distrib_discrete — distrib_discrete • fonctionr

Function describe the distribution of a discrete variable from complex survey data. It produces a table and a graphic.

Usage

distrib_discrete(
  data,
  quali_var,
  facet = NULL,
  filter_exp = NULL,
  ...,
  na.rm.facet = TRUE,
  na.rm.var = TRUE,
  probs = NULL,
  prop_method = "beta",
  reorder = FALSE,
  show_ci = TRUE,
  show_n = FALSE,
  show_value = TRUE,
  show_labs = TRUE,
  scale = 100,
  digits = 0,
  unit = "%",
  dec = ",",
  pal = "sienna2",
  dodge = 0.9,
  font = "Roboto",
  wrap_width_y = 25,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  lang = "fr",
  caption = NULL,
  theme = NULL,
  export_path = NULL
)

distrib_d(...)

Arguments

data: A dataframe or an object from the survey package or an object from the srvyr package.
quali_var: The discrete variable that is described.
facet: A variable defining the faceting group.
filter_exp: An expression that filters the data, preserving the design.
...: All options possible in as_survey_design in srvyr package.
na.rm.facet: TRUE if you want to remove observations with NA on the facet variable. FALSE if you want to create a facet with the NA value for the facet variable. Default is TRUE.
na.rm.var: TRUE if you want to remove observations with NA on the discrete variable. FALSE if you want to create a modality with the NA value for the discrete variable. Default is TRUE.
probs: Vector of probabilities for H0 of the statistical test, in the correct order (will be rescaled to sum to 1). If probs = NULL, no statistical test is performed. Default is NULL.
prop_method: Type of proportion method to use to compute confidence intervals. See svyciprop in survey package for details. Default is the beta method.
reorder: TRUE if you want to reorder the categories according to their proportion. NA value, in case if na.rm.group = FALSE, is not included in the reorder. Default is FALSE.
show_ci: TRUE if you want to show the error bars on the graphic. FALSE if you do not want to show the error bars. Default is TRUE.
show_n: TRUE if you want to show on the graphic the number of individuals in the sample in each modality of quali_var. FALSE if you do not want to show this number. Default is FALSE.
show_value: TRUE if you want to show the proportion of each category on the graphic. FALSE if you do not want to show the proportion. Default is TRUE.
show_labs: TRUE if you want to show axes, titles and caption labels. FALSE if you do not want to show any label on axes and titles. Default is TRUE.
scale: Denominator of the proportion. Default is 100 to interprets numbers as percentages.
digits: Numbers of digits showed on the values labels on the graphic. Default is 0.
unit: Unit showed in the graphic. Default is percent.
dec: Decimal mark shown on the graphic. Default is ",".
pal: Colour of the bars. NA bar, in case if na.rm.group = FALSE, and total bar are always in grey.
dodge: Width of the bar, between 0 and 1. Default is 0.9.
font: Font used in the graphic. See load_and_active_fonts() for available fonts.
wrap_width_y: Number of characters before going to the line in the labels of the categories. Default is 25.
title: Title of the graphic.
subtitle: Subtitle of the graphic.
xlab: X label on the graphic. As coord_flip() is used in the graphic, xlab refers to the X label on the graphic, after the coord_flip(), and not to the x variable in the data. If xlab = NULL, X label on the graphic will be "Distribution (total : 100 percent)". To show no X label, use xlab = "".
ylab: Y label on the graphic. As coord_flip() is used in the graphic, ylab refers to the Y label on the graphic, after the coord_flip(), and not to the Y variable in the data. If ylab = NULL, Y label on the graphic will be quali_var. To show no Y label, use ylab = "".
lang: The language of the indications on the chart. Possibilities: "fr", "nl", "en". Default is "fr".
caption: Caption in the graphic.
theme: Theme od te graphic. IWEPS adds y axis lines and ticks.
export_path: Path to export the results in an xlsx file. The file includes three sheets : the table, the graphic and the statistical test (if probs is not NULL).

Value

A list that contains a table, a graphic and a statistical test

Examples

# Loading of data
data(eusilc, package = "laeken")

# Recoding eusilc$pl030 into eusilc$pl030_rec
eusilc$pl030_rec <- NA
eusilc$pl030_rec[eusilc$pl030 == "1"] <- "Working full time"
eusilc$pl030_rec[eusilc$pl030 == "2"] <- "Working part time"
eusilc$pl030_rec[eusilc$pl030 == "3"] <- "Unemployed"
eusilc$pl030_rec[eusilc$pl030 == "4"] <- "Student"
eusilc$pl030_rec[eusilc$pl030 == "5"] <- "Retired"
eusilc$pl030_rec[eusilc$pl030 == "6"] <- "Permanently disabled"
eusilc$pl030_rec[eusilc$pl030 == "7"] <- "Fulfilling domestic tasks"

# Computation, taking sample design into account
eusilc_dist_group_d <- distrib_d(
eusilc,
pl030_rec,
strata = db040,
ids = db030,
weight = rb050,
title = "Distribution of socio-economic status",
subtitle = "Example with austrian SILC data from 'laeken' package"
)
#> Input: data.frame
#> Sampling design -> ids:  db030, strata:  db040, weights:  rb050

# Results in graph form
eusilc_dist_group_d$graph


# Results in table format
eusilc_dist_group_d$tab
#> # A tibble: 7 × 8
#>   pl030_rec            prop prop_low prop_upp n_sample n_weighted n_weighted_low
#>   <fct>               <dbl>    <dbl>    <dbl>    <int>      <dbl>          <dbl>
#> 1 Fulfilling domest… 0.0948   0.0899   0.0998     1207    640311.        605978.
#> 2 Permanently disab… 0.0155   0.0129   0.0186      178    104930.         85796.
#> 3 Retired            0.267    0.258    0.277      3146   1806954.       1746273.
#> 4 Student            0.0586   0.0544   0.0630      736    395829.        365532.
#> 5 Unemployed         0.0449   0.0411   0.0489      518    303252.        276953.
#> 6 Working full time  0.425    0.416    0.434      5162   2869868.       2797833.
#> 7 Working part time  0.0941   0.0890   0.0995     1160    636121.        600709.
#> # ℹ 1 more variable: n_weighted_upp <dbl>