Codebook For TOP Data
22 abril, 2020
In the following Tables and graphics, there is a summary of the variables and docummentation of their characteristics. This high-level summary may permit us find errors on coding, help us to understand missingness and guide us towards an objective, or lead us to more questions.
load("G:/Mi unidad/Alvacast/SISTRAT 2019 (github)/4.RData")
#load("H:/sud_cl/4.RData")
codebook(CONS_TOP_df_dup_ENE_2020_prev9)knitr::asis_output(data_info)Metadata
Description
if (exists("name", meta)) {
glue::glue(
"__Dataset name__: {name}",
.envir = meta)
}Dataset name: SENDAs Treatment Outcomes Profile
cat(description)Information About Treatment Outcomes Profile of users of SENDA, 2015 to 2019
Metadata for search engines
- Date published: 2020-04-22
meta <- meta[setdiff(names(meta),
c("creator", "datePublished", "identifier",
"url", "citation", "spatialCoverage",
"temporalCoverage", "description", "name"))]
pander::pander(meta)- keywords: hash_key, hash_rut_completo, id_mod, id, ano_bd, row, fecha_aplicacion_top, nombre_apliacador_del_top, top, etapa_del_tratamiento, fecha_nacimiento, edad, sexo, fecha_de_ingreso_a_tratamiento, plan_de_tratamiento, nombre_del_centro, tipo_centro, sustancia_principal_1, sustancia_principal_2, sustancia_principal_3, total_oh, dosis_oh, total_thc, dosis_thc, total_pbc, dosis_pbc, total_coc, dosis_coc, total_bzd, dosis_bzd, total_otra, dosis_otra, hurto, robo, venta_drogas, rina, total_vif, otro, total_transgresion, salud_psicologica, total_trabajo, total_educacion, salud_fisica, lugar_vivir, vivienda, calidad_vida, region_centro, comentario, fech_ing, fech_ing_sin_fmt, fech_ap_top, fech_nac, obs, edad_al_ing, edad_at_ap, tipo_de_plan, identidad_de_genero, sus_prin1, sus_prin2, sus_prin3 and id_centro
knitr::asis_output(survey_overview)Variables
if (detailed_variables || detailed_scales) {
knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}hash_key
Codificación del RUT/Masked Identifier (RUT)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| hash_key | Codificación del RUT/Masked Identifier (RUT) | character | 0 | 1 | 37201 | 0 | 32 | 32 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}hash_rut_completo
HASH alternativo, en el escenario en que se asuma que el individuo al que se le codificó el RUT presente mayor edad/Alternative HASH-Key
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}## Error in if (stats::median(table(x)) == 1) {: valor ausente donde TRUE/FALSE es necesariowrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}## No non-missing values to show.knitr::opts_chunk$set(fig.height = old_height)103291 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | count |
|---|---|---|---|---|---|
| hash_rut_completo | HASH alternativo, en el escenario en que se asuma que el individuo al que se le codificó el RUT presente mayor edad/Alternative HASH-Key | logical | 103291 | 0 | : |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}id_mod
ID de SENDA para Presentación en Página Web (enmascara caracteres 5 y 6)/SENDAs ID (masked characters 5 & 6)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| id_mod | ID de SENDA para Presentación en Página Web (enmascara caracteres 5 y 6)/SENDAs ID (masked characters 5 & 6) | character | 0 | 1 | 37145 | 0 | 12 | 13 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}id
Codigo Identificación de SENDA/SENDAs ID
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| id | Codigo Identificación de SENDA/SENDAs ID | character | 0 | 1 | 37953 | 0 | 12 | 13 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}ano_bd
Año de la Base de Datos/Year of the Dataset (Source)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ano_bd | Año de la Base de Datos/Year of the Dataset (Source) | numeric | 0 | 1 | 2015 | 2017 | 2019 | 2017.277 | 1.234673 | ▂▇▇▇▆ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}row
Numerador de los eventos presentes en la Base de Datos/Events in the Dataset
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| row | Numerador de los eventos presentes en la Base de Datos/Events in the Dataset | numeric | 0 | 1 | 1 | 54056 | 107307 | 53990.85 | 30957.41 | ▇▇▇▇▇ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}fecha_aplicacion_top
(original, Recodificado en fech_ap_top)/
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| fecha_aplicacion_top | (original, Recodificado en fech_ap_top)/ | character | 0 | 1 | 1593 | 0 | 10 | 10 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}nombre_apliacador_del_top
Nombre Aplicador del TOP/Name of the TOP Interviewer
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| nombre_apliacador_del_top | Nombre Aplicador del TOP/Name of the TOP Interviewer | character | 1 | 0.9999903 | 11530 | 0 | 1 | 50 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}top
TOP
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| top | TOP | factor | FALSE | 1. Egreso, 2. Ingreso, 3. Tratamiento |
0 | 1 | 3 | Tra: 49085, Ing: 44420, Egr: 9786 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}etapa_del_tratamiento
Etapa del Tratamiento/Stage of Treatment
