many_val_group — many_val_group • fonctionr

Function to compare de proportions/means/medians of a set of several binary/continuous variables between different groups. It can use complex survey data. It produces a table and a graphic.

Usage

many_val_group(
  data,
  group,
  list_vars,
  type,
  list_vars_lab = NULL,
  facet = NULL,
  filter_exp = NULL,
  ...,
  na.rm.group = T,
  na.rm.facet = T,
  na.vars = "rm",
  total = TRUE,
  prop_method = "beta",
  position = "dodge",
  show_ci = T,
  show_n = FALSE,
  show_value = TRUE,
  show_labs = TRUE,
  total_name = NULL,
  scale = NULL,
  digits = 0,
  unit = NULL,
  dec = ",",
  pal = "Egypt",
  direction = 1,
  desaturate = 0,
  lighten = 0,
  darken = 0,
  dodge = 0.9,
  font = "Roboto",
  wrap_width_y = 25,
  wrap_width_leg = 25,
  legend_ncol = 4,
  title = NULL,
  subtitle = NULL,
  xlab = NULL,
  ylab = NULL,
  legend_lab = NULL,
  lang = "fr",
  caption = NULL,
  theme = NULL,
  export_path = NULL
)

many_prop_group(..., type = "prop")

many_median_group(..., type = "median")

many_mean_group(..., type = "mean")

Arguments

data: A dataframe or an object from the survey package or an object from the srvyr package.
group: A variable defining groups to be compared.
list_vars: A vector containing names of the dummy variables on which to compute the proportions
type: "mean" to compute means by group ; "median" to compute medians by group ; "prop" to compute proportions by group.
list_vars_lab: Names of the variables
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.group: TRUE if you want to remove observations with NA on the group variable or NA on the facet variable. FALSE if you want to create a group with the NA value for the group variable and a facet with the NA value for the facet variable. NA in the variables included in prop_exp are not affected in this argument. All the observation with a NA in the variables included in prop_exp are excluded.
na.rm.facet: TRUE if you want to remove observations with NA on the group variable or NA on the facet variable. FALSE if you want to create a group with the NA value for the group variable and a facet with the NA value for the facet variable. NA in the variables included in prop_exp are not affected in this argument. All the observation with a NA in the variables included in prop_exp are excluded.
na.vars: The treatment of NA values in variables. "rm" removes NA only in each individual variable, "rm.all" removes every individual that has at least one NA in one variable.
total: TRUE if you want to calculate a total, FALSE if you don't. The default is TRUE
prop_method: Type of proportion method to use. See svyciprop in survey package for details. Default is the beta method.
position: Position adjustment for geom_bar.
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.
show_n: TRUE if you want to show on the graphic the number of individuals in the sample in each group. FALSE if you do not want to show this number. Default is FALSE.
show_value: TRUE if you want to show the proportion in each group on the graphic. FALSE if you do not want to show the proportion.
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.
total_name: Name of the total bar on the graphic. Default is Total.
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: Color palette used on the graphic. The palettes from the packages MetBrewer, MoMAColors and PrettyCols are available.
direction: Direction of the palette color. Default is 1. The opposite direction is -1.
desaturate: Numeric specifying the amount of desaturation where 1 corresponds to complete desaturation, 0 to no desaturation, and values in between to partial desaturation.
lighten: Numeric specifying the amount of lightening. Negative numbers cause darkening.
darken: Numeric specifying the amount of lightening. Negative numbers cause lightening.
dodge: Width of the bar, between 0 and 1.
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. Applies to the labels of the groups. Default is 25.
wrap_width_leg: Number of characters before going to the line. Applies to the labels of the legend. Default is 25.
legend_ncol: Number maximum of colomn in the legend. Default is 4.
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.
ylab: Y 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.
legend_lab: Legend (fill) label on the graphic.
lang: The language of the indications on the chart. Possibilities: "fr", "nl", "en". Default is "fr".
caption: Caption of 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 two sheets : the table and the graphic.

Value

A list that contains a table and a graphic

Examples

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

# Recoding variables
eusilc$worker <- 0
eusilc$worker[eusilc$pl030 == "1"]<-1
eusilc$worker[eusilc$pl030 == "2"]<-1
eusilc$austrian<-0
eusilc$austrian[eusilc$pb220a == "AT"]<-1

# Computation, taking sample design into account
eusilc_many_mean_group <- many_mean_group(
eusilc,
group = rb090,
list_vars = c(py010n,py050n,py090n,py100n),
list_vars_lab = c("Wage","Self-employement income","unemployement benefit","pension"),
strata = db040,
ids = db030,
weight = rb050,
title = "Average incomes according to gender",
subtitle = "Example with austrian SILC data from 'laeken' package"
)
#> Variable(s) entrees : py010n, py050n, py090n, py100n
#> Input: data.frame
#> Sampling design -> ids:  db030, strata:  db040, weights:  rb050

# Results in graph form
eusilc_many_mean_group$graph
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
#> Warning: Removed 8 rows containing missing values or values outside the scale range
#> (`geom_text()`).
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_text()`).


# Results in table format
eusilc_many_mean_group$tab
#> # A tibble: 12 × 9
#>    rb090  list_col     mean mean_low mean_upp n_sample n_weighted n_weighted_low
#>    <fct>  <fct>       <dbl>    <dbl>    <dbl>    <int>      <dbl>          <dbl>
#>  1 male   Wage       12445.   12102.   12787.     5844   3237897.       3178503.
#>  2 female Wage        6136.    5902.    6370.     6263   3519368.       3470221.
#>  3 Total  Wage        9159.    8946.    9372.    12107   6757264.       6683738.
#>  4 male   Self-empl…  1539.    1369.    1710.     5844   3237897.       3178503.
#>  5 female Self-empl…   689.     600.     778.     6263   3519368.       3470221.
#>  6 Total  Self-empl…  1096.    1002.    1191.    12107   6757264.       6683738.
#>  7 male   unemploye…   420.     371.     469.     5844   3237897.       3178503.
#>  8 female unemploye…   431.     387.     475.     6263   3519368.       3470221.
#>  9 Total  unemploye…   426.     393.     458.    12107   6757264.       6683738.
#> 10 male   pension     4123.    3894.    4353.     5844   3237897.       3178503.
#> 11 female pension     3438.    3254.    3623.     6263   3519368.       3470221.
#> 12 Total  pension     3767.    3606.    3927.    12107   6757264.       6683738.
#> # ℹ 1 more variable: n_weighted_upp <dbl>