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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:

30DayChartChallenge-2021 30DayMapChallenge-2021
30DayChartChallenge-2022 30DayMapChallenge-2022
30DayChartChallenge-2023

My #TidyTuesdays

Week1
Bring your own data to start 2023
Week2
Project FeederWatcher
Week3
Art History
Week4
Alone data
w1 boy w2_BFWd w3_Arhd w4_Alnd
Week5
PCUK
Week6
BTSP
Week7
Hollywood Age Gaps
Week8
Bob Ross Paintings
w5_PCUK w6_BTSP w7_HlAG w8_BbRP
Week9
African Languages
Week10
Numbat
Week11
EDD
Week12
Programming Languages
w9_AfLS w10_NmiA w11_ErDD w12_PrgL
Week13
Time zones
Week14
Premier League Match Data
Week15
US Egg Production
Week16
Neolithic Founder Crops
w13_TmZn w14_PLMD w15_UEPD w16_NFC
Week17
London Maraton
Week18
Solar/Wind utilities
Week19
NYTimes best sellers
Week20
Eurovision
w17_LM 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
NASA GISS Surface Temperature Analysis
252022-06-21 262022-06-28 272022-07-05 282022-07-12
Week29
Technology
Week30
BYOD
Week31
Oregon Spotted Frog
Week32
ferriswheels
292022-07-19 302022-07-26 312022-08-02 322022-08-09
Week33
Open Source Psychometrics
Week34
CHIP dataset
Week35
Pell Grants
Week36
LEGO database
332022-08-16 342022-08-23 352022-08-30 362022-09-06
Week37
Bigfoot
Week38
Hydro Wastewater plants
Week39
Artists in the USA
Week40
Product Hunt products
372022-09-13 382022-09-20 392022-09-27 402022-10-04
Week41
Ravelry data
Week42
Stranger things dialogue
Week43
Great British Bakeoff
Week44
Horror Movies
412022-10-11 422022-10-18 432022-10-25 442022-11-01
Week45
Radio Stations
Week46
Page Metrics
Week47
R-Ladies Chapter Events
Week48
World Cup
week 45 w46_web_page_metrics w47_rladies_chapter_events w48_fifa_world_cup
Week49
Elevators
Week50
Monthly State Retail Sales
Week51
Weather Forecast Accuracy
Week52
Star Trek Timelines
w49_elevators w50_retail_sales w51 weather_forecast_accuray w52_star_trek_timelines

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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