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pokemon! #789
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pokemon! #789
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Just a few tweaks needed overall, but the main thing is that your submission directory is ephemeral; we only keep the pieces designated in the instructions. If you want to use an article that you wrote for the article link, you need to publish it on your own GitHub account. It's also fine to link to virtually anything about Pokemon for the article.
Thanks for the submission!
title: Pokemon | ||
article: | ||
title: Gotta Catch Em All? | ||
url: github.com/frankiethull/tidytuesday/tree/pokemon/data/curated/pokemon/article.md |
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The link needs to be to an existing thing on the internet. This directory is deleted during the acceptance process, so it can't "live" here. Either publish it on your own GitHub, or provide a link to anything about Pokemon, ideally showing some use of data, but something like the source of the data or a wikipedia article about Pokemon or anything else would work.
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I was originally thinking the article would live on my fork. But I'll go with the second idea, providing a link to anything about Pokemon. Will look around for one that uses the underlying data (if possible).
# Please include at least one image, and up to three images | ||
- file: pokemon.png | ||
alt: > | ||
Image shows faceted scatterplots of pokemon dataset. x = attack; y = defense; facet = pokemon type; color = primary pokemon color; |
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Describe the image in a way that serves to replace the image when the image isn't available. What does the plot show? Are there trends? Is there one type that stands out as more defense-focused, or another that's more attack-focused? Or maybe pointing out the type with the strongest attack and the type with the strongest defense.
Co-authored-by: Jon Harmon <[email protected]>
Co-authored-by: Jon Harmon <[email protected]>
Co-authored-by: Jon Harmon <[email protected]>
Co-authored-by: Jon Harmon <[email protected]>
Co-authored-by: Jon Harmon <[email protected]>
Co-authored-by: Jon Harmon <[email protected]>
Thank you for your submission! Please leave this text as-is when you submit, and then check the checklist.
Usability:
Preparation:
cleaning.R
has code to download and clean the dataset, resulting in descriptively-named data.frames.saving.R
to createcsv
s andmd
data dictionaries for each of my data.frames.{dataset}.md
files.intro.md
file to introduce my dataset.png
file.meta.yaml
, including descriptive alt text for each image.meta.yaml
about how to credit me, and deleted any parts of that block that I do not want you to use.