Trigger warning Introduction Getting and cleaning data Trends in violence by region Is South America more dangerous for transgender people, or just for all people? Country level analysis Proportion of murder victims that are transgender Number of transgender victims by age Conclusions Trigger warning This is an exploratory data analysis of murders of transgender people. The data contains graphic descriptions of violence against transgender people.
Introduction Getting data with rgbif Data cleaning Data wrangling Make the animation Another example with Kudzu Introduction Since I discovered GBIF, I’ve been hooked. What is GBIF? From their website: “GBIF—the Global Biodiversity Information Facility—is an international network and research infrastructure funded by the world’s governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth.” In 2018, GBIF passed the mark of one billion occurence records, which is just incredible.
Introduction Trials and tribulations The solution Introduction Drama, intrigue, arrogance, dashed hopes, rock-bottom, perseverance, and eventual triumph, this post has it all! It starts with me watching Rachael Tatman’s recent live-coding video, and ends with a thrilling race-to-the-bottom between two pathetically slow functions. What lies ahead: many a WTF moment, lots of trial and error, and some useful tidyverse data wrangling tips.
Rachael Tatman is a data scientist at Kaggle, and does these awesome live coding sessions every Friday.
The more I use the tidyverse in my R coding, the more I ask myself: does Hadley Wickham hate dogs, or does he just need help with dog-related package names? See, of the packages Hadley has developed for the tidyverse, there are two that have cat-inspired names (forcats and purrr) but zero that pay homage to man’s best friend. It’s not like doggo names are hard to think of for R packages it took me 30 seconds to come up with baRk and woofR**.