There are three main verbs in tidyplots:
add 👉 to add element to the plots.adjust 👉 to modify elements in the plot.remove 👉 to remove elements form the plot.library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment)
p2 <- p1 |>
add_mean_bar() |>
add_title(title = "p2: add bars and title")
p3 <- p2 |>
adjust_title(title = "p3: adjust y axis title (red)") |>
adjust_y_axis_title(color = "#bb5566")
p4 <- p3 |>
adjust_title(title = "p4: remove y axis title") |>
remove_y_axis_title()Explore the package index (https://jbengler.github.io/tidyplots/reference/index.html) for a full list of functions.
tidyplots.add functions (e.g. bar plot)Most add funtions in tidyplots follow the same scheme
add_<statistical-entity>_<graphical-representation>
add_mean_dot(), add_mean_dash(), add_mean_bar(), add_median_bar(), add_sum_bar() etc.
add_sem_errorbar(), add_sd_errorbar() etc.
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(x = treatment, y = score, color = treatment) |>
add_sum_bar() |> add_title(title = "p1: sum bar")
p2 <- study |>
tidyplot(x = treatment, y = score, color = treatment) |>
add_median_bar() |> add_title(title = "p2: median bar")
p3 <- study |>
tidyplot(x = treatment, y = score) |>
add_mean_bar() |> add_title(title = "p3: mean bar")
p4 <- p3 |>
adjust_title(title = paste("p4: mean bar", "points", sep = "\n")) |>
add_data_points_beeswarm(white_border = TRUE, size = 2)
p5 <- p4 |>
adjust_title(title = paste("p5: mean bar", "points", "line", sep = "\n")) |>
add_mean_line()
p6 <- p5 |>
adjust_title(title = paste(
"p6: mean bar", "points", "line", "area", sep = "\n")) |>
add_mean_area(alpha = 0.6)
add functions.alpha vs. saturation (e.g. bar plot)library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_annotation_text(
text = c("see me?", "see me?"),
x = c(2, 3),
y = 100,
angle = 90,
color = "#bb5566",
fontface = "bold") |>
add_title(title = "p1") |>
adjust_y_axis(limit = c(0, 200)) |>
adjust_x_axis(limit = c(1, 4))
p2 <- p1 |>
adjust_title(title = "p2") |>
add_sum_bar()
p3 <- p1 |>
adjust_title(title = "p3: decreased intensity via `alpha`") |>
add_sum_bar(alpha = 0.5)
p4 <- p2 |>
adjust_title(title = "p4: decreased intensity via `saturation`") |>
adjust_colors(
new_colors = c(
"#ddaa33",
"#bb5566",
"#004488",
"#000000"),
saturation = 0.5)
alpha vs. saturation.library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(group, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
group score
<chr> <dbl>
1 placebo 2
2 placebo 4
3 placebo 5
4 placebo 4
5 placebo 6
6 placebo 9
7 placebo 8
8 placebo 12
9 placebo 15
10 placebo 16# Plot
p1 <- study |>
tidyplot(
x = group,
y = score,
color = group) |>
add_median_bar() |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: width reduced") |>
adjust_size(width = 20)
p3 <- p1 |>
adjust_title(title = "p3: height reduced") |>
adjust_size(height = 20)
p4 <- p1 |>
adjust_title(title = "p4: width and height enlarged") |>
adjust_size(
width = 8,
height = 8,
unit = "cm")All plots in tidyplots have absolute dimensions.
By default this is 50 mm in width and 50 mm in height.
If you want the plot to fill all available space (ggplot2 default), do adjust_size(width = NA, height = NA).
Absolute dimention is to be the only reliable way to maintain consistent proportions of plot area size, font size, line width, etc.
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_mean_bar(alpha = 0.4) |>
add_sem_errorbar() |>
add_data_points_beeswarm() |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: remove legend") |>
remove_legend()
p3 <- p2 |>
adjust_title(title = "p3: remove x axis title") |>
remove_x_axis_title()
p4 <- p3 |>
adjust_title(title = "p4: remove x axis labels") |>
remove_x_axis_labels()
p5 <- p4 |>
adjust_title(title = "p5: remove y axis title") |>
remove_y_axis_title()
p6 <- p5 |>
adjust_title(title = "p6: remove y axis labels") |>
remove_y_axis_labels()
library(tidyplots)
# View top 10 rows of the columns used
animals |>
dplyr::select(weight, size, animal) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
weight size animal
<dbl> <dbl> <chr>
1 0.00012 1.5 Honeybee
2 0.00045 10 Monarch Butterfly
3 0.0002 10 Dragonfly
4 0.00002 2.5 Firefly
5 0.003 17 Praying Mantis
6 0.0000025 1 Mosquito
7 0.000005 4 Ant
8 0.00001 0.8 Ladybug
9 0.0002 7.5 Grasshopper
10 0.0004 9 Butterfly # Plot
p1 <- animals |>
tidyplot(x = weight, y = size) |>
add_data_points(color = "#ddaa33") |>
add_title(title = "p1")
p2 <- p1 |>
add_data_points(
data = max_rows(weight, n = 3),
color = "#bb5566") |>
adjust_title(title = "p2: point highlight")
p3 <- p2 |>
add_data_labels_repel(data = max_rows(weight, n = 3),
label = animal, color = "black") |>
adjust_title(title = paste(
"p3: point highlight",
"point labels",
sep = "\n"))
p4 <- p2 |>
add_data_labels_repel(data = max_rows(weight, n = 3),
background = TRUE, background_color = "#ffcccc",
min.segment.length = 0,
label = animal,
color = "black") |>
add_title(title = paste(
"p4: point highlight",
"point labels",
"label background",
sep = "\n"))
library(tidyplots)
# View top 10 rows of the columns used
animals |>
dplyr::select(weight, size, animal) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
weight size animal
<dbl> <dbl> <chr>
1 0.00012 1.5 Honeybee
2 0.00045 10 Monarch Butterfly
3 0.0002 10 Dragonfly
4 0.00002 2.5 Firefly
5 0.003 17 Praying Mantis
6 0.0000025 1 Mosquito
7 0.000005 4 Ant
8 0.00001 0.8 Ladybug
9 0.0002 7.5 Grasshopper
10 0.0004 9 Butterfly # Plot
p0 <- animals |>
tidyplot(x = weight, y = size)
p1 <- p0 |>
add_title(title = "p1: filled circle (default)") |>
add_data_points(color = "#ddaa33", shape = 19)
p2 <- p0 |>
add_title(title = "p2: empty circle") |>
add_data_points(color = "#ddaa33", shape = 1)
p3 <- p0 |>
add_title(title = "p3: filled triangle") |>
add_data_points(color = "#ddaa33", shape = 17)
p4 <- p0 |>
add_title(title = "p4: empty triangle") |>
add_data_points(color = "#ddaa33", shape = 2)
p5 <- p0 |>
add_title(title = "p5: enlarged and filled triangle") |>
add_data_points(
color = "#ddaa33", fill = "#004488", shape = 24, size = 2)
p6 <- p0 |>
add_title(title = paste(
"p6: enlarged and filled triangle", "wider border", sep = "\n")) |>
add_data_points(
color = "#ddaa33", fill = "#004488", shape = 24, size = 2,
stroke = 2)See Section 6.1 and Section 6.2 for details about shape.
