Brenden

@beesm.bsky.social

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#rstats & public health brendenmsmith.com

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.
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.”
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.
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.
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%.
Heights of the most commonly climbed Himalayan peaks (2020-2024)
Relative change in the twenty most common words in talk titles between posit::conf 2023 and 2024
Area graph showing the number of weekly downloads of the CDCPLACES R package in 2024.
# 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 }