Visualizing Data with GGPLOT2

GEOG 5023: Quantitative Methods In Geography

# need to install package for first time use
install.packages("ggplot2")
install.packages("hexbin")

Loading libraries

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 2.15.3

library(maptools)

## Loading required package: foreign

## Loading required package: sp

## Warning: package 'sp' was built under R version 2.15.3

## Loading required package: grid

## Loading required package: lattice

## Checking rgeos availability: FALSE Note: when rgeos is not available,
## polygon geometry computations in maptools depend on gpclib, which has a
## restricted licence. It is disabled by default; to enable gpclib, type
## gpclibPermit()

Loading Data

## LOAD DATA 'USA copy.shp'
USA <- readShapePoly(choose.files())
## Remove count fields and rows with missing data
USA <- USA[, c(1:8, 14:30)]
USA <- na.omit(USA)

Build a ggplot object

plot1 <- ggplot(data = USA@data, aes(x = Obese, y = homevalu))

Notice the above line doesn't draw anything. That's because we have to assign a “geom” to the aesthetic mapping specified by aes().

plot1 + geom_point()

# transform the coordinates
plot1 + geom_point() + scale_x_log10() + scale_y_log10()

add transparency to the points to make overplotting visible

plot1 + geom_point(alpha = 1/10) + scale_x_log10() + scale_y_log10()

# add a fitted line to the plot
plot1 + geom_point(alpha = 1/10) + geom_smooth(method = "lm")

plot1 + geom_point(alpha = 1/10) + geom_smooth(method = "loess")

other ways to deal with the over-plotting problem.

library(hexbin)

## Warning: package 'hexbin' was built under R version 2.15.3

plot1 + stat_binhex()

plot1 + geom_bin2d()

plot1 + geom_density2d()

incorporate qualitative variables.

# create a qualitative variable:
USA$good_states <- ifelse(USA$STATE_NAME %in% c("New York", "Massachusetts", 
    "Rhode Island", "Wyoming"), yes = "its good", no = "its ok")
USA$good_states <- as.factor(USA$good_states)

# colors by 'good_states'
plot2 <- ggplot(data = USA@data, aes(x = Obese, y = homevalu, color = good_states))
plot2 + geom_point()

# uses a local fit
plot2 <- ggplot(data = USA@data, aes(x = Obese, y = homevalu, color = good_states, 
    shape = good_states))
plot2 + stat_smooth()

## geom_smooth: method="auto" and size of largest group is >=1000, so using
## gam with formula: y ~ s(x, bs = "cs"). Use 'method = x' to change the
## smoothing method.

# use both
plot2 + geom_point() + stat_smooth(method = "lm", se = TRUE, lwd = 0.5, lty = 1)

Adding marginalia and changing the appearance of the plot is easy. Lets look at the percent college educated (pctcoled) and the per capita income (pcincome), these two variables have a correlation of r=.7 so our plots should show some sort of relationship.

plot3 <- ggplot(data = USA@data, aes(x = pctcoled, y = pcincome))
plot3 + geom_point() + ylab("Per Capita Income") + xlab("Percent College Educated") + 
    ggtitle("US Counties (2000)\nPercent College Educated by Per Capita Income")

make multidimensional plot. Lets say we wanted to add the unemployment variable to the plot by changing the color of the dots based on the unemployment rate.

plot4 <- ggplot(data = USA@data, aes(x = pctcoled, y = pcincome, color = unemploy)) + 
    geom_point() + ylab("Per Capita Income") + xlab("Percent College Educated") + 
    ggtitle("US Counties (2000)\nPercent College Educated by Per Capita Income") + 
    scale_color_gradient2("Unemployment", breaks = c(min(USA$unemploy), mean(USA$unemploy), 
        max(USA$unemploy)), labels = c("Below Average", "Average", "Above Average"), 
        low = "green", mid = "yellow", high = "red", midpoint = mean(USA$unemploy))
plot4

split the plot into panels based upon the “good states” variable. We create “facets” or subplots that display only the data for each level of the factor:

plot4 + facet_grid(. ~ good_states)

create your own theme. edit codes to make title and legend tile visible

sethTheme <- theme(panel.background = element_rect(fill = "black"), title = element_text(colour = "white"), 
    plot.background = element_rect(fill = "black"), panel.grid.minor = element_blank(), 
    panel.grid.major = element_line(linetype = 3, colour = "white"), axis.text.x = element_text(colour = "grey80"), 
    axis.text.y = element_text(colour = "grey80"), axis.title.x = element_text(colour = "grey80"), 
    axis.title.y = element_text(colour = "grey80"), legend.key = element_rect(fill = "black"), 
    legend.text = element_text(colour = "white"), legend.title = element_text(colour = "white"), 
    legend.background = element_rect(fill = "black"), axis.ticks = element_blank())
plot4 + sethTheme

ggsave function makes it very easy to save plots in just about any graphics format. plots saved as PDFs move nicely into Adobe illustrator. Using ggsave is simple ggsave(“path/plotName.png”) saves a png file. To save a PDF file simply change the extension: ggsave(“path/plotName.pdf”).

Created by: Li Xu; Created on: 04/29/2013; Updated on: 05/04/2013