library(tidyplots)
# View top 10 rows of the columns used
animals |>
dplyr::select(weight, speed) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
weight speed
<dbl> <dbl>
1 0.00012 0.72
2 0.00045 0.432
3 0.0002 2.09
4 0.00002 0.0432
5 0.003 0.144
6 0.0000025 0.054
7 0.000005 0.00216
8 0.00001 0.054
9 0.0002 0.576
10 0.0004 0.432 p1 <- animals |>
tidyplot(x = weight, y = speed) |>
add_data_points(white_border = TRUE) |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: reference line (dotdash)") |>
add_reference_lines(
x = 4000, y = c(100, 200), linetype = "dotdash")
p3 <- p1 |>
adjust_title(title = "p3: annotation line (dashed)") |>
add_annotation_line(
x = 2000, xend = 2000, y = 20, yend = 150, linetype = "dashed") |>
add_annotation_text(text = "line", x = 2000, y = 160)
p4 <- p1 |>
adjust_title(title = "p4: annotation line with arrowhead") |>
add_annotation_line(
x = 2000, xend = 2000, y = 20, yend = 150,
arrow = grid::arrow(length = grid::unit(2, "mm"))) |>
add_annotation_text(text = "line with arrowhead", x = 2000, y = 160)
p5 <- p1 |>
adjust_title(title = "p5: annotation rectangle (dotted)") |>
add_annotation_rectangle(
xmin = 2000, xmax = 6200, ymin = 20, ymax = 150,
linetype = "dotted", color = "#000000", fill = NA)
p6 <- p1 |>
adjust_title(title = "p6: filled annotation rectangle (dotted)") |>
add_annotation_rectangle(
xmin = 2000, xmax = 6200, ymin = 20, ymax = 150,
linetype = "dotted", color = "#000000", fill = "#eecc66")
library(tidyplots)
# View the top 10 rows of the columns used
study |>
dplyr::select(group, score, dose) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
group score dose
<chr> <dbl> <chr>
1 placebo 2 high
2 placebo 4 high
3 placebo 5 high
4 placebo 4 high
5 placebo 6 high
6 placebo 9 low
7 placebo 8 low
8 placebo 12 low
9 placebo 15 low
10 placebo 16 low # Plot
p1 <- study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm(white_border = TRUE) |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(
title = "p2: rename x axis levels") |>
rename_x_axis_levels(
new_names = c(
"placebo" = "A",
"treatment" = "B"))
p3 <- p2 |>
adjust_title(
title = "p3: rename color levels") |>
rename_color_levels(
new_names = c(
"high" = "up",
"low" = "down"))
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(group, score, dose) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
group score dose
<chr> <dbl> <chr>
1 placebo 2 high
2 placebo 4 high
3 placebo 5 high
4 placebo 4 high
5 placebo 6 high
6 placebo 9 low
7 placebo 8 low
8 placebo 12 low
9 placebo 15 low
10 placebo 16 low p1 <- study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm() |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: assign colors") |>
adjust_colors(
new_colors = c(
"high" = "#bb5566",
"low" = "#ddaa33"))
p3 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm() |>
add_title(title = "p3")
p4 <- p3 |>
adjust_title(title = "p4: assign colors") |>
adjust_colors(
new_colors = c(
"A" = "#000000",
"B" = "#004488",
"C" = "#bb5566",
"D" = "#ddaa33"))
library(tidyplots)
# Define a custom color scheme
high_contrast_qualitative_colour <- new_color_scheme(
c("#000000",
"#004488",
"#bb5566",
"#ddaa33"),
name = "high_contrast_qualitative_colour")
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm(white_border = TRUE) |>
adjust_legend_position(position = "bottom") |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: a custom color scheme") |>
adjust_colors(new_colors = high_contrast_qualitative_colour)
library(tidyplots)
# View top 10 rows of the columns used
spendings |>
dplyr::select(category, amount) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
category amount
<chr> <dbl>
1 Food 100
2 Transportation 40
3 Housing 1200
4 Utilities 80
5 Education 75
6 Insurance 200
7 Food 60
8 Utilities 50
9 Food 90
10 Transportation 40# Plot
p1 <- spendings |>
tidyplot(
x = category,
y = amount,
color = category) |>
add_sum_bar(alpha = 0.2) |>
add_sum_dash() |>
add_title(title = "p1") |>
adjust_x_axis(rotate_labels = TRUE) |>
remove_legend()
p2 <- p1 |>
adjust_title(title = "p2: add sum values") |>
add_sum_value(accuracy = 1)
p3 <- p1 |>
adjust_title(title = paste(
"p3: add sum values in black",
"one decimal place",
sep = "\n")) |>
add_sum_value(accuracy = 0.1, color = "black")
p4 <- p1 |>
adjust_title(title = paste(
"p4: add sum values in black",
"rotate 45",
sep = "\n")) |>
add_sum_value(
accuracy = 1,
color = "black",
angle = 45,
hjust = -0.1) |>
adjust_padding(top = 0.2)
library(tidyplots)
# View the top 10 rows of the columns used
spendings |>
dplyr::select(category, amount) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
category amount
<chr> <dbl>
1 Food 100
2 Transportation 40
3 Housing 1200
4 Utilities 80
5 Education 75
6 Insurance 200
7 Food 60
8 Utilities 50
9 Food 90
10 Transportation 40# Plot
p1 <- spendings |>
tidyplot(
x = category,
y = amount,
color = category) |>
add_sum_bar(alpha = 0.2) |>
add_sum_dash() |>
add_sum_value(
accuracy = 1,
color = "black") |>
adjust_x_axis(rotate_labels = TRUE) |>
add_title(title = "p1") |>
remove_legend()
p2 <- p1 |>
adjust_title(title = "p2: sort x axis levels by score") |>
sort_x_axis_levels()
p3 <- p1 |>
adjust_title(title = "p3: sort x axis levels by score in reverse") |>
sort_x_axis_levels(.reverse = TRUE)
p4 <- p1 |>
adjust_title(title = "p4: sort color levels by score") |>
sort_color_levels()
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm(white_border = TRUE) |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: custom x axis level order") |>
reorder_x_axis_levels("C", "D", "A", "B")
p3 <- p1 |>
adjust_title(title = "p3: reverse x axis levels") |>
reverse_x_axis_levels()
p4 <- p1 |>
adjust_title(title = "p4: sort x axis levels by score") |>
sort_x_axis_levels()
p5 <- p1 |>
adjust_title(title = "p5: custom color level order") |>
reorder_color_levels("C", "D")
p6 <- p1 |>
adjust_title(title = "p6: sort color levels by score") |>
sort_color_levels()
