More tests

Design principles

• tidyplots aims at delivering ready-to-publish plots, eliminating cumbersome formatting tasks.

• tidyplots uses the pipe %>% (instead of + like ggplot2) to build up plots. This way you can seamlessly pipe into and out of your plot. For example, coming from fram a data wrangeling pipeline you can generate a plot and directly pipe it into save_plot(). You can also call save_plot() or view_plot() in the middle of your pipeline to output intermediate results.

• tidyplots tries to reduce the complexity of ggplot2 by choosing sensible defaults. However, you take more detailed control by via the ... argument in each function. And if you need to add plain ggplot2 code, you can do this using add() function, which will preserve the tidyplots pipeline.

• All plots have absolute dimensions by default, defined in “mm”. These dimensions refer to the plotting area, not the entire plot, thus ensuring consistent lengths of x any y axes. The dimension can be changed with the width and height parameters, either when creating the plot with tidyplot() or later with adjust_plot_area_size(). If you want to restore the ggplot2 behavior that a plot automatically takes up all available space, set width and height to NA.

• tidyplots allows to split plots into subplots by a variable of choice, similar to ggplot2::facet_wrap(). However, split_plot() provides a more consistent look and allows to distribute subplots across multiple pages.

• color and fill are always mapped to the same variable. However, you can still do one of the following: (1) reduce the opacity of fill using the alpha or saturation arguments, (2) set color or fill to a constant hex color within an add_ function, or (3) set color or fill to NA within an add_ function to to prevent it from being displayed.

Workflow

The data to be plotted needs to be tidy in the sense that (1) each variable must have its own column, (2) each observation must have its own row and (3) each value must have its own cell. To learn more about tidy data, have a look at the excellent book R for Data Science.

To create a plot, you start with the tidyplot() function. Then you pipe through add_ functions, adjust_ functions, theme_ functions and remove_ functions, preferably in that order. split_plot() must be last in this sequence and can only be followed by save_plot().

Standing on the shoulders of giants

ggplot2 and the rest of the tidyverse by Hadley Wickham et al.
patchwork by Thomas Lin Pedersen
ggpubr by Alboukadel Kassambara
ggrastr by Evan Biederstedt
ggrepel by Kamil Slowikowski

Counts & Sums

library(tidyplots)

library(ggplot2)
library(patchwork)

study %>% 
  ggplot(aes(x = treatment, y = score, color = treatment, fill = treatment)) +
  stat_summary(fun = mean, geom = "bar", color = NA, width = 0.6, alpha = 0.3) +
  stat_summary(fun.data = mean_se, geom = "errorbar", linewidth = 0.25, width = 0.4) +
  geom_point(position = position_jitterdodge(jitter.width = 0.2)) +
  scale_y_continuous(expand = expansion(c(0, 0.1))) +
  theme_bw() +
      theme(
      panel.border = element_blank(),
      axis.line = element_line(linewidth = 0.25, colour = "black"),
      plot.margin = unit(c(0.5, 0.5, 0.5, 0.5), "mm"),
      plot.background = element_rect(fill = NA, colour = NA),
      legend.background = element_rect(fill = NA, colour = NA),
      legend.key = element_rect(fill = NA, colour = NA),
      strip.background = element_rect(fill = NA, colour = NA),
      panel.background = element_rect(fill = NA, colour = NA),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      axis.ticks = element_line(colour = "black", linewidth = 0.25)
    ) +
  plot_layout(widths = unit(50, "mm"), heights = unit(50, "mm"))

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points_jitter()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

animals %>%
  tidyplot(family, size, color = family) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points() %>% 
  sort_x_axis_labels(size) %>% 
  adjust_colors(c("#B0B1B3",
    "#F18823",
    "#E23130",
    "#1D5D83"))

study %>%
  tidyplot(treatment, score, color = treatment) %>%
  add_range_bar() %>% 
  add_data_points()

study %>%
  tidyplot(treatment, score) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_error_bar() %>%
  add_data_points() %>%
  add_mean_line()

study %>%
  tidyplot(treatment, score) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_error_bar() %>%
  add_data_points() %>%
  add_line(group = participant)

