# Out-of-class

• ### Normal v. Binomial Distribution (Shiny App)

Approximating a normal distribution with a binomial distribution

• ### A Compendium of Clean Graphs in R

This compendium facilitates the creation of good graphs by presenting a set of concrete examples, ranging from the trivial to the advanced. The graphs can all be reproduced and adjusted by copy-pasting code into the R console. Almost every example in this compendium is driven by the same philosophy: A good graph is a simple graph, in the Einsteinian sense that a graph should be made as simple as possible, but not simpler.  A note for R fans: the majority of our plots have been created in base R, but you will encounter some examples in ggplot.

• ### Find-a-fit! (Shiny App)

Find the best linear fit for a given set of data points and residuals (or let this app show you how it is done).

• ### Polynomial Surface Explorer (Shiny App)

Adjust regression parameters to bend and shift a two-dimensional polynomial surface.

• ### When does a significant p-value indicate a true effect? (Shiny App)

When does a significant p-value indicate a true effect?  This app will help with understanding the Positive Predictive Value (PPV) of a p-value.

This app is based on Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124. http://doi.org/10.1371/journal.pmed.0020124

• ### Analysis Tool: 2D Outlier Analysis (Shiny App)

The app allows you to see the trade-offs on various types of outlier/anomaly detection algorithms. Outliers are marked with a star and cluster centers with an X.

• ### Feeling Bayes Factor: Height Difference Between Males and Females (Shiny App)

Can you "see" a group mean difference, just by eyeballing the data? Is your gut feeling aligned to the formal index of evidence, the Bayes factor?

• ### What does a Bayes factor look like? [The urn model] (Shiny App)

Visualizing the Bayes factor (quantification of evidence supporting a null or altermative hypothesis) using the urn model.

• ### Getting used to R, RStudio, and R Markdown

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.

• ### Getting Started with R

These handouts/links give a foundational understanding of how to set up and use R