Graphics in R

The R language has extensive graphical capabilities.

Graphics in R may be created by many different methods including base graphics and more advanced plotting packages such as lattice.

The ggplot2 package was created by Hadley Wickham and provides a intuitive plotting system to rapidly generate publication quality graphics.

ggplot2 builds on the concept of the “Grammar of Graphics” (Wilkinson 2005, Bertin 1983) which describes a consistent syntax for the construction of a wide range of complex graphics by a concise description of their components.

Why use ggplot2

The structured syntax and high level of abstraction used by ggplot2 should allow for the user to concentrate on the visualisations instead of creating the underlying code.

On top of this central philosophy ggplot2 has:

Increased flexible over many plotting systems.
An advanced theme system for professional/publication level graphics.
Large developer base – Many libraries extending its flexibility.
Large user base – Great documentation and active mailing list.

Grammar of Graphics

How to build a plot from its components.

How ggplot2 builds a plot.

Overview of example code for the ggplot2 scatter plot.

ggplot(data = <default data set>, 
       aes(x = <default x axis variable>,
           y = <default y axis variable>,
           ... <other default aesthetic mappings>),
       ... <other plot defaults>) +

       geom_scatter(aes(size = <size variable for this geom>, 
                      ... <other aesthetic mappings>),
                  data = <data for this point geom>,
                  stat = <statistic string or function>,
                  position = <position string or function>,
                  color = <"fixed color specification">,
                  <other arguments, possibly passed to the _stat_ function) +

  scale_<aesthetic>_<type>(name = <"scale label">,
                     breaks = <where to put tick marks>,
                     labels = <labels for tick marks>,
                     ... <other options for the scale>) +
  
  ggtitle("Graphics/Plot")+
  xlab("Weight")+
  ylab("Height")+

  theme(plot.title = element_text(colour = "gray"),
        ... <other theme elements>)

What users are required to specify in ggplot2 to build a plot.

Actual code for the ggplot2 scatter plot.

ggplot(data=patients_clean,
       aes(y=Weight,x=Height,colour=Sex,
           size=BMI,shape=Pet))
+geom_point()

Getting started with ggplot2

Earlier we have been working with the patients’ dataset to create a “clean and tidy” dataset.

Now we will use a cleaned dataset to demonstrate some of the plotting capabilities of ggplot2.

library(tidyr)
library(ggplot2)
library(dplyr)
library(stringr)
library(lubridate)

patients_clean <- read.delim("patients_clean_ggplot2.txt",sep="\t")
knitr:::kable(head(patients_clean))

ID	Name	Race	Age	Sex	Smokes	Height	Weight	Birth	State	Pet	Grade_Level	Died	Count	Date	Grade	BMI	Overweight
Patient-001	Demetrius	White	44	Male	Smoker	176.4791	77.07070	1972-02-11	New York	Cat	1	TRUE	-0.7440011	2015-03-04	1	24.74586	FALSE
Patient-002	Rosario	White	43	Male	Smoker	174.7485	83.26835	1972-07-22	Florida	Cat	1	TRUE	-1.0714531	2015-03-04	1	27.26799	TRUE
Patient-003	Julio	Black	44	Male	Non-Smoker	173.9449	85.63231	1971-11-23	Connecticut	Dog	2	FALSE	-0.8215636	2015-03-04	2	28.30182	TRUE
Patient-004	Lupe	White	44	Male	Non-Smoker	180.0289	88.72032	1971-10-03	Massachusetts	Dog	1	TRUE	0.4976671	2015-03-04	1	27.37403	TRUE
Patient-005	Lavern	Cat	43	Male	Non-Smoker	178.6365	97.05405	1972-11-23	Kansas	Cat	3	FALSE	-1.2099217	2015-03-04	3	30.41397	TRUE
Patient-006	Bernie	Native	43	Female	Non-Smoker	159.7778	68.31247	1972-07-30	Illinois	Cat	2	FALSE	0.5241618	2015-03-04	2	26.75882	TRUE

Our first ggplot2 graph

As seen above, in order to produce a ggplot2 graph we need a minimum of:-

Data to be used in graph
Mappings of data to the graph (aesthetic mapping)
What type of graph we want to use (The geom to use).