study %>%
  tidyplot(group, score, color = dose) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_error_bar() %>%
  add_data_points_beeswarm()

p1 <- 
  study %>%
  tidyplot(x = treatment, y = score, color = treatment) %>%
  add_mean_dash() %>%
  add_error_bar() %>%
  add_data_points_beeswarm()
p1

p2 <- 
  study %>%
  tidyplot(x = treatment, y = score, color = treatment) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_error_bar() %>%
  add_data_points_beeswarm()
p2

study %>%
  tidyplot(x = treatment, y = score, color = treatment) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_error_bar() %>%
  add_data_points_beeswarm(confetti = TRUE)

# testing adjust_colors() adjust_data_labels() split_plot() interference
# "split" always after "adjust"
# "adjust_color()" always after "adjust_data_labels()"

# providing less or more colors, not a problem
new_colors <-
  c("A" = "#B0B1B3",
    "B" = "#F18823",
    "C" = "#E23130",
    "D" = "#1D5D83")

# Providing less names, not a problem.
# Providing too many or wrong names -> Warning
new_names <-
  c("A" = "Regime A",
    "B" = "Regime B",
    "C" = "Regime C",
    "D" = "Regime D")

p2 %>% adjust_colors(new_colors)

p2 %>% reorder_x_axis_labels("C") %>% adjust_colors(new_colors)

p2 %>% adjust_colors(new_colors) %>% reorder_x_axis_labels("C")

p2 %>% adjust_colors(new_colors)

p2 %>% rename_x_axis_labels(new_names) %>% adjust_colors(new_colors)

p2 %>% adjust_colors(new_colors) %>% rename_x_axis_labels(new_names)

new_colors2 <-
  c("Regime A" = "#B0B1B3",
    "Regime B" = "#F18823",
    "Regime C" = "#E23130",
    "Regime D" = "#1D5D83")

p2 %>% adjust_colors(new_colors2)

p2 %>% rename_x_axis_labels(new_names) %>% adjust_colors(new_colors2)

p2 %>% adjust_colors(new_colors2) %>% rename_x_axis_labels(new_names)

# providing more colors, not a problem. Providing less colors -> Error


# during split plot, single subplots might only have NA for certain categories
# this has been fixed apparently by using drop = FALSE in 

# adjusting axis limits

p1 %>%
  adjust_y_axis(limits = c(-20, 60))

# x--y plots


p3 <- 
  animals %>% 
  tidyplot(x = weight, y = size, color = number_of_legs) %>% 
  add_data_points(confetti = TRUE) %>% 
  add_data_points(data = filter_rows(size > 300), color = "red") %>% 
  add_text_labels(data = max_rows(weight, n = 3), var = animal, color = "black")
  
p3

p3 %>% add_reference_lines(x = c(4000), y = c(200, 350))

# labels of out of bounds animals still appear
p3 %>%
  adjust_y_axis(limits = c(0, 600)) %>% 
  adjust_x_axis(limits = c(0, 1000))

energy_week %>%
  tidyplot(date, power, color = energy_type) %>%
  add_mean_line() %>%
  adjust_y_axis(limits = c(1000, 5000)) %>% 
  remove_plot_area_padding()

# adding titles and captions

p2 %>% add_title("My fancy long title")

p2 %>% add_caption("Here goes the fine print")

# adjusting axis titles

p2 %>% adjust_x_axis("My x axis title")

p2 %>% adjust_y_axis("My y axis title")

# adjusting legend

p2 %>% adjust_legend(title = "My legend title")

p2 %>% adjust_legend(title = "My legend title", position = "top")

# plotmath expressions

p2 %>% add_title(title = "$E==m*c^{2}~H[2]*O$")

p2 %>% add_caption(caption = "$E==m*c^{2}~H[2]*O$")

p2 %>% adjust_legend(title = "$E==m*c^{2}~H[2]*O$")

p2 %>% adjust_x_axis(title = "$Domino~E==m*c^{2}$")

p2 %>% adjust_y_axis(title = "$Domino~E==m*c^{2}$")

new_names <-
  c("A" = "$TNF*alpha$",
    "B" = "$IFN*gamma$",
    "C" = "plain text",
    "D" = "$H[2]*O$")

p2 %>% rename_x_axis_labels(new_names)