library(tidyplots)
# View top 10 rows of the columns used
spendings |>
dplyr::select(category, amount) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
category amount
<chr> <dbl>
1 Food 100
2 Transportation 40
3 Housing 1200
4 Utilities 80
5 Education 75
6 Insurance 200
7 Food 60
8 Utilities 50
9 Food 90
10 Transportation 40# Plot
p1 <- spendings |>
tidyplot(
x = category,
y = amount,
color = category) |>
add_sum_bar() |>
add_title(title = "p1") |>
adjust_x_axis(rotate_labels = TRUE)
p2 <- p1 |>
adjust_title(title = "p2: other bars in gray") |>
adjust_colors(
new_colors = c(
"Health" = "#994455",
na.value = "#dddddd"))
p3 <- p1 |>
adjust_title(title = paste(
"p3: other bars in gray",
"sum value",
sep = "\n")) |>
add_sum_value(accuracy = 1) |>
adjust_colors(
new_colors = c(
"Health" = "#994455",
na.value = "#dddddd"))
p4 <- p3 |>
adjust_title(title = paste(
"p4: other bars in gray",
"sum value",
"sort x axis",
sep = "\n")) |>
sort_x_axis_levels()
library(tidyplots)
# View top 10 rows of the columns used
spendings |>
dplyr::select(amount, category) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
amount category
<dbl> <chr>
1 100 Food
2 40 Transportation
3 1200 Housing
4 80 Utilities
5 75 Education
6 200 Insurance
7 60 Food
8 50 Utilities
9 90 Food
10 40 Transportation# Plot
p1 <- spendings |>
tidyplot(x = category, y = amount, color = category) |>
add_sum_bar(alpha = 0.2) |>
add_sum_dash() |>
add_sum_value(accuracy = 1, color = "black") |>
add_title(title = "p1") |>
sort_x_axis_levels() |>
adjust_x_axis(rotate_labels = TRUE) |>
adjust_colors(new_colors = c(
"#eecc66", "#ee99aa", "#6699cc",
"#997700", "#994455", "#004488", "#000000")) |>
remove_legend() |>
adjust_padding(right = 0.2)
p2 <- spendings |>
tidyplot(x = amount, y = category, color = category) |>
add_sum_bar(alpha = 0.2) |>
add_sum_dash() |>
add_sum_value(accuracy = 1, color = "black") |>
adjust_title(title = "p2: horizontal") |>
sort_y_axis_levels() |>
adjust_colors(new_colors = c(
"#eecc66", "#ee99aa", "#6699cc",
"#997700", "#994455", "#004488", "#000000")) |>
remove_legend()
p3 <- p2 |>
adjust_title(title = "p3: adjust right padding") |>
adjust_padding(right = 0.2)
p4 <- p2 |>
adjust_title(title = "p4: adjust x axis limit") |>
adjust_x_axis(limits = c(0, 1500))
The sum value of Housing in p2 is clipped (the actual value should be 1,200).
One way to solve this is via p3 (i.e. piping with adjust_padding(right = 0.2)).
The second way is via p4 (i.e. piping with adjust_x_axis(limits = c(0, 1500)).
The third way is to pipe with remove_clipping() if you install a specific commit (on 2026-04-01), i.e. pak("jbengler/tidyplots@4a08bfe") via the pak package, or use a later version.
library(tidyplots)
# View top 10 rows of the columns used
gene_expression |>
dplyr::select(group, expression, external_gene_name) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
group expression external_gene_name
<chr> <dbl> <chr>
1 Hin 2.20 Apol6
2 Hin 2.20 Apol6
3 Hin 2.66 Apol6
4 Hin 2.65 Apol6
5 Hin 3.44 Apol6
6 Ein 5.03 Apol6
7 Ein 5.31 Apol6
8 Ein 5.37 Apol6
9 Ein 5.54 Apol6
10 Ein 5.65 Apol6 # Plot
gene_expression |>
tidyplot(
x = group,
y = expression,
color = group) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm(
white_border = TRUE) |>
view_plot(
title = "All") |>
view_plot(
data = filter_rows(
external_gene_name %in% c(
"Apol6",
"Col5a3")),
title = "Apol6 & Col5a3")
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(group, score, dose) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
group score dose
<chr> <dbl> <chr>
1 placebo 2 high
2 placebo 4 high
3 placebo 5 high
4 placebo 4 high
5 placebo 6 high
6 placebo 9 low
7 placebo 8 low
8 placebo 12 low
9 placebo 15 low
10 placebo 16 low # eval: false
# Define a global style
my_style <- function(x) {
x |>
adjust_colors(new_colors = c("#bb5566", "#ddaa33")) |>
adjust_size(width = 50, height = 30, unit = "mm") |>
adjust_title(fontsize = 10, color = "#004488")}
# Plot
p1 <- study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_mean_bar() |>
adjust_size() |>
add_title("p1: default style")
# Set global options
tidyplots_options(my_style = my_style)
# Plot
p2 <- study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_mean_bar() |>
add_title("p2: defined global style")
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(group, score, dose) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
group score dose
<chr> <dbl> <chr>
1 placebo 2 high
2 placebo 4 high
3 placebo 5 high
4 placebo 4 high
5 placebo 6 high
6 placebo 9 low
7 placebo 8 low
8 placebo 12 low
9 placebo 15 low
10 placebo 16 low # Define a style
my_style <- function(x) {
x |>
adjust_colors(new_colors = c("#bb5566", "#ddaa33")) |>
adjust_size(width = 50, height = 30, unit = "mm") |>
adjust_title(fontsize = 10, color = "#ddaa33")}
p1 <- study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_mean_bar() |>
adjust_size() |>
add_title("p1: default style")
p2 <- study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_mean_bar() |>
add_title("p2: defined custom style") |>
my_style()
library(tidyplots)
# View top 10 rows of the columns used
energy_week |>
dplyr::select(date, power, energy_source) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
date power energy_source
<dttm> <dbl> <fct>
1 2023-09-03 22:00:00 0 Nuclear
2 2023-09-03 22:00:00 2634. Hydro Run-of-River
3 2023-09-03 22:00:00 4711. Biomass
4 2023-09-03 22:00:00 8399. Fossil brown coal / lignite
5 2023-09-03 22:00:00 1726. Fossil hard coal
6 2023-09-03 22:00:00 401. Fossil oil
7 2023-09-03 22:00:00 4900. Fossil gas
8 2023-09-03 22:00:00 17.9 Geothermal
9 2023-09-03 22:00:00 232. Hydro water reservoir
10 2023-09-03 22:00:00 586 Hydro pumped storage # Define a style
my_style <- function(x) {
x |>
adjust_font(family = "mono", face = "bold") |>
remove_x_axis_ticks() |>
remove_y_axis_ticks() |>
reverse_color_levels() |>
adjust_x_axis(labels = scales::label_date( # display dates as "Sep 05" etc.
