Resource Library

This resource is designed to provide new users to R, RStudio, and R Markdown with the introductory steps needed to begin their own reproducible research. Many screenshots and screencasts (with no audio) will be included, but if further clarification is needed on these or any other aspect of the book, please create a GitHub issue here or email me with a reference to the error/area where more guidance is necessary.  It is recommended that you have R version 3.3.0 or later, RStudio Desktop version 1.0 or higher, and rmarkdown R package version 1.0 or higher. 

Everyday, massive amounts of data are generated in every part of our lives. That makes data fluency an indispensable skill to help you succeed - no matter what industry you’re in -- and DataCamp is here to help.  With bite-sized lessons in how to use R, Python, and SQL for data science, DataCamp allows you to learn in a way that fits your schedule, on any device.

9 courses are available for those who create a free account, and 123 courses are available for individuals who sign up for a $29/month membership ($25/month is you pay yearly).  Businesses can also pay $300 per member per year for employees to learn these skills.

swirl is a software package for the R programming language that turns the R console into an interactive learning environment. Users receive immediate feedback as they are guided through self-paced lessons in data science and R programming.

RStudio Cloud makes it easy for professionals, hobbyists, trainers, teachers and students to do, share, teach and learn data science using R.  Create analyses using RStudio directly from your browser - there is no software to install and nothing to configure on your computer.  Share your projects - and access those of others - without worrying about data transfer or package installation. Each project defines its own environment, and RStudio Cloud automatically reproduces that environment whenever anyone accesses the project.  It’s easy to share analyses with the world - but it’s also simple to collaborate with a select group in a private space. You control who can enter a space - and via roles, you have fine grained control over what each user can do.  There are also many learning materials available: interactive tutorials covering the basics of data science, cheatsheets for working with popular R packages, links to Datacamp courses, and a guide to using RStudio Cloud.

Epidemiology is the study of the distribution and determinants of human disease and health outcomes, and the application of methods to improve human health. This course examines the methods used in epidemiologic research, including the design of epidemiologic studies and the collection and analysis of epidemiological data.  Perfect for students and teachers wanting to learn/acquire materials for this topic.

Those who complete this course will be able to select appropriate methods of multivariate data analysis, given multivariate data and study objectives; write SAS and/or Minitab programs to carry out multivariate data analyses; and interpret results of multivariate data analyses.  Perfect for students and teachers wanting to learn/acquire materials for this topic.

Visual ANOVA is a simple little program that lets you put all this theory we've been describing into a simple visual whole. It assumes that you've read the Meanings and Intuitions section and have have understood the the general ideas at least. Even if your understanding of the previous section is incomplete at this time, it is worth playing with Visual ANOVA since that may clear up the big picture of ANOVA for you.

This article describes an activity that illustrates contingency table (two-way table) analysis. Students use contingency tables to analyze the "unusual episode" (the sinking of the ocean liner Titanic)data (from Dawson 1995) and attempt to use their analysis to deduce the origin of the data. The activity is appropriate for use in an introductory college statistics course or in a high school AP statistics course. Key words: contingency table (two-way table), conditional distribution

In these activities designed to introduce sampling distributions and the Central Limit Theorem, students generate several small samples and note patterns in the distributions of the means and proportions that they themselves calculate from these samples. Outside of class, students generate samples of dice rolls and coin spins and draw random samples from small populations for which data is given on each individual. Students report their sample means and proportions to the instructor who then compiles the results into a single data file for in-class exploration of sampling distributions and the Central Limit Theorem. Key words: Sampling distribution, sample mean, sample proportion, central limit theorem

When two variables are related, it is possible to predict a person's score on one variable from their score on the second variable with better than chance accuracy. This section describes how these predictions are made and what can be learned about the relationship between the variables by developing a prediction equation.