The grammar of Graphics

06 May, 2022

Graphics in R

Options

Traditional (base) graphics
- isolated instructions to the device

Options

Traditional (base) graphics
- isolated instructions to the device
Grid graphics
- instruction sets
- lattice
- ggplot2

Packages

library(ggplot2) #OR better still library(tidyverse)
library(tidyverse)
library(grid)
library(gridExtra)
library(patchwork)
library(scales)

Graphics infrastructure

layers of data driven geometric objects
coordinate system
scales
faceting
themes

Typical ggplot template

ggplot() +                          # required
    geom_*(                         # required
        data = <DATA>,              # required - <DATA> is a data frame
        mapping = aes(<MAPPING>),   # required - map variables to scales
        stat = <STAT>,              # optional - map variables to geoms
        position = <POSITION>) +    # optional - adjustments to overlapping geoms
    coord_*() +                     # optional - specify coordinate system
    scale_*() +                     # optional - visual appearence of scales
    facet_*() +                     # optional - subplots
    theme_*()                       # optional - overal appearence

Motivating data

head(BOD)

##   Time demand
## 1    1    8.3
## 2    2   10.3
## 3    3   19.0
## 4    4   16.0
## 5    5   15.6
## 6    7   19.8

summary(BOD)

##       Time           demand     
##  Min.   :1.000   Min.   : 8.30  
##  1st Qu.:2.250   1st Qu.:11.62  
##  Median :3.500   Median :15.80  
##  Mean   :3.667   Mean   :14.83  
##  3rd Qu.:4.750   3rd Qu.:18.25  
##  Max.   :7.000   Max.   :19.80

ggplot - complete template

 p <- ggplot() + #single layer - points
  layer(data = BOD, #data.frame
    mapping = aes(y = demand, x = Time),
    stat = "identity", #use original data
    geom = "point", #plot data as points
    position = "identity",
    params = list(na.rm = TRUE),
    show.legend = FALSE
  )+ #layer of lines
  layer(data = BOD, #data.frame
    mapping = aes(y = demand, x = Time),
    stat = "identity", #use original data
    geom = "line", #plot data as a line
    position = "identity",
    params = list(na.rm = TRUE),
    show.legend = FALSE
  ) +
  coord_cartesian() + #cartesian coordinates
  scale_x_continuous() + #continuous x axis
  scale_y_continuous()  #continuous y axis
p #print the plot

ggplot

ggplot(data = BOD,
       map = aes(y = demand, x = Time)) +
    geom_point() +
    geom_line()

Overview

data

p<-ggplot(data = BOD)

layers (geoms)

p<-p + geom_point(aes(y = demand, x = Time))
p

Overview

data

p<-ggplot(data = BOD)

layers (geoms)

p <- p + geom_point(aes(y = demand, x = Time))

scales

p <- p + scale_x_sqrt(name = "Time (days)")
p

Layers

layers of data driven objects
- geometric objects to represent data
- statistical methods to summarize the data
- mapping of aesthetics
- position control

`geom_` and `stat_`

coupled together
engage either
stat_identity

`geom_`

Arguments

data - obvious
mapping - aesthetics

If omitted, inherited from ggplot()

stat - the stat_ function
position - overlapping geoms

`geom_`

BOD %>%
    ggplot(aes(y = demand, x = Time)) +
    geom_point()
#OR
ggplot(data = BOD, aes(y = demand, x = Time)) +
    geom_point()
#OR
ggplot(data = BOD) +
    geom_point(aes(y = demand, x = Time))
#OR
ggplot() +
    geom_point(data = BOD, aes(y = demand, x = Time))

Optional aesthetics mapping

alpha - transparency
colour - colour of the geometric features
fill - colour of the geometric features
linetype - fill colour of geometric features
size - size of geometric features such as points or text
shape - shape of geometric features such as points
weight - weightings of values

primitives

df <- data.frame(x = c(3, 1, 5), y = c(2, 4, 6), z = c('a', 'b', 'c'))
p <- ggplot(data=df, aes(y = y, x = x))

geom	Example syntax	Example plot
geom_blank	p + geom_blank()
geom_point	p + geom_point()
geom_text	p + geom_text(aes(label = z))
geom_path	p + geom_path()
geom_polygon	p + geom_polygon()
geom_area	p + geom_area()
geom_ribbon	p + geom_ribbon(aes(ymin = y-1, ymax= y+1))

