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:

dplyr::mutate creates or transforms a variable (column) while keeping the existing ones:

dplyr: get your data wrangling on.

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:

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():

Blast off into the...

For #rstats and friends!

Thanks, #rstats community!

If you bring group_by() to the party, don't forget dplyr::ungroup()

Gentle intro to t-tests thread:

in_case_you_forget:

Release the disco data:

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:

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.

• Español: rstats-artwork-ES

• Brazilian Portuguese: rstats-artwork-PT

• 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!