Introduction to Data Science Using R

A repository to house materials for a 4 session course introducing participants to data science using R.

The goal of this 4 session 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

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

Day 1

Introduction to Course (30 min)
Demo (10 min) (Sam)
Introduction to R & RStudio (50 minutes) (Sam)
BREAK ☕
Seeking Help in R (15 minutes) (Andy)
Project Management with RStudio (40 minutes) (Andy)

Day 2

Review + questions (30 minutes)
Data Structures (50 minutes) (Andy)
BREAK ☕
Exploring Data Frames (45 minutes) (Bonnie)
Making Publication Quality Plots with ggplot2 Pt. 1 (45 minutes) (Bonnie)

Day 3

Review + questions (30 minutes)
Making Publication Quality Plots with ggplot2 Pt. 2 (40 minutes) (Bonnie)
BREAK ☕
Data Manipulation with dplyr (90 minutes) (Andy)

Day 4

Review + questions (30 min)
Data Manipulation with tidyr (45 minutes) (Sam)
BREAK ☕
Introduce bcdata and practice ggplot2 and dplyr (75 minutes) (Sam)
Seeking Help from Others with reprex (30 minutes) (Sam)

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, Feb 23 and 24, March 2 & 3, 2022

Instructors: Andy Teucher, Sam Albers & Bonnie Robert
Suggested pre-reading: Good Enough Practices for Scientific Computing
Course material: https://bcgov.github.io/ds-intro-to-r-2-day/

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

Instructors: Sam Albers, Stephanie Hazlitt, Genevieve Perkins & Andy Teucher
Suggested pre-reading: Good Enough Practices for Scientific Computing
Course material: https://bcgov.github.io/ds-intro-to-r-2-day/
What we actually did on the day:

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

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.

MonkmanMH/ds-intro-to-r-2-day