Data Tidying and Importing with R

Overview

Build confidence working with messy, real-world data. In this course, you’ll learn how to import, clean, and organize data in R so that it’s ready for analysis, visualization, or modeling. Using dplyr, tidyr, and other Tidyverse tools, you’ll practice joining datasets, reshaping data, and creating efficient data pipelines that support reproducible work. You’ll also explore how to responsibly collect and scrape data from online sources, including ethical and legal considerations. By the end of this course, you’ll know how to transform raw datasets into structured, tidy formats and you’ll understand how responsible data handling and documentation are essential to high-quality, ethical data science.

Syllabus

Tidy Data

Tidy datasets have a specific structure: each variable is a column, and each observation is a row. In this module, we use functional verbs from the dplyr package in R to transform data into a ready-to-use tidy data format. Additionally, we use functional verbs to manipulate data frames.

Importing + Recoding Data

A column in our data set can be stored as many different types, such as numbers or characters. These different data types inform how R treats the data, and whether certain functions are compatible to use with certain types of data. In this module, we discuss more in detail, the different data types classified by R, data classes, as well as how to recode variables in a data set to be different types, classes, or take on different values.

Web Scraping and Programming

Web scraping is the process of extracting this information automatically and transforming it into a structured dataset. In this module, we go over how to perform basic web scraping in R to make an abundance of data online more easily accessible.