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1887 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| etapa_del_tratamiento | Etapa del Tratamiento/Stage of Treatment | factor | FALSE | 1. Egreso, 2. Inicio Tratamiento, 3. Seguimiento 12 meses, 4. Seguimiento 15 meses, 5. Seguimiento 3 meses, 6. Seguimiento 6 meses, 7. Seguimiento 9 meses |
1887 | 0.9817312 | 7 | Ini: 44417, Seg: 23694, Seg: 12074, Egr: 9786 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}fecha_nacimiento
Fecha de Nacimiento/Date of Birth
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| fecha_nacimiento | Fecha de Nacimiento/Date of Birth | character | 0 | 1 | 14483 | 0 | 10 | 10 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}edad
Edad (número entero)/Year (Discrete Number)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| edad | Edad (número entero)/Year (Discrete Number) | numeric | 0 | 1 | 3 | 39 | 102 | 40.40813 | 11.78333 | ▁▇▅▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sexo
Sexo/Sex
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| sexo | Sexo/Sex | factor | FALSE | 1. Hombre, 2. Mujer |
0 | 1 | 2 | Hom: 74059, Muj: 29232 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}fecha_de_ingreso_a_tratamiento
(original, Recodificado en fech_ing)/
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1086 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| fecha_de_ingreso_a_tratamiento | (original, Recodificado en fech_ing)/ | character | 1086 | 0.989486 | 1394 | 0 | 10 | 10 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}plan_de_tratamiento
(original, Recodificado en tipo_de_plan)/
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1088 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| plan_de_tratamiento | (original, Recodificado en tipo_de_plan)/ | character | 1088 | 0.9894667 | 9 | 0 | 4 | 8 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}nombre_del_centro
Nombre del Centro de Tratamiento/Treatment Center
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| nombre_del_centro | Nombre del Centro de Tratamiento/Treatment Center | character | 0 | 1 | 372 | 0 | 5 | 120 | 365 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}tipo_centro
Tipo de Centro/Type of Center
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| tipo_centro | Tipo de Centro/Type of Center | factor | FALSE | 1. privado, 2. publico |
0 | 1 | 2 | pub: 68223, pri: 35068 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sustancia_principal_1
(original, Recodificado en sus_prin1)/
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1086 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| sustancia_principal_1 | (original, Recodificado en sus_prin1)/ | character | 1086 | 0.989486 | 18 | 0 | 3 | 98 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sustancia_principal_2
(original, Recodificado en sus_prin2)/
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)37094 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| sustancia_principal_2 | (original, Recodificado en sus_prin2)/ | character | 37094 | 0.6408787 | 21 | 0 | 3 | 98 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sustancia_principal_3
(original, Recodificado en sus_prin3)/
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)69049 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| sustancia_principal_3 | (original, Recodificado en sus_prin3)/ | character | 69049 | 0.33151 | 21 | 0 | 3 | 98 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_oh
Total Alcohol/Total Alcohol
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)436 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_oh | Total Alcohol/Total Alcohol | numeric | 436 | 0.9957789 | 0 | 0 | 28 | 3.3881 | 6.40716 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}dosis_oh
Dosis Consumo de Alcohol/Amount of Alcohol
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)436 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dosis_oh | Dosis Consumo de Alcohol/Amount of Alcohol | numeric | 436 | 0.9957789 | 0 | 0 | 99 | 3.010072 | 6.23239 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_thc
Total Marihuana/Total Marijuana
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)443 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_thc | Total Marihuana/Total Marijuana | numeric | 443 | 0.9957111 | 0 | 0 | 28 | 2.628782 | 6.945185 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}dosis_thc
Dosis Marihuana/Dose of Marijuana
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)443 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dosis_thc | Dosis Marihuana/Dose of Marijuana | numeric | 443 | 0.9957111 | 0 | 0 | 99 | 0.4866599 | 1.783987 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_pbc
Total Pasta Base de Cocaína/Total Cocaine Paste Base
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)454 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_pbc | Total Pasta Base de Cocaína/Total Cocaine Paste Base | numeric | 454 | 0.9956047 | 0 | 0 | 28 | 2.313477 | 6.415292 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}dosis_pbc
Dosis Pasta Base de Cocaína/Dose of Cocaine Paste Base
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)454 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dosis_pbc | Dosis Pasta Base de Cocaína/Dose of Cocaine Paste Base | numeric | 454 | 0.9956047 | 0 | 0 | 99 | 0.5132588 | 2.34785 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_coc
