R is a powerful open source environment for statistics. 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 The following resources are intended to help those getting started with this system.

A Student's Guide to R and RStudio (available in printed form at Amherst Books and the Smith College bookstore)

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, there are drop-in statistics hours each week-night (7-9pm) during the semester coordinated with the Moss Quantitative Center. For the spring semester of 2017, these will take place in Merrill 300B in the 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 February 5, 2017 by Nicholas Horton