Plot a Horizontal Edgebars Chart following WJP style guidelines

wjp_edgebars() takes a data frame with a specific data structure (usually long shaped) and returns a ggplot object with an edgebar chart following WJP style guidelines.

Usage

wjp_edgebars(
  data,
  target,
  grouping,
  labels,
  cvec = NULL,
  x_lab_pos = NULL,
  y_lab_pos = 0,
  nudge_lab = 2.5,
  margin_top = 20,
  bar_width = 0.35,
  ptheme = WJP_theme()
)

Arguments

data: A data frame containing the data to be plotted.
target: A string specifying the variable in the data frame that contains the numeric values to be plotted as bars.
grouping: A string specifying the variable in the data frame that contains the categories for the x-axis.
labels: A string specifying the variable in the data frame that contains the labels to be displayed near the bars.
cvec: String. Hex code of the color for the bars. Default is NULL.
x_lab_pos: A string specifying the variable in the data frame that contains the order in which the bars will be displayed. Default is NULL.
y_lab_pos: A numeric value specifying the y-axis position for displaying labels. Default is 0.
nudge_lab: A numeric value specifying the padding for displaying labels in milimeters. Default is 2.5.
margin_top: A numeric value specifying the top margin of the plot. Default is 20.
bar_width: A numeric value specifying the width of the bars. For single bars the default value of 0.35 is recommended, for plots with two bars a value of 0.5 is more suitable.
ptheme: A ggplot aesthetic theme to be applied to the chart. Default is the WJP_theme initilized with the package.

Value

A ggplot object representing the edge bars plot.

Examples

library(dplyr)
library(tidyr)
library(haven)
library(ggplot2)
library(ggtext)

# Always load the WJP fonts (optional)
wjp_fonts()

# Preparing data
gpp_data <- WJPr::gpp

data4bars <- gpp_data %>%
  select(country, year, q1a) %>%
  group_by(country, year) %>%
  mutate(
    q1a = as.double(q1a),
    trust = case_when(
      q1a <= 2  ~ 1,
      q1a <= 4  ~ 0,
      q1a == 99 ~ NA_real_
    ),
    year = as.character(year)
  ) %>%
  summarise(
    trust   = mean(trust, na.rm = TRUE),
    .groups = "keep"
  ) %>%
  mutate(
    trust = trust*100
  ) %>%
  filter(year == "2022") %>%
  mutate(
    color_variable = country,
    value_label = paste0(
      format(
        round(trust, 0),
        nsmall = 0
      ),
      "%"
    ),
    label_position = trust + 5
  )

# Plotting chart
wjp_edgebars(
  data4bars,              
  target    = "trust",        
  grouping  = "country",
  labels    = "color_variable",
  cvec      = "#F6D8AE"
)