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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,

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