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Jan Broder Engler @jbengler.de · 02/10/2026

This is the grammar of dispersion measures in #tidyplots 🤩 You can mix and match as you like. #rstats #dataviz #phd

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Jan Broder Engler @jbengler.de · 01/10/2026

These are some examples of #tidyplots built-in color schemes 🌈 Feel free to create your own! #rstats #dataviz #phd

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Jan Broder Engler @jbengler.de · 30/09/2026

This is how you can test selected comparisons in #tidyplots 📋 #rstats #dataviz #phd

library(tidyplots)

study |>
tidyplot(x = treatment, y = score, color = treatment) |>
add_mean_dash() |>
add_sem_errorbar() |>
add_data_points() |>
add_test_pvalue(comparisons = list(c(1,3),c(2,4)))

040

Jan Broder Engler @jbengler.de · 29/09/2026

This is the anatomy of #tidyplots 🩻 They come in 50 x 50 mm, but can be resized to your linking using `adjust_size()` 🧰 #rstats #dataviz #phd

151

Jan Broder Engler @jbengler.de · 28/09/2026

This is how you can generate donut charts in #tidyplots 🍩 #rstats #dataviz #phd

library(tidyplots)

energy |>
tidyplot(y = energy, color = energy_source) |>
add_donut()

020

Jan Broder Engler @jbengler.de · 27/09/2026

Here are some #tidyplots use cases for data journalism 🔥 Complete with data and code 🚀 tidyplots.org/use-cases/#data-journ… #rstats #dataviz #phd

071

Jan Broder Engler @jbengler.de · 26/09/2026

This is how you can adjust colors in #tidyplots 🌈 #rstats #dataviz #phd

library(tidyplots)
library(patchwork)

p1 <-
energy |>
tidyplot(x = year, y = energy, color = energy_type) |>
add_barstack_absolute() |>
adjust_colors(colors_discrete_apple)

p2 <- p1 |> adjust_colors(colors_discrete_metro)

wrap_plots(p1, p2, ncol = 1)

000

Jan Broder Engler @jbengler.de · 25/09/2026

This is how you can adjust the plot size in #tidyplots 🔥 All measures are in mm. #rstats #dataviz #phd

library(tidyplots)

study |>
tidyplot(x = group, y = score, color = group) |>
add_median_bar(alpha = 0.3) |>
add_median_dash() |>
add_data_points_beeswarm(white_border = TRUE) |>
remove_x_axis_labels() |>
adjust_size(width = 20, height = 50)

030

Jan Broder Engler @jbengler.de · 24/09/2026

This is how you can add normal data ellipses in #tidyplots 🐣 #rstats #dataviz #phd

library(tidyplots)

pca |>
tidyplot(x = pc1, y = pc2, color = group) |>
add_data_points() |>
add_ellipse()

141

Jan Broder Engler @jbengler.de · 23/09/2026

This is how you can generate lollipop charts in #tidyplots 🍭 #rstats #dataviz #phd

library(tidyplots)

spendings |>
tidyplot(x = amount, y = category, color = category) |>
add_sum_bar(width = 0.05) |>
add_sum_dot()

140

Jan Broder Engler @jbengler.de · 22/09/2026

This is how you can change the color scheme in #tidyplots 🌈 #rstats #dataviz #phd

library(tidyplots)

spendings |>
tidyplot(x = category, y = amount, color = category) |>
add_sum_bar() |>
adjust_x_axis(rotate_labels = TRUE) |>
sort_x_axis_levels() |>
adjust_colors(new_colors = colors_discrete_candy)

030

Jan Broder Engler @jbengler.de · 21/09/2026

Tidyplots offers a variety of continuous color schemes 🌈 Most of the continuous color schemes are adapted from Stéfan van der Walt and Nathaniel Smith (2015) and were designed to maximize accessibility for individuals with color vision deficiencies. #rstats #dataviz

061

Jan Broder Engler @jbengler.de · 20/09/2026

Tidyplots offers a variety of diverging color schemes 🌈 Diverging color schemes are great to represent data that diverges around a central midpoint, often a meaningful neutral value like zero, an average, or a baseline. #rstats #dataviz