`geom_point`

head(CO2)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8
## 4   Qn1 Quebec nonchilled  350   37.2
## 5   Qn1 Quebec nonchilled  500   35.3
## 6   Qn1 Quebec nonchilled  675   39.2

summary(CO2)

##      Plant             Type         Treatment       conc          uptake     
##  Qn1    : 7   Quebec     :42   nonchilled:42   Min.   :  95   Min.   : 7.70  
##  Qn2    : 7   Mississippi:42   chilled   :42   1st Qu.: 175   1st Qu.:17.90  
##  Qn3    : 7                                    Median : 350   Median :28.30  
##  Qc1    : 7                                    Mean   : 435   Mean   :27.21  
##  Qc3    : 7                                    3rd Qu.: 675   3rd Qu.:37.12  
##  Qc2    : 7                                    Max.   :1000   Max.   :45.50  
##  (Other):42

`geom_point`

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_point(colour = "red")

g + geom_point(aes(colour = Type))

`geom_point`

g + geom_point(stat = "summary",
               fun = mean)

g + stat_summary(geom = "point",
                 fun = mean)

Example data sets

head(diamonds)

## # A tibble: 6 × 10
##   carat cut       color clarity depth table price     x     y     z
##   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
## 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  4.2   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

summary(diamonds)

##      carat               cut        color        clarity          depth      
##  Min.   :0.2000   Fair     : 1610   D: 6775   SI1    :13065   Min.   :43.00  
##  1st Qu.:0.4000   Good     : 4906   E: 9797   VS2    :12258   1st Qu.:61.00  
##  Median :0.7000   Very Good:12082   F: 9542   SI2    : 9194   Median :61.80  
##  Mean   :0.7979   Premium  :13791   G:11292   VS1    : 8171   Mean   :61.75  
##  3rd Qu.:1.0400   Ideal    :21551   H: 8304   VVS2   : 5066   3rd Qu.:62.50  
##  Max.   :5.0100                     I: 5422   VVS1   : 3655   Max.   :79.00  
##                                     J: 2808   (Other): 2531                  
##      table           price             x                y         
##  Min.   :43.00   Min.   :  326   Min.   : 0.000   Min.   : 0.000  
##  1st Qu.:56.00   1st Qu.:  950   1st Qu.: 4.710   1st Qu.: 4.720  
##  Median :57.00   Median : 2401   Median : 5.700   Median : 5.710  
##  Mean   :57.46   Mean   : 3933   Mean   : 5.731   Mean   : 5.735  
##  3rd Qu.:59.00   3rd Qu.: 5324   3rd Qu.: 6.540   3rd Qu.: 6.540  
##  Max.   :95.00   Max.   :18823   Max.   :10.740   Max.   :58.900  
##                                                                   
##        z         
##  Min.   : 0.000  
##  1st Qu.: 2.910  
##  Median : 3.530  
##  Mean   : 3.539  
##  3rd Qu.: 4.040  
##  Max.   :31.800  
##

Various geometric objects

Exploring distributions

diamonds %>% data.frame() %>% head(2)

##   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

g <- ggplot(diamonds, aes(x=carat))

Feature	stat	Example syntax
Histogram	_bin	g + geom_histogram()
Density	_density	g + geom_density()
Boxplot	_boxplot	g + geom_boxplot()
Violin plot	_ydensity	g + geom_violin(aes(y = carat), orientation='y')
QQ plot	_qq	g + geom_qq(aes(sample = carat), inherit.aes = FALSE)
Bar plot	_count	g + geom_bar(aes(x = cut))

Exploring distributions

diamonds %>% data.frame() %>% head(2)

##   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

g <- ggplot(diamonds, aes(x=carat))

