Intro to R Crash Course, Part 1

Friday, September 25 — for anyone new to R

Friday, Sep 25, 12:10–1:00 PM, Asmundson 242. Basic R syntax, from scratch. Optional, but open to everyone — a good refresher even if you’ve used R before. Part 1 of 2 (continues Oct 2).

Quick self-check

Before we start, tell us where you’re at with R — this helps us pace the session. Tap the link below on your phone:

Submit your answer →

“How would you describe your R coding ability?” — Totally new to R · I’ve run others’ code · I’ve written my own code · I routinely use R to code and do analyses · I develop new tools in R

Before you arrive

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

The script we’ll work through in class

This is the actual script — live, at the keyboard, not just slides. Follow along on your own laptop.

Mod1_R_intro.R

What’s in it (we’ll get as far as we get in 50 minutes — Part 2 on Oct 2 picks up where we leave off):

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 (likely Oct 2 territory)

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, once we get into the statistics fundamentals starting Mon 9/28.

Note

Back to the full Friday discussion schedule.

--- title: "Intro to R Crash Course, Part 1" subtitle: "Friday, September 25 — for anyone new to R" --- ::: {.week-header} **Friday, Sep 25, 12:10–1:00 PM, Asmundson 242.** Basic R syntax, from scratch. Optional, but open to everyone — a good refresher even if you've used R before. Part 1 of 2 (continues [Oct 2](r-crash-course-2.html)). ::: --- ## Quick self-check Before we start, tell us where you're at with R — this helps us pace the session. Tap the link below on your phone: **[Submit your answer →](https://greymonroe.github.io/live-poll-test/submit.html?poll=pls206-r-self-assessment)** *"How would you describe your R coding ability?"* — Totally new to R · I've run others' code · I've written my own code · I routinely use R to code and do analyses · I develop new tools in R ## 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 This is the actual script — live, at the keyboard, not just slides. Follow along on your own laptop. ::: {.file-list} - [Mod1_R_intro.R](../Module%201/Mod1_R_intro.R) ::: What's in it (we'll get as far as we get in 50 minutes — Part 2 on Oct 2 picks up where we leave off): - 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 (likely Oct 2 territory) ## 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, once we get into the statistics fundamentals starting Mon 9/28. --- ::: {.callout-note} Back to the [full Friday discussion schedule](index.html). :::