Add variable colors and sizes:
## Simple scatterplot
First, create a *blank* ggplot object with the data and x-y geometry set up.
```r
p <- ggplot(mtcars, aes(x=wt, y=mpg))
summary(p)
```
```
## data: mpg, cyl, disp, hp, drat, wt, qsec,
## vs, am, gear, carb [32x11]
## mapping: x = wt, y = mpg
## faceting: facet_null()
```
```r
p
```
```r
p + geom_point()
```
Or you can do both at the same time:
```r
ggplot(mtcars, aes(x=wt, y=mpg)) +
geom_point()
```
### Aesthetic map: color by # of cylinders
```r
p +
geom_point(aes(colour = factor(cyl)))
```
### Set shape using # of cylinders
```r
p +
geom_point(aes(shape = factor(cyl)))
```
### Adjust size by `qsec`
```r
p +
geom_point(aes(size = qsec))
```
### Color by cylinders and size by `qsec`
```r
p +
geom_point(aes(colour = factor(cyl),size = qsec))
```
### Multiple aesthetics
```r
p +
geom_point(aes(colour = factor(cyl),size = qsec,shape=factor(gear)))
```
### Add a linear model
```r
p + geom_point() +
geom_smooth(method="lm")
```
### Add a LOESS smooth
```r
p + geom_point() +
geom_smooth(method="loess")
```
### Change scale color
```r
p + geom_point(aes(colour = cyl)) +
scale_colour_gradient(low = "blue")
```
### Change scale shapes
```r
p + geom_point(aes(shape = factor(cyl))) +
scale_shape(solid = FALSE)
```
### Set aesthetics to fixed value
```r
ggplot(mtcars, aes(wt, mpg)) +
geom_point(colour = "red", size = 3)
```
### Transparancy: alpha=0.2
```r
d <- ggplot(diamonds, aes(carat, price))
d + geom_point(alpha = 0.2)
```
Varying alpha useful for large data sets
### Transparancy: alpha=0.1
```r
d +
geom_point(alpha = 0.1)
```
### Transparancy: alpha=0.01
```r
d +
geom_point(alpha = 0.01)
```
## Building ggplots
## Other Plot types
### Discrete X, Continuous Y
```r
p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_point()
```
### Discrete X, Continuous Y + geom_jitter()
```r
p +
geom_jitter()
```
### Discrete X, Continuous Y + geom_violin()
```r
p +
geom_violin()
```
### Discrete X, Continuous Y + geom_violin()
```r
p +
geom_violin() + geom_jitter(position = position_jitter(width = .1))
```
### Three Variables
Will return to this when we start working with raster maps.
### Stats
Visualize a data transformation
* Each stat creates additional variables with a common ``..name..`` syntax
* Often two ways: `stat_bin(geom="bar")` OR `geom_bar(stat="bin")`
### 2D kernel density estimation
Old Faithful Geyser Data on duration and waiting times.
```r
library("MASS")
```
```
##
## Attaching package: 'MASS'
```
```
## The following object is masked from 'package:dplyr':
##
## select
```
```
## The following objects are masked from 'package:raster':
##
## area, select
```
```r
data(geyser)
m <- ggplot(geyser, aes(x = duration, y = waiting))
```
[photo: Greg Willis](https://commons.wikimedia.org/wiki/File:Old_Faithful_(3679482556).jpg)
See `?geyser` for details.
```r
m +
geom_point()
```
```r
m +
geom_point() + stat_density2d(geom="contour")
```
Check `?geom_density2d()` for details
```r
m +
geom_point() + stat_density2d(geom="contour") +
xlim(0.5, 6) + ylim(40, 110)
```
Update limits to show full contours. Check `?geom_density2d()` for details
```r
m + stat_density2d(aes(fill = ..level..), geom="polygon") +
geom_point(col="red")
```
Check `?geom_density2d()` for details
### Discrete color: default
```r
b=ggplot(mpg,aes(fl))+
geom_bar( aes(fill = fl)); b
```
### Discrete color: greys
```r
b + scale_fill_grey( start = 0.2, end = 0.8,
na.value = "red")
```
### Continuous color: defaults
```r
a <- ggplot(mpg, aes(x=hwy,y=cty,col=displ)) +
geom_point(); a
```
### Continuous color: `gradient`
```r
a + scale_color_gradient( low = "red",
high = "yellow")
```
### Continuous color: `gradient2`
```r
a + scale_color_gradient2(low = "red", high = "blue",
mid = "white", midpoint = 4)
```
### Continuous color: `gradientn`
```r
a + scale_color_gradientn(
colours = rainbow(10))
```
### Discrete color: brewer
```r
b +
scale_fill_brewer( palette = "Blues")
```
## [colorbrewer2.org](http://colorbrewer2.org)
## ColorBrewer: Diverging
## ColorBrewer: Filtered
## Position
### Stacked bars
```r
ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar()
```
### Dodged bars
```r
ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar(position="dodge")
```
# Facets
Use facets to divide graphic into *small multiples* based on a categorical variable.
`facet_wrap()` for one variable:
```r
ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
geom_point()+
facet_wrap(~year)
```
`facet_grid()`: two variables
```r
ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
geom_point()+
facet_grid(year~cyl)
```
*Small multiples* (via facets) are very useful for visualization of timeseries (and especially timeseries of spatial data.)
# Themes
Set *default* display parameters (colors, font sizes, etc.) for different purposes (for example print vs. presentation) using themes.
## GGplot Themes
Quickly change plot appearance with themes.
### More options in the `ggthemes` package.
```r
library(ggthemes)
```
Or build your own!
### Theme examples: default
```r
p=ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
geom_jitter() +
labs(
x = "City mileage/gallon",
y = "Highway mileage/gallon",
color = "Cylinders"
)
```
### Theme examples: default
```r
p
```
### Theme examples: Solarized
```r
p + theme_solarized()
```
### Theme examples: Solarized Dark
```r
p + theme_solarized(light=FALSE)
```
### Theme examples: Excel
```r
p + theme_excel()
```
### Theme examples: _The Economist_
```r
p + theme_economist()
```
# Saving/exporting
## Saving using the GUI
## Saving using `ggsave()`
Save a `ggplot` with sensible defaults:
```r
ggsave(filename, plot = last_plot(), scale = 1, width, height)
```
## Saving using devices
Save any plot with maximum flexibility:
```r
pdf(filename, width, height) # open device
ggplot() # draw the plot(s)
dev.off() # close the device
```
**Formats**
* pdf
* jpeg
* png
* tif
and more...
## Save a plot: Example 1
```r
png("03_assets/test1.png",width=600,height=300)
p + theme_solarized(light=FALSE)
dev.off()
```
```
## quartz_off_screen
## 2
```
## Save a plot: Example 2
```r
png("03_assets/test2.png",width=600,height=300)
p + theme_solarized(light=FALSE, base_size=24)
dev.off()
```
```
## quartz_off_screen
## 2
```
## Save a plot: Example 2
```r
png("03_assets/test2.png",width=600,height=300)
p + theme_solarized(light=FALSE, base_size=24)
dev.off()
```
```
## quartz_off_screen
## 2
```
## Colophon
Sources:
* [ggplot cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)
Licensing:
* Presentation: [CC-BY-3.0 ](http://creativecommons.org/licenses/by/3.0/us/)
* Source code: [MIT](http://opensource.org/licenses/MIT)