Book: The Grammar of Graphics

Grammar: “the fundamental principles or rules of an art or science”

"…rules for constructing graphs mathematically and then representing them as graphics aesthetically."

• Developed by Hadley Wickam (2007)
• Takes the good things of lattice with the underlying layered grammar of graphics approach.
• Easy to draw wide range of graphics with compact syntax and independent components

areas <- c("N", "E", "W", "S", "C")
sales <- c(5, 25, 15, 20, 10)
profit <- c(2, 8, 6, 5, 3)
humble <- data.frame(areas, sales, profit)
humble$areas <-ordered(humble$areas, levels=c("N", "E", "W", "S", "C"))
humble

##   areas sales profit
## 1     N     5      2
## 2     E    25      8
## 3     W    15      6
## 4     S    20      5
## 5     C    10      3

• data: The data that you want to visualise.
• aes: A set of aesthetic mappings describing how variables in the data are mapped to aesthetic attributes that you can perceive.
• geom: Geometric objects represent what you actually see on the plot: points, lines, polygons, etc.
• stat: Statistical transformations summarise data in many useful ways. For example, binning and counting observations to create a histogram, or summarising a 2d relationship with a linear model.

• scales: The scales map values in the data space to values in an aesthetic space, whether it be colour, or size, or shape. Scales draw a legend or axes, which provide an inverse mapping to make it possible to read the original data values from the graph.
• coord: A coordinate system describes how data coordinates are mapped to the plane of the graphic. It also provides axes and gridlines to make it possible to read the graph. We normally use a Cartesian coordinate system, but a number of others are available, including polar coordinates and map projections.
• facet: A faceting specification describes how to break up the data into subsets and how to display those subsets as small multiples. This is also known as conditioning or latticing/trellising.

• data
• aes e.g. x, y, fill, color
• geom e.g. point, bar, line
• stat e.g. identity, bin, count
• scale e.g. log, reverse, color
• coord e.g. cartesian, polar, map
• facet e.g. wrap, grid

install.packages('ggplot2')
library(ggplot2)

Main arguments

• data set, usually a data.frame
• aesthetic mappings provided by aes function

General ggplot syntax

ggplot(data, aes(…)) + geom_x() + … + stat_x + …

Layer specifications

• geom_*(mapping, data, …, geom, position)
• stat_*(mapping, data, …, stat, position)

Additional components: scales, coordinates, facet

library(ggplot2)
data(diamonds)
names(diamonds)

##  [1] "carat"   "cut"     "color"   "clarity" "depth"   "table"   "price"  
##  [8] "x"       "y"       "z"

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

str(diamonds)

## 'data.frame':    53940 obs. of  10 variables:
##  $ carat  : num  0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
##  $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
##  $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
##  $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
##  $ depth  : num  61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
##  $ table  : num  55 61 65 58 58 57 57 55 61 61 ...
##  $ price  : int  326 326 327 334 335 336 336 337 337 338 ...
##  $ x      : num  3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
##  $ y      : num  3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
##  $ z      : num  2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

Categorical

Bar, Column, Stacked, CoxComb, Pie, Bullseye

summary(diamonds$clarity)

##    I1   SI2   SI1   VS2   VS1  VVS2  VVS1    IF 
##   741  9194 13065 12258  8171  5066  3655  1790

# ggplot(data = diamonds, aes(x = clarity)) + geom_bar()
ggplot(diamonds, aes(clarity)) + geom_bar()

ggplot(diamonds, aes(clarity, fill=clarity)) + geom_bar()

ggplot(diamonds, aes(clarity, fill=clarity)) + geom_bar(width = 1)

ggplot(diamonds, aes(clarity, fill=clarity)) +
  geom_bar(width = 1) + coord_polar()

ggplot(diamonds, aes(x="", fill=clarity)) +
  geom_bar() + xlab('clarity')

ggplot(diamonds, aes(x= "", fill=clarity)) +
  geom_bar() + xlab('clarity') + coord_polar(theta = "y")

ggplot(diamonds, aes(x= "", fill=clarity)) +
  geom_bar(width = 1) + xlab('clarity') + coord_polar(theta = "x")

Continuous Variables

Histogram

summary(diamonds$price)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     326     950    2400    3930    5320   18800

ggplot(diamonds, aes(price)) + geom_histogram()

## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.

ggplot(diamonds, aes(price)) + geom_histogram(binwidth = 500)

ggplot(diamonds, aes(price)) + geom_histogram(binwidth = 50)

ggplot(diamonds, aes(price)) + geom_histogram(binwidth = 0.02) +
  scale_x_log10()

## Warning: position_stack requires constant width: output may be incorrect

Categorical vs. Categorical

Stacked Bar, Mosaic

by(diamonds$cut, diamonds$clarity, summary)

