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. DataCamp Amherst College faculty have been utilizing modules and materials from DataCamp to help students learn these flexible and powerful tools.

Student Guide and other R resources

The following additional resources may be helpful to those getting started with this system.

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

Getting started videos

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

Multiple regression interpretation and diagnostics (Galton dataset)

Introduction to the mosaic package and other related resources

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

Textbooks and textbook-related resources

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

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

Merrill 131 classroom

Changing monitor display in Merrill 131

Mounting home folders

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

Statistics fellows

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).

SDM4 in R videos

These video overview of chapters of Stats: Data and Models (De Veaux, Velleman, and Bock) 4th edition provide additional guidance for the reader. The R companion resources can be found at http://nhorton.people.amherst.edu/sdm4.

Chapter 1: Introduction

Chapter 2: Categorical data

Chapter 3: Quantitative data

Chapter 4: Comparing groups

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

Chapter 6: Scatterplots, association, and correlation

Chapter 7: Linear regression

Chapter 8: Regression wisdom

Chapter 9: Re-expressing data and transformations

Chapter 10: Randomness

Chapter 11: Sample surveys

Chapter 12: Experiments and observational studies

Chapter 13: From randomness to probability

Chapter 14: Probability rules

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 22: Comparing groups

Chapter 23: Paired samples and blocking

Chapter 24: Comparing counts

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