cheatsheet.Rmd

```{r}
opts_chunk$set(warning=FALSE, message=FALSE, fig.width=8, fig.height=4)
```

# Loading ggplot

```{r}
library(ggplot2)
```

# Basic use with qplot

Load the sample data

```{r}
set.seed(1410)  # make the sample reproducible
head(diamonds)
dsmall <- diamonds[sample(nrow(diamonds), 100), ]
```

```{r}
qplot(carat, price, data=diamonds)
```

## Color, size, shape and other aesthetic attributes

```{r}
qplot(carat, price, data=dsmall, color=color, shape=cut, alpha=I(1/2))
```

## Plot geoms

```{r}
qplot(carat, price, data=dsmall, geom=c("point", "smooth"))
```

### Adding a smoother
There are many different smoother that can be used with `method` argument.

```{r}
qplot(carat, price, data=dsmall, geom=c("point", "smooth"), method="lm")
```

### Boxplots and jittered points

```{r}
qplot(color, price/carat, data=diamonds, geom="jitter")
```

```{r}
qplot(color, price/carat, data=diamonds, geom="boxplot")
```

### Histogram and density plots

```{r}
qplot(carat, data=diamonds, geom="histogram", fill=color)
```

```{r}
qplot(carat, data=diamonds, geom="density", color=color)
```

Change the amount of smoothing with `binwidth` argument.

```{r}
qplot(carat, data=diamonds, geom="histogram", binwidth=1)
qplot(carat, data=diamonds, geom="histogram", binwidth=0.1)
qplot(carat, data=diamonds, geom="histogram", binwidth=0.01)
```

### Bar charts

```{r}
qplot(color, data=diamonds, geom="bar")
```

```{r}
# bar plot of diamond color weighted by carat
qplot(color, data=diamonds, geom="bar", weight=carat) +
    scale_y_continuous("carat")
```

### Time series

```{r}
head(economics)
```

```{r}
qplot(date, unemploy/pop, data=economics, geom="line")
```

## Faceting

```{r}
qplot(carat, data=diamonds, facets=color~.,
      geom="histogram", binwidth=0.1, xlim=c(0,3))
```

## Other options
- `xlim` and `ylim`: set limits for x- and y-axis (e.g. `xlim=c(0,20)`)
- `main`: main title for the plot
- `xlab` and `ylab`: labels for x- and y-axis

```{r}
qplot(carat, price, data=dsmall,
      xlab="Price ($)",
      ylab="Weight (carats)",
      main="Price-weight relationship")
```

# Build a plot layer by layer

More complicated, multi-layer plots can be generated using `ggplot()`.

## Basic plot types

```{r}
df <- data.frame(x=c(3, 1, 5), y=c(2, 4, 6), label=c("a", "b", "c"))
p <- ggplot(df, aes(x, y, label=label)) + xlab(NULL) + ylab(NULL)
```

### `geom_point`
```{r}
p + geom_point() + ggtitle("geom_point")
```

### `geom_bar`
```{r}
p + geom_bar(stat="identity") + ggtitle("geom_bar(stat=\"identity\")")
```

### `geom_line`
```{r}
p + geom_line() + ggtitle("geom_line")
```

### `geom_area`
```{r}
p + geom_area() + ggtitle("geom_area")
```

### `geom_path`
```{r}
p + geom_path() + ggtitle("geom_path")
```

### `geom_text`
```{r}
p + geom_text() + ggtitle("geom_text")
```

### `geom_tile`
```{r}
p + geom_tile() + ggtitle("geom_tile")
```

### `geom_polygon`
```{r}
p + geom_polygon() + ggtitle("geom_polygon")
```

## Displaying distributions

For 1d data, the geom is the histogram.

### `geom_histogram` and `geom_freqpoly`

```{r}
depth_dist <- ggplot(diamonds, aes(depth)) + xlim(58, 68)
depth_dist + geom_histogram()
```

To compare the distribution between groups, couple of options

```{r}
depth_dist + geom_histogram(aes(y = ..density..), binwidth=0.1) +
    facet_grid(cut ~ .)
depth_dist + geom_histogram(aes(fill=cut), binwidth=0.1, position="fill")
depth_dist + geom_freqpoly(aes(y = ..density.., color=cut), binwidth=0.1)
```

### `geom_boxplot`

```{r}
qplot(cut, depth, data=diamonds, geom="boxplot")
library(plyr)
qplot(carat, depth, data=diamonds, geom="boxplot",
      group = round_any(carat, 0.1, floor), xlim=c(0, 3))
```

