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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Week 20 of #MakeoverMonday looked at a very sad topic - rhino poachng in South Africa. Here’s the original viz: What works well: Simple but effective colours choices Clear title The use of rhino imagery helps make it immediately clear what the chart is about What could be improved: I didn’t like the use of a unit chart as it didn’t quite make sense in terms of numbers of poached rhinos per year The use of rhino icons seemed a but too ‘fun’ for such a serious topic What I focused on: I wanted to simplify the orginal chart I wanted to annotate some of the key facts as well The interactive version can be found here

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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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Week 19 of #MakeoverMonday looked at the most efficient batters in Major League Baseball (MLB). Here’s the original viz: What works well: Simple title that says what it is about Nice colour choices There’s a lot of detail included, but without cluttering the viz What could be improved: I don’t follow baseball, I was a bit lost when trying to understand this I was confused by the highlighted stats.

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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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Jared Braggins

Data Visualisation and Analysis