ggplot2

ggplot2 is based on the grammer of graphics, the idea that you can build every graph from the same few components:

• data
• aesthetic mapping
• geometric object
• statistical transformations
• scales
• coordinate system
• position adjustments
• faceting

ggplot2 cheetsheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

ggplot2 grammers

ggplot2 usage – qplot() and ggplot2()

library(ggplot2)

## Warning: package 'ggplot2' was built under R version 3.3.2

qplot()

• qplot is a shortcut designed to be familiar if you’re used to base plot().
• It’s a convenient wrapper for creating a number of different types of plots.
• It’s great to produce plots quickly, but not suitable for complex graphics.
• qplot(x, y, data, geom = “auto”)

mpg data data

str(mpg)

## Classes 'tbl_df', 'tbl' and 'data.frame':    234 obs. of  11 variables:
##  $ manufacturer: chr  "audi" "audi" "audi" "audi" ...
##  $ model       : chr  "a4" "a4" "a4" "a4" ...
##  $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr  "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr  "f" "f" "f" "f" ...
##  $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr  "p" "p" "p" "p" ...
##  $ class       : chr  "compact" "compact" "compact" "compact" ...

head(mpg)

## # A tibble: 6 × 11
##   manufacturer model displ  year   cyl      trans   drv   cty   hwy    fl
##          <chr> <chr> <dbl> <int> <int>      <chr> <chr> <int> <int> <chr>
## 1         audi    a4   1.8  1999     4   auto(l5)     f    18    29     p
## 2         audi    a4   1.8  1999     4 manual(m5)     f    21    29     p
## 3         audi    a4   2.0  2008     4 manual(m6)     f    20    31     p
## 4         audi    a4   2.0  2008     4   auto(av)     f    21    30     p
## 5         audi    a4   2.8  1999     6   auto(l5)     f    16    26     p
## 6         audi    a4   2.8  1999     6 manual(m5)     f    18    26     p
## # ... with 1 more variables: class <chr>

basic scattered plot

qplot(displ, hwy, data = mpg) 

## aesthetic: displ, hwy. data: mpg

scattered plot with color

qplot(displ, hwy, colour = class, data = mpg) 

## aesthetic: displ, hwy, class; data: mpg

scattered plot with shape

mpg_sub <- subset(mpg, class!="suv") ## qplot support a maximum of 6 shapes
qplot(displ, hwy, shape = class, data = mpg_sub) ## aesthetic: displ, hwy, class; data: mpg

scattered plot with geom = “line”

qplot(displ, hwy, data = mpg, geom = "line") 

## aesthetic: displ, hwy
## data: mpg
## Geometries: line

scattered plot with geom = “path”

qplot(displ, hwy, data = mpg, geom = "path") 

## aesthetic: displ, hwy
## data: mpg
## Geometries: path

More about geom – geom = “boxplot”

qplot(class, displ, data = mpg, geom = "boxplot") 

## aesthetic: displ, class
## data: mpg
## Geometries: boxplot

More about geom – geom = “jitter”

qplot(class, displ, data = mpg, geom = "jitter") 

## aesthetic: displ, class
## data: mpg
## Geometries: jitter

More about geom – geom = c(“jitter”, “boxplot”)

qplot(class, displ, data = mpg, geom = c("boxplot", "jitter")) 

## aesthetic: displ, class
## data: mpg
## Geometries: jitter

More about geom – geom = “histogram”

qplot(displ, data = mpg, geom = "histogram") 

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## aesthetic: displ
## data: mpg
## Geometries: histogram

More about geom – geom = “density”

qplot(displ, data = mpg, geom = "density") 

## aesthetic: displ
## data: mpg
## Geometries: density

Facets

qplot(displ, data = mpg, geom = "density", facets = ~class) 

## aesthetic: displ
## data: mpg
## Geometries: density
## facets: ~class

ggplot() - graphics are added up by different layers

Compared to qplot(), it’s easier to use multiple dataset in ggplot().

• The basic usage is a combination of ggplot(), aes() and some geom function.
• ggplot() initializes a ggplot object. It can declare input data and a set of aesthetics.
• aes() describes how variables mapped to visual properties.
• geom function describe how the data will be visualized (point, line, histogram),
• Use “+” to add layers.
  • For example, ggplot(data) + aes(x = a, y = b) + geom point().