Feature	stat	Example syntax
Histogram	_bin	g + geom_histogram(aes(fill=cut))
Density	_density	g + geom_density(aes(fill=cut), alpha=0.3)
Boxplot	_boxplot	g + geom_boxplot(aes(y = cut))
Violin plot	_ydensity	g + geom_violin(aes(y = cut))
QQ plot	_qq	g + geom_qq(aes(sample = carat, color = cut), inherit.aes = FALSE)

Exploring distributions

diamonds %>% data.frame() %>% head(2)

##   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

g <- ggplot(diamonds, aes(x=carat))

Feature	stat	Example syntax	Example plot
Bar plot	_count	g + geom_bar(aes(x = cut, fill = clarity))
Bar plot	_count	g + geom_bar(aes(x = cut, fill = clarity), position='dodge')

Visualising trends

head(CO2, 2)

##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2 %>% filter(Plant == first(Plant)), aes(x = conc, y = uptake))

Feature	stat	Example syntax
Scatterplot	_identity	g + geom_point()
Line plot	_identity	g + geom_line()
Smoother	_smooth	g + geom_smooth()
Quantiles plot	_quantile	g + geom_quantile()

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_point()

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_point(aes(colour = Treatment))

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_point(aes(fill = Treatment),shape=21)

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_point(aes(shape=Treatment))

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_point() + geom_line()

g + geom_point() + geom_path()

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_line(aes(group = Plant))

g + geom_line(aes(color=Plant))

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_line(stat = "summary", fun.y = mean)

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_smooth(method = "lm")

g + geom_smooth(method = "loess")

Visualising trends

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(x = conc, y = uptake))

g + geom_smooth(aes(fill = Treatment))

Visualising three dimensions

head(as.data.frame(faithfuld), 3)

##   eruptions waiting     density
## 1  1.600000      43 0.003216159
## 2  1.647297      43 0.003835375
## 3  1.694595      43 0.004435548

g <- ggplot(faithfuld, aes(y = eruptions, x = waiting))

Feature	stat	Example syntax
Tiles	_tile	g + geom_tile(aes(fill = density))
Raster	_identity	g + geom_raster(aes(fill = density))
Contour	_contour	g + geom_contour(aes(z = density))
Filled contour	_contour_filled	g + geom_contour_filled(aes(z = density))

Visualising maps

library(maps)
library(mapdata)
aus <- map_data("worldHires", region="Australia")
head(aus,3)

##       long       lat group order    region              subregion
## 1 142.1461 -10.74943     1     1 Australia Prince of Wales Island
## 2 142.1430 -10.74525     1     2 Australia Prince of Wales Island
## 3 142.1406 -10.74113     1     3 Australia Prince of Wales Island

ggplot(aus, aes(x=long, y=lat, group=group)) +
    geom_polygon()

Visualising uncertainty

head(warpbreaks)

##   breaks wool tension
## 1     26    A       L
## 2     30    A       L
## 3     54    A       L
## 4     25    A       L
## 5     70    A       L
## 6     52    A       L

summary(warpbreaks)

##      breaks      wool   tension
##  Min.   :10.00   A:27   L:18   
##  1st Qu.:18.25   B:27   M:18   
##  Median :26.00          H:18   
##  Mean   :28.15                 
##  3rd Qu.:34.00                 
##  Max.   :70.00

Visualising uncertainty

warpbreaks.sum <- warpbreaks %>%
    group_by(wool) %>%
    summarise(mean_se(breaks))
g <- ggplot(warpbreaks.sum, aes(y = y, x = wool, ymin = ymin, ymax = ymax))

Feature	stat	Example syntax
Error bars	_identity	g + geom_errorbar()
Line range	_identity	g + geom_linerange()
Point range	_identity	g + geom_pointrange()

Visualising uncertainty

head(as.data.frame(warpbreaks),2)

##   breaks wool tension
## 1     26    A       L
## 2     30    A       L

g <- ggplot(warpbreaks, aes(y = breaks, x = wool))

g + geom_pointrange(stat = "summary",
      fun.data = "mean_se")

g + geom_pointrange(stat = "summary",
      fun.data = "mean_cl_boot")

Coordinate systems

head(CO2,3)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4
## 3   Qn1 Quebec nonchilled  250   34.8

g <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake))

