Introduction to the R tidyverse

Erik Erhardt
2018-02-16

• Syllabus
  • Title
  • Overview
  • Description
  • Schedule
  • Recommended Reading
  • Instructor
• Software and Computer Support
  • Software requirements
  • Local course folder
  • R packages
  • Course Materials

Course website: Course materials

Short course for the Albuquerque Chapter of the American Statistical Association (ACASA)

Prof. Erik Erhardt

Date: February 16, 2018

Time: 12:30-5pm

Location: Student Union Building at the University of New Mexico - Fiesta Rooms A&B

Registration: https://www.123signup.com/event?id=hvbrq

Syllabus

Title

Introduction to the R tidyverse:

Explore, wrangle, program, and communicate, reproducibly

Overview

An afternoon course introducing what the Tidyverse is, reasons for using it, the tools available in it, and an overview with exercises of its core functionality.

Description

This afternoon short course will introduce participants to the R Tidyverse, a set of unified packages covering the data analysis workflow implementing a single philosophy (making it easy to learn and use). Prior R programming experience is required. Through interactive lectures of R programming concepts, examples, and exercises, this course will guide participants through the 18 packages used for the steps of importing, wrangling, visualizing, and exploring data and then communicating the results. Participants will gain a high-level perspective of the data analysis cycle along with the tools needed for managing the details.

Participants will receive an electronic copy of all course materials, including lecture slides, practice datasets, software scripts, relevant supporting documentation, and recommended readings.

Skills Prerequisites

Required:

• At least limited experience programming in any computer language.
• At least limited experience creating plots and tables of data or data summaries.

Not required but advantageous:

• Data formats, such as strings, factors, dates and times, vectors, and data frames.
• Limited experience in basic statistical analysis, such as simple linear regression and contingency table analysis (for example, two-way tables).

No level of proficiency beyond basic awareness is assumed for skills related to:

• Advanced mathematical or statistical topics such as matrix algebra or likelihood theory.

Schedule

Friday February 16, 2018
12:30 Course outcomes, data and materials
12:35 Tidyverse workflow and packages
12:45 RStudio and R Markdown
 1:00 Tibble
 1:25 Import
 2:00 Snack and refreshment break
 2:10 Tidy
 2:50 Snack and refreshment break
 3:00 Visualize
 3:25 Transform 1-4: f, a, s, m
 3:55 Snack and refreshment break
 4:05 Transform 5-8: s, g, r, more
 4:40 Model
 4:45 Program
 4:50 Communicate
 4:55 Resources
 5:00 Conclude

Recommended Reading

• R packages for data science. https://www.tidyverse.org/
• Wickham, H., & Grolemund, G. (2016). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media, Inc. http://r4ds.had.co.nz/
• Grolemund, G. (2016). Hands-On Programming with R: Write Your Own Functions and Simulations. O'Reilly Media, Inc. https://www.amazon.com/dp/1449359019/ref=cm_sw_su_dp
• Peng, R. D. (2016). R programming for data science. https://bookdown.org/rdpeng/rprogdatascience/
• Peng, R. (2016). Exploratory data analysis with R. https://bookdown.org/rdpeng/exdata/
• Gillespie, C., & Lovelace, R. (2016). Efficient R Programming. O'Reilly Media, Inc. https://bookdown.org/csgillespie/efficientR/

Instructor

Prof. Erik Erhardt, PhD, is an Associate Professor of Statistics and Teaching Fellow in the Department of Mathematics and Statistics at the University of New Mexico. He has strong interests in graduate and undergraduate education, statistical computing and programming, Bayesian statistical methods, scientific replication, and data visualization. He has an invited book chapter on Visualizing Scientific Data in the Handbook of Psychophysiology (2017), extensive published lecture notes and course projects in advanced data analysis and statistical computing, and several R packages dating back to 2007. His research interests include Bayesian and Frequentist statistical methods for human brain imaging and ecological stable isotope sourcing.

Software and Computer Support

Software requirements

Participants need to bring a laptop computer with Wi-Fi capabilities with the current version of R and RStudio installed; both are free and available for Windows, Mac, and Linux at https://cran.r-project.org and https://www.rstudio.com.

Local course folder

Create a folder on your computer for all the course materials, such as "tidyverse". Save all materials there.

R packages

Installing the flightsABQ17 data package

1. Install the devtools package: Following instructions at https://www.r-project.org/nosvn/pandoc/devtools.html under the first section, "Updating to the latest version of devtools".

2. Install the flightsABQ17 package from github:

# https://github.com/erikerhardt/flightsABQ17
devtools::install_github("erikerhardt/flightsABQ17")
library(flightsABQ17)

• If that fails, you can download the package and install it locally.

  • flightsABQ17_0.0.1.tar.gz
  • install.packages("/YOUR_PATH/flightsABQ17_0.0.1.tar.gz")

Packages to install and update

pkg_list <- c("tidyverse", "gridExtra")         # packages to install
install.packages(pkg_list, dependencies = TRUE) # install
update.packages(checkBuilt = TRUE, ask = FALSE) # update all packages

Course Materials

• Slides (Erhardt_RTidyverse_20180216.pdf)
• Exercises (IntroTidyverse_ACASA_201802-exercises.zip), s
• R Studio Cheat Sheets (RStudio_cheatsheets_collection.pdf)