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TidyTuesday

TidyTuesday project is a weekly appointment that happens on every Tuesday for practicing making #DataVisualization with datasets provided by the #R4DS Online Learning Community

Several TidyTuesday interesting examples can be found in the main repository:


How to make a #TidyTuesday (more info at the bottom of this page)


My contributions are posted on:

Twitter @fgazzelloni and collected in this repository with related code.


Other #DataViz projects I contribute to:


My #TidyTuesdays

Week1
Bring your own data to start 2022
Week2
Bee Colony losses
Week3
Chocolate Bar ratings
Week4
Board games
w1 w2 w3 w4 2022-01-25
Week5
Dog breeds
Week6
Tuskegee Airmen
Week7
#DuBoisChallenge2022
Week8
World Freedom index
5 2022-02-01 62022-02-08 7 2022-02-15 8 2022-02-22
Week9
Alternative Fuel Stations
Week10
Erasmus student mobility
Week11
CRAN/BIOC Vignettes
Week12
Baby names
92022-03-01 10 2022-03-08 112022-03-15 122022-03-22
Week13
Collegiate Sports Budgets
Week14
Digital Publications
Week15
Indoor Air Pollution
Week16
Crossword Puzzles and Clues
132022-03-29 142022-04-05 152022-04-12 162022-04-19
Week17
Kaggle Hidden Gems
Week18
Solar/Wind utilities
Week19
NYTimes best sellers
Week20
Eurovision
172022-04-26 182022-05-03 19 2022-05-10 20 2022-05-17
Week21
Women's Rugby
Week22
Company reputation poll
Week23
Pride Corporate Accountability Project
Week24
US Drought
212022-05-24 222022-05-31 232022-06-07 242022-06-14
Week25
Juneteenth
Week26
UK Gender pay gap
Week27
San Francisco Rentals
Week28
European flights
252022-06-21 262022-06-28 272022-07-05 282022-07-12
Week29
Technology
Week30
BYOD
Week31
Oregon Spotted Frog
Week32
???
292022-07-19 302022-07-26 312022-08-02 32...
Week33
???
Week34
???
Week35
???
Week36
???
33... 34... 35... 36...
Week37
???
Week38
???
Week39
???
Week40
???
37... 38... 39... 40...

INFO: How to make a #TidyTuesday

  • Go to R4DataScience GitHub repository

  • import data found in the README at the middle bottom of the page is a table with the most updated data provided for the year/week

  • click on the corresponding data tab in the table

  • load the data, two options are available:

    1. Install {tidytuesdayR} package from CRAN via: install.packages("tidytuesdayR"), then load the data as suggested assigning a tuesdata variable name using the tt_load() function:

      tuesdata <- tidytuesdayR::tt_load("date")

      tuesdata <- tidytuesdayR::tt_load(year, week)

    2. Import the data directly from the .csv file provided

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