### `geom_jitter`

```{r}
qplot(class, cty, data=mpg, geom="jitter")
qplot(class, drv, data=mpg, geom="jitter")
```

### `geom_density`

```{r}
qplot(depth, data=diamonds, geom="density", xlim=c(54, 70))
qplot(depth, data=diamonds, geom="density", xlim=c(54, 70), fill=cut, alpha=I(0.2))
```

## Deal with overplotting

- Make the points smaller

```{r}
df <- data.frame(x=rnorm(2000), y=rnorm(2000))
norm <- ggplot(df, aes(x, y))
norm + geom_point()
norm + geom_point(shape=1)
norm + geom_point(shape = ".")          # pixel-sized
```

- Use alpha blending

```{r}
library(scales)
norm + geom_point(color=alpha("black", 1/3))
norm + geom_point(color=alpha("black", 1/5))
norm + geom_point(color=alpha("black", 1/10))
```

- Randomly jitter if there is some discreteness

```{r}
td <- ggplot(diamonds, aes(table, depth)) + xlim(50, 70) + ylim(50, 70)
td + geom_point()
td + geom_jitter()
jit <- position_jitter(width=0.5)
td + geom_jitter(position=jit)
td + geom_jitter(position=jit, color=alpha("black", 1/10))
td + geom_jitter(position=jit, color=alpha("black", 1/50))
td + geom_jitter(position=jit, color=alpha("black", 1/200))
```

## Surface plots

## Drawing maps

```{r}
library(maps)
data(us.cities)
big_cities <- subset(us.cities, pop>500000)
qplot(long, lat, data=big_cities) + borders("state", size=0.5)
```

### [Choropleth map](http://en.wikipedia.org/wiki/Choropleth_map)
```{r}
states <- map_data("state")
arrests <- USArrests
names(arrests) <- tolower(names(arrests))
arrests$region <- tolower(rownames(USArrests))

choro <- merge(states, arrests, by="region")
# reorder the rows because order matters when drawing polygons and merge
# destroys the original ordering
choro <- choro[order(choro$order), ]
qplot(long, lat, data=choro, group=group, fill=assault, geom="polygon")
qplot(long, lat, data=choro, group=group, fill=assault/murder, geom="polygon")
```

## Annotating a plot

Just extra data

- adding one at a time
- many at once

```{r}
unemp <- qplot(date, unemploy, data=economics, geom="line",
               xlab="", ylab="No. unemployed (1000s)")

presidential <- presidential[-(1:3), ]

yrng <- range(economics$unemploy) 
xrng <- range(economics$date)
unemp + geom_vline(aes(xintercept=as.numeric(start)), data=presidential)
unemp + geom_rect(aes(NULL, NULL, xmin=start, xmax=end, fill=party),
                  ymin=yrng[1], ymax=yrng[2], data=presidential) +
    scale_fill_manual(values=alpha(c("blue", "red"), 0.2))

last_plot() + geom_text(aes(x=start, y=yrng[1], label=name),
                        data=presidential, size=3, hjust=0, vjust=0)

caption <- paste(strwrap("Unemployment rates in the US have varied
alot over the years", 40), collapse="\n")
unemp + geom_text(aes(x, y, label=caption),
                  data=data.frame(x=xrng[2], y=yrng[2]),
                  hjust=1, vjust=1, size=4)

highest <- subset(economics, unemploy==max(unemploy))
unemp + geom_point(data=highest, size=3, color=alpha("red", 0.3))
```

# Faceting

## Facet grid

```{r}
qplot(cty, hwy, data=mpg) + facet_grid(. ~ cyl)
qplot(cty, data=mpg, geom="histogram", binwidth=2) + facet_grid(cyl ~ .)
qplot(cty, hwy, data=mpg) + facet_grid(drv ~ cyl)
```

### Margins

```{r}
p <- qplot(displ, hwy, data=mpg) + geom_smooth(method="lm", se=F)
p + facet_grid(cyl ~ drv)
p + facet_grid(cyl ~ drv, margins=T)
```

## Facet wrap

```{r}
library(plyr)
movies$decade <- round_any(movies$year, 10, floor)
qplot(rating, ..density.., data=subset(movies, decade > 1890),
      geom="histogram", binwidth=0.5) +
    facet_wrap(~ decade, ncol=6)
```