Intro to Data Wrangling with data.table

Your Instructors

Julia Ellingwood and Renato Franco

Materials

RPub version of slidedeck here: http://rpubs.com/jellingwood-ftw/829410

Session Content

• Background on data.table what it’s for, who’s behind it, and how does it fit into the tidyverse
• Intro to data.table objects and syntax
• Data manipulation with data.table how to subset, extract, summarize, group, etc
• An in-class exercise

Main Learning Objectives

• You will learn the advantages of data.table over other data wrangling tools, and compare its functionality with dplyr.
• You will be able to create data.table objects and use data.table syntax to subset, filter, extract, compute, and group your dataset.

Some additional sources on data.table

• https://rdatatable.gitlab.io/data.table/
• The CRAN vignette: https://cran.r-project.org/web/packages/data.table/vignettes/datatable-intro.html
• data.table cheatsheet: /~https://github.com/intro-to-data-science-21-workshop/09-JuliaEllingwood-data.table_data_wrangling/blob/master/datatable_cheatsheet.pdf
• Datacamp course on data manipulation with data.table: https://app.datacamp.com/learn/skill-tracks/data-manipulation-with-r
• Simulated datasets and processing time with data.table, dplyr, and other data wrangling tools: https://h2oai.github.io/db-benchmark/

License

The material in this repository is made available under the MIT license.

Statement of contributions

Julia Ellingwood created the slides and Renato Franco wrote the session R script. Both contributed to the recording of the video and the in-class exercises.