Coordinates	Example syntax	Example plot
Cartesian	g + coord_cartesian()
Flip	g + coord_flip()
Polar	g + coord_polar()

Coordinate systems

#Orthographic coordinates
library(maps)
library(mapdata)
aus <- map_data("worldHires", region = "Australia")
ggplot(aus, aes(x = long, y = lat, group = group)) +
  coord_map("ortho", orientation=c(-20, 125, 23.5))+
  geom_polygon()

Scales

x, y axes scales
size of points (thickness of lines)
shape of points
linetype of lines
color of lines or points
fill of shapes

g + scale_<SCALE>_<TYPE>(
    name,     # scale title
    breaks,   # axis/legend ticks/breaks
    labels,   # alternative axis/legend text labels
    values,   # scale dependent properties (sizes/colours etc)
    limits,   # range of values accommodated
    ...,      # additional scale dependent options
    )

<TYPE> - prepackaged scale modifier

X and Y axes

head(CO2,2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(y = uptake, x = conc)) + geom_point()

Scale	Example syntax	Example plot
Continuous	g + scale_x_continuous()
log	g + scale_x_log10()
sqrt	g + scale_x_sqrt()
reverse	g + scale_x_reverse()

X and Y axes

Axis titles with math

head(CO2,2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g + scale_x_continuous(name = expression(
         Ambient~CO[2]~concentration~(mg/l)))

X and Y axes

Axis more padding

g + scale_x_continuous(name = "CO2 conc",
         expand = c(0,200))

X and Y axes

Axis on a log scale

g + scale_x_log10(name = "CO2 conc",
    breaks = as.vector(c(1, 2, 5, 10) %o% 10^(-1:2)))

X and Y axes

Axis representing categorical data

ggplot(CO2, aes(y = uptake, x = Treatment)) + geom_point()+
  scale_x_discrete(name = "Treatment")

Size and shape scales

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake
## 1   Qn1 Quebec nonchilled   95   16.0
## 2   Qn1 Quebec nonchilled  175   30.4

g <- ggplot(CO2, aes(y = uptake, x = conc))

Scale	Example syntax	Example plot
Size	g + geom_point(aes(size = uptake)) + scale_size_continuous()
Size	g + geom_point(aes(size = Type)) + scale_size_discrete(range = c(2,5))
Shape	g + geom_point(aes(shape = Type)) + scale_shape_manual(values = c(2,3))

Size

g + geom_point(aes(size = uptake)) +
    scale_size_continuous()

Size

g + geom_point(aes(size = Type)) +
    scale_size_discrete(range = c(2,5))

Shape

g + geom_point(aes(shape = Type)) +
    scale_shape_manual(values = c(2,3))

Color and fill scales

Continuous scale

head(CO2, 2)

## Grouped Data: uptake ~ conc | Plant
##   Plant   Type  Treatment conc uptake              Comb
## 1   Qn1 Quebec nonchilled   95   16.0 Quebec.nonchilled
## 2   Qn1 Quebec nonchilled  175   30.4 Quebec.nonchilled

g <- ggplot(CO2, aes(y = uptake, x = conc)) +
    geom_point(aes(colour = uptake))

Color and fill scales

Continuous scale

Scale	Example syntax	Example plot
Gradient	g + scale_color_gradient(low = 'red', high = 'blue')
Gradient2	g + scale_color_gradient2(low = 'red', mid = "white", high = 'blue')
Gradientn	g + scale_color_gradientn(colors = terrain.colors(12))
Distiller	g + scale_color_distiller(palette = "Reds")
Viridis	g + scale_color_viridis_c(option = "D")

Colours

657 named colours

colors()

Colour palettes

library(colorspace)
pal <- choose_pal()

Colour palettes

RColorBrewer::display.brewer.all()

Colour palettes

RColorBrewer::brewer.pal(n=8, name='Set1')

## [1] "#E41A1C" "#377EB8" "#4DAF4A" "#984EA3" "#FF7F00" "#FFFF33" "#A65628"
## [8] "#F781BF"

RColorBrewer::display.brewer.pal(n=8, name='Set1')

