Hands-On Ex 1

Layered Grammar of Graphics: ggplot2 methods

1.1 Loading of the Required Packages

pacman::p_load(tidyverse)

1.2 Loading the data set

For this exercise, we will be using the Year End Examination Grades of a cohort of Primary 3 students from a local school.

We will use the read_csv function of readr/tidyverse to read the provided data.

exam_data <- read_csv("data/Exam_data.csv")

head (exam_data)

# A tibble: 6 × 7
  ID         CLASS GENDER RACE    ENGLISH MATHS SCIENCE
  <chr>      <chr> <chr>  <chr>     <dbl> <dbl>   <dbl>
1 Student321 3I    Male   Malay        21     9      15
2 Student305 3I    Female Malay        24    22      16
3 Student289 3H    Male   Chinese      26    16      16
4 Student227 3F    Male   Chinese      27    77      31
5 Student318 3I    Male   Malay        27    11      25
6 Student306 3I    Female Malay        31    16      16

We use the head() to take a look at the columns - which comprise seven columns - 4 categorical (ID, Class, Gender and Race) and 3 continuous data (Scores for English, Maths and Science).

1.3 Introducing ggplot

We will first look at ggplot.

ggplot cheat sheet:

1.3.1 R Graphics vs ggplot

Comparing the choice of using R Graphics vs ggplot:

R Graphics

hist(exam_data$MATHS)

ggplot2

ggplot(data=exam_data, aes(x = MATHS)) +
  geom_histogram(bins=10, 
                 boundary = 100,
                 color="black", 
                 fill="grey") +
  ggtitle("Distribution of Maths scores")

Good to learn ggplot2 as it provides a more powerful tool that is customisable, although it means learning its way of language first. That said, we all need to do more customisation at some point in time - so just jump into it!

1.4 Grammar of Graphics

Grammar of Graphics is a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. It was introduced by Leland Wilkinson (1999) Grammar of Graphics, Springer. The grammar of graphics is an answer to a question:

What is a statistical graphic?

In the nutshell, Grammar of Graphics defines the rules of structuring mathematical and aesthetic elements into a meaningful graph.

There are two principles in Grammar of Graphics, they are:

• Graphics = distinct layers of grammatical elements

• Meaningful plots through aesthetic mapping

A good grammar of graphics will allow us to gain insight into the composition of complicated graphics, and reveal unexpected connections between seemingly different graphics (Cox 1978). It also provides a strong foundation for understanding a diverse range of graphics. Furthermore, it may also help guide us on what a well-formed or correct graphic looks like, but there will still be many grammatically correct but nonsensical graphics.

ggplot2 is an implementation of Leland Wilkinson’s Grammar of Graphics. Figure below shows the seven grammars of ggplot2.

A short description of each building block are as follows:

• Data: The dataset being plotted.

• Aesthetics take attributes of the data and use them to influence visual characteristics, such as position, colours, size, shape, or transparency.

• Geometrics: The visual elements used for our data, such as point, bar or line.

• Facets split the data into subsets to create multiple variations of the same graph (paneling, multiple plots).

• Statistics, statiscal transformations that summarise data (e.g. mean, confidence intervals).

• Coordinate systems define the plane on which data are mapped on the graphic.

• Themes modify all non-data components of a plot, such as main title, sub-title, y-aixs title, or legend background.

1.5 Essential elements of ggplot2

Lets break down the elements of ggplot2.

When we use ggplot() , it initialises a ggplot object and creates a blank canvas. The data argument defines the dataset that we want to plot. If the dataset is not already a data.frame, it will be converted to one by fortify().

ggplot(data=exam_data)

1.6 Aesthetic mappings

Refer to this linkfor more info.

The aesthetic mappings take attributes of the data and and use them to influence visual characteristics, such as position, colour, size, shape, or transparency. Each visual characteristic can thus encode an aspect of the data and be used to convey information.

