fgazzelloni / tidytuesday

Explore fascinating TidyTuesday projects in my portfolio, showcasing data visualization and analysis skills.

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:

• https://github.com/rfordatascience/tidytuesday

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

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My contributions are posted on:

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

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Other #DataViz projects I contribute to:

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

My #TidyTuesdays

2021 | 2022 | 2023

Week1 Bring your own data to start 2023	Week2 Project FeederWatcher	Week3 Art History	Week4 Alone data
Week5 PCUK	Week6 BTSP	Week7 Hollywood Age Gaps	Week8 Bob Ross Paintings
Week9 African Languages	Week10 Numbat	Week11 EDD	Week12 Programming Languages
Week13 Time zones	Week14 Premier League Match Data	Week15 US Egg Production	Week16 Neolithic Founder Crops
Week17 London Maraton	Week18 Solar/Wind utilities	Week19 NYTimes best sellers	Week20 Eurovision
Week21 Women's Rugby	Week22 Company reputation poll	Week23 Pride Corporate Accountability Project	Week24 US Drought
Week25 Juneteenth	Week26 UK Gender pay gap	Week27 San Francisco Rentals	Week28 NASA GISS Surface Temperature Analysis
Week29 Technology	Week30 BYOD	Week31 Oregon Spotted Frog	Week32 ferriswheels
Week33 Open Source Psychometrics	Week34 CHIP dataset	Week35 Pell Grants	Week36 LEGO database
Week37 Bigfoot	Week38 Hydro Wastewater plants	Week39 Artists in the USA	Week40 Product Hunt products
Week41 Ravelry data	Week42 Stranger things dialogue	Week43 Great British Bakeoff	Week44 Horror Movies
Week45 Radio Stations	Week46 Page Metrics	Week47 R-Ladies Chapter Events	Week48 World Cup
Week49 Elevators	Week50 Monthly State Retail Sales	Week51 Weather Forecast Accuracy	Week52 Star Trek Timelines

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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