In the code below we define the data as our cleaned patients data frame.

pcPlot <- ggplot(data=patients_clean)
class(pcPlot)

## [1] "gg"     "ggplot"

pcPlot$data[1:4,]

##            ID      Name  Race Age  Sex     Smokes   Height   Weight
## 1 Patient-001 Demetrius White  44 Male     Smoker 176.4791 77.07070
## 2 Patient-002   Rosario White  43 Male     Smoker 174.7485 83.26835
## 3 Patient-003     Julio Black  44 Male Non-Smoker 173.9449 85.63231
## 4 Patient-004      Lupe White  44 Male Non-Smoker 180.0289 88.72032
##        Birth         State Pet Grade_Level  Died      Count       Date
## 1 1972-02-11      New York Cat           1  TRUE -0.7440011 2015-03-04
## 2 1972-07-22       Florida Cat           1  TRUE -1.0714531 2015-03-04
## 3 1971-11-23   Connecticut Dog           2 FALSE -0.8215636 2015-03-04
## 4 1971-10-03 Massachusetts Dog           1  TRUE  0.4976671 2015-03-04
##   Grade      BMI Overweight
## 1     1 24.74586      FALSE
## 2     1 27.26799       TRUE
## 3     2 28.30182       TRUE
## 4     1 27.37403       TRUE

Now we can see that we have gg/ggplot object (pcPlot) and in this the data has been defined.

Important information on how to map the data to the visual properties (aesthetics) of the plot as well as what type of plot to use (geom) have however yet to specified.

pcPlot$mapping

## * NULL ->

pcPlot$theme

## list()

pcPlot$layers

## list()

The information to map the data to the plot can be added now using the aes() function.

pcPlot <- ggplot(data=patients_clean)

pcPlot <- pcPlot+aes(x=Height,Weight)

pcPlot$mapping

## $x
## Height
## 
## $y
## Weight

pcPlot$theme

## list()

pcPlot$layers

## list()

But we are still missing the final component of our plot, the type of plot to use (geom).

Below the geom_point function is used to specify a point plot, a scatter plot of Height values on the x-axis versus Weight values on the y values.

pcPlot <- ggplot(data=patients_clean)

pcPlot <- pcPlot+aes(x=Height,Weight)
pcPlot <- pcPlot+geom_point()

pcPlot$mapping

## $x
## Height
## 
## $y
## Weight

pcPlot$theme

## list()

pcPlot$layers

## [[1]]
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity

Now we have all the components of our plot, we need we can display the results.

pcPlot

More typically, the data and aesthetics are defined within ggplot function and geoms applied afterwards.

pcPlot <- ggplot(data=patients_clean,
                 mapping=aes(x=Height,Weight))
pcPlot+geom_point()

Geoms - Plot types

As we have seen, an important element of a ggplot is the geom used. Following the specification of data, the geom describes the type of plot used.

Several geoms are available in ggplot2:-

geom_point() - Scatter plots
geom_line() - Line plots
geom_smooth() - Fitted line plots
geom_bar() - Bar plots
geom_boxplot() - Boxplots
geom_jitter() - Jitter to plots
geom_hist() - Histogram plots
geom_density() - Density plots
geom_text() - Text to plots
geom_errorbar() - Errorbars to plots
geom_violin() - Violin plots

Geoms - Line plots

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Height,Weight))

pcPlot_line <- pcPlot+geom_line() 

pcPlot_line

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Height,Weight))

pcPlot_smooth <- pcPlot+geom_smooth() 

pcPlot_smooth

Geoms - Bar and frequency plots

pcPlot <- ggplot(data=patients_clean[1:3,],
        mapping=aes(x=ID,y=Weight))

pcPlot_bar <- pcPlot+geom_bar(stat="identity") 

pcPlot_bar

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Height))

pcPlot_hist <- pcPlot+geom_histogram() 

pcPlot_hist

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

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Height))

pcPlot_density <- pcPlot+geom_density() 

pcPlot_density

Geoms - Box and violin plots

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Sex,y=Height))

pcPlot_boxplot <- pcPlot+geom_boxplot() 

pcPlot_boxplot

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Sex,y=Height))

pcPlot_violin <- pcPlot+geom_violin() 

pcPlot_violin

An overview of geoms and thier arguments can be found at ggplot2 documentation or within the ggplot2 cheatsheet.

