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")

This is how you can preview a subset of the dataset in #tidyplots 🔎

#rstats #dataviz #phd

This is how to get started with #tidyplots 🚀

https://tidyplots.org/getstarted

#rstats #dataviz #phd

library(tidyplots) # Define style my_style <- function(x) x |> adjust_colors(colors_discrete_candy) |> adjust_font(family = "mono") |> adjust_size(height = 30) # Set global options tidyplots_options(my_style = my_style) study |> tidyplot(x = group, y = score, color = dose) |> add_mean_bar() # Reset global options tidyplots_options()

This is how you can set a default style in #tidyplots 🎨

jbengler.github.io/tidyplots/reference/tidy...

#rstats #dataviz #phd

Started work on my first data product. Currently a #wip, it's a tutorial/course/walkthrough around running Linear Regressions. These are sample charts generated using #Tidyplots working with a special dataset.

Looking forward to sharing this once it's done.
#DataViz #RStats #DataScience

If you regularly create visualizations in R, the tidyplots package is worth exploring.

The figure below shows examples from the tidyplots website: tidyplots.org

I recently released a new Statistics Globe Hub module about tidyplots: statisticsglobe.com/hub

#rstats #datascience #tidyverse

Go try it 🤩

Some progress on the model exploration visuals using #tidyplots. Patterns are starting to lock in.

#dataviz #ggplot2 #rstats

Here are some #tidyplots use cases for bioinformatics 🔥

Complete with data and code 🚀

https://tidyplots.org/use-cases/#bioinformatics

#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)

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

#rstats #dataviz #phd

🚀

Thanks for the opportunity to speak at the RNVSU March Meetup 🙏

Is was great to see so many people interested in #tidyplots 😍

Talk slides can be found here: github.com/jbengler/tid...

Best wishes!

#rstats #dataviz #phd

library(tidyheatmaps) tidyheatmap(df = data_exprs, rows = external_gene_name, columns = sample, values = expression, scale = "row", annotation_col = c(sample_type, condition, group), annotation_row = c(is_immune_gene, direction), gaps_row = direction, gaps_col = group )

This is how you can visualize gene expression data in #tidyheatmaps 🤩

https://jbengler.github.io/tidyheatmaps/

#rstats #dataviz #phd

I have also been there 😂

Thanks a lot!

Nooo, you cannot do that 😂

Github star number 666 for #tidyplots 😈

github.com/jbengler/tid...

#rstats #dataviz #phd

Tomorrow I will give a talk about "Tidyplots for easy data visualization" 📣

We will explore the concept behind tidyplots, walk through its available functions, and dive into live coding on common plotting tasks.

www.meetup.com/r-nvsu/event...

#rstats #dataviz #phd

pak is a package manger that installs the package version into your library thereby affecting all of your projects.

Thus, it really depends what you are looking for.

Great to hear that you enjoy my posts! I always try to include code in the image alt text to make it easy to copy and paste.

However, it might be that sometimes a post without alt text slips through.

Yes, that is right! Thank you for pointing out.

Package versions in renv are linked to your project, so you can use different versions across your projects without one affecting the other. This is great for isolation, portability and reproducibility.

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")

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(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 = c("Health" = "#ff3b30"), na.value = "lightgrey")

This is how you can guide attention in #tidyplots 🎯

#rstats #dataviz #phd

This is how you can use plotmath expressions in #tidyplots 🤓

jbengler.github.io/tidyplots/articles/Advan...

#rstats #dataviz #phd

Yes, I also find this a tough one. Often times the most engaging color schemes are not particularly color deficiency friendly.

All key features at a glance – with the #tidyplots cheatsheet 🤩

https://tidyplots.org/cheatsheet

#rstats #dataviz #phd

I still have to implement ggpubr::stat_pvalue_manual().
It is on the list.

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)))

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

#rstats #dataviz #phd

The main scheme "friendly" is a variation of the original Okabe & Ito 2008 scheme that was optimized for color vision deficiency:

jfly.uni-koeln.de/color/

The other schemes are basically a random bunch I found esthetically pleasing but are not selected for being color vision friendly.

library(tidyplots) study |> tidyplot(x = treatment, y = score, color = treatment) |> add_violin() |> add_data_points_beeswarm(white_border = TRUE) |> rename_x_axis_levels(new_names = c( "A" = "Dog", "B" = "Cat", "C" = "Hamster", "D" = "Dinosaur"))

This is how you can rename axis levels in #tidyplots 🎉

#rstats #dataviz #phd