## diamonds$clarity: I1
##      Fair      Good Very Good   Premium     Ideal 
##       210        96        84       205       146 
## -------------------------------------------------------- 
## diamonds$clarity: SI2
##      Fair      Good Very Good   Premium     Ideal 
##       466      1081      2100      2949      2598 
## -------------------------------------------------------- 
## diamonds$clarity: SI1
##      Fair      Good Very Good   Premium     Ideal 
##       408      1560      3240      3575      4282 
## -------------------------------------------------------- 
## diamonds$clarity: VS2
##      Fair      Good Very Good   Premium     Ideal 
##       261       978      2591      3357      5071 
## -------------------------------------------------------- 
## diamonds$clarity: VS1
##      Fair      Good Very Good   Premium     Ideal 
##       170       648      1775      1989      3589 
## -------------------------------------------------------- 
## diamonds$clarity: VVS2
##      Fair      Good Very Good   Premium     Ideal 
##        69       286      1235       870      2606 
## -------------------------------------------------------- 
## diamonds$clarity: VVS1
##      Fair      Good Very Good   Premium     Ideal 
##        17       186       789       616      2047 
## -------------------------------------------------------- 
## diamonds$clarity: IF
##      Fair      Good Very Good   Premium     Ideal 
##         9        71       268       230      1212

ggplot(diamonds, aes(x=cut, fill=clarity)) +
  geom_bar()

ggplot(diamonds, aes(x=cut, fill=clarity)) +
  geom_bar(position = "dodge")

ggplot(diamonds, aes(x=cut, fill=clarity)) +
  geom_bar(position = "fill")

No direct function - But you can easily write it

ggMMplot <- function(var1, var2){
  require(ggplot2)
  levVar1 <- length(levels(var1))
  levVar2 <- length(levels(var2))

  jointTable <- prop.table(table(var1, var2))
  plotData <- as.data.frame(jointTable)
  plotData$marginVar1 <- prop.table(table(var1))
  plotData$var2Height <- plotData$Freq / plotData$marginVar1
  plotData$var1Center <- c(0, cumsum(plotData$marginVar1)[1:levVar1 -1]) +
    plotData$marginVar1 / 2

  ggplot(plotData, aes(var1Center, var2Height)) +
    geom_bar(stat = "identity", aes(width = marginVar1, fill = var2), col = "white") +
    geom_text(aes(label = as.character(var1), x = var1Center, 
                  y = 1.05), size = 4, color = "grey")}

ggMMplot(diamonds$cut, diamonds$clarity)

## Warning: position_stack requires constant width: output may be incorrect

Continuous vs. Categorical

Histogram - Aesthetics, Facets, Frequency Polygon, Density Box_plot

by(diamonds$price, diamonds$cut, summary)

## diamonds$cut: Fair
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     337    2050    3280    4360    5210   18600 
## -------------------------------------------------------- 
## diamonds$cut: Good
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     327    1140    3050    3930    5030   18800 
## -------------------------------------------------------- 
## diamonds$cut: Very Good
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     336     912    2650    3980    5370   18800 
## -------------------------------------------------------- 
## diamonds$cut: Premium
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     326    1050    3180    4580    6300   18800 
## -------------------------------------------------------- 
## diamonds$cut: Ideal
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     326     878    1810    3460    4680   18800

ggplot(diamonds, aes(price, fill=cut)) +
  geom_bar(binwidth = 500)

ggplot(diamonds, aes(price, fill=cut)) +
  geom_bar(binwidth = 500) + facet_wrap(~ cut)

ggplot(diamonds, aes(price, fill=cut)) +
  geom_bar(binwidth = 500) + facet_wrap(~ cut, scales="free")

ggplot(diamonds, aes(price, color = cut)) +
  geom_freqpoly(binwidth = 500)

ggplot(diamonds, aes(price, ..density.., color=cut)) +
  geom_freqpoly(binwidth = 500)

ggplot(diamonds, aes(cut, price, color = cut)) + 
  geom_point()

ggplot(diamonds, aes(cut, price, color = cut)) + 
  geom_point(alpha = 0.1)

ggplot(diamonds, aes(cut, price, color = cut)) + 
  geom_jitter()

ggplot(diamonds, aes(cut, price, fill = cut)) +
  geom_boxplot()

ggplot(diamonds, aes(cut, price, fill = cut)) +
 geom_jitter(size = 1) + geom_boxplot()

Continuous vs. Continuous

Scatterplot - Aesthetics, Facets

ggplot(diamonds, aes(carat, price)) + geom_point()

ggplot(diamonds, aes(carat, price)) + geom_point(size = 1)

ggplot(diamonds, aes(carat, price)) + geom_point(alpha = 0.2)

ggplot(diamonds, aes(carat, price)) + geom_bin2d()

ggplot(diamonds, aes(carat, price)) + geom_hex()

ggplot(diamonds, aes(carat, price)) + geom_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.

ggplot(diamonds, aes(carat, price)) + geom_point() + xlim(c(0, 3.1))

## Warning: Removed 14 rows containing missing values (geom_point).

ggplot(diamonds, aes(carat, price)) + geom_point() +
  scale_y_log10()

ggplot(diamonds, aes(carat, price)) + geom_point(alpha = 0.3) +
  scale_y_log10() + scale_x_log10()

ggplot(diamonds, aes(carat, price)) + geom_point(alpha = 0.3) +
  scale_y_log10() + scale_x_log10() +
  geom_smooth(method = lm)

ggplot(diamonds, aes(carat, price, color = cut)) + 
  geom_point(alpha = 0.3)

ggplot(diamonds, aes(carat, price, color = cut)) +
   geom_point(size=1) + facet_wrap(~ cut)

