---
title: "IS4 in R: Displaying and Describing Catergorical Data (Chapter 2)"
author: "Patrick Frenett, Vickie Ip, and Nicholas Horton (nhorton@amherst.edu)"
date: "July 17, 2017"
output: 
  pdf_document:
    fig_height: 4
    fig_width: 6
  html_document:
    fig_height: 3
    fig_width: 5
  word_document:
    fig_height: 4
    fig_width: 6
---


```{r, include=FALSE}
# Don't delete this chunk if you are using the mosaic package
# This loads the mosaic and dplyr packages
require(mosaic)
```

```{r, include=FALSE}
# Some customization.  You can alter or delete as desired (if you know what you are doing).

# This changes the default colors in lattice plots.
trellis.par.set(theme=theme.mosaic())  

# knitr settings to control how R chunks work.
require(knitr)
opts_chunk$set(
  tidy=FALSE,     # display code as typed
  size="small"    # slightly smaller font for code
)
```

## Introduction and Background 

This document is intended to help describe how to undertake analyses introduced 
as examples in the Fourth Edition of \emph{Intro Stats} (2013) by De Veaux, Velleman, and Bock.
More information about the book can be found at http://wps.aw.com/aw_deveaux_stats_series.  This
file as well as the associated R Markdown reproducible analysis source file used to create it can be found at https://nhorton.people.amherst.edu/is4.

This work leverages initiatives undertaken by Project MOSAIC (http://www.mosaic-web.org), an NSF-funded effort to improve the teaching of statistics, calculus, science and computing in the undergraduate curriculum. In particular, we utilize the `mosaic` package, which was written to simplify the use of R for introductory statistics courses. A short summary of the R needed to teach introductory statistics can be found in the mosaic package vignettes (http://cran.r-project.org/web/packages/mosaic).  A paper describing the mosaic approach was published in the *R Journal*: https://journal.r-project.org/archive/2017/RJ-2017-024.

## Chapter 2: Displaying and Describing Categorical Data

### Section 2.1: Summarizing and Displaying a Single Categorical Variable

See displays on page 17.

```{r message=FALSE}
library(mosaic); library(readr); library(ggformula)
options(digits=3)
Titanic <- read_delim("https://nhorton.people.amherst.edu/sdm4/data/Titanic.txt", delim="\t")
```
```{r}
tally(~ Class, data=Titanic)
tally(~ Class, format="percent", data=Titanic)
```

```{r fig.align="center"}
gf_bar(~Class, data=Titanic, stat="count",fill="aquamarine3")
```

### Section 2.2: Exploring the Relationship Between Two Categorical Variables

See display on page 19.

```{r}
tally(~ Survived + Class, margin=TRUE, data=Titanic)
tally(~ Survived | Class, format="percent", data=Titanic)
```

See display on page 22.

```{r fig.align="center"}
gf_bar( ~ Class, fill= ~Survived, data=Titanic, position = position_dodge())
```

```{r fig.align="center"}
mosaicplot(tally(~ Survived + Class, data=Titanic),
           main="Mosaic plot of Class by Survival", col= c("lightskyblue","lightslateblue","lightskyblue3", "lightseagreen"))
```