ggplot2 redux

• facet_wrap, facet_grid
• themes, preset and custom
• ggmap
• ggally

What can you do w/ ggplot2 that you can't do w/ base functions?

• Facetting is one of the more powerful aspects of ggplot2

• plot y vs x (or just x) by z, where z is some categorical variable

• facet_grid or facet_wrap

data(diamonds)
head(diamonds)

##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48

• more important for multiple facet variables
• facet_wrap always has one horizontal facet label on the top

p1 + facet_wrap(~ cut + color)

• more important for multiple facet variables
• facet_grid can have both horizontal, vertical facet labels

p1 + facet_grid(cut ~ color)

• order of variables affects position of facets
• facet_wrap orders facets by position in the call

p1 + facet_wrap(~ color + cut) # same as facet_wrap(color ~ cut)

• order of variables affects position of facets
• facet_wrap orders facets by position in the call

p1 + facet_wrap(~ cut + color) # same as facet_wrap(cut ~ color)

• order of variables affects position of facets
• facet_grid orders vertical/horizontal facets by left/right of tilde

p1 + facet_grid(cut ~ color) # not the same as facet_grid(~ cut + color)

• order of variables affects position of facets
• facet_grid orders vertical/horizontal facets by left/right of tilde

p1 + facet_grid(color ~ cut) # not the same as facet_grid(~ color + cut)

• both use the scales argument for axes, otherwise fixed

p1 + facet_wrap(~ color + cut, scales = 'free') # or 'free_x', 'free_y' 

• facet_grid treats scales differently

p1 + facet_grid(color ~ cut, scales = 'free') # or 'free_x', 'free_y' 

• facet_wrap uses the ncol argument

p1 + facet_wrap(~ color + cut, ncol = 7)

• facet_grid always creates a symmetrical plot

p1 + facet_grid(~ color + cut)

• Very easy to quickly evaluate a variable by multiple categories
• Adding some fake variables to diamonds...

diamonds$fake1 <- sample(c('A', 'B'), nrow(diamonds), replace = T)
diamonds$fake2 <- sample(c('C', 'D'), nrow(diamonds), replace = T)
diamonds$fake3 <- sample(c('E', 'F'), nrow(diamonds), replace = T)
diamonds$fake4 <- sample(c('G', 'H'), nrow(diamonds), replace = T)
head(diamonds[, grep('fake', names(diamonds))])

##   fake1 fake2 fake3 fake4
## 1     B     D     F     G
## 2     A     D     E     H
## 3     A     D     F     G
## 4     A     C     E     G
## 5     B     D     E     G
## 6     A     C     F     H

p1 + facet_grid(fake1 + fake2 ~ fake3 + fake4)

facet_wrap and facet_grid accomplish similar tasks, with slight differences

facet_wrap

• only horizontal facet labels
• not symmetric, uses ncol
• scales affect all facets

facet_grid

• horizontal/vertical facet labels
• always symmetric

• scales only affect outer facets

  • Not apparent why you would use one over the other...

• Themes are simply complete calls to the ggplot2::theme function
• A custom theme is easily made

ugly_theme <- theme(
    panel.background = element_rect(fill = "green"), 
    axis.line = element_line(size = 3, colour = "red", linetype = "dotted"),
    axis.text = element_text(colour = "blue"),
    axis.ticks.length = unit(.85, "cm")
    )

• Complete list of theme options here

• The user-defined theme can also be set as default by updating an existing theme

ugly_default <- function(){
    theme_grey() %+replace%
    theme(
        panel.background = element_rect(fill = "green"), 
        axis.line = element_line(size = 3, colour = "red", linetype = "dotted"),
        axis.text = element_text(colour = "blue"),
        axis.ticks.length = unit(.85, "cm")
        )
    }
theme_set(ugly_default())

p2

• The ggthemes library provides additional themes

library(devtools)
install_github('ggthemes', username = 'jrnold')
library(ggthemes)   

• Check the repo on Github for more info

The basic idea of ggmap is to take a downloaded map image, plot it as a context layer using ggplot2, and then plot additional content layers of data, statistics, or models on top of the map.

• install/load ggmap
• download the images and format for plotting, done with get_map

install.packages('ggmap')
library(ggmap)

# get map by location
loc <- 'Environmental Protection Agency, 1 Sabine Drive, Gulf Breeze, FL'
my_map <- get_map(
    location = loc, 
    source = 'google', 
    maptype = 'terrain', 
    zoom = 13
    )

• Many geoms

ls(pattern = '^geom_', env = as.environment('package:ggplot2'))

##  [1] "geom_abline"     "geom_area"       "geom_bar"       
##  [4] "geom_bin2d"      "geom_blank"      "geom_boxplot"   
##  [7] "geom_contour"    "geom_crossbar"   "geom_density"   
## [10] "geom_density2d"  "geom_dotplot"    "geom_errorbar"  
## [13] "geom_errorbarh"  "geom_freqpoly"   "geom_hex"       
## [16] "geom_histogram"  "geom_hline"      "geom_jitter"    
## [19] "geom_line"       "geom_linerange"  "geom_map"       
## [22] "geom_path"       "geom_point"      "geom_pointrange"
## [25] "geom_polygon"    "geom_quantile"   "geom_raster"    
## [28] "geom_rect"       "geom_ribbon"     "geom_rug"       
## [31] "geom_segment"    "geom_smooth"     "geom_step"      
## [34] "geom_text"       "geom_tile"       "geom_violin"    
## [37] "geom_vline"

• Easy facetting, pre-loaded and customized themes, spatial data, pairs plots...

• See online documentation for additional functionality

• Presentation materials available here

• EPA, NHEERL slidify template from Jeff's repo