## troops <- read.table(url("http://amitkaps.com/data/minard-troops.txt"), header = TRUE)
troops <- read.table("data/minard-troops.txt", header = TRUE)
troops

##    long  lat survivors direction group
## 1  24.0 54.9    340000         A     1
## 2  24.5 55.0    340000         A     1
## 3  25.5 54.5    340000         A     1
## 4  26.0 54.7    320000         A     1
## 5  27.0 54.8    300000         A     1
## 6  28.0 54.9    280000         A     1
## 7  28.5 55.0    240000         A     1
## 8  29.0 55.1    210000         A     1
## 9  30.0 55.2    180000         A     1
## 10 30.3 55.3    175000         A     1
## 11 32.0 54.8    145000         A     1
## 12 33.2 54.9    140000         A     1
## 13 34.4 55.5    127100         A     1
## 14 35.5 55.4    100000         A     1
## 15 36.0 55.5    100000         A     1
## 16 37.6 55.8    100000         A     1
## 17 37.7 55.7    100000         R     1
## 18 37.5 55.7     98000         R     1
## 19 37.0 55.0     97000         R     1
## 20 36.8 55.0     96000         R     1
## 21 35.4 55.3     87000         R     1
## 22 34.3 55.2     55000         R     1
## 23 33.3 54.8     37000         R     1
## 24 32.0 54.6     24000         R     1
## 25 30.4 54.4     20000         R     1
## 26 29.2 54.3     20000         R     1
## 27 28.5 54.2     20000         R     1
## 28 28.3 54.3     20000         R     1
## 29 27.5 54.5     20000         R     1
## 30 26.8 54.3     12000         R     1
## 31 26.4 54.4     14000         R     1
## 32 25.0 54.4      8000         R     1
## 33 24.4 54.4      4000         R     1
## 34 24.2 54.4      4000         R     1
## 35 24.1 54.4      4000         R     1
## 36 24.0 55.1     60000         A     2
## 37 24.5 55.2     60000         A     2
## 38 25.5 54.7     60000         A     2
## 39 26.6 55.7     40000         A     2
## 40 27.4 55.6     33000         A     2
## 41 28.7 55.5     33000         A     2
## 42 28.7 55.5     33000         R     2
## 43 29.2 54.2     30000         R     2
## 44 28.5 54.1     30000         R     2
## 45 28.3 54.2     28000         R     2
## 46 24.0 55.2     22000         A     3
## 47 24.5 55.3     22000         A     3
## 48 24.6 55.8      6000         A     3
## 49 24.6 55.8      6000         R     3
## 50 24.2 54.4      6000         R     3
## 51 24.1 54.4      6000         R     3

plot_troops <- ggplot(troops, aes(long, lat)) +
  geom_path(aes(size = survivors, color = direction, group = group))
plot_troops

## cities <- read.table(url("http://amitkaps.com/data/minard-cities.txt"), header = TRUE)
cities <- read.table("data/minard-cities.txt", header = TRUE)
cities

##    long  lat           city
## 1  24.0 55.0          Kowno
## 2  25.3 54.7          Wilna
## 3  26.4 54.4       Smorgoni
## 4  26.8 54.3      Moiodexno
## 5  27.7 55.2      Gloubokoe
## 6  27.6 53.9          Minsk
## 7  28.5 54.3     Studienska
## 8  28.7 55.5        Polotzk
## 9  29.2 54.4           Bobr
## 10 30.2 55.3        Witebsk
## 11 30.4 54.5         Orscha
## 12 30.4 53.9        Mohilow
## 13 32.0 54.8       Smolensk
## 14 33.2 54.9    Dorogobouge
## 15 34.3 55.2          Wixma
## 16 34.4 55.5          Chjat
## 17 36.0 55.5        Mojaisk
## 18 37.6 55.8         Moscou
## 19 36.6 55.3      Tarantino
## 20 36.5 55.0 Malo-Jarosewii

plot_troops_cities <- plot_troops +
  geom_text(aes(label = city), size = 4, data = cities)
plot_troops_cities

library(maps)
library(mapproj)

plot_polished <- plot_troops_cities +
  scale_size(range = c(1, 10), 
             breaks = c(1, 2, 3) * 10^5,
             labels = c(1, 2, 3) * 10^5 )+
  scale_color_manual(values = c("grey50","red")) +
  xlab(NULL) +
  ylab(NULL) +
  coord_map()

plot_polished

Resources & Books

• doc.ggplot2.org
• R Graphics Cookbook by Winston Change
• ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham

Courses

• Exploratory Data Analysis using R

Amit Kapoor

Partner, narrativeVIZ Consulting

amit@narrativeviz.com

narrativeviz.com

You can find this presentation and more at:

narrativeviz.com/playbook