wrangling-03

Lesson Overview

This repository contains the third lesson for the Data Wrangling in R seminar. This lesson covers the basics of using dplyr to modify existing observations.

Lesson Objectives

By the end of this lesson, learners should be able to:

1. Create a new data frame with only a selection (i.e. a "subset") of observations based on characteristics present in the data.
2. Create a new data frame with a set group of observations based on the current sort order.
3. Create a new data frame with a random sample of observations.

Lesson Resources

• The SETUP.md file in the references/ directory contains a list of packages required for this lesson
• The notebook/ directory contains our primary teaching materials, included a completed version of the notebook we will be working on during the seminar.
• The lesson slides provide an overview of the DSS and data cleaning.
• The references/ directory also contains other notes on changes to the repository, key topics, terms, data sources, and software.

Extra Resources

• R for Data Science, Chapter 5 - Data Transformation
• RStudio's dplyr Cheat Sheet

Lesson Quick Start

Install Necessary Packages

The packages we'll need for today can be installed using:

install.packages(c("tidyverse", "here", "knitr", "rmarkdown", "usethis"))

Download Lesson Materials

You can download this lesson to your Desktop easily using usethis:

usethis::use_course("https://github.com/slu-dss/wrangling-03/archive/master.zip")

By using usethis::use_course, all of the lesson materials will be downloaded to your computer, automatically extracted, and saved to your desktop. You can then open the .Rproj file to get started.

Contributor Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

About the SLU DSS

Data Wrangling in R

About the SLU Data Science Seminar

The SLU Data Science Seminar (DSS) is a collaborative, interdisciplinary group at Saint Louis University focused on building researchers’ data science skills using open source software. We currently host seminars focused on the programming language R. The SLU DSS is co-organized by Christina Gacia, Ph.D., Kelly Lovejoy, Ph.D., and Christopher Prener, Ph.D.. You can keep up with us here on GitHub, on our website, and on Twitter.

About Saint Louis University

Founded in 1818, Saint Louis University is one of the nation’s oldest and most prestigious Catholic institutions. Rooted in Jesuit values and its pioneering history as the first university west of the Mississippi River, SLU offers nearly 13,000 students a rigorous, transformative education of the whole person. At the core of the University’s diverse community of scholars is SLU’s service-focused mission, which challenges and prepares students to make the world a better, more just place.