For #TidyTuesday week 20 we looked at Nobel Prize Laureates. The Data The data for week 20 can be found on Kaggle. The Analysis For this week I wanted to continue my focus on creating maps, so I decided to visualise Nobel Prize Laureates by country of origin. For a bit of additional practice I created a boxplot to visualise Nobel Prize Laureates by gender and category. Here’s the code:
For #TidyTuesday week 19 we looked at global student to teacher ratios. The Data The data for week 19 comes from the UNESCO Institute of Statistics. The Analysis For this week I wanted to focus on student to teacher ratios for tertiary education. I wanted to see what this looked like on a gobal scale, so I decided to show this on a map. Here’s the code: # Load packages library(tidyverse) library(extrafont) # Import data student_ratio <- readr::read_csv("https://raw.
For #TidyTuesday week 18 we looked at bird collisions in Chicago. The Data The data for week 18 comes from a study conducted by the Royal Society. The Analysis I wanted to show the number of collisions per bird family over time. This ended up being a perfect excuse to try a ridgeline chart for the first time. Here’s the code: # Load libraries library(tidyverse) library(lubridate) library(ggridges) # Import data bird_collisions <- read_csv("https://raw.
For #TidyTuesday week 17 I got a little too ambitious. I had decided to focus on my favourite anime, Cowboy Bebop. My original goal was to use gganimate to plot the top 20 anime for each year, but to also include any anime for previous years back to 1998. This was to show a rolling top 20 from the year that Cowboy Bebop was released. The Data The data for week 17 comes from MyAnimeList.
My focus for #TidyTuesday Week 16 was on trying to replicate a chart. For context, the idea was to explore the data behind some before and after charts from the Economist. Here are the charts I was looking at: Instead of trying to make my own version of the better chart, I decided to try and figure out how to replicate some of the elements in it. Specifically: Highlighting points in a scatterplot Label the highlighted points, but to also add custom formatting to some of the labels only Here’s the code:
My focus for #TidyTuesday Week 15 was on getting better at exploratory analysis. This is a bit of a contrast to previous weeks where most of my effort has gone into data visualisation. The Data Week 15’s data comes from Wikipedia. Inspiration for this week came from John Burn-Murdoch’s amazing article in the Financial Times. The Analysis I decided to look at the top Grand Slam winners, but I broke this down by court type (grass, clay or hard).
#TidyTuesday Week 14 was a good challenge for me. I wanted to focus on getting better at creating line charts with ggplot2, and I feel like I achieved that goal. The Data Week 14’s data comes from Seattle’s open data portal and contains information about bike traffic in Seattle. The Analysis Since I’m still trying to get back up to speed with using R, I decided to keep it simple and focus on a) showing traffic over time, and b) showing which crossing registered the most traffic each year.
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