format = "%b %d", locale = "en"))}
# Plot
p1 <- energy_week |>
tidyplot(
x = date,
y = power,
color = energy_source) |>
add_areastack_relative() |>
add_title(title = "p1: areastack relative")
p2 <- p1 |>
adjust_title(
title = paste("p3: areastack relative", "defined custom style", sep = "\n")) |>
adjust_legend_position(position = "left") |>
my_style()
p3 <- energy_week |>
tidyplot(
x = date,
y = power,
color = energy_source) |>
add_areastack_absolute() |>
add_title(
title = paste("p3: areastack absolute", "defined custom style", sep = "\n")) |>
my_style()
Please note the Chinese character “月” on the x-axis of p1, as the code was run on an x86_64-w64-mingw32/x64 platform with the locale set to Chinese (Simplified)_China.utf8.
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Define a style to reuse
my_style <- function(x) {
x |>
add_mean_bar(alpha = 0.4) |>
add_sem_errorbar() |>
add_data_points_beeswarm()}
p1 <- study |>
tidyplot(x = treatment, y = score, color = treatment) |>
my_style() |>
add_title(title = "p1")
p2 <- study |>
tidyplot(x = treatment, y = score, color = treatment, paper = "#dddddd") |>
my_style() |>
add_title(title = "p2: paper/background color changed")
p3 <- study |>
tidyplot(x = treatment, y = score, color = treatment, paper = "#dddddd", ink = "#004488") |>
my_style() |>
add_title(title = "p3: paper/background and ink/foreground\n color changed")# Set global options
tidyplots_options(paper = "#dddddd", ink = "#004488")
p4 <- study |>
tidyplot(x = treatment, y = score, color = treatment) |>
my_style() |>
add_title(
title = "p4: paper/background and ink/foreground\n color changed via 'tidyplots_options()'")
# Reset tidyplots options
tidyplots_options()
The title color of p3 and p4 should be in the ink color (i.e. blue #004488). However, it has been changed to red (i.e. #bb5566) to follow the global setting applied throughout this book.
By the way, it seems unreasonable to change the default paper and ink colors for scientific figures.
library(tidyplots)
# View top 10 rows of the columns used
spendings |>
dplyr::select(category, amount) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
category amount
<chr> <dbl>
1 Food 100
2 Transportation 40
3 Housing 1200
4 Utilities 80
5 Education 75
6 Insurance 200
7 Food 60
8 Utilities 50
9 Food 90
10 Transportation 40# Plot
p1 <- spendings |>
tidyplot(
x = category,
y = amount,
color = category) |>
add_sum_bar() |>
add_title(title = "p1") |>
adjust_x_axis(rotate_labels = TRUE) |>
adjust_size(width = 40) |>
sort_x_axis_levels()
p2 <- p1 |>
adjust_title(title = "p2: colors_discrete_friendly (3rd & 4th colors)") |>
adjust_colors(new_colors = colors_discrete_friendly[3:4])
p3 <- p1 |>
adjust_title(title = "p3: custom color scheme (2 colors)") |>
adjust_colors(new_colors = c("#eecc66", "#997700"))
p4 <- p1 |>
adjust_title(title = "p4: colors_continuous_cividis (265 colors)") |>
adjust_colors(new_colors = colors_continuous_cividis)Tidyplots comes with a number of default color schemes.
A color scheme (default or custom) might contain limited number of colors (e.g. p3), tidyplots will automatically fill up the gaps between colors to deliver exactly the number that is required for the plot.
Similarly, when a color scheme contains more colors than needed (e.g. p4), tidyplots will select the required number of colors by attempting to evenly sample from the supplied color vector.
