Introduction to R Data Wrangling Lisa Federer MLIS

Introduction to R: Data Wrangling Lisa Federer, MLIS, MA, AHIP Research Data Informationist

Course Overview • Introduction to R and RStudio – Why R? – What is RStudio? – Terminology and syntax • Hands-on practice – Deciphering error messages and troubleshooting – Basics of data processing/wrangling – Getting more help

Why R? One command to carry out complex tasks

Why R? Easy to share code, which enhances reproducibility

Why R? Everything in one places: data processing, analysis, and visualization

What is RStudio? IDE (integrated development environment) for R

Data input options • Input data directly into R by typing it in • Import data from many, many common file types (. txt, . csv, Excel files, XML, SAS, SPSS, Stata, etc) • Connect to web resources and other remote servers

Data types: data frame a list of variables of the same number of rows with unique row names (like a spreadsheet)

Data types within data frames • Numeric variable = numeric or integer Ex: 1, 1. 5, 200000, 3. 14159 • Text variable = character Ex: a, b, hello, 3 b • Nominal variable = factor Ex: cat, dog, pig, rhino, horse • Ordinal variable = ordered factor Ex: xsmall, medium, large, xlarge • True/false = logical Ex: TRUE, FALSE

Talking to R getwd() dat <- read. csv(file = "all. Found. csv", header = TRUE) Function: how to tell R what to do, always followed by ()

Talking to R getwd() dat <- read. csv(file = "all. Found. csv", header = TRUE) Argument: instructions that specify how a function should be run. Not always required, may be more than one, they are separated by commas, and they all go in the ()

Talking to R getwd() dat <- read. csv(file = "all. Found. csv", header = TRUE) Assignment operator (<-): assigns the output of the function to a name that you can then refer back to