ggplot2 makes GREAT visuals
“Mappings” & “Geoms”
install.packages("ggplot2")
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install.packages("maptools")
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library(ggplot2)
## Warning: package 'ggplot2' was built under R version 2.15.3
library(maptools)
## Warning: package 'maptools' was built under R version 2.15.3
## 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: TRUE
## LOAD DATA
USA <- readShapePoly("C:/Users/Hallie/Desktop/Spring 2013 Courses/Quantitative Methods/Data/USA copy.shp")
## Remove count fields and rows with missing data
USA <- USA[, c(1:8, 14:30)]
USA <- na.omit(USA)
plot1 <- ggplot(data = USA@data, aes(x = Obese, y = homevalu))
plot1 + geom_point()
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 fitted line to the plot
plot1 + geom_point(alpha = 1/10) + geom_smooth(method = "lm")
# other ways to deal w/ over-plotting problem
install.packages("hexbin")
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library(hexbin)
## Warning: package 'hexbin' was built under R version 2.15.3
plot1 + stat_binhex()
plot1 + geom_bin2d()
plot1 + geom_density2d()
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)
ggplot2 makes it very easy to incorporate qualitative variables. These can be used in several ways: 1. Facets: Each level of a factor can be plotted in its own panel. 2. Groups: Each level of a factor can be assigned its own group. For example, plotting fitted lines for each group through a scatter plot. 3. Appearance: Color, symbols, line weight, fill, and other variables can be assigned to a factor (qualitative variable).
Lets create a qualitative variable:
# MODIFY PLOT 1
plot2 <- ggplot(data = USA@data, aes(x = Obese, y = homevalu, color = good_states))
plot2 + geom_point()
plot2 <- ggplot(data = USA@data, aes(x = Obese, y = homevalu, color = good_states,
shape = good_states))
plot2 + stat_smooth() #uses a local fit
## 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.
plot2 + geom_point() + stat_smooth(method = "lm", se = TRUE, lwd = 0.5, lty = 1)
# lwd controls line thickness lty controls line type 1= solid line, higher
# numbers various forms of dashed lines. se can be used to turn off the
# grey standard error envelopes.
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")
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
plot4 + facet_grid(. ~ good_states)
# If you wanted to go crazy you could do: plot4 + facet_grid(.~
# STATE_NAME)
plot4 + theme_classic()
install.packages("ggthemes", dependencies = TRUE)
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## Error: unable to install packages
library(ggthemes)
## Warning: package 'ggthemes' was built under R version 2.15.3
plot4 + theme_economist()
plot4 + theme_solarized() #OUCH!
plot4 + theme_tufte()
Seth has made have a custom theme that he often uses in presentations:
sethTheme <- theme(panel.background = element_rect(fill = "black"), plot.background = element_rect(fill = "black"),
panel.grid.minor = element_blank(), panel.grid.major = element_line(linetype = 3,
colour = "white"), title = element_text(colour = "grey80"), 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 = "grey80"),
legend.background = element_rect(fill = "black"), axis.ticks = element_blank())
plot4 + sethTheme
`?`(theme())
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## done
Making Maps
install.packages("rgdal")
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library(rgdal)
## Warning: package 'rgdal' was built under R version 2.15.3
## rgdal: version: 0.8-6, (SVN revision Unversioned directory) Geospatial
## Data Abstraction Library extensions to R successfully loaded Loaded GDAL
## runtime: GDAL 1.9.2, released 2012/10/08 Path to GDAL shared files:
## C:/Users/Hallie/Documents/R/win-library/2.15/rgdal/gdal GDAL does not use
## iconv for recoding strings. Loaded PROJ.4 runtime: Rel. 4.7.1, 23
## September 2009, [PJ_VERSION: 470] Path to PROJ.4 shared files:
## C:/Users/Hallie/Documents/R/win-library/2.15/rgdal/proj
install.packages("rgeos")
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library(rgeos)
## Warning: package 'rgeos' was built under R version 2.15.3
## rgeos version: 0.2-16, (SVN revision 389) GEOS runtime version:
## 3.3.6-CAPI-1.7.6 Polygon checking: TRUE
install.packages("gpclib")
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library(gpclib)
## Warning: A specification for class "gpc.poly" in package 'gpclib' seems
## equivalent to one from package 'rgeos' and is not turning on duplicate
## class definitions for this class
## Warning: A specification for class "gpc.poly.nohole" in package 'gpclib'
## seems equivalent to one from package 'rgeos' and is not turning on
## duplicate class definitions for this class
## General Polygon Clipper Library for R (version 1.5-1) Type 'class ?
## gpc.poly' for help
## Attaching package: 'gpclib'
## The following object(s) are masked from 'package:rgeos':
##
## append.poly, area.poly, get.bbox, get.pts, read.polyfile, scale.poly,
## triangulate, tristrip, write.polyfile
gpclibPermit()
## [1] TRUE
## Use fortify to extract ploygon boundaries from the spatialDataFrame
## (its slow)
usa_geom <- fortify(USA, region = "FIPS")
## reattach data to polygon boundaries
usa_map_df <- merge(usa_geom, USA, by.x = "id", by.y = "FIPS")
## make a map of bush_pct
map1 <- ggplot(usa_map_df, aes(long, lat, group = group)) + geom_polygon(data = usa_map_df,
aes(fill = Bush_pct)) + coord_equal() + scale_fill_gradient(low = "yellow",
high = "red") + geom_path(data = usa_geom, aes(long, lat, group = group),
lty = 3, lwd = 0.1, color = "white")
map1
# Apply the Seth theme
map1 + sethTheme
library(classInt)
## Warning: package 'classInt' was built under R version 2.15.3
## Loading required package: class
## Loading required package: e1071
classIntervals(USA$Bush_pct, n = 5, style = "quantile")
## style: quantile
## [0,50.52) [50.52,58.07) [58.07,64.37) [64.37,71.31) [71.31,92.83]
## 622 622 622 622 623
breaks <- c(0, 50, 58, 64, 71, 93) #approximate quantiles
labels = c("[0 - 50%]", "[50% - 58%]", "[58% - 64%]", "[64% - 71%]", "[71% - 93%]")
usa_map_df$bushBreaks <- cut(usa_map_df$Bush_pct, breaks = breaks, labels = labels)
map2 <- ggplot(aes(long, lat, group = group), data = usa_map_df) + geom_polygon(data = usa_map_df,
aes(fill = bushBreaks)) + coord_equal()
map2
install.packages("RColorBrewer")
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library(RColorBrewer)
map2 + scale_fill_brewer("Votes for Bush in 2004 (%)", palette = "YlGnBu") +
sethTheme + ggtitle("Votes for Bush in 2004 (%)") + theme(plot.title = element_text(size = 24,
face = "bold", color = "white", hjust = 2))