All aesthetics of a plot are specified in the aes() function call (in later part of this lesson, you will see that each geom layer can have its own aes specification)

Code chunk below adds the aesthetic element into the plot.

ggplot(data=exam_data, 
       aes(x= MATHS))

Note

• ggplot includes the x-axis and the axis’s label.

1.7 geom

Geometric objects are the actual marks we put on a plot. Examples include:

• geom_point for drawing individual points (e.g., a scatter plot)

• geom_line for drawing lines (e.g., for a line charts)

• geom_smooth for drawing smoothed lines (e.g., for simple trends or approximations)

• geom_bar for drawing bars (e.g., for bar charts)

• geom_histogram for drawing binned values (e.g. a histogram)

• geom_polygon for drawing arbitrary shapes

• geom_map for drawing polygons in the shape of a map! (You can access the data to use for these maps by using the map_data() function).

• A plot must have at least one geom; there is no upper limit. You can add a geom to a plot using the + operator.

• For complete list, please refer to here.

1.7.1 Geometric Objects: geom_bar

The code chunk below plots a bar chart by using geom_bar().

ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar()

1.7.2 Geometric Objects: geom_dotplot

In a dot plot, the width of a dot corresponds to the bin width (or maximum width, depending on the binning algorithm), and dots are stacked, with each dot representing one observation.

In the code chunk below, geom_dotplot() of ggplot2 is used to plot a dot plot.

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot(dotsize = 0.5)

Warning

The y scale is not very useful, in fact it is very misleading.

Note

The code chunk below performs the following two steps:

• scale_y_continuous() is used to turn off the y-axis, and

• binwidth argument is used to change the binwidth to 2.5.

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_dotplot(binwidth=2.5,         
               dotsize = 0.5) +      
  scale_y_continuous(NULL,           
                     breaks = NULL)  

1.7.3 Geometric Objects: geom_histogram()

In the code chunk below, geom_histogram() is used to create a simple histogram by using values in MATHS field of exam_data.

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_histogram()       

Note

Note that the default bin is 30.

1.7.4 Modifying a geometric object by changing geom()

In the code chunk below,

• bins argument is used to change the number of bins to 20,

• fill argument is used to shade the histogram with light blue color, and

• color argument is used to change the outline colour of the bars in black

ggplot(data=exam_data, 
       aes(x= MATHS)) +
  geom_histogram(bins=20,            
                 color="black",      
                 fill="light blue")  

1.7.5 Modifying a geometric object by changing aes()

The code chunk below changes the interior colour of the histogram (i.e. fill) by using sub-group of aesthetic().

ggplot(data=exam_data, 
       aes(x= MATHS, 
           fill = GENDER)) +
  geom_histogram(bins=20, 
                 color="grey30")

Note

This approach can be used to colour, fill and alpha of the geometric.

1.7.6 Geometric Objects: geom-density()

geom-density() computes and plots kernel density estimate, which is a smoothed version of the histogram.

It is a useful alternative to the histogram for continuous data that comes from an underlying smooth distribution.

The code below plots the distribution of Maths scores in a kernel density estimate plot.

ggplot(data=exam_data, 
       aes(x = MATHS)) +
  geom_density()           

The code chunk below plots two kernel density lines by using colour or fill arguments of aes()

ggplot(data=exam_data, 
       aes(x = MATHS, 
           colour = GENDER)) +
  geom_density()

1.7.7 Geometric Objects: geom_boxplot

geom_boxplot() displays continuous value list. It visualises five summary statistics (the median, two hinges and two whiskers), and all “outlying” points individually.

The code chunk below plots boxplots by using geom_boxplot().

ggplot(data=exam_data, 
       aes(y = MATHS,       
           x= GENDER)) +    
  geom_boxplot()            

Notches are used in box plots to help visually assess whether the medians of distributions differ. If the notches do not overlap, this is evidence that the medians are different.

The code chunk below plots the distribution of Maths scores by gender in notched plot instead of boxplot.