# adjust colors

new_colors <-
  c("A" = "#B0B1B3",
    "B" = "#F18823",
    "C" = "#E23130",
    "D" = "#1D5D83")

p2 %>% adjust_colors(new_colors)

p2 %>% adjust_colors(new_colors)

# add_heatmap()

gene_expression %>%
  tidyplot(x = sample, y = external_gene_name, color = expression) %>%
  add_heatmap(scale = "row") %>%
  adjust_plot_area_size(height = 90)

gene_expression %>% 
  tidyplot(x = sample, y = external_gene_name, color = expression) %>% 
  add_heatmap() %>% 
  adjust_plot_area_size(height = 90)

hm <- 
  gene_expression %>% 
  tidyplot(x = sample, y = external_gene_name, color = expression) %>% 
  add_heatmap(scale = "row") %>% 
  adjust_plot_area_size(height = 90)

hm %>% reorder_y_axis_labels("Bsn")

hm %>% reorder_y_axis_labels(c("Bsn", "Apol6"))

hm %>% reorder_y_axis_labels(sample)

hm %>% sort_y_axis_labels(direction)

hm %>% sort_y_axis_labels(direction, -padj)

hm

hm %>% reorder_y_axis_labels("Bsn")

hm %>% reorder_x_axis_labels("Hin_3")

hm %>% reorder_x_axis_labels(sample)

hm

hm %>% sort_y_axis_labels(expression)

hm %>% sort_y_axis_labels(direction)

hm %>% sort_y_axis_labels(direction, -padj)

hm %>% sort_x_axis_labels(group)

hm %>% sort_x_axis_labels(sample_type)

hm %>% sort_x_axis_labels(desc(sample_type))

hm %>% sort_x_axis_labels(condition)

hm

hm %>% reverse_x_axis_labels()

hm %>% reverse_y_axis_labels()

hm

hm %>% rename_x_axis_labels(new_names = c("Ein_1" = "Hallo"))

hm %>% rename_y_axis_labels(new_names = c("Bsn" = "Juhooooooooooooooo!",
                                     "Aip" = "Rabbithoooooooooooole"))

dinosaurs %>% 
  tidyplot(speed, dinosaur) %>% 
  add_mean_bar() %>% 
  sort_y_axis_labels(speed)

animals %>%
  tidyplot(family, size, color = family) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points() %>% 
  sort_x_axis_labels(size)

study %>% 
  tidyplot(treatment, score, color = treatment) %>% 
  add_data_points_beeswarm() %>% 
  reorder_x_axis_labels("D") %>% 
  sort_x_axis_labels(score) %>% 
  reverse_x_axis_labels() %>% 
  rename_x_axis_labels(c("A" = "Hallo"))

p4 <- 
  study %>% 
  tidyplot(group, score, color = dose) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

p4

p4 %>% reverse_color_labels()

p4

p4 %>% rename_color_labels(c("low" = "Nearly nothing", "high" = "Sky high"))

gene_expression %>% 
  tidyplot(x = sample, y = external_gene_name, color = expression) %>% 
  add_heatmap(scale = "row", rasterize = TRUE, rasterize_dpi = 20) %>% 
  sort_y_axis_labels(direction) %>%
  adjust_plot_area_size(height = 90)

gene_expression %>% 
  tidyplot(x = sample, y = external_gene_name, color = expression) %>% 
  add_heatmap(scale = "column") %>% 
  adjust_plot_area_size(height = 90)

# plotmath expressions

p2 %>% adjust_x_axis(title = "$Domino~E==m*c^{2}$")

p2 %>% adjust_y_axis(title = "$Domino~E==m*c^{2}$")

# next chapter

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm(rasterize = TRUE, rasterize_dpi = 50)

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(alpha = 0.3)