Total Cocaína/Total Snort Cocaine
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)474 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_coc | Total Cocaína/Total Snort Cocaine | numeric | 474 | 0.995411 | 0 | 0 | 28 | 0.9993873 | 3.784745 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}dosis_coc
Dosis Cocaína/Dose of Snort Cocaine
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)474 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dosis_coc | Dosis Cocaína/Dose of Snort Cocaine | numeric | 474 | 0.995411 | 0 | 0 | 99 | 0.1909704 | 2.026866 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_bzd
Total Sedantes o Tranquilizantes/Total Sedatives and Tranquillizers
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)464 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_bzd | Total Sedantes o Tranquilizantes/Total Sedatives and Tranquillizers | numeric | 464 | 0.9955078 | 0 | 0 | 28 | 0.5632762 | 3.561032 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}dosis_bzd
Dosis Sedantes o Tranquilizantes/Dose of Sedatives and Tranquillizers
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)503 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dosis_bzd | Dosis Sedantes o Tranquilizantes/Dose of Sedatives and Tranquillizers | numeric | 503 | 0.9951303 | 0 | 0 | 99 | 0.4146107 | 2.868782 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_otra
Total Otra sustancia problema/Total Other Substances
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)503 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_otra | Total Otra sustancia problema/Total Other Substances | numeric | 503 | 0.9951303 | 0 | 0 | 28 | 0.933251 | 4.85147 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}dosis_otra
Dosis Otra sustancia problema/Dose of Other Substances
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)503 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| dosis_otra | Dosis Otra sustancia problema/Dose of Other Substances | numeric | 503 | 0.9951303 | 0 | 0 | 99 | 0.4146107 | 2.868782 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}hurto
Hurto/Theft
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3599 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| hurto | Hurto/Theft | factor | FALSE | 1. N, 2. S |
3599 | 0.9651567 | 2 | N: 94383, S: 5309 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}robo
Robo/Robbery
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3608 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| robo | Robo/Robbery | factor | FALSE | 1. N, 2. S |
3608 | 0.9650696 | 2 | N: 96606, S: 3077 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}venta_drogas
Venta de Drogas/Drug selling
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3626 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| venta_drogas | Venta de Drogas/Drug selling | factor | FALSE | 1. N, 2. S |
3626 | 0.9648953 | 2 | N: 97425, S: 2240 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}rina
Riña/Fights
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3586 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| rina | Riña/Fights | factor | FALSE | 1. N, 2. S |
3586 | 0.9652826 | 2 | N: 93297, S: 6408 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_vif
Total Violencia Intrafamiliar/Total Domestic Violence
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)567 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_vif | Total Violencia Intrafamiliar/Total Domestic Violence | numeric | 567 | 0.9945107 | 0 | 0 | 28 | 0.6885246 | 3.243149 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}otro
Otra Acción/Another Action
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)4227 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| otro | Otra Acción/Another Action | factor | FALSE | 1. N, 2. S |
4227 | 0.9590768 | 2 | N: 96403, S: 2661 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_transgresion
Total Transgresión a la Norma Social/Total Behavior that transgresses social norms
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_transgresion | Total Transgresión a la Norma Social/Total Behavior that transgresses social norms | numeric | 0 | 1 | 0 | 0 | 5 | 0.1906749 | 0.5958001 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}salud_psicologica
Salud Psicológica/Psychological Health
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)345 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| salud_psicologica | Salud Psicológica/Psychological Health | numeric | 345 | 0.9966599 | 0 | 12 | 20 | 11.87126 | 5.145294 | ▂▃▇▇▆ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_trabajo
Total Trabajo Pagado Formal o Informal/Total of Paid Work
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)391 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_trabajo | Total Trabajo Pagado Formal o Informal/Total of Paid Work | numeric | 391 | 0.9962146 | 0 | 7 | 28 | 10.31753 | 10.60293 | ▇▁▁▃▃ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}total_educacion
Total Asistencia a Establecimiento Educacional o Capacitación Laboral/Total College or school