Facets

g <- ggplot(CO2) +
    geom_point(aes(x = conc, y = uptake, colour = Type))

g + facet_wrap(~Plant)

Facets

g <- ggplot(CO2) +
    geom_point(aes(x = conc, y = uptake, colour = Type))

g + facet_wrap(~Plant, scales = 'free_y')

Facets

g <- ggplot(CO2) +
    geom_point(aes(x = conc, y = uptake, colour = Type))

g + facet_grid(Type~Treatment)

Arranging multiple plots

Multiple plots

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
library(gridExtra)
grid.arrange(g1,  g2)

Multiple plots

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
library(patchwork)
g1/g2

Multiple plots

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
library(patchwork)
g1+g2

Multiple plots

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
library(patchwork)
(g1+g2)/g1

Multiple plots

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
g2 <- ggplot(CO2) + geom_point(aes(x = Treatment, y = uptake))
library(patchwork)
wrap_plots(g1,g2, g1, g2, widths = c(2,1,1,1), guides = 'collect',
           design = "AAB\nCDE") +
    guide_area() +
    plot_annotation(tag_levels = 'a',
                    tag_suffix = ')')

wrap_plots(g1,g2, g1, g2, guides = 'collect',
           design = "AAAA\nBCDE") +
    guide_area() +
    plot_annotation(tag_levels = 'a',
                    tag_suffix = ')')

Themes

`theme_classic`

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_classic()

`theme_bw`

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_bw()

`theme_grey`

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_grey()

`theme_minimal`

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_minimal()

`theme_linedraw`

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_linedraw()

`theme_light`

ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth() +
    geom_point() +
    theme_light()

others

png('resources/xkcd.png', width=500, height=500, res=200)
library(xkcd)  
## remotes::install_version("Rttf2pt1", version = "1.3.8")
library(Rttf2pt1)
library(sysfonts)
library(extrafont)
## download.file("http://simonsoftware.se/other/xkcd.ttf", dest="xkcd.ttf")
#download.file("https://github.com/ekmett/arcade/blob/master/static/fonts/xkcd.ttf", dest="xkcd.ttf")
#font_import(".", prompt = FALSE)
#loadfonts()
xrange <- range(CO2$conc)
yrange <- range(CO2$uptake)
ggplot(CO2, aes(y = uptake, x = conc)) + geom_smooth(position='jitter', size=1.5) +
    #geom_point() +
        theme_minimal()+theme(text=element_text(size=16, family='xkcd'))+
        xkcdaxis(xrange, yrange)

dev.off()

others

Saving ggplot figures

g1 <- ggplot(CO2) + geom_point(aes(x = conc, y = uptake, colour = Type))
ggsave(filename='figure1.pdf', g1,  width=7,  height=5)

Practice

state=data.frame(state.x77, state.region, state.division, state.center) %>%
    select(Illiteracy, state.region, x, y)
head(state)

##            Illiteracy state.region         x       y
## Alabama           2.1        South  -86.7509 32.5901
## Alaska            1.5         West -127.2500 49.2500
## Arizona           1.8         West -111.6250 34.2192
## Arkansas          1.9        South  -92.2992 34.7336
## California        1.1         West -119.7730 36.5341
## Colorado          0.7         West -105.5130 38.6777

Calculate the mean and 95% confidence interval of Illiteracy per state.region and plot them.

Practice

library(gmodels)
state.sum = state %>% group_by(state.region) %>%
    summarise(Mean = mean(Illiteracy),
              Lower = ci(Illiteracy)[2],
              Upper = ci(Illiteracy)[3])
state.sum

## # A tibble: 4 × 4
##   state.region   Mean Lower Upper
##   <fct>         <dbl> <dbl> <dbl>
## 1 Northeast      1    0.786 1.21 
## 2 South          1.74 1.44  2.03 
## 3 North Central  0.7  0.610 0.790
## 4 West           1.02 0.655 1.39

ggplot(state.sum, aes(y = Mean, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = Lower, ymax = Upper), width=0.1)

Practice

ggplot(state.sum, aes(y = Mean, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.1) +
    scale_x_discrete("Region") +
    scale_y_continuous("Illiteracy rate (%)")+
    theme_classic() +
    theme(axis.line.y = element_line(),
          axis.line.x = element_line())

