Resources for R

Learning

The following online books are useful for expanding your R knowledge and skills:

• the most recent ADC training materials
  • The cleaning and data manipulation section is useful for working with attribute tables
• Efficient R Programming
  • In particular Chapter 3 Efficient Programming
• R for Data Science
  • Section on Strings
• R Packages
  • contributing to arcticdatatutils, datamgmt and EML
• Advanced R
  • Object-oriented programming in R for S4 to understand how datapack and dataone packages are written
• bookdown: Authoring Books and Technical Documents with R Markdown
• formatting, troubleshooting and updating the training document

Others

• Hands-On Programming with R
• R Programming for Data Science
• Exploratory Data Analysis with R
• Mastering Software Development in R
• Geocomputation with R
• R Markdown: The Definitive Guide
• The Tidyverse Style Guide

The RStudio cheatsheets are also useful references for functions in tidyverse and other packages.

Packages

The data team uses and develops a number of R packages. Here is a listing and description of the main packages:

• dataone
  • reading and writing data at DataONE member nodes
  • http://doi.org/10.5063/F1M61H5X
• datapack
  • creating and managing data packages
  • https://github.com/ropensci/datapack
• EML
  • creating and editing EML metadata documents
  • https://ropensci.github.io/EML
• arcticdatautils
  • utility functions for processing data for the Arctic Data Center
  • https://nceas.github.io/arcticdatautils/
• datamgmt
  • data management utilities for curating, documenting, and publishing data (sandbox package)
  • https://nceas.github.io/datamgmt/
• metadig
  • authoring MetaDIG quality checks
  • https://github.com/NCEAS/metadig-r
• metajam
  • downloading and reading data and metadata from DataONE member nodes
  • https://nceas.github.io/metajam/