Grammar of Data Manipulation

Resources tagged Grammar of Data Manipulation#

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/ - http://dplyr.tidyverse.org/reference/setops.html - http://dplyr.tidyverse.org/reference/join.html ---------------- 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 ______________________________________________________________

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 ______________________________________________________________

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

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. http://tidyr.tidyverse.org/reference/ - http://tidyr.tidyverse.org/reference/gather - http://tidyr.tidyverse.org/reference/spread - http://tidyr.tidyverse.org/reference/unite - http://tidyr.tidyverse.org/reference/separate ---------------- 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 ______________________________________________________________

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/ - http://dplyr.tidyverse.org/reference/union.html - http://dplyr.tidyverse.org/reference/intersect.html - http://dplyr.tidyverse.org/reference/set_diff.htm ---------------- 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 ______________________________________________________________