-ggplot2 documentation

-ggplot2 Cheatsheet

Aesthetics

In order to change the property on an aesthetic of a plot into a constant value (e.g. set colour of all points to red) we can supply the colour argument to the geom_point() function.

pcPlot <- ggplot(data=patients_clean,
                 mapping=aes(x=Height,y=Weight))
pcPlot+geom_point(colour="red")

As we discussed earlier however, ggplot2 makes use of aesthetic mappings to assign variables in the data to the properties/aesthetics of the plot. This allows the properties of the plot to reflect variables in the data dynamically.

In these examples we supply additional information to the aes() function to define what information to display and how it is represented in the plot.

First we can recreate the plot we saw earlier.

pcPlot <- ggplot(data=patients_clean,
                 mapping=aes(x=Height,y=Weight))
pcPlot+geom_point()

Now we can adjust the aes mapping by supplying an argument to the colour parameter in the aes function. (Note that ggplot2 accepts “color” or “colour” as parameter name)

This simple adjustment allows for identifaction of the separation between male and female measurements for height and weight.

pcPlot <- ggplot(data=patients_clean,
                 mapping=aes(x=Height,y=Weight,colour=Sex))
pcPlot+geom_point()

Similarly the shape of points may be adjusted.

pcPlot <- ggplot(data=patients_clean,
                 mapping=aes(x=Height,y=Weight,shape=Sex))
pcPlot+geom_point()

The aesthetic mappings may be set directly in the geom_points() function as previously when specifying red. This can allow the same ggplot object to be used by different aesethetic mappings and varying geoms

pcPlot <- ggplot(data=patients_clean)
pcPlot+geom_point(aes(x=Height,y=Weight,colour=Sex))

pcPlot+geom_point(aes(x=Height,y=Weight,colour=Smokes))

pcPlot+geom_point(aes(x=Height,y=Weight,colour=Smokes,shape=Sex))

pcPlot+geom_violin(aes(x=Sex,y=Height,fill=Smokes))

Again, for a comprehensive list of parameters and aesthetic mappings used in geom_type functions see the ggplot2 documentation for individual geoms by using ?geom_type

?geom_point

or visit the ggplot2 documentations pages and cheatsheet

Facets

One very useful feature of ggplot is faceting. This allows you to produce plots subset by variables in your data.

To facet our data into multiple plots we can use the facet_wrap or facet_grid function specifying the variable we split by.

The facet_grid function is well suited to splitting the data by two factors.

Here we can plot the data with the Smokes variable as rows and Sex variable as columns.

facet_grid(Rows~Columns)

pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(Smokes~Sex)

To split by one factor we can apply the facet_grid() function ommiting the variable before the “~”" to facet along columns in plot.

facet_grid(~Columns)

pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(~Sex)

To split along rows in plot, the variable is placed before the “~.”.

facet_grid(Rows~.)

pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_grid(Sex~.)

The facet_wrap() function offers a less grid based structure but is well suited to faceting data by one variable.

For facet_wrap() we follow as similar syntax to facet_grid()

pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_wrap(~Smokes)

For more complex faceting both facet_grid and facet_wrap can accept combinations of variables.

Using facet_wrap

pcPlot <- ggplot(data=patients_clean,aes(x=Height,y=Weight,colour=Sex))+geom_point()
pcPlot + facet_wrap(~Pet+Smokes+Sex)

Or in a nice grid format using facet_grid() and the Smokes variable against a combination of Gender and Pet.

pcPlot + facet_grid(Smokes~Sex+Pet)

Exercise set 1

Link_to_exercises

Link_to_exercises_with_images

Link_to_Rmarkdown template

Link_to_answers

Scales

Scales and their legends have so far been handled using ggplot2 defaults. ggplot2 offers functionality to have finer control over scales and legends using the scale methods.

Scale methods are divided into functions by combinations of

the aesthetics they control.
the type of data mapped to scale.

scale_aesthetic_type

Try typing in scale_ then tab to autocomplete. This will provide some examples of the scale functions available in ggplot2.

Although different scale functions accept some variety in their arguments, common arguments to scale functions include -

name - The axis or legend title
limits - Minimum and maximum of the scale
breaks - Label/tick positions along an axis
labels - Label names at each break

Controlling the X and Y scale.

