---

title : The ggplot2 Plotting System - Part 1

subtitle :

author : Roger D. Peng, Associate Professor of Biostatistics

job : Johns Hopkins Bloomberg School of Public Health

logo : bloomberg_shield.png

framework : io2012 # {io2012, html5slides, shower, dzslides, ...}

highlighter : highlight.js # {highlight.js, prettify, highlight}

hitheme : tomorrow #

url:

lib: ../../libraries

assets: ../../assets

widgets : [mathjax] # {mathjax, quiz, bootstrap}

mode : selfcontained # {standalone, draft}

---

```{r setup, cache = F, echo = F, message = F, warning = F, tidy = F}

# make this an external chunk that can be included in any file

options(width = 80)

opts_chunk$set(message = F, error = F, warning = F, comment = NA, fig.align = 'center', dpi = 100, tidy = F, cache.path = '.cache/', fig.path = 'fig/', fig.height = 4, cache = TRUE, fig.show = 'hold')

options(xtable.type = 'html')

knit_hooks$set(inline = function(x) {

if(is.numeric(x)) {

round(x, getOption('digits'))

} else {

paste(as.character(x), collapse = ', ')

}

})

knit_hooks$set(plot = knitr:::hook_plot_html)

```

## What is ggplot2?

- An implementation of _The Grammar of Graphics_ by Leland Wilkinson

- Written by Hadley Wickham (while he was a graduate student at Iowa State)

- A “third” graphics system for R (along with __base__ and __lattice__)

- Available from CRAN via `install.packages()`

- Web site: http://ggplot2.org (better documentation)

---

## What is ggplot2?

- Grammar of graphics represents an abstraction of graphics ideas/objects

- Think “verb”, “noun”, “adjective” for graphics

- Allows for a “theory” of graphics on which to build new graphics and graphics objects

- “Shorten the distance from mind to page”

---

## Grammer of Graphics

### “In brief, the grammar tells us that a statistical graphic is a __mapping__ from data to __aesthetic__ attributes (colour, shape, size) of __geometric__ objects (points, lines, bars). The plot may also contain statistical transformations of the data and is drawn on a specific coordinate system”

- from _ggplot2_ book

---

## Plotting Systems in R: Base

- “Artist’s palette” model

- Start with blank canvas and build up from there

- Start with `plot` function (or similar)

- Use annotation functions to add/modify (`text`, `lines`, `points`, `axis`)

---

## Plotting Systems in R: Base

- Convenient, mirrors how we think of building plots and analyzing data

- Can’t go back once plot has started (i.e. to adjust margins); need to plan in advance

- Difficult to “translate” to others once a new plot has been created (no graphical “language”)

- Plot is just a series of R commands

---

## Plotting Systems in R: Lattice

- Plots are created with a single function call (`xyplot`, `bwplot`, etc.)

- Most useful for conditioning types of plots: Looking at how $y$ changes with $x$ across levels of $z$

- Things like margins/spacing set automatically because entire plot is specified at once

- Good for putting many many plots on a screen

---

## Plotting Systems in R: Lattice

- Sometimes awkward to specify an entire plot in a single function call

- Annotation in plot is not intuitive

- Use of panel functions and subscripts difficult to wield and requires intense preparation

- Cannot “add” to the plot once it’s created

---

## Plotting Systems in R: ggplot2

- Split the difference between base and lattice

- Automatically deals with spacings, text, titles but also allows you to annotate by “adding”

- Superficial similarity to lattice but generally easier/more intuitive to use

- Default mode makes many choices for you (but you _can_ customize!)

---

## The Basics: `qplot()`

- Works much like the `plot` function in base graphics system

- Looks for data in a data frame, similar to lattice, or in the parent environment

- Plots are made up of _aesthetics_ (size, shape, color) and _geoms_ (points, lines)

---

## The Basics: `qplot()`

- Factors are important for indicating subsets of the data (if they are to have different properties); they should be __labeled__

- The `qplot()` hides what goes on underneath, which is okay for most operations

- `ggplot()` is the core function and very flexible for doing things `qplot()` cannot do

---

## Example Dataset

```{r}

library(ggplot2)

str(mpg)

```

---

## ggplot2 “Hello, world!”

```{r}

qplot(displ, hwy, data = mpg)

```

---

## Modifying aesthetics

```{r}

qplot(displ, hwy, data = mpg, color = drv)

```

---

## Adding a geom

```{r}

qplot(displ, hwy, data = mpg, geom = c("point", "smooth"))

```

---

## Histograms

```{r}

qplot(hwy, data = mpg, fill = drv)

```

---

## Facets

```{r, fig.width=4.5}

qplot(displ, hwy, data = mpg, facets = . ~ drv)

qplot(hwy, data = mpg, facets = drv ~ ., binwidth = 2)

```

---

## MAACS Cohort

- Mouse Allergen and Asthma Cohort Study

- Baltimore children (aged 5—17)

- Persistent asthma, exacerbation in past year

- Study indoor environment and its relationship with asthma morbidity

- Recent publication: http://goo.gl/WqE9j8

```{r,echo=FALSE}

eno <- read.csv("eno.csv")

skin <- read.csv("skin.csv")

env <- read.csv("environmental.csv")

m <- merge(eno, env, by = "id")

maacs <- merge(m, skin, by = "id")

```

---

## Example: MAACS

```{r}

str(maacs)

```

---

## Histogram of eNO

```{r}

qplot(log(eno), data = maacs)

```

---

## Histogram by Group

```{r}

qplot(log(eno), data = maacs, fill = mopos)

```

---

## Density Smooth

```{r, fig.width=4.5}

qplot(log(eno), data = maacs, geom = "density")

qplot(log(eno), data = maacs, geom = "density", color = mopos)

```

---

## Scatterplots: eNO vs. PM$_{2.5}$

```{r, fig.width=2.7}

qplot(log(pm25), log(eno), data = maacs)

qplot(log(pm25), log(eno), data = maacs, shape = mopos)

qplot(log(pm25), log(eno), data = maacs, color = mopos)

```

---

## Scatterplots: eNO vs. PM$_{2.5}$

```{r}

qplot(log(pm25), log(eno), data = maacs, color = mopos,

geom = c("point", "smooth"), method = "lm")

```

---

## Scatterplots: eNO vs. PM$_{2.5}$

```{r, fig.width=9}

qplot(log(pm25), log(eno), data = maacs, geom = c("point", "smooth"),

method = "lm", facets = . ~ mopos)

```

---

## Summary of qplot()

- The `qplot()` function is the analog to `plot()` but with many built-in features

- Syntax somewhere in between base/lattice

- Produces very nice graphics, essentially publication ready (if you like the design)

- Difficult to go against the grain/customize (don’t bother; use full ggplot2 power in that case)

---

## Resources

- The _ggplot2_ book by Hadley Wickham

- The _R Graphics Cookbook_ by Winston Chang (examples in base plots and in ggplot2)

- ggplot2 web site (http://ggplot2.org)

- ggplot2 mailing list (http://goo.gl/OdW3uB), primarily for developers