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.
tidyr.tidyverse...
tidyr.tidyverse...
tidyr.tidyverse...
tidyr.tidyverse...
tidyr.tidyverse...
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Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup • What is data wrangling...
/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 • Tidy Data and tidyr --...
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` • Data Manipulation Tool...
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 • Working with Two Datas...
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`,
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Cheatsheets: www.rstudio.co...
Documentation:
`tidyr` docs: tidyr.tidyverse.org/reference/
`tidyr` vignette: cran.r-project...
`dplyr` docs: dplyr.tidyverse...
`dplyr` one-table vignette: cran.r-project...
`dplyr` two-table (join operations) vignette: cran.r-project...
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13 окт 2024