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1067 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| total_educacion | Total Asistencia a Establecimiento Educacional o Capacitación Laboral/Total College or school | numeric | 1067 | 0.98967 | 0 | 0 | 28 | 0.5376624 | 2.991359 | ▇▁▁▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}salud_fisica
Total Salud Física/Total Physical Health
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)353 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| salud_fisica | Total Salud Física/Total Physical Health | numeric | 353 | 0.9965825 | 0 | 14 | 20 | 12.82918 | 5.101704 | ▂▃▇▇▇ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}lugar_vivir
Lugar estable para vivir/Stable Place to Live
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3133 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| lugar_vivir | Lugar estable para vivir/Stable Place to Live | factor | FALSE | 1. N, 2. S |
3133 | 0.9696682 | 2 | S: 93448, N: 6710 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}vivienda
Vivienda con Condiciones Básicas/Housing conditions
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)3194 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| vivienda | Vivienda con Condiciones Básicas/Housing conditions | factor | FALSE | 1. N, 2. S |
3194 | 0.9690777 | 2 | S: 92755, N: 7342 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}calidad_vida
Total Calidad de Vida/Total Quality of Life (QoL)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)368 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| calidad_vida | Total Calidad de Vida/Total Quality of Life (QoL) | numeric | 368 | 0.9964373 | 0 | 14 | 20 | 12.88733 | 5.306473 | ▂▃▆▇▇ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}region_centro
Región del Centro/Chilean Region of the Center
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| region_centro | Región del Centro/Chilean Region of the Center | character | 0 | 1 | 16 | 0 | 8 | 44 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}comentario
Comentarios relacionados con la aplicación del TOP/Comments related to the application of TOP
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)97776 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| comentario | Comentarios relacionados con la aplicación del TOP/Comments related to the application of TOP | character | 97776 | 0.0533928 | 4560 | 0 | 1 | 170 | 27 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}fech_ing
Fecha de Ingreso a Tratamiento/Date of Admission to Treatment
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}## 1394 unique, categorical values, so not shown.knitr::opts_chunk$set(fig.height = old_height)1073 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | min | median | max |
|---|---|---|---|---|---|---|---|---|
| fech_ing | Fecha de Ingreso a Tratamiento/Date of Admission to Treatment | Date | 1073 | 0.9896119 | 1394 | 2015-05-01 | 2017-05-17 | 2019-11-07 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}fech_ing_sin_fmt
Fecha de Ingreso de Tratamiento (Sin Formato de Fecha)/Date of Admission (unformatted)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1086 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| fech_ing_sin_fmt | Fecha de Ingreso de Tratamiento (Sin Formato de Fecha)/Date of Admission (unformatted) | character | 1086 | 0.989486 | 1394 | 0 | 10 | 10 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}fech_ap_top
Fecha de Aplicación de TOP/Date of Application of TOP
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}## 1593 unique, categorical values, so not shown.knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | min | median | max |
|---|---|---|---|---|---|---|---|---|
| fech_ap_top | Fecha de Aplicación de TOP/Date of Application of TOP | Date | 0 | 1 | 1593 | 2015-01-08 | 2017-10-20 | 2019-11-29 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}fech_nac
Fecha de Nacimiento/Date of Birth
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}## 14483 unique, categorical values, so not shown.knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | min | median | max |
|---|---|---|---|---|---|---|---|---|
| fech_nac | Fecha de Nacimiento/Date of Birth | Date | 0 | 1 | 14483 | 1917-07-15 | 1980-08-02 | 2015-12-16 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}obs
Observaciones al Proceso de Limpieza y Estandarización de Casos/Observations to the Process of Data Tidying & Standardization
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| obs | Observaciones al Proceso de Limpieza y Estandarización de Casos/Observations to the Process of Data Tidying & Standardization | character | 0 | 1 | 26 | 97356 | 0 | 169 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}edad_al_ing
Edad a la Fecha de Ingreso a Tratamiento (numérico continuo)/Age at Admission to Treatment
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1073 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| edad_al_ing | Edad a la Fecha de Ingreso a Tratamiento (numérico continuo)/Age at Admission to Treatment | numeric | 1073 | 0.9896119 | -0.16 | 37 | 100 | 38.46781 | 11.72702 | ▁▇▅▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}edad_at_ap
