Data visualisation

I love the different ways we can present data. Whilst academic data visualisation is often based on enabling readers to accurately perceive the absolute values of the data, and hence make inferences, there is obviously merit in making visualisations that are aesthetically pleasing and engaging. As I have developed coding-based skills through my academic work, I have tried to use these skills to help with computerised data visualtions.

On this page

TidyTuesday

Football

Pokemon


TidyTuesday ๐Ÿ’ป

TidyTuesday is a weekly project produced by the R4DS Online Learning Community where each week a raw datset, chart, or article is posted. One is then able to tidy and explore the data, and produce an informative(?) visualisation.

An important aspect of TidyTuesday is this, taken directly from the TidyTuesday website:

We will have many sources of data and want to emphasize that no causation is implied. There are various moderating variables that affect all data, many of which might not have been captured in these datasets. As such, our guidelines are to use the data provided to practice your data tidying and plotting techniques. Participants are invited to consider for themselves what nuancing factors might underlie these relationships.

TidyTuesday is a bit of fun, and is often used to learn and improve R skills, data visualation techniques, and connect with the R community!

My GitHub with all of my contributions:

2021 week 44 - Ultra trail running ๐Ÿƒโ€โ™€๏ธ

Ultra trail data courtesy of Benjamin Nowak by way of International Trail Running Association (ITRA)

utmb


2021 week 43 - Giant pumpkins ๐ŸŽƒ

Giant pumpkin data from BigPumpkins.com

giant_pumpkins_med
giant_pumpkins_max_19102021


2021 week 42 - Global Seafood ๐ŸŸ

Global fishing data from OurWorldinData.org

global_fishing


2021 week 41 - US registered nurses ๐Ÿฅ

US Nurse data from Data.World

emmys


2021 week 39 - Emmy awards ๐Ÿ†

Emmy award data from emmys.com

emmys


2021 week 38 - US Billboard 100 ๐ŸŽผ

US Billboard data from Data.World by way of Sean Miller, Billboard.com and Spotify

us_billboard


2021 week 37 - Formula One ๐ŸŽ

Formula One data from Ergast API

formula_1

formula_1

formula_1


2021 week 36 - Australian bird baths ๐Ÿฆœ

Bird bath data from Cleary et al., (2016) PLOS ONE 11(3): e0150899

aus_bird


2021 week 35 - Lorises ๐Ÿ™‰

Strepsirrhine primate data from the Duke Lemur Center

lemur


2021 week 34 - Star Trek voice commands ๐Ÿš€๐Ÿ––

Star Trek voice commands data from the SpeechInteraction.org

star_trek


2021 week 33 - BEA Infrastructure investment ๐Ÿ’ฐ

U.S. Infrastructure investment data from the Bureau of Economic Analysis

bea


2021 week 32 - Paralympics ๐Ÿ…๐Ÿ‡ฎ๐Ÿ‡ช

Paralympics data from the International Paralympic Committee

paralympics


2021 week 31 - Olympics ๐Ÿ…

Olympics data from Kaggle

summer_olympics_1
summer_olympics_2


2021 week 30 - US Droughts ๐ŸŒต

Data of US droughts from U.S. Drought Monitor

ca_droughts


2021 week 29 - Scooby Doo episodes ๐Ÿ•๐Ÿ‘ป

Scooby Doo episode data from Kaggle thanks to manual data aggregation by plummye

scooby_doo


2021 week 28 - Independence days ๐ŸŒ๐ŸŽ†

Independence Days data from Wikipedia thanks to Isabella Velasquez

independence_days


2021 week 27 - London animal rescues ๐Ÿฑ๐Ÿถ๐Ÿ‡บ๐Ÿ‡ธ

Animal rescue data from London.gov by way of Data is Plural and Georgios Karamanis

battersea_rescue


2021 week 26 - US Public Park access ๐ŸŒณ๐Ÿ‡บ๐Ÿ‡ธ

Park access data from The Trust for Public Land

ny_park_access
il_park_access


2021 week 25 - #DuBoisChallenge tweets โœŠ๐Ÿฟ

#DuBoisChallenge data from Anthony Starks, Allen Hillery, and Sekou Tyler

dubois_twitter


2021 week 24 - Great Lakes Fisheries ๐ŸŽฃ

Fishery data from Great Lakes Fishery Commission

great_lakes_fish


2021 week 23 - Survivor TV Show ๐Ÿ“บ๐Ÿ

Survivor data from Daniel Oehm who produced the {survivoR} package

Survivor


2021 week 22 - Mario Kart 64 ๐ŸŽ๐Ÿ

Mario Kart 64 World Records from Benedikt Claus & MKWR

mario_kart


2021 week 21 - Salary survey ๐Ÿ’ฐ

Salary survey data from Ask a Manager

salary_data


2021 week 20 - Internet usage ๐Ÿ’ป

US internet usage data from Microsoft

internet_usage


2021 week 19 - Water sources ๐Ÿ’ฆ

Water access points data from Water Point Data Exchange

water_sources


2021 week 18 - CEO departures ๐Ÿ“Š

CEO departure data from Gentry et al. 2021 & DataIsPlural

CEO_shows


2021 week 17 - Netflix Titles ๐Ÿ“บ

Netflix show data from Shivam Bansal (Kaggle)

netflix_shows


2021 week 16 - US Post Offices โœ‰๏ธ๐Ÿ“ช

US Post Office data from Blevins & Helbock, 2021, "US Post Offices", Harvard Dataverse

us_post_offices


2021 week 15 - Deforestation ๐ŸŒณ๐Ÿชต

Deforestation data from Our World in Data

deforestation


2021 week 14 - Make up shades ๐Ÿ’„

Makeup shades data from The Pudding | See original article here

makeup_shades


2021 week 13 - UN votes ๐ŸŒ๐ŸŒ

UN voting data from Harvard Dataverse

UN vote


2021 week 12 - Video Games ๐Ÿ‘พ

Video game data from the video game distribution service Steam

Steam_gaming


2021 week 11 - Bechdel Test ๐ŸŽฅ๐Ÿ™‹โ€โ™€๏ธ

The Bechdel test is a measure of the representation of women in fiction

Bechdel Test


Football data โšฝ

I have produced an R Shiny App with an updating 2021 / 2022 Premier League Table

With this, you can view:

  1. The Premier League Table at a set date

  2. The Premier League Table between two dates - the media love to do this to see, for example, the table since Christmas or since a managerial sacking

  3. A lineplot of the weekly league position for each team

  4. A lineplot of the total number of points attained by each team, on a weekly basis.

The previous version for 2020/21 can be found here: Premier League Table

Please note that I only have a free shinyapps account, so use is limited to 25 active hours per month


Pokรฉmon

There is a plethora of Pokรฉmon data visualisation online, with much providing informative insights on specific Pokรฉmon stats (HP, Attack, Sp. Atk etc). I thought it would be interesting to visualise the different “type” that each Pokรฉmon is.

I downloaded a dataset from Kaggle that contains the typing of each Pokรฉmon (some Pokรฉmon have two types), and used {geom_tile} to produce a tile representing each Pokรฉmon, where the colour of the tile maps to each Pokรฉmon’s typing.