research-02

Lesson Overview

Objectives

At the end of this lesson, participants should be able to:

1. Construct file paths in R in a way that ensures maximum reproducibility.
2. Construct data frames from a variety of raw data sources including .rda, .csv, various statistical package data sets, and Excel workbooks.
3. Identify common issues with Excel workbooks and modify their input into R to address these issues.

Lesson Quick Start

Install Necessary Packages

You can copy and paste this syntax into your console:

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/research-02/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.

Lesson Resources

• The SETUP.md file in the references/ directory contains a list of packages required for this lesson
• The notebook/ directory contains both the seminar and completed versions of our lesson notebooks
• The lesson slides provide an overview of file paths and working directory issues for reproducibility
• The references/ directory also contains other notes on changes to the repository, key topics, terms, data sources, and software.

Extra Resources

• the tidyverse website
• the readr website
• the haven website
• the readxl website

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

Reproducible Research in R

This seminar is our introductory series of lessons for using R and RStudio with an eye towards reproducibility. We focus on some basic features of R itself, organizing with R projects, loading data using the haven package, crafting R notebooks and using RMarkdown syntax, and using the knitr package. More details are available on our website.

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.