Edad a la Aplicación del Tratamiento (numérico continuo)/Age at the Application of TOP
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)0 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| edad_at_ap | Edad a la Aplicación del Tratamiento (numérico continuo)/Age at the Application of TOP | numeric | 0 | 1 | -0.071 | 37 | 101 | 38.84788 | 11.78334 | ▁▇▅▁▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}tipo_de_plan
Tipo de Plan/Type of Plan
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1165 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| tipo_de_plan | Tipo de Plan/Type of Plan | factor | FALSE | 1. M-PAI, 2. M-PR, 3. PG-PAB, 4. PG-PAI, 5. PG-PR |
1165 | 0.9887212 | 5 | PG-: 43267, PG-: 32489, PG-: 13091, M-P: 7996 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}identidad_de_genero
Identidad de Género/Gender Identity
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)84121 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | n_unique | empty | min | max | whitespace |
|---|---|---|---|---|---|---|---|---|---|
| identidad_de_genero | Identidad de Género/Gender Identity | character | 84121 | 0.1855922 | 2 | 0 | 8 | 9 | 0 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sus_prin1
Sustancia Principal de Consumo (1)/Primary Substance of Consumption (1)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)1086 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| sus_prin1 | Sustancia Principal de Consumo (1)/Primary Substance of Consumption (1) | factor | FALSE | 1. Alcohol, 2. Alucinógenos, 3. Cocaína, 4. Estimulante tipo anfetaminas, 5. Inhalables, 6. Marihuana, 7. Opioides, 8. Otros, 9. Pasta Base, 10. Tranquilizantes e Hipnóticos |
1086 | 0.989486 | 10 | Alc: 42036, Pas: 35220, Coc: 18886, Mar: 4495 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sus_prin2
Sustancia Principal de Consumo (2)/Primary Substance of Consumption (2)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)38546 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| sus_prin2 | Sustancia Principal de Consumo (2)/Primary Substance of Consumption (2) | factor | FALSE | 1. Alcohol, 2. Alucinógenos, 3. Cocaína, 4. Estimulante tipo anfetaminas, 5. Inhalables, 6. Marihuana, 7. Opioides, 8. Otros, 9. Pasta Base, 10. Tranquilizantes e Hipnóticos |
38546 | 0.6268213 | 10 | Alc: 26283, Mar: 19910, Coc: 11358, Pas: 4773 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}sus_prin3
Sustancia Principal de Consumo (3)/Primary Substance of Consumption (3)
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)69437 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | ordered | value_labels | n_missing | complete_rate | n_unique | top_counts |
|---|---|---|---|---|---|---|---|---|
| sus_prin3 | Sustancia Principal de Consumo (3)/Primary Substance of Consumption (3) | factor | FALSE | 1. Alcohol, 2. Alucinógenos, 3. Cocaína, 4. Estimulante tipo anfetaminas, 5. Inhalables, 6. Marihuana, 7. Opioides, 8. Otros, 9. Pasta Base, 10. Tranquilizantes e Hipnóticos |
69437 | 0.3277536 | 10 | Mar: 12897, Alc: 10430, Coc: 6212, Pas: 2141 |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}id_centro
ID de Centro/Center ID
Distribution
show_missing_values <- FALSE
if (has_labels(item)) {
missing_values <- item[is.na(haven::zap_missing(item))]
attributes(missing_values) <- attributes(item)
if (!is.null(attributes(item)$labels)) {
attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
}
if (is.double(item)) {
show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
item <- haven::zap_missing(item)
}
if (length(item_attributes$labels) == 0 && is.numeric(item)) {
item <- haven::zap_labels(item)
}
}
item_nomiss <- item[!is.na(item)]
# unnest mc_multiple and so on
if (
is.character(item_nomiss) &&
any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
!is.null(item_info) &&
(exists("type", item_info) &&
any(stringr::str_detect(item_info$type,
pattern = stringr::fixed("multiple"))))
) {
item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)
old_height <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
if ( go_vertical ) {
# numeric items are plotted horizontally (because that's what usually expected)
# categorical items are plotted vertically because we can use the screen real estate better this way
if (is.null(choices) ||
dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
non_missing_choices <- unique(item_nomiss)
names(non_missing_choices) <- non_missing_choices
}
choice_multiplier <- old_height/6.5
new_height <- 2 + choice_multiplier * length(non_missing_choices)
new_height <- ifelse(new_height > 20, 20, new_height)
new_height <- ifelse(new_height < 1, 1, new_height)
if(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss)) {
new_height <- old_height
}
knitr::opts_chunk$set(fig.height = new_height)
}
wrap_at <- knitr::opts_chunk$get("fig.width") * 10# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
if (is.character(item_nomiss)) {
char_count <- stringr::str_count(item_nomiss)
attributes(char_count)$label <- item_label
plot_labelled(char_count,
item_name, wrap_at, FALSE, trans = "log1p", "characters")
} else {
cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
}
}
knitr::opts_chunk$set(fig.height = old_height)221 missing values.