Both continous and discrete X/Y scales can be controlled in ggplot2 using the

scale_(x/y)_(continous/discrete)

In this example we control the continuous sale on the x-axis by providing a name, X-axis limits, the positions of breaks (ticks/labels) and the labels to place at breaks.

pcPlot +
  geom_point() +
  facet_grid(Smokes~Sex)+
  scale_x_continuous(name="height ('cm')",
                     limits = c(100,200),
                     breaks=c(125,150,175),
                     labels=c("small","justright","tall"))

Similary control over discrete scales is shown below.

pcPlot <- ggplot(data=patients_clean,aes(x=Sex,y=Height))
pcPlot +
  geom_violin(aes(x=Sex,y=Height)) +
  scale_x_discrete(labels=c("Girls","Guys"))

Multiple X/Y scales can be combined to give full control of axis marks.

pcPlot <- ggplot(data=patients_clean,aes(x=Sex,y=Height,fill=Smokes))
pcPlot +
  geom_violin(aes(x=Sex,y=Height)) +
  scale_x_discrete(labels=c("Guys","Girls"))+
  scale_y_continuous(breaks=c(160,180),labels=c("Petite","Tall"))

Controlling other scales.

When using fill,colour,linetype, shape, size or alpha aesthetic mappings the scales are automatically selected for you and the appropriate legends created.

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,colour=Sex))
pcPlot + geom_point(size=4)

In the above example the discrete colours for the Sex variable was selected by default.

Manual discrete colour scale

Manual control of discrete variables can be performed using scale_aes_Of_Interest_manual with the values parameter. Additionally in this example an updated name for the legend is provided.

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,colour=Sex))
pcPlot + geom_point(size=4) + 
  scale_color_manual(values = c("Green","Purple"),
                     name="Gender")

Colorbrewer for discrete colour scale

Here we have specified the colours to be used (hence the manual) but when the number of levels to a variable are high this may be impractical and often we would like ggplot2 to choose colours from a scale of our choice.

The brewer set of scale functions allow the user to make use of a range of palettes available from colorbrewer.

Diverging

BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral

Qualitative

Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3

Sequential

Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu, YlOrBr, YlOrRd

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,colour=Pet))
pcPlot + geom_point(size=4) + 
  scale_color_brewer(palette = "Set2")

For more details on palette sizes and styles visit the colorbrewer website and ggplot2 reference page.

Continous colour scales

So far we have looked a qualitative scales but ggplot2 offers much functionality for continuous scales such as for size, alpha (transparancy), colour and fill.

scale_alpha_continuous() - For Transparancy
scale_size_continuous() - For control of size.

Both these functions accept the range of alpha/size to be used in plotting.

Below the range of alpha to be used in plot is limited to between 0.5 and 1

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,alpha=BMI))
pcPlot + geom_point(size=4) + 
  scale_alpha_continuous(range = c(0.5,1))

Below the range of sizes to be used in plot is limited to between 3 and 6

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point(alpha=0.8) + 
  scale_size_continuous(range = c(3,6))

The limits of the scale can also be controlled but it is important to note data outside of scale is removed from plot.

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3,6), limits = c(25,40))

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

What points of scale to be labeled and labels text can also be controlled.

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,size=BMI))
pcPlot + geom_point() + scale_size_continuous(range = c(3,6) ,breaks=c(25,30),labels=c("Good","Good but not 25"))

Control of colour/fill scales can be best achieved through the gradient subfunctions of scale.

scale_(colour/fill)_gradient - 2 colour gradient (eg. low to high BMI)
scale_(colour/fill)_gradient2 - Diverging colour scale with a midpoint colour (e.g. Down, No Change, Up)

Both functions take a common set of arguments:-

low - Colour for low end of gradient scale
high - Colour for high end of gradient scale.
na.value - Colour for any NA values.

An example using scale_colour_gradient below sets the low and high end colours to White and Red respectively

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) + 
  scale_colour_gradient(low = "White",high="Red")

Similarly we can use the scale_colour_gradient2 function which allows for the specification of a midpoint value and its associated colour.

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) + 
  scale_colour_gradient2(low = "Blue",mid="Black",high="Red",midpoint = median(patients_clean$BMI))

As with previous continous scales, limits and custom labels in scale legend can be added.

