Ploting with ggplot
Overview
Teaching: 30 min
Exercises: 0 minQuestions
How to plot in R using ggplot2 package
Objectives
Learn professional plotting tool in R using ggplot2
Learn multiple way to use ggplot2
What is ggplot
- Grammar of Graphics by Leland Wilkinson
- Written by Hadley Wickham - a grad student at Iowa State
- Third graphic system in for R (along with Base and Lattice)
> install.packages("ggplot2") > library(ggplot2)
Basic component of ggplot
- A data frame
- aes: aesthetic mappings showing how data are mapped to color, size
- geoms: geometric objects like points, lines, shapes.
- facets: for conditional plots.
- stats: statistical transformations like binning, quanti les, smoothing.
- scales: what scale an aesthetic map uses
- coordinate system
Type of ggplot
- Basic qplot ```
- Same as plot in Base plot
- Nicer graphics than Base plot
- Difficult for customize ```
- Advanced ggplot ```
- Flexible with many built-in function ```
Basic qplot: Scatter plot
- Plots are made of aes (size, shape, color) and geom (points, lines)
- Look for data in frame (or from parent directory)
qplot(Sepal.Length, Sepal.Width, data=iris) - Add aesthetic (shape, color)
qplot(Sepal.Length, Petal.Length, data=iris, color=factor(Species), shape=factor(Species)) #aesthetic
- Add geom (points, lines)
qplot(Sepal.Length, Petal.Length, data=iris, geom=c("point","smooth")) #geometry
- Linear Fitting
qplot(Sepal.Length, Petal.Length, data=iris, color=Species, geom=c("point","smooth"),method="lm")
Basic qplot: Histogram
qplot(Sepal.Length,fill=Species, data=iris)
qplot(Sepal.Length,data=iris,geom="density")
qplot(Sepal.Length,data=iris,geom="density",
color=Species)
Basic qplot: Facets
qplot(Sepal.Length,Petal.Length,facets=.~Species, data=iris)
Advanced ggplot
Sample plot
gp <- ggplot(mpg, aes(hwy, cty))
gp+geom_point(aes(color=cyl))
gp+geom_point(aes(color=factor(cyl)))
gp+geom_point(aes(color=factor(cyl)))+geom_smooth(method="lm")
gp+geom_point(aes(color=factor(cyl)))+geom_smooth(method="lm")
+facet_grid(.~cyl)
# Save plot to file
ggsave("plot.png",width=5,height=5)
Annotation
- Labels: xlab(), ylab(), labs(), ggtitle()
- global annotation: use theme()
- Standard appearance: theme_bw()
gp+geom_point(aes(color=factor(cyl), size=factor(cyl)))+ geom_smooth(method="lm")+ xlab("Highway miles per gallon")+ ylab("city miles per gallon")+ ggtitle("Scatter plot for cty & hwy")+ xlim(10,40)+ylim(10,40)+ theme_bw(base_size = 15)
Some nice ggplots featuring
Boxplot
ggplot(mpg,aes(x=manufacturer,y=hwy,
fill=factor(manufacturer)))+
geom_boxplot()+
geom_jitter()+
labs(title="Boxplot for Hwy per manufacturer",x="Manufacturer",y="Highway milage")+
theme_bw()+coord_flip()+
theme(legend.position = "none")
Violin plot
g <- ggplot(mpg, aes(class, cty))
g + geom_violin(aes(fill=class)) +
labs(title="Violin plot",
subtitle="City Mileage vs Class of vehicle",
caption="Source: mpg",
x="Class of Vehicle",
y="City Mileage")
Histogram
g <- ggplot(mpg, aes(displ)) + scale_fill_brewer(palette = "Spectral")
g + geom_histogram(aes(fill=class),
bins=10,
col="black",
size=.1) + # change number of bins
labs(title="Histogram with Fixed Bins",
subtitle="Engine Displacement across Vehicle Classes",
x="enginer displacement (m)",
y="Frequency count")
Scatter plot
data("midwest")
gg <- ggplot(midwest, aes(x=area, y=poptotal)) +
geom_point(aes(col=state, size=popdensity)) +
geom_smooth(method="loess", se=F) +
xlim(c(0, 0.1)) +
ylim(c(0, 500000)) +
labs(subtitle="Area Vs Population",
y="Population",
x="Area",
title="Scatterplot",
caption = "Source: midwest")
plot(gg)
Density
g <- ggplot(mpg, aes(cty))
g + geom_density(aes(fill=factor(cyl)), alpha=0.8) +
labs(title="Density plot",
subtitle="City Mileage Grouped by Number of cylinders",
caption="Source: mpg",
x="City Mileage",
fill="# Cylinders")+
theme_bw()
Density 2D
gg <- ggplot(faithful,aes(x=eruptions,y=waiting))
gg + stat_density_2d(aes(fill=..level..),
geom="polygon",color="black")+
geom_smooth(method="lm",linetype=2,color="red")+
scale_fill_continuous(low="green",high="red")+
geom_point() +
theme_bw()
Geographic visualization with ggplot
library(maps)
states <- map_data("state")
ggplot(data = states)+
geom_polygon(aes(x=long,y=lat,fill=region),
color="black")+
coord_fixed(1.3)+
guides(fill=FALSE)
counties <- map_data("county")
SC_counties <- subset(counties,region == "south carolina")
ggplot(data = SC_counties)+
geom_polygon(aes(x=long,y=lat,fill=subregion),
color="black")+
coord_fixed(1.3)+
guides(fill=FALSE)
some.eu.countries <- c(
"Portugal", "Spain", "France", "Switzerland", "Germany",
"Austria", "Belgium", "UK", "Netherlands",
"Denmark", "Poland", "Italy",
"Croatia", "Slovenia", "Hungary", "Slovakia",
"Czech republic"
)
# Retrievethe map data
some.eu.maps <- map_data("world", region = some.eu.countries)
ggplot(some.eu.maps, aes(x = long, y = lat)) +
geom_polygon(aes( group = group, fill = region))+
scale_fill_viridis_d()+
theme_void()+
theme(legend.position = "none")
Plot Shapefile for geography study
Download shape file data [here](https://opendata.arcgis.com/datasets/a21fdb46d23e4ef896f31475217cbb08_1.zip)
Store it in your folder: c:/R/GIS/ in Windows or /user/R/GIS in MacOS
Unzip it and rename all files to `Countries_WGS84.*` under `C:/GIS/`
Install additional packages:
install.packages("rgdal")
install.packages("colorspace")
Perform plotting
library(rgdal)
library(colorspace)
library(maps)
setwd('c:/R/GIS/')
gfile <- readOGR(dsn="Countries_WGS84.shp")
names(gfile)
gfile$CNTRY_NAME
plot(gfile)
plot(gfile,col=rainbow_hcl(50))
llgridlines(gfile,lty=5)
Plot raster
Here we will plot a raster data base using Global land cover data set. The data can be downloaded from here. Unzip and put the raster data to working directory:
install.packages("raster")
library(raster)
library(rgdal)
setwd('c:/R/GIS/')
#import raster
Gcover <- raster("GLOBCOVER_L4_200901_200912_V2.3.tif")
#plot raster
plot(Gcover,main="GLobal Land cover")
Key Points
ggplot2