ABSTRACT
This chapter examines data wrangling – getting development practitioners' data into the form they want. It focuses on: subsetting, transforming, tidying, summarising, ordering, and joining. Typically, in conservation and development projects the person analysing the data is not the same person who collected the data, entered the data, or planned the study. Consequently, the person analysing the data will often have to rearrange it to meet their needs. Often this means subsetting, modifying, summarising, and ordering the original data – known as data wrangling. While base R can be used to wrangles data, the tidyverse set of packages makes data wrangling easier. Consequently, practitioners will focus on using the functions within the tidyverse. Another useful package that they will also use is janitor which is designed for cleaning and summarising data. Tibbles never change the original format of the data. Tibbles also only show a portion of the data. This is done to prevent the console window from becoming unreadable.