Practice

library(gmodels)
state.sum = state %>% group_by(state.region) %>%
    mutate(mean_sdl(Illiteracy))
state.sum

## # A tibble: 50 × 6
## # Groups:   state.region [4]
##    Illiteracy state.region       x     y   ymin  ymax
##         <dbl> <fct>          <dbl> <dbl>  <dbl> <dbl>
##  1        2.1 South          -86.8  1.74  0.633 2.84 
##  2        1.5 West          -127.   1.02 -0.194 2.24 
##  3        1.8 West          -112.   1.02 -0.194 2.24 
##  4        1.9 South          -92.3  1.74  0.633 2.84 
##  5        1.1 West          -120.   1.02 -0.194 2.24 
##  6        0.7 West          -106.   1.02 -0.194 2.24 
##  7        1.1 Northeast      -72.4  1     0.443 1.56 
##  8        0.9 South          -75.0  1.74  0.633 2.84 
##  9        1.3 South          -81.7  1.74  0.633 2.84 
## 10        2   South          -83.4  1.74  0.633 2.84 
## 11        1.9 West          -126.   1.02 -0.194 2.24 
## 12        0.6 West          -114.   1.02 -0.194 2.24 
## 13        0.9 North Central  -89.4  0.7   0.417 0.983
## 14        0.7 North Central  -86.1  0.7   0.417 0.983
## 15        0.5 North Central  -93.4  0.7   0.417 0.983
## 16        0.6 North Central  -98.1  0.7   0.417 0.983
## 17        1.6 South          -84.8  1.74  0.633 2.84 
## 18        2.8 South          -92.3  1.74  0.633 2.84 
## 19        0.7 Northeast      -69.0  1     0.443 1.56 
## 20        0.9 South          -76.6  1.74  0.633 2.84 
## 21        1.1 Northeast      -71.6  1     0.443 1.56 
## 22        0.9 North Central  -84.7  0.7   0.417 0.983
## 23        0.6 North Central  -94.6  0.7   0.417 0.983
## 24        2.4 South          -89.8  1.74  0.633 2.84 
## 25        0.8 North Central  -92.5  0.7   0.417 0.983
## 26        0.6 West          -109.   1.02 -0.194 2.24 
## 27        0.6 North Central  -99.6  0.7   0.417 0.983
## 28        0.5 West          -117.   1.02 -0.194 2.24 
## 29        0.7 Northeast      -71.4  1     0.443 1.56 
## 30        1.1 Northeast      -74.2  1     0.443 1.56 
## 31        2.2 West          -106.   1.02 -0.194 2.24 
## 32        1.4 Northeast      -75.1  1     0.443 1.56 
## 33        1.8 South          -78.5  1.74  0.633 2.84 
## 34        0.8 North Central -100.   0.7   0.417 0.983
## 35        0.8 North Central  -82.6  0.7   0.417 0.983
## 36        1.1 South          -97.1  1.74  0.633 2.84 
## 37        0.6 West          -120.   1.02 -0.194 2.24 
## 38        1   Northeast      -77.4  1     0.443 1.56 
## 39        1.3 Northeast      -71.1  1     0.443 1.56 
## 40        2.3 South          -80.5  1.74  0.633 2.84 
## 41        0.5 North Central  -99.7  0.7   0.417 0.983
## 42        1.7 South          -86.5  1.74  0.633 2.84 
## 43        2.2 South          -98.8  1.74  0.633 2.84 
## 44        0.6 West          -111.   1.02 -0.194 2.24 
## 45        0.6 Northeast      -72.5  1     0.443 1.56 
## 46        1.4 South          -78.2  1.74  0.633 2.84 
## 47        0.6 West          -120.   1.02 -0.194 2.24 
## 48        1.4 South          -80.7  1.74  0.633 2.84 
## 49        0.7 North Central  -90.0  0.7   0.417 0.983
## 50        0.6 West          -107.   1.02 -0.194 2.24

ggplot(state.sum, aes(y = y, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.1)

Practice

library(gmodels)
state.sum = state %>% group_by(state.region) %>%
    mutate(mean_cl_boot(Illiteracy))
state.sum

