Graphs tweeted in 2019

Here are 166 graphs I made and tweeted in 2019. There’s minimal commentary, and I copied them from my tweets rather than track down the originals, so not sure about the image quality. I omitted a few near-duplicates. All were made with JMP except where noted. Each image should be hyperlinked to its tweet (but I probably missed a few).


I started out 2019 with some data I collected from the Radio Paradise online playlist. This packed bar chart of artists is not a great fit for packed bars, which is a good sign for the radio station: it means the artist distribution is not very skewed.

I also collected the song ratings, hoping to understand why they keep playing Joni Mitchell.

I tried a couple variations on hockey attendance data for #MakeoverMonday. I still haven’t figured out the right way to participate there.

More Radio Paradise data.

Trying a few alternative ways to compare two small distributions.

Still milking that Radio Paradise data.

These next four views of the data data led to collaborating with Nick Desbarats on A Friendlier Dot Plot blog post.

A fun chart I made with Google Trends.

A couple alternative views of some tea survey data.

Four alternatives to some political radar charts I saw on I don’t find radar charts useful in general, but maybe everything else is worse.


Recreating a W. E. B. Du Bois map, first mimicking his colors and then coloring by a data value.

I turned the dot plot alternatives into an Observable notebook. Here are some results that I shared.

Here’s my first plot of sign-ups for the local Analytics>Forward unconference.

Charting a colleague’s movie ticket price history.


I tried this month’s Story Telling With Data challenge, looking at country-to-country financial aid.

Playing with force-directing dodging on Observable.

A couple final Analytics>Forward attendance charts.

My full submission for the #SWDchallenge.

Remade a pie chart grid as a heatmap.

Trying a static view of one of the bar chart race data sets.


I discovered I could download my water usage data.

I made an Observable notebook for making packed bar charts.

Trying to reproduce a strange regression line in a NYT graphic. This proved to be a rich data set and would later become the example data for my JSM interactivity presentation.


Another radar makeover.

Trying to remake a suspicious journal plot.

Makeover of some soccer league rankings.

Carbon-dioxide emissions are so skewed you can see the top five and all the other countries packed in the same graph.

I made and remade a few charts from this paper on gender and the effect of temperature on cognitive performance.

With some effort, I was able to collect my crossword puzzle solving times. I later made good use of the data in my JMP 15 keynote segments in Tucson and Tokyo.


Found the Global Power Plant Database.

More crossword graphs. This time comparing ways of comparing distributions.

College majors for data scientists from a Twitter poll.

Started looking at Greenland ice melt data. The first graph just verifies that I was reading the gridded data values correctly, but I ended up switching to a different source with summary values for each day.

A word cloud from Apple keynote transcripts.

Another journal chart makeover.


Testing the limits of packed bars on audiobook counts. Is 26 too few items? Is 69,321 too many?

Some results I graphed from a salary survey of statisticians.

I also make and share graphs on Cross Validated Q&A site. Here’s one I also tweeted, simulating overlapping bars.

A makeover of a bar-mekko chart.

Demonstrating bars with labels inside the bars.

A way to show gains and losses along with the net result.

A makeover of a questionable ISOTYPE graph from UNC.

Teaser graph for the blog post to go along with my JSM talk on interactivity modes in data visualization.


My one graph creation from attending JSM: sponsor booth space.

Another #MakeoverMonday data set: Britain’s power generation.

Not including them here, but I shared a dozen animated GIFs showing the nine interactivity techniques from my JSM talk.

Another #MakeoverMonday data set: clinical trials counts.

Trying to breakdown UK suicides data. Thought it might be a baby boom effect, but there seems to have been less of a baby boom in the UK.

I made this comparison of early box plot forms.

A zoomed-in axis inset to explore showing both long and short time scales.

I took the coal production data Tukey used for the smoothers in his book and tried a few other smoothers.

Violins versus Highest Density Region plots:


Updated Greenland ice melt cumulative view.

Verifying that the bars in a NYT Notre Dame graphic were sized on a square root scale instead of linear.

Checking default Y axis scaling for a line plot in JMP.

Exploring how packing affects aggregate size.

Looking at school district diversity data from a Washington Post graphic.


Discovered livestock data at UN’s Food and Agriculture Organization site. Showing 4 of 11 packed bar charts here.

Comparing the effects of aggregation on regression after seeing an odd analysis in Significance magazine. They later published this graph in their correspondence section.

Tourism in Portugal

Some alternatives to a truncated bar chart I saw in a paper.

A mini-gallery I made to show some new JMP 15 features.

I remade an emoji pie chart as packed bars and then someone suggested packed circles, so I tried that, too.


Looking at UK Conservative votes versus deprivation measures.

After a marriage statistics paper shared their data and details, I was able to reproduce the results.

Here’s a sampling of several graphs I posted in a study of a chord diagram (see also my previous blog post).

Makeover of a study paper, removing a dubious log scale.

Answering a question about histogram binning.

This underground water leak took a while to find and fix, but it was nice to see the data, at least.

Making an example geographic scatter plot.

Trying out a shading idea from Len Kiefer.

Data from Steam gaming usage.

Looking at data from an Economist graphic about earning for college graduates versus the colleges’ admission rates.


Despite knowing next to nothing about UK politics, I tried some graphical reproductions and explorations based on an Economist ternary chart.

Comparing stats masters programs. I’ve still don’t know why Columbia is so far above the others in number of students.

Remake of an animated ozone chart.

Makeovers of a Reuters bar-mekko chart.

Restyling a journal’s scatter plot..

Alaska area comparison.