Summary statistics
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
escaped_table(codebook_table(df))| name | label | data_type | n_missing | complete_rate | min | median | max | mean | sd | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| id_centro | ID de Centro/Center ID | numeric | 221 | 0.9978604 | 104 | 290 | 779 | 334.0786 | 175.0481 | ▇▆▃▂▁ |
if (show_missing_values) {
plot_labelled(missing_values, item_name, wrap_at)
}if (!is.null(item_info)) {
# don't show choices again, if they're basically same thing as value labels
if (!is.null(choices) && !is.null(item_info$choices) &&
all(names(na.omit(choices)) == item_info$choices) &&
all(na.omit(choices) == names(item_info$choices))) {
item_info$choices <- NULL
}
item_info$label_parsed <-
item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
pander::pander(item_info)
}if (!is.null(choices) && length(choices) && length(choices) < 30) {
pander::pander(as.list(choices))
}missingness_reportMissingness report
if (length(md_pattern)) {
if (knitr::is_html_output()) {
rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
} else {
knitr::kable(md_pattern)
}
}itemsCodebook table
export_table(metadata_table)jsonldJSON-LD metadata
The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.
{
"name": "SENDAs Treatment Outcomes Profile",
"description": "Information About Treatment Outcomes Profile of users of SENDA, 2015 to 2019\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.8.2).",
"datePublished": "2020-04-22",
"keywords": ["hash_key", "hash_rut_completo", "id_mod", "id", "ano_bd", "row", "fecha_aplicacion_top", "nombre_apliacador_del_top", "top", "etapa_del_tratamiento", "fecha_nacimiento", "edad", "sexo", "fecha_de_ingreso_a_tratamiento", "plan_de_tratamiento", "nombre_del_centro", "tipo_centro", "sustancia_principal_1", "sustancia_principal_2", "sustancia_principal_3", "total_oh", "dosis_oh", "total_thc", "dosis_thc", "total_pbc", "dosis_pbc", "total_coc", "dosis_coc", "total_bzd", "dosis_bzd", "total_otra", "dosis_otra", "hurto", "robo", "venta_drogas", "rina", "total_vif", "otro", "total_transgresion", "salud_psicologica", "total_trabajo", "total_educacion", "salud_fisica", "lugar_vivir", "vivienda", "calidad_vida", "region_centro", "comentario", "fech_ing", "fech_ing_sin_fmt", "fech_ap_top", "fech_nac", "obs", "edad_al_ing", "edad_at_ap", "tipo_de_plan", "identidad_de_genero", "sus_prin1", "sus_prin2", "sus_prin3", "id_centro"],
"@context": "http://schema.org/",
"@type": "Dataset",
"variableMeasured": [
{
"name": "hash_key",
"description": "Codificación del RUT/Masked Identifier (RUT)",
"@type": "propertyValue"
},
{
"name": "hash_rut_completo",
"description": "HASH alternativo, en el escenario en que se asuma que el individuo al que se le codificó el RUT presente mayor edad/Alternative HASH-Key",
"@type": "propertyValue"
},
{
"name": "id_mod",
"description": "ID de SENDA para Presentación en Página Web (enmascara caracteres 5 y 6)/SENDAs ID (masked characters 5 & 6)",
"@type": "propertyValue"
},
{
"name": "id",
"description": "Codigo Identificación de SENDA/SENDAs ID",
"@type": "propertyValue"
},
{
"name": "ano_bd",
"description": "Año de la Base de Datos/Year of the Dataset (Source)",
"@type": "propertyValue"
},
{
"name": "row",
"description": "Numerador de los eventos presentes en la Base de Datos/Events in the Dataset",
"@type": "propertyValue"
},
{
"name": "fecha_aplicacion_top",
"description": "(original, Recodificado en fech_ap_top)/",
"@type": "propertyValue"
},
{
"name": "nombre_apliacador_del_top",
"description": "Nombre Aplicador del TOP/Name of the TOP Interviewer",
"@type": "propertyValue"
},
{
"name": "top",
"description": "TOP",
