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.
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/
dplyr
(90 minutes) (Julie)ggplot2
Pt. 1 (60 minutes) (Bonnie)ggplot2
Pt. 2 (60 minutes) (Bonnie)tidyr
(60 minutes) (Julie)dplyr
(45 minutes) (Julie)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/
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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.