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_boxplot(notch=TRUE)

1.7.8 Geometric Objects: geom_violin

geom_violin is designed for creating violin plot. Violin plots are a way of comparing multiple data distributions. With ordinary density curves, it is difficult to compare more than just a few distributions because the lines visually interfere with each other. With a violin plot, it’s easier to compare several distributions since they’re placed side by side.

The code below plot the distribution of Maths score by gender in violin plot.

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_violin()

1.7.9 Geometric Objects: geom_point()

geom_point() is especially useful for creating scatterplot.

The code chunk below plots a scatterplot showing the Maths and English grades of pupils by using geom_point().

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point()            

1.7.10 geom objects can be combined

The code chunk below plots the data points on the boxplots by using both geom_boxplot() and geom_point().

ggplot(data=exam_data, 
       aes(y = MATHS, 
           x= GENDER)) +
  geom_boxplot() +                    
  geom_point(position="jitter", 
             size = 0.5)        

1.8 stat function

The Statistics functions statistically transform data, usually as some form of summary. For example:

• frequency of values of a variable (bar graph)

  • a mean

  • a confidence limit

• There are two ways to use these functions:

  • add a stat_() function and override the default geom, or

  • add a geom_() function and override the default stat

1.8.1 Working with stat()

The boxplots below are incomplete because the positions of the means were not shown.

ggplot(data=exam_data, 
       aes(y = MATHS, x= GENDER)) +
  geom_boxplot()

1.8.2 Working with stat - the stat_summary() method

The code chunk below adds mean values by using stat_summary() function and overriding the default geom.

ggplot(data=exam_data, 
       aes(y = MATHS, x= GENDER)) +
  geom_boxplot() +
  stat_summary(geom = "point",       
               fun.y="mean",         
               colour ="red",        
               size=4)               

1.8.3 Working with stat - the geom() method

The code chunk below adding mean values by using geom_() function and overriding the default stat.

ggplot(data=exam_data, 
       aes(y = MATHS, x= GENDER)) +
  geom_boxplot() +
  geom_point(stat="summary",        
             fun.y="mean",           
             colour ="red",          
             size=4)          

1.8.4 Adding a best fit curve on a scatterplot?

The scatterplot below shows the relationship of Maths and English grades of pupils. The interpretability of this graph can be improved by adding a best fit curve.

In the code chunk below, geom_smooth() is used to plot a best fit curve on the scatterplot.

ggplot(data=exam_data, 
       aes(x= MATHS, y=ENGLISH)) +
  geom_point() +
  geom_smooth(linewidth=0.5)

Note

• The default method used is loess.

The default smoothing method can be overridden as shown below.

ggplot(data=exam_data, 
       aes(x= MATHS, 
           y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              linewidth=0.5)

1.9 Facets

Facetting generates small multiples (sometimes also called trellis plot), each displaying a different subset of the data. They are an alternative to aesthetics for displaying additional discrete variables. ggplot2 supports two types of factes, namely: facet_grid() and facet_wrap.

1.9.1 Working with facet_wrap()

facet_wrap wraps a 1d sequence of panels into 2d. This is generally a better use of screen space than facet_grid because most displays are roughly rectangular.

The code chunk below plots a trellis plot using facet-wrap().

ggplot(data=exam_data, 
       aes(x= MATHS)) +
  geom_histogram(bins=20) +
    facet_wrap(~ CLASS)

1.9.2 facet_grid() function

facet_grid() forms a matrix of panels defined by row and column facetting variables. It is most useful when you have two discrete variables, and all combinations of the variables exist in the data.

The code chunk below plots a trellis plot using facet_grid().

ggplot(data=exam_data, 
       aes(x= MATHS)) +
  geom_histogram(bins=20) +
    facet_grid(~ CLASS)

1.10 Coordinates

The Coordinates functions map the position of objects onto the plane of the plot. There are a number of different possible coordinate systems to use, they are:

• coord_cartesian() - the default cartesian coordinate systems, where you specify x and y values (e.g. allows you to zoom in or out).

• coord_flip() - a cartesian system with the x and y flipped.

