# gastonstat/shiny-introstats

Shiny apps for introduction to statistics
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Latest commit a614429 Oct 13, 2018
 Failed to load latest commit information. ch03-histograms Mar 18, 2017 ch08-corr-coeff-diagrams Mar 29, 2017 ch08-football-shape Apr 14, 2016 ch10-heights-data Mar 29, 2017 ch10-regression-strips Apr 30, 2016 ch11-regression-errors Apr 29, 2016 ch12-regression-heteroscedastic Apr 16, 2016 ch16-chance-error Apr 27, 2016 ch17-demere-games Mar 30, 2017 ch17-expected-value-std-error Apr 1, 2017 ch17-roulette-wheel Apr 10, 2016 ch18-coin-tossing Apr 2, 2017 ch18-roll-dice-product Apr 2, 2017 ch18-roll-dice-sum Oct 13, 2018 ch18-tossing-coins Oct 11, 2018 ch20-sampling-men Apr 12, 2016 ch21-percentage-estimation Apr 14, 2016 ch23-average-number Apr 14, 2016 regression-effect Mar 9, 2016 regression-galton Mar 9, 2016 regression-ropes Mar 9, 2016 scatterplot-ropes Mar 9, 2016 .gitignore Mar 9, 2016 LICENSE Mar 9, 2016 README.md Mar 27, 2017

# Shiny Apps for STAT 2, 20, 21, 131A

This is a collcetion of Shiny apps for introductory statistics courses based on the classic textbook Statistics by David Freedman, Robert Pisani, and Roger Purves (2007). Fourth Edition. Norton & Company.

I originally developed these apps for the course Stat 2, and refined them for Stat 20 / Stat 131A, at UC Berkeley. The main motivation behind the apps is to have teaching materials (in the form of interactive graphics) that I can use for living demos during lecture.

The apps are not specifically intended for Stat 2, 20 or 131A. They can be used for any of the introductory Statistics courses at UC Berkeley: STAT 2, STAT 20, STAT 21, STAT W21, STAT 131A, etc. And pretty much in any statistics introductory course in general.

## Running the apps

Both R and RStudio are free, and are available for Mac OS X, Windows, and Linux.

Assuming that you have both R and RStudio, the other thing you need is the R package "shiny". In case of doubt, run:

`install.packages("shiny")`

The easiest way to run an app is with the `runGitHub()` function from the `"shiny"` package. For instance, to run the app contained in the regression-galton folder, run the following code in R:

```library(shiny)

# Run an app from a subdirectory in the repo
runGitHub("shiny-introstats", "gastonstat", subdir = "regression-galton")```

Another way to run the apps is by cloning the git repository, then use `runApp()`:

```# First clone the repository with git. If you have cloned it into
# ~/regression-galton, first go to that directory, then use runApp().
setwd("~/regression-galton")
runApp()```