## # A tibble: 50 × 6
## # Groups:   state.region [4]
##    Illiteracy state.region       x     y  ymin  ymax
##         <dbl> <fct>          <dbl> <dbl> <dbl> <dbl>
##  1        2.1 South          -86.8  1.74 1.48  2.01 
##  2        1.5 West          -127.   1.02 0.708 1.36 
##  3        1.8 West          -112.   1.02 0.708 1.36 
##  4        1.9 South          -92.3  1.74 1.48  2.01 
##  5        1.1 West          -120.   1.02 0.708 1.36 
##  6        0.7 West          -106.   1.02 0.708 1.36 
##  7        1.1 Northeast      -72.4  1    0.844 1.17 
##  8        0.9 South          -75.0  1.74 1.48  2.01 
##  9        1.3 South          -81.7  1.74 1.48  2.01 
## 10        2   South          -83.4  1.74 1.48  2.01 
## 11        1.9 West          -126.   1.02 0.708 1.36 
## 12        0.6 West          -114.   1.02 0.708 1.36 
## 13        0.9 North Central  -89.4  0.7  0.625 0.775
## 14        0.7 North Central  -86.1  0.7  0.625 0.775
## 15        0.5 North Central  -93.4  0.7  0.625 0.775
## 16        0.6 North Central  -98.1  0.7  0.625 0.775
## 17        1.6 South          -84.8  1.74 1.48  2.01 
## 18        2.8 South          -92.3  1.74 1.48  2.01 
## 19        0.7 Northeast      -69.0  1    0.844 1.17 
## 20        0.9 South          -76.6  1.74 1.48  2.01 
## 21        1.1 Northeast      -71.6  1    0.844 1.17 
## 22        0.9 North Central  -84.7  0.7  0.625 0.775
## 23        0.6 North Central  -94.6  0.7  0.625 0.775
## 24        2.4 South          -89.8  1.74 1.48  2.01 
## 25        0.8 North Central  -92.5  0.7  0.625 0.775
## 26        0.6 West          -109.   1.02 0.708 1.36 
## 27        0.6 North Central  -99.6  0.7  0.625 0.775
## 28        0.5 West          -117.   1.02 0.708 1.36 
## 29        0.7 Northeast      -71.4  1    0.844 1.17 
## 30        1.1 Northeast      -74.2  1    0.844 1.17 
## 31        2.2 West          -106.   1.02 0.708 1.36 
## 32        1.4 Northeast      -75.1  1    0.844 1.17 
## 33        1.8 South          -78.5  1.74 1.48  2.01 
## 34        0.8 North Central -100.   0.7  0.625 0.775
## 35        0.8 North Central  -82.6  0.7  0.625 0.775
## 36        1.1 South          -97.1  1.74 1.48  2.01 
## 37        0.6 West          -120.   1.02 0.708 1.36 
## 38        1   Northeast      -77.4  1    0.844 1.17 
## 39        1.3 Northeast      -71.1  1    0.844 1.17 
## 40        2.3 South          -80.5  1.74 1.48  2.01 
## 41        0.5 North Central  -99.7  0.7  0.625 0.775
## 42        1.7 South          -86.5  1.74 1.48  2.01 
## 43        2.2 South          -98.8  1.74 1.48  2.01 
## 44        0.6 West          -111.   1.02 0.708 1.36 
## 45        0.6 Northeast      -72.5  1    0.844 1.17 
## 46        1.4 South          -78.2  1.74 1.48  2.01 
## 47        0.6 West          -120.   1.02 0.708 1.36 
## 48        1.4 South          -80.7  1.74 1.48  2.01 
## 49        0.7 North Central  -90.0  0.7  0.625 0.775
## 50        0.6 West          -107.   1.02 0.708 1.36

ggplot(state.sum, aes(y = y, x = state.region)) + geom_point() +
    geom_errorbar(aes(ymin = ymin, ymax = ymax), width = 0.1)

