Bar plot function — bar_plot • slrplotfun

Plot a bar plot using ggplot2.

bar_plot(
  df,
  x_var,
  fill_var = NULL,
  y_var = NULL,
  style = c("stack", "fill", "dodge")[1],
  group_by_x_var = TRUE,
  y_percent = TRUE,
  percent_accuracy = 1,
  y_lim = NULL,
  y_breaks = 2000,
  x_breaks = NULL,
  y_breaks_end = 1e+05,
  title = NULL,
  subtitle = NULL,
  y_lab = NULL,
  x_lab = NULL,
  fill_colors = NULL,
  legend_labels = ggplot2::waiver(),
  label_breaks = ggplot2::waiver(),
  legend_row = NULL,
  legend_col = NULL,
  expand = FALSE,
  flip = FALSE,
  ...
)

Arguments

df: Data frame.
x_var: Variable for x axis, use string name. Recommended that x_var is in character in df.
fill_var: Variable for the different colors in bars, use string name. Use NULL if only one color for bars.
y_var: Variable for y axis, if NULL, count is used.
style: 3 different styles of bar plots, "stack", "fill", or "dodge". fill requires y_percent = TRUE.
group_by_x_var: Only relevant for style dodge. Boolean indicating if percentages should be for x_var or fill_var.
y_percent: If TRUE, y axis is in percent form. Otherwise in count form.
percent_accuracy: Set accuracy for scales::percent_format().
y_lim: Limit on y axis.
x_breaks, y_breaks: Length between each break on x/y axis.
y_breaks_end: Break end, default for 100,000. Works for all count values below that.
title: Plot title, NULL if no title.
subtitle: Small text under title, NULL if no subtitle.
y_lab: Y-axis label, use NULL for no label.
x_lab: X-axis label, use NULL for no label.
fill_colors: Color of the different categories in fill_var.
legend_labels: Label for each legend key.
label_breaks: Order of the legend keys.
legend_row: How many rows for the legends.
legend_col: How many columns for the legends.
expand: If TRUE, the margins around the data are kept.
flip: If TRUE, x and y axis changes positions making the bars go horizontally instead of vertically.
...: arguments passed to theme_slr()

Value

              ggplot object containing bar plot.

Examples

# Example data
df <- ggplot2::diamonds

# Style stack
bar_plot(df, 'color', 'cut', y_breaks = 2)

bar_plot(df, 'color', 'cut', y_percent = FALSE, y_breaks = 2000)


# Style stack with y variable included
df2 <-
  dplyr::group_by(df, color, cut) %>%
  dplyr::summarise(y = dplyr::n(), .groups = "drop_last")
bar_plot(df2, 'color', 'cut', y_var = 'y', y_breaks = 2)


# Style fill
bar_plot(df, 'color', 'cut', y_breaks = 10, style = 'fill')


# Style dodge grouped by x_var (color in this case)
bar_plot(df, 'color', 'cut', style = 'dodge', y_breaks = 10)

bar_plot(df, 'color', 'cut', style = 'dodge', y_percent = FALSE, y_breaks = 2000)


# Style dodge grouped by fill_var (cut in this case)
bar_plot(df, 'color', 'cut', style = 'dodge', group_by_x_var = FALSE, y_breaks = 10)


# Since bar_plot() returns ggplot object, it is possible to add more features
# Here we zoom the plot using coord_cartesian():
df3 <- dplyr::filter(df, clarity %in% c('SI1', 'SI2', 'VS2'))
bar_plot(df3, 'clarity', style = 'dodge', y_percent = FALSE, y_breaks = 2000) +
  ggplot2::coord_cartesian(ylim = c(8000, 14000))