tidytuesday VS dataRetrieval

TidyTuesday is a weekly data cleaning project where a new, interesting data source is linked to each week: https://github.com/rfordatascience/tidytuesday

For R, I don't have specific resources, but I remember I started out with doing tidytuesdays challenge (https://github.com/rfordatascience/tidytuesday).

Tidy Tuesday has data and links to more data. The nice thing about those data sets is that you can search for what other people did with the data on social media (e.g. Twitter).

Dataset: https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-11-01

What you're describing is contributing to open source. Some things I'd suggest doing: - learn some git first - create GitHub account and create at least a practice repo - look at learning community-related repos, like Tidy Tuesday - follow R "power" users, people associated with RStudio, and similar folks on social media. Those folks will sometimes mention projects aimed at beginners.

Data: TidyTuesday April 4

The Tidy Tuesday git repo has a lot of example datasets to work with.

I mostly use ggplot2 in R for visualisations which means that The R Graph Gallery is my starting point for inspiration. The best thing to do is start with a simple idea that tells a story, and one of the best guys out there that does this is Cedric Scherer. He is involved a bit with the TidyTuesday project which I wish I had more time to play around with, and is a great starting point for developing a library of vis techniques.

The tile map was made in R (see code) using ggplot2 and the geofacet packages. Data are from the USGS National Water Information System, accessed in R using dataRetrieval. See it on twitter

I’d look into using the R package data retrieval (https://github.com/USGS-R/dataRetrieval) for downloading data. You could download and write the data to a csv if you want excel to open it.

The gage and spatial data were processed in R using dataRetrieval, sf, sp, rgeos, and dplyr. The animation frames were created in R using standard base R graphics, then the frames were stitched together into a video using FFmpeg. Source code can be found on our gage conditions GitHub repository: https://github.com/USGS-VIZLAB/gage-conditions-gif.