library(tidyplots)
# View top 10 rows of the columns used
pca |>
dplyr::select(pc1, pc2, group) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
pc1 pc2 group
<dbl> <dbl> <chr>
1 -10.8 -9.17 Hin
2 -11.5 -7.84 Hin
3 -10.2 -9.11 Hin
4 -8.18 -7.30 Hin
5 -10.7 -8.15 Hin
6 -17.6 3.75 Ein
7 -20.8 7.37 Ein
8 -15.8 -0.644 Ein
9 -17.6 4.16 Ein
10 -16.0 4.13 Ein # Plot
p1 <- pca |>
tidyplot(
x = pc1,
y = pc2,
color = group) |>
add_data_points(white_border = TRUE) |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: ellipses") |>
add_ellipse()
p3 <- p1 |>
adjust_title(title = "p3: dotted ellipses") |>
add_ellipse(linetype = "dotted")
p4 <- p1 |>
adjust_title(title = "p4: filled ellipses") |>
add_ellipse(geom = "polygon", alpha = 0.2)
p5 <- p1 |>
adjust_title(title = paste("p5: filled ellipses",
"reduced level", sep = "\n")) |>
add_ellipse(geom = "polygon", alpha = 0.2, level = 0.8)
p6 <- p1 |>
adjust_title(title = paste("p6: filled ellipses",
"reduced level", "'norm' type", sep = "\n")) |>
add_ellipse(geom = "polygon", alpha = 0.2, level = 0.8, type = "norm")
library(tidyplots)
# View top 10 rows of the column used
climate |>
dplyr::select(month, max_temperature) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
month max_temperature
<chr> <dbl>
1 01 -1.49
2 02 3.37
3 03 5.05
4 04 8.34
5 05 16.2
6 06 18.2
7 07 20.5
8 08 18.2
9 09 18.3
10 10 14.2 # Plot
p1 <- climate |>
tidyplot(x = month, y = max_temperature) |>
add_data_points_beeswarm(alpha = 0.3, color = "#bbbbbb", white_border = TRUE) |>
add_mean_line() |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: sem ribbon and errorbar") |>
add_sem_ribbon() |>
add_sem_errorbar()
p3 <- p1 |>
adjust_title(title = "p3: sd ribbon and errorbar") |>
add_sd_ribbon() |>
add_sd_errorbar()
p4 <- p1 |>
adjust_title(title = "p4: ci95 ribbon and errorbar") |>
add_ci95_ribbon() |>
add_ci95_errorbar()
p5 <- p1 |>
adjust_title(title = "p5: range ribbon and bar") |>
add_range_ribbon() |>
add_range_errorbar()
p6 <- p1 |>
adjust_title(title = paste(
"p6: range ribbon (defined color)",
"range bar",
sep = "\n")) |>
add_range_ribbon(color = "#bb5566", fill = "#bb5566") |>
add_range_errorbar()
library(tidyplots)
# View top 10 rows of the columns used
time_course |>
dplyr::select(day, score, treatment) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 3
day score treatment
<dbl> <dbl> <chr>
1 0 0 untreated
2 0 0 untreated
3 0 0 untreated
4 0 0 untreated
5 0 0 untreated
6 0 0 untreated
7 0 0 untreated
8 0 0 untreated
9 0 0 untreated
10 0 0 untreated# Plot
p1 <- time_course |>
tidyplot(
x = day,
y = score,
color = treatment) |>
add_mean_line() |>
add_mean_dot(size = 1.5) |>
add_title(title = "p1: mean line/dot")
p2 <- p1 |>
adjust_title(title = "p2: sem ribbon") |>
add_data_points(alpha = 0.3) |>
add_sem_ribbon()
p3 <- p1 |>
adjust_title(title = "p3: sem errorbar") |>
add_sem_errorbar(width = 1.5)
p4 <- p1 |>
adjust_title(title = paste("p4: sem ribbon", "sem errorbar", sep = "\n")) |>
add_sem_ribbon() |>
add_sem_errorbar(width = 1.5)
p5 <- p1 |>
adjust_title(title = "p5: sd errorbar") |>
add_sd_errorbar(width = 1.5)
p6 <- p1 |>
adjust_title(title = paste("p6: sd ribbon", "sd errorbar", sep = "\n")) |>
add_sd_ribbon() |>
add_sd_errorbar(width = 1.5)
library(tidyplots)
# View top 10 rows of the columns used
time_course |>
dplyr::select(score, day) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
score day
<dbl> <dbl>
1 0 0
2 0 0
3 0 0
4 0 0
5 0 0
6 0 0
7 0 0
8 0 0
9 0 0
10 0 0# Plot
p1 <- time_course |>
tidyplot(
x = score,
y = day) |>
add_title(title = "p1") |>
add_data_points(alpha = 0.2, color = "#dddddd")
p2 <- p1 |>
adjust_title(title = "p2: orientation = y") |>
add_curve_fit(
orientation = "y", color = "#bb5566", fill = "#bb5566")
p3 <- p1 |>
adjust_title(title = "p3: orientation = x") |>
add_curve_fit(
orientation = "x", color = "#bb5566", fill = "#bb5566")
p4 <- p1 |>
adjust_title(title = paste("p4: orientation = x", "sem ribbon", sep = "\n")) |>
add_curve_fit(
orientation = "x", color = "#bb5566", fill = "#bb5566") |>
add_sem_ribbon(fill = "#000000")
p5 <- p1 |>
adjust_title(title = paste("p5: orientation = x", "method: 'lm'", sep = "\n")) |>
add_curve_fit(
method = "lm", orientation = "x", color = "#bb5566", fill = "#bb5566")
p6 <- p1 |>
adjust_title(title = paste("p6: orientation = x", "method: 'gam'", sep = "\n")) |>
add_curve_fit(
method = "gam", orientation = "x", color = "#bb5566", fill = "#bb5566")
# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# eval: false
# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_data_points() |>
add_mean_bar(alpha = 0.4) |>
adjust_title(title = "p1") |>
add_sem_errorbar()
p2 <- p1 |>
adjust_title(
title =
"$p2:~italic(M)==frac(1,italic(c)(italic(c)-1))*sum(AUC[italic(list(k,l))])$",
family = "sans") |>
add_caption(caption = "family = 'sans'")
p3 <- p1 |>
adjust_title(
title =
"$p3:~italic(M)==frac(1,italic(c)(italic(c)-1))*sum(AUC[italic(list(k,l))])$",
family = "serif") |>
add_caption(caption = "family = 'serif'")
p4 <- p1 |>
adjust_title(
title =
"$p4:~italic(M)==frac(1,italic(c)(italic(c)-1))*sum(AUC[italic(list(k,l))])$",
family = "mono") |>
add_caption(caption = "family = 'mono'")Just surrond your string by “$” characters.
Plotmath expressions work with plot titles, legend titles, axis titles, axis labels, caption, and more.
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(x = treatment, y = score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
x y
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_data_points() |>
add_mean_bar(alpha = 0.4) |>
add_sem_errorbar() |>
add_title(title = "p1")
p2 <- p1 |>
rename_x_axis_levels(
new_names = c(
"A" = "$IFN*gamma$",
"B" = "$TNF*alpha$",
"C" = "$H[2]*O$",
"D" = "$C[8]*H[10]*N[4]*0[2]$")) |>
adjust_title(
title = "$p2:~italic(E)==italic(m)*italic(c^{2})$") |>
add_caption(
caption = "$Caption:~H[2]*O~level$") |>
add_annotation_text(
text = "$H[2]*O$", x = 2, y = 45) |>
adjust_x_axis_title(
title = "$italic(Mycobacterium~tuberculosis)$") |>
adjust_y_axis_title(
title = "$A==pi*italic(r)^{2}$")Just surrond your string by “$” characters.
