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Brenden

@beesm.bsky.social

124 followers 230 following 53 posts

#rstats & public health brendenmsmith.com

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Brenden @beesm.bsky.social · 04/02/2026

First #TidyTuesday using python 🐍 🫣

Horizontal stacked bar chart showing water needs by plant cultivation class. High-water plants include Salad, Cucurbit, and Brassica, while Allium, Chenopodiaceae, and Solanum require mostly medium water levels.

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Brenden @beesm.bsky.social · 30/07/2025

Might be the best github issue I’ve ever read 😂

A screenshot of an open github issue that reads: “Who could forget the classic 11 second
"skrrerrrrrrrrrrrt" on Halftime, or one of his many
"SLATT"s? Perhaps even some of whatever is going on in the beginning of Audemars? Even Nicki Minaj saying "Thugger" in the opening of Anybody. Thug is criminally underrepresented in this package.”

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Brenden @beesm.bsky.social · 10/07/2025

Quick look at the most common adjectives used among the top identified colors in the xkcd color survey. #TidyTuesday #rstats

A bar graph showing the top ten adjectives used to describe colors in the xkcd color survey. This is among the top 949 most commonly identified colors. Light and dark are by far the two most common words. Others include pale, bright, deep, very, baby, dull, dusty, and pastel.

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Brenden @beesm.bsky.social · 02/07/2025

#TidyTuesday week 26 - It ain't much but it's honest work. I chose to look at the average differences in prices between conventional and reformulated gasoline -- which honestly I have not ever thought much about! Interesting to see such differences increase over the years. #rstats #dataviz

bar graph showing the average price difference between conventional and reformulated gasoline over time. The main metric is an annual average of this difference, showing how reformulated (cleaner burning) gasoline is more expensive. Over the years, this difference has grown larger. Interestingly, midgrade fuel seems to have the highest price disparity compared to regular and premium.

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Brenden @beesm.bsky.social · 04/02/2025

We have dplyr at home… #rstats

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Brenden @beesm.bsky.social · 28/01/2025

#TidyTuesday week 4 ✅ This week’s data is from the U.S. Census Bureau’s American Community Survey. I took a look at how the data on lack of indoor plumbing among U.S Counties compares to other census provided geographies, specifically American Indian/Alaska Native/Native Hawaiian Areas. #rstats

Percentage of households lacking plumbing (2023). Four out of the five U.S. Counties with the highest percentage of households without complete indoor plumbing overlap with the boundaries of the Navajo Nation Reservation and Off-Land Trust Land. The maximum percentage among counties was 3.9%, while the maximum among American Indian/Alaska Native/Native Hawaiian Areas was 5%.

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Brenden @beesm.bsky.social · 21/01/2025

#TidyTuesday Week 3: The History of Himalayan Mountaineering Expeditions ✅ Had some fun with `geom_polygon` and `geom_text`. Also used a beautiful palette from {NatParksPalettes}. #rstats #dataviz #ggplot2

Heights of the most commonly climbed Himalayan peaks (2020-2024)

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Brenden @beesm.bsky.social · 14/01/2025

Week 2 of #TidyTuesday looks at posit::conf data for 2023 and 2024. I examined the most common words in talk titles between the two years and plotted the relative change. Looks like there was a big shift towards AI, learning, art, and reproducibility in 2024. #rstats #dataviz #ggplot2

Relative change in the twenty most common words in talk titles between posit::conf 2023 and 2024

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Brenden @beesm.bsky.social · 07/01/2025

Quick #TidyTuesday for the new year looking at the CDCPLACES package downloads over last year! Shout out to cranlogs for the data! #rstats #ggplot2 Code: tinyurl.com/yufuz9rz CDCPLACES package: tinyurl.com/46z6tfaa

Area graph showing the number of weekly downloads of the CDCPLACES R package in 2024.

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Brenden @beesm.bsky.social · 01/12/2024

I've completed "Historian Hysteria" - Day 1 - Advent of Code 2024 #AdventOfCode #rstats adventofcode.com/2024/day/1

$# Part one data <- read.table("input/day1_input.txt") sum(abs(sort(data$V1) - sort(data$V2))) # Part two score <- 0 for(i in 1:length(data$V1)){ num_oc <- sum(data$V2 %in% data$V1[i]) added_score <- num_oc * data$V1[i] score <- score + added_score }$

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