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_mean_dash() %>% 
  add_mean_value()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = group, y = score, color = dose) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = group, y = score, color = dose) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_mean_dash() %>% 
  add_mean_value()

study %>% 
  tidyplot(x = group, y = score, color = dose, dodge_width = 0.6) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_mean_dash() %>% 
  add_mean_value()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(width = 0.02) %>% 
  add_mean_dot()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_bar(width = 0.02) %>% 
  add_mean_dot() %>% 
  add_mean_value(vjust = -1, extra_padding = 0.2)

study %>% 
  tidyplot(x = group, y = score, color = dose) %>% 
  add_mean_bar(width = 0.02) %>% 
  add_mean_dot()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_range_bar() %>% 
  add_mean_dot()

# box and violin

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_range_bar() %>% 
  add_mean_dash()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_boxplot()

study %>% 
  tidyplot(x = dose, y = score, color = group) %>% 
  add_boxplot() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_violin() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = dose, y = score, color = group) %>% 
  add_violin() %>% 
  add_data_points_beeswarm()

# adjust_plot_area_size

p1

p1 %>% adjust_plot_area_size(width = 70)

p1 %>% adjust_plot_area_size(width = 44, height = 25)

p1 %>% adjust_plot_area_size(width = NA, height = NA)

# line plots

study %>% 
  tidyplot(x = group, y = score, group = participant, color = dose) %>% 
  add_line() %>% 
  add_area()

# summary lines

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_mean_line()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_mean_line() %>% 
  add_mean_dot()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_mean_line() %>% 
  add_mean_dot() %>% 
  add_error_bar()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_mean_line() %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_error_ribbon() %>% 
  add_mean_line() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_range_ribbon() %>% 
  add_mean_line() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_sd_ribbon() %>% 
  add_mean_line() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_ci95_ribbon() %>% 
  add_mean_line() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = treatment, y = score) %>% 
  add_curve_fit() %>% 
  add_data_points_beeswarm()

# grouped line

study %>% 
  tidyplot(x = dose, y = score, color = group) %>% 
  add_mean_line() %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = dose, y = score, color = group) %>% 
  add_error_ribbon() %>% 
  add_mean_line() %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = dose, y = score, color = group, dodge_width = 0) %>% 
  add_mean_line() %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

study %>% 
  tidyplot(x = dose, y = score, color = group, dodge_width = 0) %>% 
  add_error_ribbon() %>% 
  add_mean_line() %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

# apparently there can be no lines between doged data points with the same x axis label
# because they need to be in a different aes(group) to be dodged AND they need to in the same aes(group) to be connected by a geom_line()
# solution: bring them to different x axis labels

study %>% 
  tidyplot(x = treatment, y = score, color = group) %>% 
  add_line(group = participant, color = "grey") %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points()

study %>%
  tidyplot(treatment, score, color = group) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_error_bar() %>%
  add_data_points() %>%
  add_line(group = participant, color = "grey") %>%
  sort_x_axis_labels(dose)

# proportions

animals %>% 
  tidyplot(x = diet, color = family) %>% 
  add_barstack_absolute()

animals %>% 
  tidyplot(y = diet, color = family) %>% 
  add_barstack_absolute()

animals %>% 
  tidyplot(color = family) %>% 
  add_barstack_absolute()

animals %>% 
  tidyplot(x = number_of_legs, color = family) %>% 
  add_barstack_relative()

animals %>% 
  tidyplot(x = family, color = number_of_legs) %>% 
  add_barstack_relative()

animals %>% 
  tidyplot(x = family, y = speed, color = number_of_legs) %>% 
  add_barstack_relative()

animals %>%
  tidyplot(number_of_legs, color = family) %>%
  add_areastack_absolute()

animals %>%
  tidyplot(number_of_legs, color = family) %>%
  add_areastack_relative()

animals %>%
  tidyplot(number_of_legs, speed, color = family) %>%
  add_areastack_absolute()

animals %>%
  tidyplot(number_of_legs, speed, color = family) %>%
  add_areastack_relative()