Plotmath expressions work with plot titles, legend titles, axis titles, axis labels, caption, and more.
library(tidyplots)
# View top 10 rows of the columns used
study |> dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_mean_bar(alpha = 0.3) |>
add_sem_errorbar() |>
add_data_points() |>
add_title(title = "p1") |>
sort_x_axis_levels()
p2 <- p1 |>
adjust_title(title = "p2: among groups") |>
add_test_pvalue()
p3 <- p1 |>
adjust_title(title = "p3: hide statistical info") |>
add_test_pvalue(hide_info = TRUE)
p4 <- p1 |>
adjust_title(title = "p4: test_pvalue at fixed y position") |>
add_test_pvalue(hide_info = TRUE, y = 90)
p5 <- p1 |>
adjust_title(
title = paste(
"p5: test_pvalue at fixed y position",
"minimize overlap", sep = "\n")) |>
add_test_pvalue(hide_info = TRUE, y = 90, step.increase = 0.3)
p6 <- p1 |>
adjust_title(title = "p6: to group A") |>
add_test_pvalue(ref.group = "A", hide_info = TRUE)
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_mean_dash() |>
add_sem_errorbar() |>
add_data_points() |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: selected comparisons") |>
add_test_pvalue(
comparisons = list(c(1, 3), c(2, 4)))
p3 <- p1 |>
adjust_title(
title = paste(
"p3: selected comparisons",
"padding_top",
sep = "\n")) |>
add_test_pvalue(
comparisons = list(c(1, 3), c(2, 4)),
padding_top = 0.5)
p4 <- p1 |>
adjust_title(
title = paste(
"p4: selected comparisons",
"hide statistical info",
sep = "\n")) |>
add_test_asterisks(
comparisons = list(c(1, 3), c(2, 4)),
hide_info = TRUE)
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(group, score, dose, treatment) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 4
group score dose treatment
<chr> <dbl> <chr> <chr>
1 placebo 2 high A
2 placebo 4 high A
3 placebo 5 high A
4 placebo 4 high A
5 placebo 6 high A
6 placebo 9 low B
7 placebo 8 low B
8 placebo 12 low B
9 placebo 15 low B
10 placebo 16 low B p1 <- study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_boxplot() |>
add_data_points() |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: test asterisks") |>
add_test_asterisks()
p3 <- p1 |>
adjust_title(title = "p3: test p value") |>
add_test_pvalue(hide_info = TRUE)
p4 <- study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_boxplot() |>
add_data_points() |>
add_test_asterisks(hide_info = TRUE) |>
add_title(title = "p4: test asterisks")
library(tidyplots)
y <- c(2.3, 4.5, 6.3, 3.4, 7.8, 6.7)
df <- data.frame(
y = c(y, y + c(0.8, 0.75)),
group = paste0("g", rep(c(1, 2), each = 6)),
batch = paste0("b", c(1:6, 1:6)))
# View df
df |> tibble::tibble()# A tibble: 12 × 3
y group batch
<dbl> <chr> <chr>
1 2.3 g1 b1
2 4.5 g1 b2
3 6.3 g1 b3
4 3.4 g1 b4
5 7.8 g1 b5
6 6.7 g1 b6
7 3.1 g2 b1
8 5.25 g2 b2
9 7.1 g2 b3
10 4.15 g2 b4
11 8.6 g2 b5
12 7.45 g2 b6 # Plot
p1 <- df |>
tidyplot(
x = group,
y = y,
color = group) |>
add_boxplot() |>
add_data_points() |>
add_title(title = "p1")
p2 <- p1 |>
remove_legend() |>
adjust_title(title = "p2: p value") |>
add_test_pvalue() |>
add_test_asterisks(bracket.nudge.y = 0.25, hide.ns = FALSE,
hide_info = TRUE, vjust = -0.3)
p3 <- p1 |>
remove_legend() |>
adjust_title(title = "p3: paired p value") |>
add_test_pvalue(paired_by = batch, hide_info = TRUE) |>
add_test_asterisks(paired_by = batch, bracket.nudge.y = 0.25, hide_info = TRUE)
p4 <- p1 |>
remove_legend() |>
adjust_title(title = paste("p4: paired p value", "paired lines", sep = "\n")) |>
add_test_asterisks(paired_by = batch, hide_info = TRUE) |>
add_line(group = batch, color = "#000000", linetype = "dotted")
ggplot2 (extension) code (e.g. dot plot)Because tidyplots is based on ggplot2 … thereby, tidyplots provides access to a wide range of features that are implemented within ggplot2 or ggplot2 extension packages … add() helper function.
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p1 <- study |>
tidyplot(x = treatment, y = score, color = treatment) |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = paste(
"p2: add points",
"via ggplot2::geom_point()",
sep = "\n")) |>
add(ggplot2::geom_point()) |>
add_data_points(shape = 1, white_border = TRUE)
p3 <- p2 |>
adjust_title(title = "p3: *M. tuberculosis*-infected cells")
p4 <- p2 |>
adjust_title(title =
"p4: *M. tuberculosis*
via ggtext::element_markdown()") |>
add(ggplot2::theme(plot.title = ggtext::element_markdown()))For the title of p4: “p4: M. tuberculosis” is followed by clicking Space bar twice and Enter button once, then followed with “via ggtext::element-markdown()”.
ggplot2 (extention) code (three more plots)library(tidyplots)
# View top 10 rows of the columns used
climate |> dplyr::select(max_temperature, month) |> dplyr::slice_head(n = 10)# A tibble: 10 × 2
max_temperature month
<dbl> <chr>
1 -1.49 01
2 3.37 02
3 5.05 03
4 8.34 04
5 16.2 05
6 18.2 06
7 20.5 07
8 18.2 08
9 18.3 09
10 14.2 10 p1 <- climate |>
tidyplot(x = max_temperature, y = month, color = month) |>
add_title(title = "p1") |> remove_legend() |>
add_data_points_beeswarm(white_border = TRUE)
p2 <- p1 |>
add(ggplot2::coord_polar(theta = "y")) |>
theme_ggplot2() |>
adjust_title(
title = "p2: polar coordinates", fontsize = 10, color = "#bb5566") |>
remove_legend()
p3 <- climate |>
tidyplot(x = max_temperature, y = month, color = month) |>
add_title(title = "p3: ridge plot") |> remove_legend() |>
add(
ggridges::geom_density_ridges(alpha = 0.3, point_alpha = 0.2,
jittered_points = TRUE, point_size = 1))
p4 <- climate |>
tidyplot(x = month, y = max_temperature, color = month) |>
add_title(title = "p4: violin plot") |>
remove_legend() |> add_violin(trim = FALSE) |>
add_data_points_beeswarm(alpha = 0.5, size = 0.5, white_border = TRUE)
p5 <- climate |>
tidyplot(x = month, y = max_temperature, color = month) |>
add_title(title = "p5: violin/dot plot") |> remove_legend() |>
add(
see::geom_violindot(dots_size = 12, trim = FALSE,
alpha = 0.5, color_dots = "#00000033"))Tidyplots is easier for many common plotting tasks.
