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jbengler.de
Jan Broder Engler
@jbengler.de
1.3K followers 640 following 440 posts
Scientist and software developer. Creator of tidyplots.org. Website jbengler.de.
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jbengler.de
Jan Broder Engler @jbengler.de · Dec 6
Hey educators, tidyplots.org is great for teaching students data visualization because it is

🕊️ Free and open-source
🚀 Easy, intuitive and fast
💾 Loaded with demo datasets
🏆 Embracing best practices
🌈 Beautiful

#rstats #phd #dataviz #education
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jbengler.de
Jan Broder Engler @jbengler.de · 1d
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()
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Jan Broder Engler @jbengler.de · 1d
Being free and open-source, this is how you can support #tidyplots ❤️

tidyplots.org
github.com/jbengler/tid...

#rstats #dataviz #phd
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jbengler.de
Jan Broder Engler @jbengler.de · 2d
This is how you can preview a subset of the dataset in #tidyplots 🔎

#rstats #dataviz #phd
library(tidyplots) gene_expression |> tidyplot(x = group, y = expression, color = group) |> add_violin() |> add_data_points_beeswarm(white_border = TRUE) |> view_plot(data = filter_rows(external_gene_name == "Apol6"), title = "Apol6")
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Jan Broder Engler @jbengler.de · 5d
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)))
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Jan Broder Engler @jbengler.de · 6d
This is how you can do paired testing in #tidyplots 🍒

#rstats #dataviz #phd
library(tidyplots) x <- c(2.3, 4.5, 6.3, 3.4, 7.8, 6.7) df <- data.frame( x = c(x, x + c(0.8, 0.75)), group = paste0("g", rep(c(1, 2), each = 6)), batch = paste0("b", c(1:6, 1:6)), shuffle = paste0("c", c(1:6, 6:1)) ) df |> tidyplot(group, x, color = group) |> add_boxplot() |> add_data_points() |> add_test_pvalue(paired_by = batch) |> add_line(group = batch, color = "black")
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Jan Broder Engler @jbengler.de · 6d
Thank you for citing #tidyplots 🙏

Marcel S. Woo et al. The immunoproteasome disturbs neuronal metabolism and drives neurodegeneration in multiple sclerosis. Cell (2025).

www.cell.com/cell/fulltex...

#rstats #dataviz #phd
The immunoproteasome disturbs neuronal metabolism and drives neurodegeneration in multiple sclerosis
Neuronal proteasomal dysfunction is a hallmark of neuroinflammatory and neurodegenerative diseases. This study reveals that the interferon-induced immunoproteasome drastically decreases proteasomal ac...
www.cell.com
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Jan Broder Engler @jbengler.de · 7d
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)
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Jan Broder Engler @jbengler.de · 7d
Thank you for citing #tidyplots 🙏

Jakub Idkowiak et al. Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data. Nature Communications (2025).

doi.org/10.1038/s414...

#rstats #dataviz #phd
Best practices and tools in R and Python for statistical processing and visualization of lipidomics and metabolomics data - Nature Communications
Mass spectrometry-based lipidomics and metabolomics generate large, complex datasets requiring effective analysis. Here, authors review key statistical and visualization methods alongside widely used R and Python tools, and provide a GitBook with step-by-step code for accessible, reproducible data analysis.
doi.org
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jbengler.de
Jan Broder Engler @jbengler.de · 8d
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)
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Jan Broder Engler @jbengler.de · 9d
This is how you can do stacked bar charts in #tidyplots 🐝

#rstats #dataviz #phd
library(tidyplots) energy |> tidyplot(x = year, y = energy, color = energy_type) |> add_barstack_absolute()
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Jan Broder Engler @jbengler.de · 10d
Here are some #tidyplots use cases for data journalism 🔥

Complete with data and code 🚀

tidyplots.org/use-cases/#d...

#rstats #dataviz #phd
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Jan Broder Engler @jbengler.de · 12d
Thanks for leading me to the right path, man!
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Jan Broder Engler @jbengler.de · 12d
This is how you can do horizontal bars in #tidyplots 😉

#rstats #dataviz #phd
library(tidyplots) spendings |> tidyplot(x = amount, y = category, color = category) |> add_sum_bar(alpha = 0.2) |> add_sum_dash() |> add_sum_value(accuracy = 1, color = "black") |> sort_y_axis_levels() |> remove_legend()
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jbengler.de
Jan Broder Engler @jbengler.de · 13d
You are welcome 😂
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jbengler.de
Jan Broder Engler @jbengler.de · 13d
This is how you can sort the x-axis in #tidyplots 🤩

#rstats #dataviz #phd
library(tidyplots) 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) |> sort_x_axis_levels()
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jbengler.de
Jan Broder Engler @jbengler.de · 14d
This is how you can add sum values in #tidyplots 🤩

#rstats #dataviz #phd
library(tidyplots) 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)
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Jan Broder Engler @jbengler.de · 15d
😂
jbengler.de
Jan Broder Engler @jbengler.de · 15d
This is how you can generate donut charts in #tidyplots 🍩

#rstats #dataviz #phd
library(tidyplots) energy |> tidyplot(y = energy, color = energy_source) |> add_donut()
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jbengler.de
Jan Broder Engler @jbengler.de · 15d
Right. I am looking for a ggsave() that does not need to be provided with the dimensions but rather extracts them from the absolutely sized plot itself.

Here are two implementations of this
- teunbrand.github.io/ggh4x/refere...
- jbengler.github.io/tidyplots/re...
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jbengler.de
Jan Broder Engler @jbengler.de · 15d
I see. Thank you for the link!
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jbengler.de
Jan Broder Engler @jbengler.de · 15d
For me it works with ggplots and tidyplots.

However, the display of plots with absolute dimensions could be improved.
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jbengler.de
Jan Broder Engler @jbengler.de · 15d
Patchwork was not initially designed to handle plots with absolute dimensions. But it would be really great to be able to use it for arranging plots of different predefined sizes.
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Jan Broder Engler @jbengler.de · 15d
The ggsave() function could check whether the plot has absolute dimensions. If so, the device size should be automatically adapted to fit the plot.
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jbengler.de
Jan Broder Engler @jbengler.de · 15d
Currently, the plot gets either cut off or has a lot of white space around.

Great would be a "scale to fit" option.

Similar to a responsive web image.

www.w3schools.com/css/css_rwd_...
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jbengler.de
Jan Broder Engler @jbengler.de · 15d
Now that ggplot2 4.0.0 supports absolute plot dimensions, we need better support in

1. Positron & RStudio viewer pane
2. ggsave()
3. quarto
4. shiny
5. patchwork

😜

#rstats #dataviz #phd
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