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,colour=BMI))
pcPlot + geom_point(size=4,alpha=0.8) + scale_colour_gradient2(low = "Blue",
                         mid="Black",
                         high="Red",
                         midpoint = median(patients_clean$BMI),
                         breaks=c(25,30),labels=c("Low","High"),
                         name="Body Mass Index")

Multiple scales may be combined to create high customisable plots and scales

pcPlot <- ggplot(data=patients_clean,
                 aes(x=Height,y=Weight,colour=BMI,shape=Sex))
pcPlot + geom_point(size=4,alpha=0.8)+ scale_shape_discrete(name="Gender") +scale_colour_gradient2(low = "Blue",mid="Black",high="Red",midpoint = median(patients_clean$BMI),
                         breaks=c(25,30),labels=c("Low","High"),
                         name="Body Mass Index")

Statistical transformations.

In ggplot2 many of the statistical transformations are performed without any direct specification e.g. geom_histogram() will use stat_bin() function to generate bin counts to be used in plot.

An example of statistical methods in ggplot2 which are very useful include the stat_smooth() and stat_summary() functions.

The stat_smooth() function can be used to fit a line to the data being displayed.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))
pcPlot+geom_point()+stat_smooth()

By default a “loess” smooth line is plotted by stat_smooth. Other methods available include lm, glm,gam,rlm.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))
pcPlot+geom_point()+stat_smooth(method="lm")

A useful feature of ggplot2 is that it uses previously defined grouping when performing smoothing.

If colour by Sex is an aesthetic mapping then two smooth lines are drawn, one for each sex.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height,colour=Sex))
pcPlot+geom_point()+stat_smooth(method="lm")

This behaviour can be overridden by specifying an aes within the stat_smooth() function and setting inherit.aes to FALSE.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height,colour=Sex))
pcPlot+geom_point()+stat_smooth(aes(x=Weight,y=Height),method="lm",inherit.aes = F)

Another useful method is stat_summary() which allows for a custom statistical function to be performed and then visualised.

The fun.y parameter specifies a function to apply to the y variables for every value of x.

In this example we use it to plot the quantiles of the Female and Male Height data

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Sex,y=Height))+geom_jitter()
pcPlot+stat_summary(fun.y=quantile,geom="point",colour="purple",size=8)

Themes

Themes specify the details of data independent elements of the plot. This includes titles, background colour, text fonts etc.

The graphs created so far have all used the default themes ,theme_grey(), but ggplot2 allows for the specification of theme used.

Predefined themes

Predefined themes can be applied to a ggplot2 object using a family of functions theme_style()

In the example below the minimal theme is applied to the scatter plot seen earlier.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme_minimal()

Several predifined themes are available within ggplot2 including:

theme_bw
theme_classic
theme_dark
theme_gray
theme_light
theme_linedraw
theme_minimal

Packages such as ggthemes also contain many useful collections of predined theme_style functions.

Creating your themes

As well as making use of predifened theme styles, ggplot2 allows for control over the attributes and elements within a plot through a collection of related functions and attributes.

theme() is the global function used to set attributes for the collections of elements/components making up the current plot.

Within the theme functions there are 4 general graphic elements which may be controlled:-

rect
line
text
title

and 5 groups of related elements:-

axis
legend
strip
panel (plot panel)
plot (Global plot parameters)

These elements may be specified by the use of their appropriate element functions including:

element_line()
element_text()
element_rect()

and additionally element_blank() to set an element to “blank”

A detailed description of controlling elements within a theme can be seen at the ggplot2 vignette and by typing ?theme into the console.

ggplot2 themes

To demonstrate customising a theme, in the example below we alter one element of theme. Here we will change the text colour for the plot.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme(
            text = element_text(colour="red"),
            axis.text = element_text(colour="red")
           )

Note because we are changing a text element we use the element_text() function.

A detailed description of which elements are available and their associated element functions can be found by typing ?theme.

