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instructions.md

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Prepare the dataset

These instructions are for preparing a dataset using the R programming language. We hope to provide instructions for other programming languages eventually.

If you have not yet set up your computer for submitting a dataset, please see the full instructions at https://github.com/rfordatascience/tidytuesday/blob/main/.github/pr_instructions.md.

  1. cleaning.R: Modify the cleaning.R file to get and clean the data.

    • Write the code to download and clean the data in cleaning.R.
    • If you're getting the data from a github repo, remember to use the 'raw' version of the URL.
    • This script should result in one or more data.frames, with descriptive variable names (eg players and teams, not df1 and df2).

  2. saving.R: Usesaving.R to save your datasets. This process creates both the .csv file(s) and the data dictionary template file(s) for your datasets. Don't save the CSV files using a separate process because we also need the data dictionaries.

    • Run the first line of saving.R to create the functions we'll use to save your dataset.
    • Provide the name of your directory as dir_name.
    • Use ttsave() for each dataset you created in cleaning.R, substituting the name for the dataset for YOUR_DATASET_DF.

  3. {dataset}.md: Edit the {dataset}.md files to describe your datasets (where {dataset} is the name of the dataset). These files are created by saving.R. There should be one file for each of your datasets. You most likely only need to edit the "description" column to provide a description of each variable.

  4. intro.md: Edit the intro.md file to describe your dataset. You don't need to add a # Title at the top; this is just a paragraph or two to introduce the week.

  5. Find at least one image for your dataset. These often come from the article about your dataset. If you can't find an image, create an example data visualization, and save the images in your folder as png files.

  6. meta.yaml: Edit meta.yaml to provide information about your dataset and how we can credit you. You can delete lines from the credit block that do not apply to you.

Submit your pull request with the data

  1. Commit the changes with this folder to your branch. In RStudio, you can do this on the "Git" tab (the "Commit" button).

  2. Submit a pull request to https://github.com/rfordatascience/tidytuesday. In R, you can do this with usethis::pr_push(), and then follow the instructions in your browser.