For more complex plots, you can either augment tidyplots with custom ggplot2 code using the add() function or switch to ggplot2 for full flexibility.
library(tidyplots)
# View
study# A tibble: 20 × 7
treatment group dose participant age sex score
<chr> <chr> <chr> <chr> <dbl> <chr> <dbl>
1 A placebo high p01 23 female 2
2 A placebo high p02 45 male 4
3 A placebo high p03 32 female 5
4 A placebo high p04 37 male 4
5 A placebo high p05 24 female 6
6 B placebo low p06 23 female 9
7 B placebo low p07 45 male 8
8 B placebo low p08 32 female 12
9 B placebo low p09 37 male 15
10 B placebo low p10 24 female 16
11 C treatment high p01 23 female 32
12 C treatment high p02 45 male 35
13 C treatment high p03 32 female 24
14 C treatment high p04 37 male 45
15 C treatment high p05 24 female 56
16 D treatment low p06 23 female 23
17 D treatment low p07 45 male 25
18 D treatment low p08 32 female 21
19 D treatment low p09 37 male 22
20 D treatment low p10 24 female 23# Plot and save
study |>
tidyplot(
x = group,
y = score,
color = dose) |>
add_data_points_beeswarm() |>
add_sem_errorbar() |>
save_plot(
"images/multiple-formats-via-pipe.pdf",
view_plot = FALSE) |>
save_plot(
"images/multiple-formats-via-pipe.png",
view_plot = FALSE) |>
save_plot(
"images/multiple-formats-via-pipe.svg",
view_plot = FALSE)List the figures saved:
images/
├── multiple-formats-via-pipe.pdf
├── multiple-formats-via-pipe.png
└── multiple-formats-via-pipe.svglibrary(tidyplots)
# View top 10 rows of the columns used
gene_expression |>
dplyr::select(
group, expression, condition,
sample_type, is_immune_gene) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 5
group expression condition sample_type is_immune_gene
<chr> <dbl> <chr> <chr> <chr>
1 Hin 2.20 healthy input no
2 Hin 2.20 healthy input no
3 Hin 2.66 healthy input no
4 Hin 2.65 healthy input no
5 Hin 3.44 healthy input no
6 Ein 5.03 disease input no
7 Ein 5.31 disease input no
8 Ein 5.37 disease input no
9 Ein 5.54 disease input no
10 Ein 5.65 disease input no # Plot and save
gene_expression |>
tidyplot(
x = group,
y = expression,
color = group) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm(white_border = TRUE) |>
save_plot(
"images/multiple-pdf-by-piping-1.pdf",
view_plot = FALSE) |>
split_plot(
by = condition,
axis.titles = "margins") |>
save_plot(
"images/multiple-pdf-by-piping-2.pdf",
view_plot = FALSE) |>
split_plot(
by = sample_type) |>
save_plot(
"images/multiple-pdf-by-piping-3.pdf",
view_plot = FALSE) |>
split_plot(
rows = condition,
cols = is_immune_gene,
axis.titles = "margins") |>
save_plot(
"images/multiple-pdf-by-piping-4.pdf",
view_plot = FALSE)List the figures saved:
images
├── multiple-pdf-by-piping-1.pdf
├── multiple-pdf-by-piping-2.pdf
├── multiple-pdf-by-piping-3.pdf
└── multiple-pdf-by-piping-4.pdflibrary(tidyplots)
# View 10 rows of the columns used
gene_expression |>
dplyr::select(
group,
expression,
external_gene_name) |>
dplyr::slice(c(1:2, 101:102, 201:202, 301:302, 401:402))# A tibble: 10 × 3
group expression external_gene_name
<chr> <dbl> <chr>
1 Hin 2.20 Apol6
2 Hin 2.20 Apol6
3 Hin 7.39 Vgf
4 Hin 6.86 Vgf
5 Hin 8.21 Dpf2
6 Hin 8.29 Dpf2
7 Hin 5.87 Ankrd54
8 Hin 5.62 Ankrd54
9 Hin 7.84 Fam96b
10 Hin 7.52 Fam96b # Plot and save
gene_expression |>
tidyplot(
x = group,
y = expression,
color = group) |>
add_violin(trim = FALSE) |>
add_data_points_beeswarm(white_border = TRUE) |>
adjust_size(overall_width = 100) |> # see the note of next page
adjust_theme_details(
strip.text.x.top = ggplot2::element_text(face = "italic")) |> # gene names in italic
split_plot(
by = external_gene_name,
ncol = 2,
nrow = 2) |>
save_plot(
"images/multiple-pdf-at-once.pdf",
multiple_files = TRUE,
view_plot = FALSE)List the figure names saved:
images/
├── multiple-pdf-at-once_01.pdf
├── multiple-pdf-at-once_02.pdf
├── multiple-pdf-at-once_03.pdf
├── multiple-pdf-at-once_04.pdf
├── multiple-pdf-at-once_05.pdf
├── multiple-pdf-at-once_06.pdf
├── multiple-pdf-at-once_07.pdf
├── multiple-pdf-at-once_08.pdf
├── multiple-pdf-at-once_09.pdf
└── multiple-pdf-at-once_10.pdfThe overall_width and overall_height parameters of the adjust_size() function control the overall dimensions of a multiplot layout generated with split_plot().