Aesthetics

• Colour related aesthetics: colour, fill and alpha
• Group related aesthetics: group
• Differentiation related aesthetics: linetype, size, shape
• Position related aesthetics: x, y, xmin, xmax, ymin, ymax, xend, yend

ggplot example

ggplot(mpg) + aes(x=displ, y=hwy, size=cyl) + geom_point()

ggplot: combine layers

myggplot <- ggplot(mpg) + aes(x=displ, y=hwy, color=cyl)
myggplot + geom_point()

ggplot: geom_line by group

ggplot(data = mpg, aes(displ, hwy, colour=class)) + geom_point(aes(size=cyl)) + geom_line(aes(group = class))

Geom functions

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

##  [1] "geom_abline"     "geom_area"       "geom_bar"       
##  [4] "geom_bin2d"      "geom_blank"      "geom_boxplot"   
##  [7] "geom_col"        "geom_contour"    "geom_count"     
## [10] "geom_crossbar"   "geom_curve"      "geom_density"   
## [13] "geom_density_2d" "geom_density2d"  "geom_dotplot"   
## [16] "geom_errorbar"   "geom_errorbarh"  "geom_freqpoly"  
## [19] "geom_hex"        "geom_histogram"  "geom_hline"     
## [22] "geom_jitter"     "geom_label"      "geom_line"      
## [25] "geom_linerange"  "geom_map"        "geom_path"      
## [28] "geom_point"      "geom_pointrange" "geom_polygon"   
## [31] "geom_qq"         "geom_quantile"   "geom_raster"    
## [34] "geom_rect"       "geom_ribbon"     "geom_rug"       
## [37] "geom_segment"    "geom_smooth"     "geom_spoke"     
## [40] "geom_step"       "geom_text"       "geom_tile"      
## [43] "geom_violin"     "geom_vline"

smooth by group 1

ggplot(data = mpg, aes(displ, hwy, colour=class)) + geom_point() + geom_smooth(aes(group = class), method="lm", se = F, size = 1) 

smooth by group 2

ggplot(data = mpg, aes(displ, hwy, colour=class)) + geom_smooth(aes(group = class), method="lm", se = T, size = 2) 

ggplot() boxplot

mpgbox <- ggplot(mpg, aes(class, hwy)) + geom_boxplot(aes(fill=class))
mpgbox

ggplot() histogram basic

ggplot(mpg, aes(x = hwy)) + geom_histogram(binwidth = 3, fill = "steelblue")

ggplot() histogram fill by color

ggplot(mpg, aes(x = hwy)) + geom_histogram(binwidth = 3, aes(fill = class))

ggplot() histogram facets by group

ggplot(mpg, aes(x = hwy, fill = class)) + geom_histogram(binwidth = 3) + facet_grid(class ~ .)

bar plot with standard deviation

mpgSummary <- data.frame(class = with(mpg, tapply(class, class, unique)), 
  meanDispl = with(mpg, tapply(displ, class, mean)),
                         sdDispl = with(mpg, tapply(displ, class, sd)))

ggplot(mpgSummary, aes(x=class, y=meanDispl, fill=class)) + 
  geom_bar(position=position_dodge(), stat="identity",
           colour="black", # Use black outlines,
           size=.3) +      # Thinner lines
  geom_errorbar(aes(ymin=meanDispl-sdDispl, ymax=meanDispl+sdDispl),
                size=.3,    # Thinner lines
                width=.2,
                position=position_dodge(.9))

facet

• facet_grid: lay out panels in a grid.
• facet_null: the default setting (single panel).
• facet_wrap: wrap a 1d ribbon of panels into proper 2d display

ggplot() histogram facet_wrap

ggplot(mpg, aes(x = hwy, fill = class)) + geom_histogram(binwidth = 3) + facet_wrap(~ class)

facet_null: back to single panel

ggplot(mpg, aes(x = hwy, fill = class)) + geom_histogram(binwidth = 3) + facet_grid(class ~ .)  + facet_null()

bar plot

ggplot(mpg, aes(class, fill=as.factor(cyl))) + geom_bar(position="dodge") #side by side

