Data Wrangling with R

"You can have data without information, but you cannot have information without data."
– Daniel Keys Moran

Data wrangling has been one of the core strengths of R, given its capabilities of relatively fast in-memory processing on demand and a wide array of packages that facilitate the fast data curation processes that data wrangling involves.

R is especially invaluable when working with datasets in excess of 1 million rows—the limit in Microsoft Excel—or when working with files that are in the order of gigabytes. Due to several easy-to-use functions for common day-to-day tasks such as aggregations, joins, and pivots, R is also arguably much simpler to use relative to some of the GUI-based tools that are available for similar...