# orientation = "y" needed

animals %>%
  tidyplot(y = number_of_legs, color = family) %>%
  add_areastack_absolute()

animals %>%
  tidyplot(y = number_of_legs, color = family) %>%
  add_areastack_relative()

animals %>%
  tidyplot(y = diet, color = family) %>%
  add_areastack_absolute()

animals %>%
  tidyplot(y = diet, color = family) %>%
  add_areastack_absolute() %>% 
  reverse_y_axis_labels()

animals %>%
  tidyplot(y = diet, x = speed, color = family) %>%
  add_areastack_absolute()

animals %>%
  tidyplot(speed, number_of_legs, color = family) %>%
  add_areastack_absolute()

animals %>%
  tidyplot(speed, number_of_legs, color = family) %>%
  add_areastack_absolute() %>% 
  reorder_y_axis_labels("2")

animals %>%
  tidyplot(speed, number_of_legs, color = family) %>%
  add_areastack_relative()

spendings %>% 
  tidyplot(x = amount, y = date, color = category) %>% 
  add_barstack_absolute()

spendings %>% 
  tidyplot(x = amount, y = date, color = category) %>% 
  add_area()

## end orientation = "y"

animals %>%
  tidyplot(number_of_legs, speed, color = family) %>%
  add_areastack_absolute()

animals %>%
  tidyplot(number_of_legs, speed, color = family) %>%
  add_areastack_relative()

animals %>% 
  tidyplot(color = diet) %>% 
  add_donut()

animals %>% 
  tidyplot(color = activity) %>% 
  add_donut()

animals %>% 
  tidyplot(color = number_of_legs) %>% 
  add_donut()

# spendings

spendings %>% 
  tidyplot(color = category) %>% 
  add_donut()

spendings %>% 
  tidyplot(y = amount, color = category) %>% 
  add_donut()

spendings %>% 
  tidyplot(color = category) %>% 
  add_pie()

spendings %>% 
  tidyplot(y = amount, color = category) %>% 
  add_pie()

##

spendings %>% 
  tidyplot(y = amount, color = category) %>% 
  add_barstack_absolute()

spendings %>% 
  tidyplot(x = amount, color = category) %>% 
  add_barstack_absolute()

spendings %>% 
  tidyplot(y = date, color = category) %>% 
  add_barstack_absolute()

spendings %>% 
  tidyplot(x = date, color = category) %>% 
  add_barstack_absolute()

spendings %>% 
  tidyplot(x = date, y = amount, color = category) %>% 
  add_barstack_absolute()

spendings %>% 
  tidyplot(color = category) %>% 
  add_barstack_absolute()

spendings %>% 
  tidyplot(x = category, y = amount, color = category) %>% 
  add_sum_bar() %>% 
  add_sum_value(accuracy = 1) %>% 
  adjust_x_axis(rotate_labels = TRUE)

spendings %>% 
  tidyplot(x = amount, y = category, color = category) %>% 
  add_sum_bar() %>% 
  add_sum_value(accuracy = 1, extra_padding = 0.3) %>% 
  adjust_x_axis()

spendings %>% 
  tidyplot(x = amount, y = category, color = category) %>% 
  add_sum_bar(alpha = 0.3) %>% 
  add_sum_dash() %>% 
  add_sum_value(accuracy = 1, extra_padding = 0.3) %>% 
  adjust_x_axis()

spendings %>% 
  tidyplot(x = date, y = amount, color = category) %>% 
  add_sum_dot() %>%
  add_sum_value(accuracy = 1)

spendings %>% 
  dplyr::mutate(cum_amount = cumsum(amount)) %>% 
  tidyplot(x = date, y = cum_amount) %>% 
  add_line() %>% 
  add_data_points()

spendings %>% 
  dplyr::mutate(cum_amount = cumsum(amount)) %>% 
  tidyplot(x = date, y = cum_amount) %>% 
  add_area() %>% 
  add_data_points()

spendings %>% 
  dplyr::mutate(cum_amount = cumsum(amount), .by = category) %>% 
  tidyplot(x = date, y = cum_amount, color = category) %>% 
  add_area()

spendings %>% 
  tidyr::complete(date, category, fill = list(title = NA, amount = 0)) %>% 
  dplyr::mutate(cum_amount = cumsum(amount), .by = category) %>% 
  tidyplot(x = date, y = cum_amount, color = category) %>% 
  add_area()

spendings %>% 
  tidyr::complete(date, category, fill = list(title = NA, amount = 0)) %>% 
  dplyr::mutate(cum_amount = cumsum(amount), .by = category) %>% 
  tidyplot(x = date, y = cum_amount, color = category) %>% 
  add_areastack_absolute(alpha = 0.3)