"value": "1. Egreso,\n2. Ingreso,\n3. Tratamiento",
"@type": "propertyValue"
},
{
"name": "etapa_del_tratamiento",
"description": "Etapa del Tratamiento/Stage of Treatment",
"value": "1. Egreso,\n2. Inicio Tratamiento,\n3. Seguimiento 12 meses,\n4. Seguimiento 15 meses,\n5. Seguimiento 3 meses,\n6. Seguimiento 6 meses,\n7. Seguimiento 9 meses",
"@type": "propertyValue"
},
{
"name": "fecha_nacimiento",
"description": "Fecha de Nacimiento/Date of Birth",
"@type": "propertyValue"
},
{
"name": "edad",
"description": "Edad (número entero)/Year (Discrete Number)",
"@type": "propertyValue"
},
{
"name": "sexo",
"description": "Sexo/Sex",
"value": "1. Hombre,\n2. Mujer",
"@type": "propertyValue"
},
{
"name": "fecha_de_ingreso_a_tratamiento",
"description": "(original, Recodificado en fech_ing)/",
"@type": "propertyValue"
},
{
"name": "plan_de_tratamiento",
"description": "(original, Recodificado en tipo_de_plan)/",
"@type": "propertyValue"
},
{
"name": "nombre_del_centro",
"description": "Nombre del Centro de Tratamiento/Treatment Center",
"@type": "propertyValue"
},
{
"name": "tipo_centro",
"description": "Tipo de Centro/Type of Center",
"value": "1. privado,\n2. publico",
"@type": "propertyValue"
},
{
"name": "sustancia_principal_1",
"description": "(original, Recodificado en sus_prin1)/",
"@type": "propertyValue"
},
{
"name": "sustancia_principal_2",
"description": "(original, Recodificado en sus_prin2)/",
"@type": "propertyValue"
},
{
"name": "sustancia_principal_3",
"description": "(original, Recodificado en sus_prin3)/",
"@type": "propertyValue"
},
{
"name": "total_oh",
"description": "Total Alcohol/Total Alcohol",
"@type": "propertyValue"
},
{
"name": "dosis_oh",
"description": "Dosis Consumo de Alcohol/Amount of Alcohol",
"@type": "propertyValue"
},
{
"name": "total_thc",
"description": "Total Marihuana/Total Marijuana",
"@type": "propertyValue"
},
{
"name": "dosis_thc",
"description": "Dosis Marihuana/Dose of Marijuana",
"@type": "propertyValue"
},
{
"name": "total_pbc",
"description": "Total Pasta Base de Cocaína/Total Cocaine Paste Base",
"@type": "propertyValue"
},
{
"name": "dosis_pbc",
"description": "Dosis Pasta Base de Cocaína/Dose of Cocaine Paste Base",
"@type": "propertyValue"
},
{
"name": "total_coc",
"description": "Total Cocaína/Total Snort Cocaine",
"@type": "propertyValue"
},
{
"name": "dosis_coc",
"description": "Dosis Cocaína/Dose of Snort Cocaine",
"@type": "propertyValue"
},
{
"name": "total_bzd",
"description": "Total Sedantes o Tranquilizantes/Total Sedatives and Tranquillizers",
"@type": "propertyValue"
},
{
"name": "dosis_bzd",
"description": "Dosis Sedantes o Tranquilizantes/Dose of Sedatives and Tranquillizers",
"@type": "propertyValue"
},
{
"name": "total_otra",
"description": "Total Otra sustancia problema/Total Other Substances",
"@type": "propertyValue"
},
{
"name": "dosis_otra",
"description": "Dosis Otra sustancia problema/Dose of Other Substances",
"@type": "propertyValue"
},
{
"name": "hurto",
"description": "Hurto/Theft",
"value": "1. N,\n2. S",
"@type": "propertyValue"
},
{
"name": "robo",
"description": "Robo/Robbery",
"value": "1. N,\n2. S",
"@type": "propertyValue"
},
{
"name": "venta_drogas",
"description": "Venta de Drogas/Drug selling",
"value": "1. N,\n2. S",
"@type": "propertyValue"
},
{
"name": "rina",
"description": "Riña/Fights",
"value": "1. N,\n2. S",
"@type": "propertyValue"
},
{
"name": "total_vif",
"description": " Total Violencia Intrafamiliar/Total Domestic Violence",
"@type": "propertyValue"
},
{
"name": "otro",
"description": "Otra Acción/Another Action",
"value": "1. N,\n2. S",
"@type": "propertyValue"
},
{
"name": "total_transgresion",
"description": "Total Transgresión a la Norma Social/Total Behavior that transgresses social norms",
