Each year, the World Health Organization releases a report with estimates of global immunization coverage. The report lots of visualizations of their data, and they release the data used for each visualization publicly. About a year ago, fresh out of Data Challenge Lab, the class offered by the Stanford Data Lab which I then TA’d a year later, I decided to practice some of my new vis skills by trying to replicate some of the visualizations in the 2016 report.
I started this jupyter notebook to help me learn to (1) use both Python and R in one Jupyter notebook and (2) translate tidyverse syntax into Python (specifically pandas & seaborn) syntax. I figured I would post it in the hopes it might be helpful to someone else. Also, I wanted to learn how to post a jupyter notebook on this website.
Rmagic Seaborn Tidyverse -> Pandas (Most of this is actually taken straight from this post) R + Jupyter This link helped me set up my environment so that I could use R and Python in one notebook.
This week’s RWeekly digest included an awesome dataset from the World Bank that documents the percentage of women in lower house parliament across the globe from 1997 to 2018. The dataset is nearly complete and also includes summary statistics for numerous geographic aggregations.
All of my code to process and explore the data is available on my GitHub. There’s lots of interesting things to look at in this data, so I will leave most of the boring stuff out of this post.
It’s not often I see a cool data project focused on Australia, and in particular Australian politics, so I was especially excited to see freerangestats’ exploration of the 2016 Australian Election Study featured in RWeekly this week! The RWeekly email included this visualization, showing the responses to 6 questions about Australia’s parliamentary system, organized by party preference for Senate in the 2013 Federal Election.
At first, I was excited to see where voters for my party came in relative to other voters in parliamentary trivia, but then… how can I even compare?
This month I decided to take part in the Storytelling With Data monthly challenge for the first time! The dataset we were given to explore contains global aid exchanges between 47 countries across the world across the years 1973-2013. The goal is to create visualizations that answers the broad question: Who Donates?, as well as some bonus questions about distribution of donations geographically, temporally and by purpose of donation. Here’s my initial attempt!
As my senior year at Stanford nears the end, I’ve started to think more and more about what I’ve really learned here, and what I’ll be taking away from my undergraduate experience. Sure, there’s plenty of sappy stuff about coming of age and figuring out what I want to start doing with my life (spoiler: haven’t figured it out), but what about concrete knowledge I’ve gained from my classes.