ecdf_plot: Empirical Cumulative Distribution Plot
In DJAnderson07/esvis: Visualization and Estimation of Effect Sizes

This is a wrapper function for the stat_ecdf function and helps make it easy to directly compare distributions at specific locations along the scale.

ecdf_plot(
  data,
  formula,
  cuts = NULL,
  linewidth = 1.2,
  ref_line_cols = "gray40",
  ref_linetype = "solid",
  center = FALSE,
  ref_rect = TRUE,
  ref_rect_col = "gray40",
  ref_rect_alpha = 0.15
)

`data`	A tidy data frame containing the data to be plotted.
`formula`	A formula of the type `out ~ group` where `out` is the outcome variable and `group` is the grouping variable. Note this variable can include any arbitrary number of groups. Additional variables can be included with `+` to produce separate plots by the secondary or tertiary varaible (e.g., `out ~ group + characteristic1 + characteristic2`). No more than two additional characteristics can be supplied at this time.
`cuts`	Optional numeric vector stating the location of reference line(s) and/or rectangle(s).
`linewidth`	Width of ECDF lines. Note that the color of the lines can be controlled through additional functions (e.g., `scale_color_brewer, scale_color_manual`).
`ref_line_cols`	Optional vector (or single value) of colors for `cuts` lines.
`ref_linetype`	Optional vector (or single value) of line types for `cuts` lines. Takes any of the arguments supplied by linetype.
`center`	Logical. Should the functions be centered prior to plotting? Defaults to `FALSE`. Note that if paneled/faceted plots are produced, the centering occurs by group.
`ref_rect`	Logical, defaults to `TRUE` when `cuts` takes any non-null value. Should semi-transparent rectangle(s) be plotted at the locations of `cuts`?
`ref_rect_col`	Color of the fill for the reference rectangles. Defaults to a dark gray.
`ref_rect_alpha`	Transparency of the fill for the reference rectangles. Defaults to 0.7.

ecdf_plot(benchmarks, math ~ ell, 
          cuts = c(190, 205, 210), 
          ref_line_cols = c("#D68EE3", "#9BE38E", "#144ECA"))

# Customize the plot with ggplot2 functions
library(ggplot2)
ecdf_plot(benchmarks, math ~ ell, 
          cuts = c(190, 205, 210), 
          ref_line_cols = c("#D68EE3", "#9BE38E", "#144ECA")) +
  theme_minimal() +
  theme(legend.position = "bottom")

ecdf_plot(seda, mean ~ grade) +
  scale_fill_brewer(palette = "Set2") +
  theme_minimal()
  
# Use within the dplyr pipeline
library(dplyr)
benchmarks %>% 
  mutate(season = factor(season, 
                         levels = c("Fall", "Winter", "Spring"))) %>% 
  ecdf_plot(math ~ ell + season + frl)