At my first data science job I worked with a guy who would throw his hands up and shout “We did it, Reddit!” any time something went really well. And last night was a huge “We did it, Reddit!” moment for me, and I hope for many of you as well.
My goal with streaming #TidyTuesday Unfiltered is to show what it’s like to work with an unfamiliar dataset, make mistakes in front of others, learn on the fly, and build a sense of community while also creating beginner-friendly content. You can check out the inaugural stream here.
Looking to skip past the 90 minutes of cats and conversation and dive right into creating our boxplot? I’ve got you covered. Here’s a 10-minute video walking through the code:
And the code!
tuesdata <- tidytuesdayR::tt_load('2021-05-25') records <- tuesdata$records
glimpse(records) records %>% skim() records_tt <- records %>% mutate(track = factor(track)) glimpse(records_tt)
records_tt %>% ggplot(aes(x = track, y = record_duration)) + geom_boxplot()
records_tt %>% ggplot(aes(x = record_duration, y = track)) + geom_boxplot(alpha = 0.6)
records_tt %>% ggplot(aes(x = record_duration, y = track, fill = type)) + geom_boxplot(alpha = 0.6)
ggsave("25-05-2021_mario_kart.png", last_plot(), device = "png")
The #TidyTuesday project is a weekly data visualization challenge that asks community members to take a dataset, tidy it as needed, and create code and visualizations to share on Twitter. The visualizations are phenomenal and well worth perusing each week.