• coord_fixed() - a cartesian system with a “fixed” aspect ratio (e.g. 1.78 for a “widescreen” plot).

• coord_quickmap() - a coordinate system that approximates a good aspect ratio for maps.

1.10.1 Working with Coordinate

By the default, the bar chart of ggplot2 is in vertical form.

ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar()

The code chunk below flips the horizontal bar chart into vertical bar chart by using coord_flip().

ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar() +
  coord_flip()

1.10.2 Changing the y- and x-axis range

The scatterplot on the right is slightly misleading because the y-aixs and x-axis range are not equal.

ggplot(data=exam_data, 
       aes(x= MATHS, y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, linewidth=0.5)

The code chunk below fixed both the y-axis and x-axis range from 0-100.

ggplot(data=exam_data, 
       aes(x= MATHS, y=ENGLISH)) +
  geom_point() +
  geom_smooth(method=lm, 
              linewidth=0.5) +  
  coord_cartesian(xlim=c(0,100),
                  ylim=c(0,100))

1.11 Themes

Themes control elements of the graph not related to the data. For example:

• background colour

• size of fonts

• gridlines

• colour of labels

Built-in themes include: - theme_gray() (default) - theme_bw() - theme_classic()

A list of theme can be found at this link. Each theme element can be conceived of as either a line (e.g. x-axis), a rectangle (e.g. graph background), or text (e.g. axis title).

1.11.1 Working with Themes

The code chunk below plot a horizontal bar chart using theme_gray().

ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar() +
  coord_flip() +
  theme_gray()

A horizontal bar chart plotted using theme_classic().

ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar() +
  coord_flip() +
  theme_classic()

A horizontal bar chart plotted using theme_minimal().

ggplot(data=exam_data, 
       aes(x=RACE)) +
  geom_bar() +
  coord_flip() +
  theme_minimal()

1.12 Reference

• Hadley Wickham (2023) ggplot2: Elegant Graphics for Data Analysis. Online 3rd edition.

• Winston Chang (2013) R Graphics Cookbook 2nd edition. Online version.

• Healy, Kieran (2019) Data Visualization: A practical introduction. Online version

• Learning ggplot2 on Paper – Components

• Learning ggplot2 on Paper – Layer

• Learning ggplot2 on Paper – Scale

2.1 Trying out ggplot

Box Plot of Maths

ggplot(data=exam_data, 
       aes(y = MATHS, x= GENDER)) +
  geom_boxplot(colour ="black", fill="#88abff", alpha=0.5) +
  geom_point(stat="summary",        
             fun=mean,           
             colour ="darkblue",          
             size=2) +
  theme_light() +
  labs(title="Maths scores across Gender", y="Maths Score", x="Gender")

Box Plots of Maths, Eng and Science

data_subset <- exam_data[, c("MATHS", "SCIENCE", "ENGLISH", "GENDER")]

# Reshape the data for plotting
data_long <- reshape2::melt(data_subset, id.vars = "GENDER", variable.name = "Score")

# Create the boxplot
ggplot(data_long, aes(x = Score, y = value, fill = GENDER)) +
  geom_boxplot() +
    xlab("Subject") +
    ylab("Score") +
    ggtitle("Boxplots of Scores by Subjects and Gender") +
    theme(legend.position = "top") +
  stat_summary(fun=mean, geom="point", shape=18, size=2, color="black", position = position_dodge(width=0.75)) +  # Add average
  stat_summary(fun=median, geom="point", shape=16, size=2, color="grey", position = position_dodge(width=0.75)) +  # Add median
  stat_summary(fun=quantile, geom="errorbar", width=0.4, color="black", fun.args=list(probs=c(0.25, 0.75)))

Boxplots using Facet

ggplot(data = data_long, aes(x = GENDER, y = value, fill=GENDER)) +
  geom_boxplot() +
  facet_wrap(~ Score, scales = "free_y", nrow = 1) +  
  xlab("Gender") +
  ylab("Score") +
  ggtitle("Boxplots of Scores by Gender for each Subject")