If we wished to set the y-axis label to be at an angle we can adjust that as well.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+theme(
            text = element_text(colour="red"),
            axis.text = element_text(colour="red"),
            axis.title.y = element_text(angle=0)
           )

Finally we may wish to remove axis line, set the background of plot panels to be white and give the strips (title above facet) a cyan background colour.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+
  geom_point()+
  facet_grid(Sex~Smokes)
pcPlot+theme(
            text = element_text(colour="red"),
            axis.text = element_text(colour="red"),
            axis.title.y = element_text(angle=0),
            axis.line = element_line(linetype = 0),
            panel.background=element_rect(fill="white"),
            strip.background=element_rect(fill="cyan")
           )

+ and %+replace%

When altering themes we have been using the + operator to add themes as we would adding geoms,scales and stats.

When using the + operator

Themes elements specified in new scheme replace elements in old theme
Theme elements in the old theme which have not been specified in new theme are maintained.

This makes the + operator useful for building up from old themes.

In the example below, we maintain all elements set by theme_bw() but overwrite the theme element attribute of the colour of text.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+
  theme_bw()+
  theme(text = element_text(colour="red"))

The consequence can be seen comparing the effect of theme() on a plot with a default theme or theme_minimal.

Since the default theme, theme_grey() contains a specification for axis.text colour, i will not replace it with “+” operator.

pcPlot+
  theme(text = element_text(colour="red"))

pcPlot+
  theme_minimal()+
  theme(text = element_text(colour="red"))

In contrast %+replace% replaces all elements within a theme regardless of whether they have been previously specfied in old theme.

When using the %+replace% operator

Theme elements specified in new scheme replace elements in old theme
Theme elements in the old theme which have not been specified in new theme are also replaced by blank theme elements.

  oldTheme <- theme_bw()
  newTheme_Plus <- theme_bw() +
  theme(text = element_text(colour="red"))
  newTheme_Replace <- theme_bw() %+replace%
  theme(text = element_text(colour="red"))
  oldTheme$text

## List of 10
##  $ family    : chr ""
##  $ face      : chr "plain"
##  $ colour    : chr "black"
##  $ size      : num 12
##  $ hjust     : num 0.5
##  $ vjust     : num 0.5
##  $ angle     : num 0
##  $ lineheight: num 0.9
##  $ margin    :Classes 'margin', 'unit'  atomic [1:4] 0 0 0 0
##   .. ..- attr(*, "valid.unit")= int 8
##   .. ..- attr(*, "unit")= chr "pt"
##  $ debug     : logi FALSE
##  - attr(*, "class")= chr [1:2] "element_text" "element"

  newTheme_Plus$text

## List of 10
##  $ family    : chr ""
##  $ face      : chr "plain"
##  $ colour    : chr "red"
##  $ size      : num 12
##  $ hjust     : num 0.5
##  $ vjust     : num 0.5
##  $ angle     : num 0
##  $ lineheight: num 0.9
##  $ margin    :Classes 'margin', 'unit'  atomic [1:4] 0 0 0 0
##   .. ..- attr(*, "valid.unit")= int 8
##   .. ..- attr(*, "unit")= chr "pt"
##  $ debug     : logi FALSE
##  - attr(*, "class")= chr [1:2] "element_text" "element"

  newTheme_Replace$text

## List of 10
##  $ family    : NULL
##  $ face      : NULL
##  $ colour    : chr "red"
##  $ size      : NULL
##  $ hjust     : NULL
##  $ vjust     : NULL
##  $ angle     : NULL
##  $ lineheight: NULL
##  $ margin    : NULL
##  $ debug     : NULL
##  - attr(*, "class")= chr [1:2] "element_text" "element"

This means that %+replace% is most useful when creating new themes.

theme_get and theme_set

Adding titles for plot and labels.

So far no plot titles have been specified. Plot titles can be specified using the labs functions.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+geom_point()
pcPlot+labs(title="Weight vs Height",y="Height (cm)")

or specified using the ggtitle and xlab/ylab functions.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Height,y=Weight))+geom_point()
pcPlot+ggtitle("Weight vs Height")+ylab("Height (cm)")

Saving plots

Plots produced by ggplot can be saved from the interactive viewer as with standard plots.

The ggsave() function allows for additional arguments to be specified including the type, resolution and size of plot.

By default ggsave() will use the size of your current graphics window when saving plots so it may be important to specify width and height arguments desired.

pcPlot <- ggplot(data=patients_clean,
        mapping=aes(x=Weight,y=Height))+geom_point()
ggsave(pcPlot,filename = "anExampleplot.png",width = 15,height = 15,units = "cm")

Exercise set 2