Jen Richmond

Super excited to curate the @weare.rladies.org account this week. Follow me over there for data viz inspo, some things I think are magic, as well as chat about burnout, navigating career change and showing up for the #rstats community. Thanks for the nudge @pipinghotdata.com !

Nice facets!!! thanks for sharing!!

Woot! Awesome plots!!

This is a good reminder that the #TidyTuesday repo has HUNDREDS of datasets.

If the dataset this week doesn't inspire, it is TOTALLY fine to go back and find something that you are more interested in. The goal is to make something and share it!!

I think they are awesome! Well done putting your plots out into the world!!

Welcome to #TidyTuesday, it is so great to see more people making plots and sharing them. This gradient background is cool! well done!!

Yay for more people using TidyTuesday to practice ggplot skills and putting their plots out into the world! well done Morgana!!

I would like to be on this list, thanks for compiling!

I need to play more but Quoncierge seems to make getting started with python in positron MUCH smoother!! THANK YOU!!

I am a baby python user and had tried and given up on positron because what the hell is a virtual environment and what do you mean I am not authorised to install that on my machine and why do I have to use home brew and but I already installed that package... and... and... and...

tidytuesdayR::tt_curate_data() ??

a tile plot of the xkcd survey colours that are inspired by bodily fluids, there is one yellow colour that refers to piss, 4 that refer to snot or booger, 8 that refer to vomit or puke, and 14 that refer to poo, shit, poop or diarrhea. Each tile is coloured according to the hex code of that colour name.

#TidyTuesday this week is SO FUN! Channelling my friend @fontikar.bsky.social and making something very silly... gross colours. Which colours reference bodily fluids?

Also... babies are gross 😂

Read about my #rstats process jenrichmond.github.io/tidytuesday/...

Lists of lab guides

This repo of lab onboarding/handbook guides might be useful docs.google.com/spreadsheets...

Yay! Go you!!

I reckon you might get pretty far with #rstats ...

1. tidyverse (9 packages for the price of 1, wrangling and viz)
2. here (avoid filepath drama)
3. janitor (data cleaning usefulness)

The #RYouWithMe modules we made for RLadies Sydney might be helpful rladiessydney.org/courses/

Quick #TidyTuesday plot this week. Diesel has become more expensive than gasoline over time. #rstats

jenrichmond.github.io/tidytuesday/...

And the best thing about curating a dataset is that you get to see all the cool things that people with all kinds of different skills make with it. Can highly recommend!!

#rstats #pydata #julialang #datascience #TidyTuesday

I bet @allisonhorst.bsky.social has good suggestions

Data cleaning is time consuming but dealing with data types shouldn’t be.

In this quick demo, I show you how to use mutate and across from the #rstats `dplyr` package to change the format of several columns fast.

youtu.be/1H1w4KhTfn8

yeah I was using .css for custom fonts and colours and to center the text on the slide. The font family and colour would render to pdf, but the background and centering didn't work in pdf format. With your .scss, I just used <br> to get the centering (ish) and the colours/font looked great, thanks!

ooooo thanks Cecilia! I have borrowed your .scss file and it is working better! To clarify, adding pdf: true to the yaml doesn't necessarily make it render a pdf automatically... it just makes the Print button in the Presentation pane generate a reasonable looking pdf output??

I made these really nice quarto slides, custom css and everything, but when I try print to pdf much of the beautifulness disappears. Anyone have tips about yaml specs or css that you need to have revealjs slides print the same as they render. thanks! #rstats #quarto

Over a decade of Measles cases in Africa🌍 for #TidyTuesday, Week 25.

#rstats #dataviz #ggplot2

Bivariate choropleth map of Africa showing measles incidence rates and laboratory confirmation rates by country in 2024. Countries are colored using a blue-to-purple matrix where blue indicates low incidence with high lab confirmation (like Botswana), red indicates high incidence with low lab confirmation (like Liberia), and purple indicates high incidence with high lab confirmation (like Somalia). The map shows significant variation in both disease burden and diagnostic capacity across African countries, with 89,765 total measles cases reported.

#TidyTuesday week 25
📊 Exploring #measles incidence and laboratory confirmation rates in Africa (2024).
Code: github.com/rajodm/TidyT...

#dataviz #rstats #ggplot2

A stacked column chart showing monthly measles cases from 2020 onwards across six WHO regions. Each region displays the top 3 countries with the most cases plus an "Other" category: AFR (DR Congo, Ethiopia, Nigeria), AMR (Brazil, Canada, Mexico), EMR (Iraq, Pakistan, Yemen), EUR (Kazakhstan, Romania, Russia), SEAR (India, Indonesia, Thailand), and WPR (China, Malaysia, Philippines). AFR and EUR regions had surges in late 2023 through early 2024, while SEAR surged about one year earlier. Recent years show a reappearance of cases even in regions that previously had few cases, continuing into 2025.

Measles cases by WHO region for this week's #TidyTuesday

Code: github.com/gkaramanis/t...

#RStats #dataviz

code at: barreiro-r.github.io/tidytuesday/...

For this #TidyTuesday I made an animation showing measles lab confirmed cases per 100k in EU.

Animation with {gganimate}, legend added with weird {magick} tricks.
I'm not fully satisfied with this one, working with animations is tough!

code: github.com/mic-wypych/t...

#rstats #ggplot2 #dataviz

For this week's #TidyTuesday dataset, I explored India's journey toward measles elimination.
The visual layout draws inspiration from newspaper format.
Charts were created in R using #ggplot2
Layout designed in Figma.

#DataVisualization #RStats

There is zero evidence of "reshaping of cognitive functions" in that study, nor could the methods and measures of that study design even reveal such a thing

Infographic showing yearly increments in global measles cases

Used this week's #TidyTuesday dataset to have a go at polar coordinates in ggplot! 🌐🦠

Bar charts faceted by region (basically continent); x-axis is year from 2014 to 2024, and y-axis is measles cases per 1 million population.

My submission for #TidyTuesday, Week 25 on Measles cases across the world. I explore measles incidence rates by region and year.

Code: github.com/mitsuoxv/tid...

Measles incidence in Europe over time

Depressing time-lapse stats about measles in Europe this week.

Enjoy

Code here: pgdatavizandstats.netlify.app/data_visuali...

#TidyTuesday | #PyDayTuesday | #dataviz