Hey folks, This week I hosted the first live ensemble programming session. It went really well. We had fun and learned a lot. If you’d like to get in on these types of sessions, let me know and I’ll be sure you get a special invitation for the next series. I really believe that this form of instruction is critical to making the material learned in compact workshops stick for the long term. I hope you had fun working with the broken axis chart last week! This week I want you to look at Figures 5 of “Strategies for effective high pressure germination or inactivation of Bacillus spores involving nisin ” by Rosa Heydenreich and colleagues, which was recently published in Applied and Environmental Microbiology. You probably would like a little context. This is from a paper looking at using pressure to get bacteria to form spores or leave the spore state. The analysis was done before and after a heat treatment (as indicated in the legend) using four different methods (across the x-axis). They measured the number of spores observed for each condition and expressed it as the log fraction of the number of the number of spores put into the experiment (No = 10^9). The error bars indicate the standard deviation across at least three independent experiments. What type of plot is this? What stands out to you about this figure? What do you like about it? What don’t you like about it? Can you outline the steps you would take to generate the figure? What are some of the steps you aren’t sure about and would like to learn? These are questions that I’d strongly encourage you to ask about any visual you are looking at because I think they’ll help you to develop your “taste” in data visualizations and strengthen you skills in generating those visualizations. This is a bar plot. Here are five things that caught my eye. First, this bar plot has it’s x-axis at the top and descends into negative log values. Second, they have hashing in the bars for the “after heat” category. Third, their legend is below the plot, has italics, and has a box around it. Fourth, they only have horizontal grid lines with a thicker, dashed grid line to indicate the limit of detection at -8. Finally, I noticed that the tick marks move into plot rather than default of plot. Here’s some data for you to experiment with:
First, let’s talk about the bar plot. You may be tempted to use The second eye catcher is that they have diagonal lines for the bars representing what happened after the heat treatment. I think this general look comes to us from many years of using M$Excel. My personal preference would be to leave out the diagonal hashing since I think it unnecessarily clutters the bars. Why not use the two shades of blue and call it a day? Anyway, there is a cool looking Third, they were able to format their treatment categories so that they could nicely tuck the legend on the left side of the axis. How’d they do that? I’d likely use Fourth, they have done some interesting things with their grid lines. If you use the Finally, the plot is doing interesting things with the x-axis ticks by having them go into the plot and by removing them from the y-axis. How would you do that? If your mind went to There’s a lot of cool stuff going on in a relatively simple plot! I’m not sure what software they used to make this plot, but it has some really nice points. The more I looked at this figure, the more things I noticed are different from the default As always if you have a cool plot you’d like to share with me for a future newsletter, feel free to reply to this email. Oh yeah, that
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Hey folks, I’m at the end of a day after I pulled an all-nighter trying to hit a grant proposal deadline. I don’t recall ever doing this in college. I seem to pull an all-nighter every five years or so. I’m too old for this! Anyway, the proposal is in and now I’m ready to move on to fun things… like talking to you about visualizing data! A few years back Whitney Battle-Baptiste and Britt Rusert put together an amazing collection of visualizations by WEB DuBois that he presented at the 1900...
Hey folks, Wow, I really didn’t expect my overview of Positron to resonate with so many people last week on YouTube! I’ll work on coming up with another video showing Virtual Studio Code (VS Code) in action. As others have mentioned in the episode’s comments, I’m not really sure why Posit is building Positron instead of making things easier within VS Code for R users. To me the need for an IDE that allows people to use multiple programming languages is a red herring - VS Code does that...
Hey folks, I hope you’ve noticed that this newsletter and the YouTube channel have nearly caught up. At this point there’s a 10-day lag between when I post a newsletter describing a data visualization and when I post the recreation video. I could possibly push that to a 3-day lag, but I’d like people to have a chance to work through the code on their own before I give my solution. After having existential dread last week that I’d never find another good plot to share, it appears my cup...