Intro to R Crash Course, Part 2

Friday, October 2 — continuation, incl. loops & apply functions

Friday, Oct 2, 12:10–1:00 PM, Asmundson 242. Continuation of Part 1 (Sep 25) — picks up where we left off, ending with loops vs. apply functions. Optional, but open to everyone.

Before you arrive

If you haven’t already, install R and RStudio — see the Setup guide.

The script we’ll work through in class

Same script as Part 1 — we continue from wherever we stopped on Sep 25. Follow along on your own laptop.

Mod1_R_intro.R

What’s in it:

Objects and 1D types in R (numeric, character, logical)
Beyond numeric: factors, dates, and other types
Vectors: creating, indexing, and vectorized operations
Beyond 1D: lists and data frames
Files, directories, and reading in data (read.csv)
Basic plotting with ggplot2
The pipe operator |>
Beginner+ skills: writing your own functions, and for loops vs. apply-family functions — the focus for this session

Data

The script reads in this dataset partway through — download it and keep it in your working directory:

winedata.csv

Extra resources

Optional — if you’re new to R, or want more practice than we have time for in class.

Cheat sheets (quick reference, print or keep open in a tab):

Posit cheat sheets — RStudio IDE, data visualization (ggplot2), data transformation (dplyr/tidyr), and more, all one page each.

Guided tutorials / practice:

swirl — learn R interactively, right inside the R console. No browser, no account. Good for drilling the basics at your own pace.
Data Analysis and Visualization in R for Ecologists (Data Carpentry) — a full beginner-friendly workshop: R/RStudio basics, ggplot2, data frames, and data wrangling, aimed at researchers with no programming background.

Free online book:

R for Data Science (2e) — the standard reference for the tidyverse approach to importing, wrangling, and visualizing data in R. More than you need for this course, but a good thing to have on hand.

Video:

StatQuest with Josh Starmer — short, clear explainer videos on R and statistics topics. A good channel to bookmark for later in the course too, now that the statistics fundamentals (Mon 9/28, Wed 9/30) are underway.

Note

Back to the full Friday discussion schedule.

--- title: "Intro to R Crash Course, Part 2" subtitle: "Friday, October 2 — continuation, incl. loops & apply functions" --- ::: {.week-header} **Friday, Oct 2, 12:10–1:00 PM, Asmundson 242.** Continuation of [Part 1 (Sep 25)](r-crash-course-1.html) — picks up where we left off, ending with loops vs. `apply` functions. Optional, but open to everyone. ::: --- ## Before you arrive If you haven't already, install R and RStudio — see the **[Setup guide](../modules/setup.html)**. ## The script we'll work through in class Same script as Part 1 — we continue from wherever we stopped on Sep 25. Follow along on your own laptop. ::: {.file-list} - [Mod1_R_intro.R](../Module%201/Mod1_R_intro.R) ::: What's in it: - Objects and 1D types in R (numeric, character, logical) - Beyond numeric: factors, dates, and other types - Vectors: creating, indexing, and vectorized operations - Beyond 1D: lists and data frames - Files, directories, and reading in data (`read.csv`) - Basic plotting with `ggplot2` - The pipe operator `|>` - Beginner+ skills: writing your own functions, and `for` loops vs. `apply`-family functions — the focus for this session ## Data The script reads in this dataset partway through — download it and keep it in your working directory: ::: {.file-list} - [winedata.csv](../data/winedata.csv) ::: ## Extra resources Optional — if you're new to R, or want more practice than we have time for in class. **Cheat sheets** (quick reference, print or keep open in a tab): - [Posit cheat sheets](https://posit.co/resources/cheatsheets/) — RStudio IDE, data visualization (`ggplot2`), data transformation (`dplyr`/`tidyr`), and more, all one page each. **Guided tutorials / practice:** - [swirl](https://swirlstats.com) — learn R interactively, right inside the R console. No browser, no account. Good for drilling the basics at your own pace. - [Data Analysis and Visualization in R for Ecologists (Data Carpentry)](https://datacarpentry.org/R-ecology-lesson/) — a full beginner-friendly workshop: R/RStudio basics, `ggplot2`, data frames, and data wrangling, aimed at researchers with no programming background. **Free online book:** - [R for Data Science (2e)](https://r4ds.hadley.nz) — the standard reference for the tidyverse approach to importing, wrangling, and visualizing data in R. More than you need for this course, but a good thing to have on hand. **Video:** - [StatQuest with Josh Starmer](https://www.youtube.com/@statquest) — short, clear explainer videos on R and statistics topics. A good channel to bookmark for later in the course too, now that the statistics fundamentals (Mon 9/28, Wed 9/30) are underway. --- ::: {.callout-note} Back to the [full Friday discussion schedule](index.html). :::