Chapter 6 Data Wrangling

Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks.

Please read the data wrangling cheat-sheet here.

6.1 Wrangling Tutorial

In order to review-basic wrangling procedures, in this tutorial we will discuss how to perform basic data manipulations, such as filtering data rows that meet certain conditions, choosing data columns, and arranging data in ascending or descending order.

6.2 Wrangling Tutorial 2

In the second tutorial we will continue with basic data manipulations, now moving on to grouping and summarizing, making data tables wider or longer, and joining data tables.

6.3 Data Manipulations

Before analyzing data, you should know how to manipulate data with dplyr essentials (easy data manipulation in R): select, mutate, filter, group_by, summarise, & more!