Practice

Overlay illiteracy data onto map of US

library(mapdata)
US <- map_data("worldHires", region = "USA")
ggplot(US) +
    geom_polygon(aes(x = long, y = lat, group = group)) +
    geom_point(data = state, aes(y = y, x = x, size = Illiteracy),
               color = "red")

Practice

Overlay illiteracy data onto map of US

library(mapdata) 
US <- map_data("worldHires", region="USA")
ggplot(US) +
    geom_polygon(aes(x = long, y = lat, group = group)) +
    geom_point(data = state,aes(y = y, x = x, size = Illiteracy),
               color = "red")+
    coord_map(xlim = c(-150, -50), ylim = c(20, 60)) +
    theme_minimal()

Practice

MACNALLY <- read.csv('../data/macnally.csv',
  header = TRUE, row.names = 1, strip.white = TRUE)
head(MACNALLY)

##                 HABITAT GST EYR
## Reedy Lake        Mixed 3.4 0.0
## Pearcedale  Gipps.Manna 3.4 9.2
## Warneet     Gipps.Manna 8.4 3.8
## Cranbourne  Gipps.Manna 3.0 5.0
## Lysterfield       Mixed 5.6 5.6
## Red Hill          Mixed 8.1 4.1

Calculate the mean and standard error of GST and plot them

Practice

Calculate the mean and standard error of GST and plot mean and confidence bars

library(gmodels)
ci(MACNALLY$GST)

##   Estimate   CI lower   CI upper Std. Error 
##   4.878378   4.035292   5.721465   0.415704

MACNALLY.agg = MACNALLY %>% group_by(HABITAT) %>%
  summarize(Mean = mean(GST), Lower = ci(GST)[2], Upper = ci(GST)[3])
ggplot(MACNALLY.agg, aes(y = Mean, x = HABITAT)) +
    geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.1)+
    geom_point() + theme_classic()

Practice

You can also use ggplot’s summary

library(tidyverse)
MACNALLY.melt = MACNALLY %>%
  pivot_longer(-HABITAT, names_to = "variable",  values_to = "value")
ggplot(MACNALLY.melt, aes(y = value, x = HABITAT)) +
    stat_summary(fun.y = "mean", geom = "point")+
    stat_summary(fun.data = "mean_cl_normal", geom = "errorbar",
                 width = 0.1)+
    facet_grid(~variable)

Practice

You can also use ggplot’s summary

#and bootstrapped means..
ggplot(MACNALLY.melt, aes(y = value, x = HABITAT)) +
    stat_summary(fun.y = "mean", geom = "point")+
    stat_summary(fun.data = "mean_cl_boot", geom = "errorbar",
                 width = 0.1)+
    facet_grid(~variable)

Graphics in R

Options

Options

Packages

Graphics infrastructure

Typical ggplot template

Motivating data

ggplot - complete template

ggplot

ggplot

Overview

Overview

Layers

Layers

geom_ and stat_

geom_

geom_

Optional aesthetics mapping

primitives

geom_point

geom_point

geom_point

Example data sets

Various geometric objects

Exploring distributions

Exploring distributions

Exploring distributions

Visualising trends

Visualising trends

Visualising trends

Visualising trends

Visualising trends

Visualising trends

Visualising trends

Visualising trends

Visualising trends

Visualising trends

Visualising three dimensions

Visualising maps

Visualising uncertainty

Visualising uncertainty

Visualising uncertainty

Coordinate systems

Coordinate systems

Coordinate systems

Scales

Scales

X and Y axes

X and Y axes

X and Y axes

X and Y axes

X and Y axes

Size and shape scales

Color and fill scales

Continuous scale

Color and fill scales

Continuous scale

Colours

Colour palettes

Colour palettes

Colour palettes

Colour palettes

Facets

Facets

Facets

Facets

Facets

Arranging multiple plots

Multiple plots

Multiple plots

Multiple plots

Multiple plots

Multiple plots

Multiple plots

Themes

theme_classic

theme_bw

theme_grey

theme_minimal

theme_linedraw

`geom_` and `stat_`

`geom_`

`geom_`

`geom_point`

`geom_point`

`geom_point`

`theme_classic`

`theme_bw`

`theme_grey`

`theme_minimal`

`theme_linedraw`

`theme_light`