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.
beepr let's you pick and play a notification sound when your code/analysis is done running:
broom makes messy model / statistical outputs into tidy tibbles:
dplyr::mutate creates or transforms a variable (column) while keeping the existing ones:
dplyr: get your data wrangling on.
dplyr::across() makes it easy to apply a function (or functions) across selected columns!
dplyr::relocate: a friendly function for moving columns around (in dplyr 1.0.0)!
gganimate: get a little action in(to your graphs)...
ggplot2 for visual data exploration:
...and use ggplot2 for creating beautiful data masterpieces!
here for more peaceful (file) paths:
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:
Like lubridate_ymd() to easily parse year/month/day data!
Use readr::parse_number() to just keep the numeric parts, & remove characters:
Part of tidymodels, the parsnip package creates standardized syntax across model engines:
Easily arrange and combine ggplots with patchwork!
You can do it!
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!
Get your code, text & outputs in the same (reproducible) place with Rmarkdown:
Be an Rmarkdown knitting wizard.
Use the sf package for simpler spatial data analysis with geometries that stick to attributes:
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()):
Blast off into the...
For #rstats and friends!
Thanks, #rstats community!
If you bring group_by() to the party, don't forget dplyr::ungroup()
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.
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:
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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!