Plot a Slope Chart following WJP style guidelines

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

Usage

wjp_slope(
  data,
  target,
  grouping,
  ngroups,
  colors,
  cvec = NULL,
  labels = NULL,
  repel = FALSE,
  ptheme = WJP_theme()
)

Arguments

data

Data frame containing the data to plot

target

String. Column name of the variable that will supply the values to plot.

grouping

String. Column name of the variable that supplies the grouping values (X-Axis).

ngroups

Vector containing each of the groups for the lines. If there is only a single group, please input c = (1).

colors

String. Column name of the variable that contains the color grouping.

cvec

Named vector with the colors to apply to each line.

labels

String. Column name of the variable containing the value labels to display in plot. Default is NULL.

repel

Boolean. If TRUE, function will apply the ggrepel package to repel labels. Default is FALSE.

ptheme

ggplot theme function to apply to the plot. By default, function applies WJP_theme()

Value

A ggplot object

Examples

library(dplyr)
library(tidyr)
library(haven)
library(ggplot2)
library(ggrepel)

# Always load the WJP fonts if not passing a custom theme to function
wjp_fonts()

# Preparing data
gpp_data <- WJPr::gpp

data4slopes <- gpp_data %>%
select(year, gend, q1a) %>%
  filter(
    year %in% c(2017, 2019)
  ) %>%
  mutate(
    q1a = as.double(q1a),
    trust = case_when(
      q1a <= 2  ~ 1,
      q1a <= 4  ~ 0
    ),
    gender = case_when(
      gend == 1 ~ "Male",
      gend == 2 ~ "Female"
    )
  ) %>%
  group_by(year, gender) %>%
  summarise(
    trust = mean(trust, na.rm = T)*100,
    .groups = "keep"
  ) %>%
  mutate(
    value_label = paste0(
      format(
        round(trust, 0),
        nsmall = 0
      ),
      "%"
    )
  )

# Plotting chart
wjp_slope(
  data4slopes,                    
  target    = "trust",             
  grouping  = "year",
  ngroups   = data4slopes$gender,                 
  labels    = "value_label",
  colors    = "gender",
  cvec      = c("Male"   = "#08605F",
                "Female" = "#9E6240"),
  repel     = TRUE
)