Introduction to R Programming and Tidyverse

Overview

This course is a gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if: • you want to do data analysis but don’t know programming • you know programming but aren’t familiar with R • you know some R programming but want to learn the tidyverse verbs You will learn to do data visualization and analysis in a reproducible manner and use functions that allow your code to be easily read and understood. You will use RMarkdown to create nice documents and reports that execute your code freshly every time it’s run and that capture your thoughts about the data along the way. This course has been designed for learners from non-STEM backgrounds to help prepare them for more advanced data science courses by providing an introduction to programming and to the R language. I am excited for you to join me on the journey! The course logo was created using images of stickers from the RStudio shop. Please visit https://swag.rstudio.com/s/shop.

Syllabus

Introduction to R, RStudio and RMarkdown

In the first module of this course, you will install and configure R and RStudio. You will review the fundamentals of R and reproducibility, install R packages required for the course, and input basic commands using the RStudio console. Finally, you will create an RMarkdown document - the deliverable for this module.

Functions

In this module, we will explore functions in R. You will review the syntax of functions and best practices of function creation. You will also practice writing functions with default arguments and argument validation.

Data Visualization using ggplot2

In this module, you will be introduced to ggplot2 - an R package for data visualization. You will explore the different grammatical elements and aesthetic mappings (layers) that are essential to visualize data in ggplot2.

Data Analysis with dplyr

In the final module of this course, you will be introduced to data analysis using dplyr. You will learn and practice with the many dplyr verbs including select, filter, arrange, mutate, group_by, and summarize.