# subset data

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points()

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points(data = filter_rows(treatment == "C"))

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points(data = max_rows(score, n = 1, by = treatment))

# adjust_description

p1 %>% add_title(title = "New title")

# add stats

p2

p2 %>% add_stats_pvalue(ref.group = 1)

p2 %>% add_stats_pvalue(ref.group = 1, p.adjust.method = "bonferroni")

p2 %>% add_stats_asterisks(ref.group = 1, p.adjust.method = "bonferroni")

# grouped

p3 <- 
  study %>% 
  tidyplot(x = dose, y = score, color = group) %>% 
  add_mean_bar(alpha = 0.3) %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm()

p3

p3 %>% add_stats_pvalue()

p3 %>% add_stats_pvalue(p.adjust.method = "bonferroni")

p3 %>% add_stats_asterisks(p.adjust.method = "bonferroni")

# animals

animals %>%
  tidyplot(x = family) %>%
  add_count_bar(alpha = 0.3) %>% 
  add_count_value(accuracy = 1) %>% 
  add_count_dash()

animals %>%
  tidyplot(x = family, y = weight) %>%
  add_sum_bar(alpha = 0.3) %>% 
  add_sum_value() %>% 
  add_sum_dash()

animals %>%
  tidyplot(x = family, y = weight) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_mean_value()

animals %>%
  tidyplot(x = weight, y = family) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_mean_value(extra_padding = 0.3)

animals %>%
  tidyplot(x = family, y = speed) %>%
  add_mean_bar(alpha = 0.3) %>%
  add_error_bar() %>%
  add_data_points_beeswarm() %>%
  adjust_x_axis(rotate_labels = TRUE)

# split_plot

gene_expression %>% 
  tidyplot(x = condition, y = expression, color = sample_type) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm() %>% 
  adjust_x_axis(title = "") %>% 
  split_plot(by = external_gene_name, ncol = 4, nrow = 4)

gene_expression %>% 
  tidyplot(x = condition, y = expression, color = sample_type) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm() %>% 
  adjust_x_axis(title = "") %>% 
  split_plot(by = external_gene_name, ncol = 4, nrow = 4, widths = 15, heights = 15)

gene_expression %>% 
  tidyplot(x = condition, y = expression, color = sample_type) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm() %>% 
  adjust_x_axis(title = "") %>% 
  split_plot(by = external_gene_name, ncol = 4, nrow = 4, widths = NA, heights = NA)

# stats with split_plot

gene_expression %>% 
  tidyplot(x = condition, y = expression, color = sample_type) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm() %>% 
  add_stats_pvalue(include_info = FALSE) %>% 
  adjust_x_axis(title = "") %>% 
  split_plot(by = external_gene_name, ncol = 4, nrow = 4, widths = 25, heights = 25)

gene_expression %>% 
  tidyplot(x = condition, y = expression, color = sample_type) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm() %>% 
  add_stats_asterisks(include_info = FALSE) %>% 
  adjust_x_axis(title = "") %>% 
  split_plot(by = external_gene_name, ncol = 4, nrow = 4, widths = 25, heights = 25)

# save_plot

gene_expression %>% 
  tidyplot(x = condition, y = expression, color = sample_type) %>% 
  add_mean_dash() %>% 
  add_error_bar() %>% 
  add_data_points_beeswarm() %>% 
  adjust_x_axis(title = "") %>% 
  split_plot(by = external_gene_name, ncol = 4, nrow = 4) %>%
  save_plot(filename = "test_multipage.pdf")

# save intermediate plots

study %>% 
  tidyplot(x = treatment, y = score, color = treatment) %>% 
  add_mean_dash() %>% 
  save_plot(filename = "test1.pdf") %>%
  add_error_bar() %>% 
  save_plot(filename = "test2.pdf") %>% 
  add_data_points_beeswarm() %>% 
  save_plot(filename = "test3.pdf")