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Introduction to Data Science Using R

A repository to house materials for a 2 day course introducing participants to data science using R.

The goal of this 2 day workshop is to teach new-to-programming data professionals to import data, clean up and summarize a data set, and make some static data visualizations using the program R. This is an introductory course to programming, specifically programming with R. R is a popular statistical computing language, commonly used in many scientific disciplines for statistical analysis, generating production-quality graphics, and automating data workflow tasks. The workshop content will follow best practices for using R for data analysis, giving attendees a foundation in the fundamentals of R and scientific computing.

Who should take this course?

  • Anyone who works with data or who is interested in learning efficient ways to make meaning from data
  • Anyone comfortable troubleshooting issues on their computer
  • Anyone keen to learn a programming language

Workshop Schedule

Daily schedule

Activity Start Time End time
Module #1 9:00 10:30
Break 10:30 10:45
Module #2 10:45 12:00
Lunch 12:00 1:00
Module #3 1:00 2:30
Break 2:30 2:45
Module #4 2:45 4:30

Course Notes: https://bcgov.github.io/ds-intro-to-r-2-day/

Day 1

  • Introduction to Course (30 min) (Bonnie)
  • Demo (10 min) (Bonnie)
  • Introduction to R & RStudio (50 minutes) (Bonnie)
  • BREAK ☕
  • Seeking Help in R (15 minutes) (Julie)
  • Project Management with RStudio (60 minutes) (Julie)
  • Lunch
  • Data Structures (75 minutes) (Bonnie)
  • Extra Practice + Questions (15 minutes)
  • BREAK ☕
  • Data Manipulation with dplyr (90 minutes) (Julie)
  • Day 1 Wrap-up + Questions (15 minutes)

Day 2

  • Review + Questions (30 minutes)
  • Making Publication Quality Plots with ggplot2 Pt. 1 (60 minutes) (Bonnie)
  • BREAK ☕
  • Making Publication Quality Plots with ggplot2 Pt. 2 (60 minutes) (Bonnie)
  • Extra Practice + Questions (15 minutes)
  • Lunch
  • Data Manipulation with tidyr (60 minutes) (Julie)
  • Extra Practice + Questions (15 minutes)
  • BREAK ☕
  • Joins with dplyr (45 minutes) (Julie)
  • Mini-Analysis Project: (45 minutes)
  • Day 2 Wrap-up + Questions (15 minutes)

Most of the above lesson material is sourced or an adaptation of the Software Carpentry Foundation (now The Carpenties) R for Reproducible Scientific Analysis lesson material: Thomas Wright and Naupaka Zimmerman (eds): Software Carpentry: R for Reproducible Scientific Analysis. Version 2016.06, June 2016, https://github.com/swcarpentry/r-novice-gapminder, 10.5281/zenodo.57520.

The R Markdown sections also draw material from Nicholas Tierney's excellent R Markdown for Scientists course which is available here: https://rmd4sci.njtierney.com/

Course Offerings

Introduction to Data Science in R, January 25 & 26, 2023

Introduction to Data Science in R, Feb 23 and 24, March 2 & 3, 2022

Introduction to Data Science in R, May 6-7, 2020

Getting Help or Reporting an Issue

To report bugs/issues/feature requests, please file an issue.

How to Contribute

If you would like to contribute, please see our CONTRIBUTING guidelines.

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.

License

Creative Commons License.

Original Work Copyright © Software Carpentry, content modified by the Province of British Columbia.

This work is licensed under the Creative Commons Attribution 4.0 International License.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

This project was created using the bcgovr package.