Here's how quickly you can create plots using 'ggauto' in #RStats using this week's #TidyTuesday data as an example 📊

In just one line of code you get a bar chart, arranged from highest to lowest.

#DataViz

Recent @DSLC club meetings:

 The Art of Data Visualization with ggplot2: The TidyTuesday Cookbook: Time zones: spatial data and mapping with sf https://youtu.be/7cetwm8592w #RStats #DataViz #ggplot2 #TidyTuesday

:Python: Deep Learning with Python (3e): Image generation https://youtu.be/b_a6QGveXDU #PyData #DeepLearning #AI

Support the Data Science Learning Community at https://patreon.com/DSLC

The TidyTuesday Cookbook: Time zones: spatial data and mapping with sf (ttcookbook01 12)

YouTube

A quick timeline bubble chart for this week's #TidyTuesday data on Papal Encyclicals showing the average number is generally decreasing

Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-06-23

#DataViz #ggplot2 #RStats

📊 #TidyTuesday – 2026 W25 | Papal Encyclicals: Industrial Revolution vs. AI Revolution
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🔗: https://stevenponce.netlify.app/data_visualizations/TidyTuesday/2026/tt_2026_25.html
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#rstats | #r4ds | #dataviz | #ggplot2

For W24 of #TidyTuesday I explored how UK baby names have changed since 1997. The most popular names of 1997 now account for a much smaller share of published records, while the number of distinct names has grown for both girls and boys.

Code: https://github.com/majaurankar/TidyTuesday/tree/main/2026/W24

#dataviz #rstats #ggplot2

This week's #TidyTuesday data compares baby names in Scotland, Northern Ireland, and England & Wales 📊

One of the interesting things about this data is that you can often spot trends related to current events 👸

Code: https://github.com/nrennie/tidytuesday/tree/main/2026/2026-06-16

#DataViz #ggplot2 #RStats #QuartoPub