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:

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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.

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The focus of this month’s #SWDChallenge was on artisanal data. I found this challenge to be incredibly enjoyable. Why? Because I got to design a visualisation using data that I knew inside and out (since it was about my work history after all!). But hey, that was kinda the point, right? I want to take a step back here though. I want to address why I used data about my work history.

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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.

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Week 18 of #MakeoverMonday looked at spacewalks at the International Space Station. Here’s the original viz: What works well: The heading is a succinct summary of what the viz is showing Using the BANS (Big-ass numbers) for USA and Russia as a legend is a good idea The labels on the bars make the chart easier to read The colours are a good choice as they are very close to the NASA palette What could be improved: The years on the x-axis are on an angle, making them harder to read There are numerous images on the viz, making it very busy What I focused on: Changing the bar chart to a line chart Changing the orientation of the dates on the x-axis to be horizontal Adding the BANs into the description The interactive version can be found here

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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.

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Week 17 of #MakeoverMonday looked at Stephen Curry’s ranking of stadium popcorn. Here’s the original viz: What works well: A heat chart is perfect for this data. The labels and colours work well There’s a clear title The scoring is simple What could be improved: The colours aren’t great What I focused on: Although I do like the original chart, I decided to try something different.

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Jared Braggins

Data Visualisation and Analysis