Stat transformation

• stat ecdf: Empirical Cumulative Density Function.
• stat ellipse: Plot data ellipses.
• stat function: Superimpose a function.
• stat identity: Identity statistic.
• stat qq: Calculation for quantile-quantile plot.
• stat summary 2d: Bin and summarise in 2d.
• stat unique: Remove duplicates.

empirical CDF

df <- data.frame(x = rnorm(1000))
ggplot(df, aes(x)) + stat_ecdf(geom = "step")

stat_function

n <- 100
set.seed(32611)
df <- data.frame(
  x = rnorm(n)
)
x <- df$x
base <- ggplot(df, aes(x)) + geom_density()
base + stat_function(fun = dnorm, colour = "red") + xlim(c(-3,3))

stat_ellipse

ggplot(mpg, aes(x = displ, y = hwy)) + geom_point() +   stat_ellipse()

stat_ellipse by group

ggplot(mpg, aes(x = displ, y = hwy, color=displ > 4)) + geom_point() +   stat_ellipse()

Coordinate

• coord_cartesian: Cartesian coordinates.
• coord_fixed: control_fixed ratio between x and y scales.
• coord_flip: flip x and y.
• coord_map: map projections.
• coord_polar: polar coordinates.
• coord_trans: transformed cartesian coordinate system.

Our old friend mpg

p <- ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  geom_smooth()

p

## `geom_smooth()` using method = 'loess'

Setting the limits on the coordinate system performs a visual zoom.

p + coord_cartesian(xlim = c(3, 5), expand = FALSE)

## `geom_smooth()` using method = 'loess'

Setting the limits on a scale converts all values outside the range to NA.

p + xlim(3, 5)

## `geom_smooth()` using method = 'loess'

## Warning: Removed 136 rows containing non-finite values (stat_smooth).

## Warning: Removed 136 rows containing missing values (geom_point).

#the same as p + scale_x_continuous(limits = c(325, 500))

resize the plot

p <- ggplot(mpg, aes(displ, hwy)) +  geom_point()
p + coord_fixed(ratio = 0.5)

p + coord_fixed(ratio = 0.1)

flip x and y

ggplot(mpg, aes(class, hwy)) +
  geom_boxplot() +
  coord_flip()

draw a world map

world <- map_data("world")

## Warning: package 'maps' was built under R version 3.3.2

worldmap <- ggplot(world, aes(x=long, y=lat, group=group)) +
  geom_path() +
  scale_y_continuous(breaks = (-2:2) * 30) +
  scale_x_continuous(breaks = (-4:4) * 45)

worldmap

polar coordinte for pie chart

pie <- ggplot(mpg, aes(x = factor(1), fill = factor(cyl))) +
  geom_bar(width = 1)
pie + coord_polar(theta = "y")

Theme

• theme_grey: signature ggplot2 theme.
• theme_bw:dark-on-light theme.
• theme_linedraw: flip x and y.
• theme_light: light grey background.
• theme_dark: dark background.
• theme_minimal: no background annotations.
• theme_classic: classic-looking theme.
• theme_void: empty theme.

examples on different themes

p <- ggplot(mpg) + geom_point(aes(x = displ, y = hwy,
                                     colour=factor(cyl))) + facet_wrap(~class)
p

More about theme

p <- ggplot(mpg) + geom_point(aes(x = displ, y = hwy,
                                     colour=factor(cyl))) 
p + theme(text = element_text(size=20),
        axis.text.x = element_text(angle=90, hjust=1)) 

labels

p <- ggplot(mpg) + geom_point(aes(x = displ, y = hwy,
                                     colour=factor(cyl))) 
p + 
  labs(title = "New plot title", x = "New x label", y = "New y label")

## more please check ?labs

How to learn more about ggplot2

1. ggplot2 cheetsheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf
2. google

knitr::purl("Rgraph_ggplot2.rmd", output = "Rgraph_ggplot2.R ", documentation = 2)

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## output file: Rgraph_ggplot2.R

## [1] "Rgraph_ggplot2.R "