061

Jan Broder Engler @jbengler.de · 19/09/2026

This is how you can add ggplot2 code to your #tidyplots 🔥 #rstats #dataviz #phd

050

Jan Broder Engler @jbengler.de · 18/09/2026

In case you ever wondered about the differences between magrittr pipe and base pipe 🤔 This table is taken from a great stackoverflow answer by @GeorgKindermann stackoverflow.com/questions/6763302… #rstats #dataviz #phd

1122

Jan Broder Engler @jbengler.de · 17/09/2026

This is how you can install a specific version of a package 🤓 In these cases 📦 pak.r-lib.org comes in very handy #rstats #dataviz #phd

library(pak)

# install the latest CRAN release
pak("tidyplots")

# install the development version
pak("jbengler/tidyplots")

# install a specific version
pak("jbengler/tidyplots@v0.0.1")

# install a specific commit
pak("jbengler/tidyplots@9f7294a")

060

Jan Broder Engler @jbengler.de · 16/09/2026

You can easily switch between magrittr pipe `%>%` and base pipe `|>` in the RStudio settings 🧰 #rstats #dataviz #phd

040

Jan Broder Engler @jbengler.de · 14/09/2026

This is how you can plot z-scores in #tidyplots heatmaps 🚀 This is especially useful when you want to focus on the dynamics within rows or columns. #rstats #dataviz #phd

030

Jan Broder Engler @jbengler.de · 12/09/2026

This is how you can add the mean and standard deviation to summarize the raw data points in your #tidyplots 🤩 Alternatively, you can also display the median, standard error of the mean, 95% confidence interval, and more. #rstats #dataviz #phd

library(tidyplots)

climate |>
tidyplot(x = month, y = max_temperature) |>
add_data_points_beeswarm(alpha = 0.3, color = "grey") |>
add_mean_line(linewidth = 0.5) |>
add_sd_ribbon()

050

Jan Broder Engler @jbengler.de · 10/09/2026

These are the discrete color schemes in #tidyplots 🌈 The default scheme `colors_discrete_friendly` is inspired by Okabe and Ito (2008) and was designed to be accessible for individuals with color vision deficiencies. #rstats #dataviz #phd

0101

Jan Broder Engler @jbengler.de · 09/09/2026

This is the grammar for summary statistics in #tidyplots 🤩 You can mix and match as you like. #rstats #dataviz #phd

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Jan Broder Engler @jbengler.de · 08/09/2026

This is how you can label selected data points in #tidyplots 🔥 In this case, we label the three animals with the highest weight using `max_rows()`. #rstats #dataviz #phd

library(tidyplots)

animals |>
tidyplot(x = weight, y = size) |>
add_data_points() |>
add_data_labels_repel(data = max_rows(weight, n = 3),
label = animal, color = "black")

030

Jan Broder Engler @jbengler.de · 07/09/2026

This is how you can define a custom style to reuse it across several #tidyplots 🤩 #rstats #dataviz #phd

$library(tidyplots) my_style <- function(x) { x |> adjust_colors(colors_discrete_apple) |> adjust_font(family = "mono", face = "bold") |> remove_x_axis_ticks() |> remove_y_axis_ticks() } energy_week |> tidyplot(date, power, color = energy_source) |> add_areastack_relative() |> my_style()$

031

Jan Broder Engler @jbengler.de · 06/09/2026

Too many vector shapes can slow down PDF performance. This is how you can selectively rasterize data points in #tidyplots 🪩 In this example, I used 100 DPI for demonstration purposes. In practice, you would typically use 300 DPI for better quality 😉 #rstats #dataviz #phd

library(tidyplots)

climate |>
tidyplot(x = month, y = max_temperature) |>
add_data_points_beeswarm(white_border = TRUE, rasterize = TRUE, rasterize_dpi = 100)

140

Jan Broder Engler @jbengler.de · 05/09/2026

When you execute a #tidyplots color scheme like `colors_continuous_viridis` in the Console, you will get a preview in the RStudio Viewer pane 🌈 Pro tip: You can highlight and copy individual hex codes directly from the Viewer pane. #rstats #dataviz #phd