R is a powerful open source environment for statistics and data science. RStudio is an integrated development environment that facilitates the use of R by students and instructors. An RStudio server is available for members of the Amherst community at r.amherst.edu. Amherst College faculty have been utilizing modules and materials from DataCamp to help students learn these flexible and powerful tools.

A Student's Guide to R and RStudio (note that a Spanish language translation is available here)

Getting started with RStudio: logging in to the server

Getting started with RStudio: first steps in R

Getting started with RStudio: second steps in R

Getting started with RStudio: first steps with R Markdown

Getting started with RStudio: second steps with R Markdown

Getting started with RStudio: sample homework in markdown

Prezi presentation on R markdown

Getting started with RStudio: dealing with files

Getting started with RStudio: all about packages

Getting started with RStudio: other resources

Github and RStudio: getting started

Multiple regression interpretation and diagnostics (Galton dataset)

Minimal (1 page) guide to R for intro stats

Cheatsheets for R, data wrangling, markdown, and Shiny

Visualizing data manipulation operations (Shiny)

Building precursors for data science in R

Using R for Data Management, Statistical Analysis, and Graphics (second edition)

Stats: Data and Models (fourth edition) examples in R

Changing monitor display in Merrill 131

Users can mount their home folder (on the RStudio server) locally with the following instructions, which makes it a lot easier to move items back and forth, print them, etc.: https://www.amherst.edu/offices/it/knowledge_base/academic-resources/unix_servers/unix_network_space

In case of questions, during the semester there are drop-in statistics hours each weeknight (7-9pm) coordinated with the Moss Quantitative Center. These drop-in hours normally take place in Merrill 300B (Science Library).

Chapter 5: Standard deviation as a ruler and the normal model

Chapter 6: Scatterplots, association, and correlation

Chapter 9: Re-expressing data and transformations

Chapter 12: Experiments and observational studies

Chapter 13: From randomness to probability

Chapter 15: Random variables (part 1 of 2):

Chapter 15: Random variables (part 2 of 2):

Chapter 16: Probability models

Chapter 17: Sampling distribution models

Chapter 18: Confidence intervals for proportions

Chapter 19: Testing hypotheses about proportions

Chapter 20: Inferences about means

Chapter 21: More about tests and intervals

Chapter 23: Paired samples and blocking

Chapter 25: Inferences for regression

Chapter 26: Analysis of variance (ANOVA)

Chapter 27: Multifactor analysis of variance

Chapter 28: Multiple regression (part 1 of 2: descriptive models)

Chapter 28: Multiple regression (part 2 of 2: inferential models)

Chapter 29: Multiple regression wisdom

Last updated June 19, 2018 by Nicholas Horton