"@type": "propertyValue"
},
{
"name": "salud_psicologica",
"description": "Salud Psicológica/Psychological Health",
"@type": "propertyValue"
},
{
"name": "total_trabajo",
"description": "Total Trabajo Pagado Formal o Informal/Total of Paid Work",
"@type": "propertyValue"
},
{
"name": "total_educacion",
"description": "Total Asistencia a Establecimiento Educacional o Capacitación Laboral/Total College or school ",
"@type": "propertyValue"
},
{
"name": "salud_fisica",
"description": "Total Salud Física/Total Physical Health",
"@type": "propertyValue"
},
{
"name": "lugar_vivir",
"description": "Lugar estable para vivir/Stable Place to Live",
"value": "1. N,\n2. S",
"@type": "propertyValue"
},
{
"name": "vivienda",
"description": "Vivienda con Condiciones Básicas/Housing conditions",
"value": "1. N,\n2. S",
"@type": "propertyValue"
},
{
"name": "calidad_vida",
"description": "Total Calidad de Vida/Total Quality of Life (QoL)",
"@type": "propertyValue"
},
{
"name": "region_centro",
"description": "Región del Centro/Chilean Region of the Center",
"@type": "propertyValue"
},
{
"name": "comentario",
"description": "Comentarios relacionados con la aplicación del TOP/Comments related to the application of TOP",
"@type": "propertyValue"
},
{
"name": "fech_ing",
"description": "Fecha de Ingreso a Tratamiento/Date of Admission to Treatment",
"@type": "propertyValue"
},
{
"name": "fech_ing_sin_fmt",
"description": "Fecha de Ingreso de Tratamiento (Sin Formato de Fecha)/Date of Admission (unformatted)",
"@type": "propertyValue"
},
{
"name": "fech_ap_top",
"description": "Fecha de Aplicación de TOP/Date of Application of TOP",
"@type": "propertyValue"
},
{
"name": "fech_nac",
"description": "Fecha de Nacimiento/Date of Birth",
"@type": "propertyValue"
},
{
"name": "obs",
"description": "Observaciones al Proceso de Limpieza y Estandarización de Casos/Observations to the Process of Data Tidying & Standardization",
"@type": "propertyValue"
},
{
"name": "edad_al_ing",
"description": "Edad a la Fecha de Ingreso a Tratamiento (numérico continuo)/Age at Admission to Treatment",
"@type": "propertyValue"
},
{
"name": "edad_at_ap",
"description": "Edad a la Aplicación del Tratamiento (numérico continuo)/Age at the Application of TOP",
"@type": "propertyValue"
},
{
"name": "tipo_de_plan",
"description": "Tipo de Plan/Type of Plan",
"value": "1. M-PAI,\n2. M-PR,\n3. PG-PAB,\n4. PG-PAI,\n5. PG-PR",
"@type": "propertyValue"
},
{
"name": "identidad_de_genero",
"description": "Identidad de Género/Gender Identity",
"@type": "propertyValue"
},
{
"name": "sus_prin1",
"description": "Sustancia Principal de Consumo (1)/Primary Substance of Consumption (1)",
"value": "1. Alcohol,\n2. Alucinógenos,\n3. Cocaína,\n4. Estimulante tipo anfetaminas,\n5. Inhalables,\n6. Marihuana,\n7. Opioides,\n8. Otros,\n9. Pasta Base,\n10. Tranquilizantes e Hipnóticos",
"@type": "propertyValue"
},
{
"name": "sus_prin2",
"description": "Sustancia Principal de Consumo (2)/Primary Substance of Consumption (2)",
"value": "1. Alcohol,\n2. Alucinógenos,\n3. Cocaína,\n4. Estimulante tipo anfetaminas,\n5. Inhalables,\n6. Marihuana,\n7. Opioides,\n8. Otros,\n9. Pasta Base,\n10. Tranquilizantes e Hipnóticos",
"@type": "propertyValue"
},
{
"name": "sus_prin3",
"description": "Sustancia Principal de Consumo (3)/Primary Substance of Consumption (3)",
"value": "1. Alcohol,\n2. Alucinógenos,\n3. Cocaína,\n4. Estimulante tipo anfetaminas,\n5. Inhalables,\n6. Marihuana,\n7. Opioides,\n8. Otros,\n9. Pasta Base,\n10. Tranquilizantes e Hipnóticos",
"@type": "propertyValue"
},
{
"name": "id_centro",
"description": "ID de Centro/Center ID",
"@type": "propertyValue"
}
]
}`