This repo contains my #rstats, data science & stats illustrations shared on my twitter account (@allison_horst).
All of this artwork is 100% available (and encouraged!) for open use by CC-BY license. That means: Hooray! I'm so happy that you want to share this artwork - especially if it helps when teaching R/rstats/stats. You can just cite with "Artwork by @allison_horst". That's it! Click on the images below for the hi-res versions.
This work is licensed under a Creative Commons Attribution 4.0 International License.
This artwork is available for free to anyone who wants to use it for your teaching, learning, presentations, etc.. If you are an instructor / faculty member / etc. and feel that your course materials & students benefit from the artwork, and you can do so without stress or burden, I would be so grateful if you'd consider donating to Data 4 Black Lives.
Me, working independently:
Monster supporters:
beepr let's you pick and play a notification sound when your code/analysis is done running:
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broom makes messy model / statistical outputs into tidy tibbles:
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dplyr::mutate creates or transforms a variable (column) while keeping the existing ones:
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dplyr: get your data wrangling on.
dplyr::across() makes it easy to apply a function (or functions) across selected columns!
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dplyr::case_when() for friendly if_else statements:
dplyr::filter() to subset rows based on your conditions:
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dplyr::relocate: a friendly function for moving columns around (in dplyr 1.0.0)!
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gganimate: get a little action in(to your graphs)...
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ggplot2 for visual data exploration:
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...and use ggplot2 for creating beautiful data masterpieces!
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here for more peaceful (file) paths:
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The janitor package contains multiple user-friendly functions for cleaning messy data, including clean_names() to update all of your column names to a nice case of your choosing (snake_case! lowerCamel! UpperCamel! SCREAMING_SNAKE! ...and more) all at once:
Use lubridate to work more easily & intuitively with dates & times:
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Like lubridate_ymd() to easily parse year/month/day data!
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Use readr::parse_number() to just keep the numeric parts, & remove characters:
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Part of tidymodels, the parsnip package creates standardized syntax across model engines:
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Easily arrange and combine ggplots with patchwork!
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You can do it!
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Use @tylermorganwall's rayshader package to create amazing 3D maps and graphs!
Use recipes to streamline data preprocessing for stats & machine learning models:
Create reproducible examples to get (and give) help more easily with reprex!
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Get your code, text & outputs in the same (reproducible) place with Rmarkdown:
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Be an Rmarkdown knitting wizard.
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Do your data sci like it's going to need an alibi with Rmarkdown:
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Use the sf package for simpler spatial data analysis with geometries that stick to attributes:
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Soon to be pivot_wider() & pivot_longer()! tidyr::spread() & gather():
stringr::str_squish() removes whitespace before and after strings, and reduced repeated interior whitespace to a single space (see also: str_trim()):
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Blast off into the...
For #rstats and friends!
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Thanks, #rstats community!
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If you bring group_by() to the party, don't forget dplyr::ungroup()
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The following illustrations are in Hadley's ACM talk, which you can watch HERE. Please cite the following artwork with "Illustrations from Hadley Wickham's talk "The Joy of Functional Programming (for Data Science)."
For looped:
Wrangler:
purrr feels like:
Presenting results:
I'm building this library of samples, faces & arms so that statistics teachers can create their own fun, charismatic samples to include in stats lectures, slides & materials. The files below contain different graphs (dotplots, histograms, more to come) with matching arms doing different things, along with a file of faces you can add on top to give them some personality. I recommend playing with transparency, brightness, cropping & size in whatever program you use to piece these together! Working on making these PNGs & SVGs.
Choose the expression to add to your sample:
More coming, feel free to send suggestions.
I start with "pretend you are this whale shark..."
For the love of pie charts:
Creatures and their distance matrix:
Find the clusters with the minimum distance between elements in them & merge:
Repeat!
Ta-da!
Meet your MLR teaching assistants:
Interpret coefficients for categorical predictor variables:
And for continuous predictor variables:
Or make predictions using the regression model:
Understand residuals:
And check for residuals normality:
in_case_you_forget:
Release the disco data:
Type I errors:
Type II errors:
Normality?
Continuous & discrete data:
Nominal, ordinal & binary data:
The expanded version of the classic Grolemund & Wickham R4DS workflow, including environmental data & sci comm bookends! Envisioned by Dr. Julia Lowndes for her useR!2019 keynote.
Dog & whale training art:
Make a data shark:
Data to make the shark is HERE. Created with drawdata.xyz.
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Español: rstats-artwork-ES
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Brazilian Portuguese: rstats-artwork-PT
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Please submit translations as an issue!
Thank you to all the R developers, maintainers, contributors, teachers and communicators who actually MAKE all of these amazing packages and documentation that have inspired this #rstats artwork. When I create an illustration with your package it's with immense gratitude for how your hard work has allowed me to do mine (using and teaching #rstats) more efficiently, more clearly, more reproducibly....just plain better. THANK YOU!