cheatsheets

Posit Cheat Sheets - Can also be found at https://posit.co/resources/cheatsheets/

rstudio/cheatsheets

rstudio.github.io/cheatsheets

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The Posit cheatsheets are visual quick-reference guides for popular R packages and tools, available as PDFs and increasingly as accessible HTML versions. They provide scannable layouts with visual aids to help users quickly locate functions and commands they need.

These cheatsheets solve the problem of navigating package documentation by organizing functions into meaningful groups with visual mnemonics rather than text-heavy explanations. They’re designed as quick-reference tools that guide users to essential information faster than traditional documentation. The HTML versions improve accessibility for users with visual impairments by minimizing images and providing alternative text.

Contributors#

Resources featuring cheatsheets#

Data Manipulation Tools: dplyr – Pt 3 Intro to the Grammar of Data Manipulation with R

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks.

dplyr docs: dplyr.tidyverse.org/reference/

Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw

/01:44 Intro and what’s covered Ground Rules
/02:40 What’s a tibble
/04:50 Use View
/05:25 The Pipe operator:
/07:20 What do I mean by data wrangling?

Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM

/00:48 Goal 1 Making your data suitable for R
/01:40 tidyr “Tidy” Data introduced and motivated
/08:10 tidyr::gather
/12:30 tidyr::spread
/15:23 tidyr::unite
/15:23 tidyr::separate

Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U

00.40 setup
02:00 dplyr::select
03:40 dplyr::filter
05:05 dplyr::mutate
07:05 dplyr::summarise
08:30 dplyr::arrange
09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
11:45 dplyr::group_by

Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together

/00.42 dplyr::bind_cols
/01:27 dplyr::bind_rows
/01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff
/02:15 joining data dplyr::left_join, dplyr::inner_join, dplyr::right_join, dplyr::full_join,

Cheatsheets: https://www.rstudio.com/resources/cheatsheets/

Documentation: tidyr docs: tidyr.tidyverse.org/reference/

tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/
dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html

Tidy Data and tidyr – Pt 2 Intro to Data Wrangling with R and the Tidyverse

http://tidyr.tidyverse.org/reference/

Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw

/01:44 Intro and what’s covered Ground Rules
/02:40 What’s a tibble
/04:50 Use View
/05:25 The Pipe operator:
/07:20 What do I mean by data wrangling?

Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM

00:48 Goal 1 Making your data suitable for R
01:40 tidyr “Tidy” Data introduced and motivated
08:10 tidyr::gather
12:30 tidyr::spread
15:23 tidyr::unite
15:23 tidyr::separate

Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U

00.40 setup
/02:00 dplyr::select
/03:40 dplyr::filter
/05:05 dplyr::mutate
/07:05 dplyr::summarise
/08:30 dplyr::arrange
/09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
/11:45 dplyr::group_by
/15:00 dplyr::group_by

Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together

/00.42 dplyr::bind_cols
/01:27 dplyr::bind_rows
/01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff
/02:15 joining data dplyr::left_join, dplyr::inner_join, dplyr::right_join, dplyr::full_join,

Cheatsheets: https://www.rstudio.com/resources/cheatsheets/

Documentation: tidyr docs: tidyr.tidyverse.org/reference/

tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/
dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html

What is data wrangling? Intro, Motivation, Outline, Setup – Pt. 1 Data Wrangling Introduction

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. These videos introduce you to these tools. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks.

Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw

01:44 Intro and what’s covered Ground Rules
02:40 What’s a tibble
04:50 Use View
05:25 The Pipe operator:
07:20 What do I mean by data wrangling?

Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM

/00:48 Goal 1 Making your data suitable for R
/01:40 tidyr “Tidy” Data introduced and motivated
/08:15 tidyr::gather
/12:38 tidyr::spread
/15:30 tidyr::unite
/15:30 tidyr::separate

Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U

00.40 setup
/02:00 dplyr::select
/03:40 dplyr::filter
/05:05 dplyr::mutate
/07:05 dplyr::summarise
/08:30 dplyr::arrange
/09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
/11:45 dplyr::group_by
/15:00 dplyr::group_by

Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together

/00.42 dplyr::bind_cols
/01:27 dplyr::bind_rows
/01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff
/02:15 joining data dplyr::left_join, dplyr::inner_join, dplyr::right_join, dplyr::full_join,

Cheatsheets: https://www.rstudio.com/resources/cheatsheets/

Documentation: tidyr docs: tidyr.tidyverse.org/reference/

tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/
dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html
dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html

New York Times “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights”, By STEVE LOHRAUG. 17, 2014 https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html

Working with Two Datasets: Binds, Set Operations, and Joins – Pt 4 Intro to Data Manipulation

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks.

dplyr docs: dplyr.tidyverse.org/reference/

Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw

/01:44 Intro and what’s covered Ground Rules:
/02:40 What’s a tibble
/04:50 Use View
/05:25 The Pipe operator:
/07:20 What do I mean by data wrangling?

Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM

/00:48 Goal 1 Making your data suitable for R
/01:40 tidyr “Tidy” Data introduced and motivated
/08:10 tidyr::gather
/12:30 tidyr::spread
/15:23 tidyr::unite
/15:23 tidyr::separate

Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U

/00.40 setup
/02:00 dplyr::select
/03:40 dplyr::filter
/05:05 dplyr::mutate
/07:05 dplyr::summarise
/08:30 dplyr::arrange
/09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
/11:45 dplyr::group_by

Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together

00.42 dplyr::bind_cols
01:27 dplyr::bind_rows
01:42 Set operations dplyr::union, dplyr::intersect, dplyr::set_diff
02:15 joining data - dplyr::left_join, dplyr::inner_join, - dplyr::right_join, dplyr::full_join,