---
title: "Discussion on 2D plots & Tables"
author: "Paul E. Johnson"
date: '2015-01-28'
output: ioslides_presentation
---

## This accompanies ScatterBoxBarPlots lecture

I'm testing out a slide-making framework R Markdown. 

In case you want to see how this is done, review the file "ScatterBoxBarPlots-1-discussion.Rmd"

That Rmd file gets converted to "ScatterBoxBarPlots-1-discussion.md" and that thing in turn
gets turned into HTML or PDF output

I'm not a fan of this yet, but I'm trying.

# Scatterplots

## Statistics in the News

- New England Patriots accused of deflating balls so that their team is more able to handle them.

- Warren Sharp's blog, "The New England Patriots Prevention of Fumbles is Nearly Impossible" http://www.sharpfootballanalysis.com/blog/2015

## Here's a peculiar scatterplot
<img src="importfigs/Fumble-Chart-1.png" width="700" alt="Fumbles">

## Critique that(?!)

- The line is ridiculous

- I'd like to see a Histogram

- What distribution should we expect if everybody plays with the same balls?

## Replies Appear!

- Gregory J. Matthews and Michael Lopez, "Why Those Statistics About the Patriots' Fumbles Are Mostly Junk", http://regressing.deadspin.com/why-those-statistics-about-the-patriots-fumbles-are-mos-1681805710

- Problem has something to do with whether we calculate plays/fumbles or fumbles/play (maybe)

## A qq plot appears in Mattews and Lopez!

<img src="importfigs/Matthews-Lopez-1.png" width="500" alt="Fumbles 1">

## Change from fumble/play to play/fumble

<img src="importfigs/Matthews-Lopez-2.png" width="500" alt="Fumbles 2">

## And a Barplot

<img src="importfigs/Matthews-Lopez-3.png" width="600" alt="Fumbles 3">

# Cross Tabulation Tables

## rockchalk development 1.8.90

```{r pctable1}
library(rockchalk)
dat <- data.frame(x1 = sample(c("A", "B", "C"), size = 200, replace = TRUE),
                  y1 = sample(c("Denver", "Kansas City", "St. Louis"), size = 200, replace = TRUE))
t1 <- pctable(y1 ~ x1, data = dat)
```

## That produced "defaults"

- I (almost always) want column percents
- Includes missings in display by default, unlike R table. To undo that, include EXCLUDE = NA as an argument.
- This adds marginal totals, they come in handy
- I'm printing the raw count and percent in each cell (different from my past idea)

## Variations
```{r pctable2}
summary(t1, rowpct = TRUE, colpct = FALSE)
```

## Having both is admittedly frustrating
```{r pctable3}
summary(t1, rowpct = TRUE, colpct = TRUE)
```

## Features

- rounded = TRUE is "privacy protection"

- Arguments intended for table will be passed through

## Can make fancy tables that can go into documents

- The tables package by Duncan Murdoch provides some convenience features that we can ride.
- For people that use LaTeX, the example demonstrates.

## html output
```{r include=FALSE}
library(tables)
```

## Can make fancy tables that can go into documents

```{r pctable4, results='asis'}
html(as.tabular(summary(t1)))
```