The function adjust_theme_details() is a wrapper around ggplot2::theme().
library(tidyplots)
# View top 10 rows of the columns used
study |> dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot and save
study |>
tidyplot(
x = treatment,
y = score,
color = treatment) |>
add_mean_bar(alpha = 0.5) |>
adjust_legend_position(position = "none") |>
add_title("stage_1") |>
save_plot(
"images/stage_1.png",
view_plot = FALSE) |>
adjust_title("stage_2") |>
add_sem_errorbar() |>
save_plot(
"images/stage_2.png",
view_plot = FALSE) |>
adjust_title("stage_3") |>
add_data_points_beeswarm(white_border = TRUE) |>
save_plot(
"images/stage_3.png",
view_plot = FALSE) |>
adjust_title("stage_4") |>
add_test_asterisks(hide_info = TRUE) |>
save_plot(
"images/stage_4.png",
view_plot = FALSE)List the figures saved:
images/
├── stage_1.png
├── stage_2.png
├── stage_3.png
└── stage_4.png
library(tidyplots)
# View top 10 rows of the columns used
animals |> dplyr::select(weight, size) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
weight size
<dbl> <dbl>
1 0.00012 1.5
2 0.00045 10
3 0.0002 10
4 0.00002 2.5
5 0.003 17
6 0.0000025 1
7 0.000005 4
8 0.00001 0.8
9 0.0002 7.5
10 0.0004 9 # Plot
p1 <- animals |>
tidyplot(x = weight, y = size, color = size, paper = "#dddddd") |>
add_data_points(white_border = TRUE, size = 3) |>
add_title(title = "p1") |>
remove_legend()
p2 <- p1 |>
adjust_title(title = "p2: different padding in four panel sides") |>
adjust_padding(
top = 0.2,
right = 0.3,
bottom = 0.4,
left = 0.5)
p3 <- p1 |>
adjust_title(title = "p3: same padding in four panel sides") |>
adjust_padding(
all = 0.2)
p4 <- p1 |>
adjust_title(title = "p4: remove panel padding") |>
remove_padding()
p5 <- p1 |>
adjust_title(title = "p5: different padding in four plot sides") |>
adjust_theme_details(
plot.margin = ggplot2::margin(t = 0.5, r = 0.5, b = 1, l = 1, unit = "cm"))
p6 <- p1 |>
adjust_title(title = "p6: same plot padding via saving") |>
save_plot(
"images/plot_panel_padding_p6.png",
padding = 0.3) # defaults to 0.1 meaning 10%
library(tidyplots)
set.seed(42)
y <- runif(n = 500, min = 0, max = 100)
# change 30 values to zero
y[sample(NROW(y), size = 30)] <- 0
df <- tibble::tibble(x = "test", y = c(y, y * -1))
# View rows 496 - 505
df |>
dplyr::slice(496:505)# A tibble: 10 × 2
x y
<chr> <dbl>
1 test 18.3
2 test 0
3 test 30.5
4 test 16.6
5 test 3.28
6 test -91.5
7 test -93.7
8 test -28.6
9 test -83.0
10 test -64.2 # Plot
p1 <- df |>
tidyplot(
x = x,
y = y) |>
add_data_points_beeswarm(white_border = TRUE) |>
add_title(title = "p1")
p2 <- p1 |>
adjust_title(title = "p2: zero points in red") |>
add_data_points_beeswarm(
data = filter_rows(y == 0),
white_border = TRUE,
color = "#bb5566")
p3 <- p2 |>
adjust_title(title = "p3: log") |>
adjust_y_axis(
transform = "pseudo_log",
breaks = c(-100, -10, -1, 0, 1, 10, 100))
p4 <- p3 |>
adjust_title(title = "p4: log + axis_logticks") |>
add(ggplot2::guides(y = "axis_logticks"))
library(tidyplots)
# View top 10 rows of the columns used
study |>
dplyr::select(treatment, score) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
treatment score
<chr> <dbl>
1 A 2
2 A 4
3 A 5
4 A 4
5 A 6
6 B 9
7 B 8
8 B 12
9 B 15
10 B 16# Plot
p0 <- study |>
tidyplot(x = treatment, y = score, color = treatment) |>
add_data_points() |>
add_median_dash() |>
add_sem_errorbar()
p1 <- p0 |>
add_title(title = "p1: theme_tidyplot (default)")
p2 <- p0 |>
add_title(title = "p2: theme_ggplot2") |>
theme_ggplot2() |>
adjust_title(fontsize = 10, color = "#bb5566")
p3 <- p0 |>
add_title(title = "p3: theme_minimal_x") |>
theme_minimal_x() |>
adjust_title(fontsize = 10, color = "#bb5566")
p4 <- p0 |>
add_title(title = "p4: theme_minimal_y") |>
theme_minimal_y() |>
adjust_title(fontsize = 10, color = "#bb5566")
p5 <- p0 |>
add_title(title = "p5: theme_minimal_xy") |>
theme_minimal_xy() |>
adjust_title(fontsize = 10, color = "#bb5566")
p6 <- p0 |>
add_title(title = "p6: ggplot2::theme_void") |>
add(ggplot2::theme_void()) |>
adjust_title(fontsize = 10, color = "#bb5566")
Too many vector shapes can or might slow down software performance. This is how you can selectively rasterize data points.
library(tidyplots)
library(svglite)
# View top 10 rows of the columns used
climate |>
dplyr::select(month, max_temperature) |>
dplyr::slice_head(n = 10)# A tibble: 10 × 2
month max_temperature
<chr> <dbl>
1 01 -1.49
2 02 3.37
3 03 5.05
4 04 8.34
5 05 16.2
6 06 18.2
7 07 20.5
8 08 18.2
9 09 18.3
10 10 14.2 # Save vector images without rasterization
climate |>
tidyplot(x = month, y = max_temperature, color = month) |>
add_data_points_beeswarm(white_border = TRUE) |>
adjust_legend_position(position = "none") |>
save_plot("images/rasterize_data-points_no.pdf", view_plot = FALSE) |>
save_plot("images/rasterize_data-points_no.svg", view_plot = FALSE)
# Save vector images with rasterization (dpi = 100)
climate |>
tidyplot(x = month, y = max_temperature, color = month) |>
add_data_points_beeswarm(
white_border = TRUE,
rasterize = TRUE,
rasterize_dpi = 100) |>
adjust_legend_position(position = "none") |>
save_plot("images/rasterize_data-points_yes_dpi100.pdf", view_plot = FALSE) |>
save_plot("images/rasterize_data-points_yes_dpi100.svg", view_plot = FALSE)
# Save vector images with rasterization (dpi = 300)
climate |>
tidyplot(x = month, y = max_temperature, color = month) |>
add_data_points_beeswarm(
white_border = TRUE,
rasterize = TRUE,
rasterize_dpi = 300) |>
adjust_legend_position(position = "none") |>
save_plot("images/rasterize_data-points_yes_dpi300.pdf", view_plot = FALSE) |>
save_plot("images/rasterize_data-points_yes_dpi300.svg", view_plot = FALSE)List the figures saved:
images
├── rasterize_data-points_no.pdf
├── rasterize_data-points_no.svg
├── rasterize_data-points_yes_dpi100.pdf
├── rasterize_data-points_yes_dpi100.svg
├── rasterize_data-points_yes_dpi300.pdf
└── rasterize_data-points_yes_dpi300.svg