Exploratory Data Analysis

Graphing with `ggplot2`

The R library ggplot2 is very powerful for plotting but you may find the syntax little strange. There are plenty of examples at the ggplot2 online help website. The ggplot2 package is loaded as part of the tidyverse set of packages.

Advantages of ggplot2 are the following:

employs the “grammar of graphics” of (Wilkinson 2005)
plotting involves a high level of abstraction
very flexible and complete graphics system
theme system for getting attractive plots
Fast growing and actively developed

Some disadvantages of ggplot2 are the following:

3-dimensional graphics (opt for rgl package instead)
Graph-theory type graphs (nodes/edges layout; opt for igraph and other packages)
Interactive graphics (opt for plotly, ggvis and other packages)

Grammar of Graphics

The main idea behind the grammar of graphics of (Wilkinson 2005) is to mimic the manual graphing approach by defining building blocks and combining them to create the figure.

The building blocks of a graph are:

data
aesthetic mapping
geometric object
transformation or re-expression of data
scales
coordinate system
position adjustments
faceting

Aesthetic Mapping (`aes`)

In ggplot land aesthetic means visualisation features or aesthetics. These are

position (i.e., on the x and y axes)
color (“outside” color)
fill (“inside” color)
shape (of points)
linetype
size

Aesthetic mappings are set with the aes() function.

Geometric Objects (`geom`)

Geometric objects or geoms are the actual marking or inking on a plot such as:

points (geom_point, for scatter plots, dot plots, etc)
lines (geom_line, for time series, trend lines, etc)
boxplot (geom_boxplot, for boxplots)

A plot must have at least one geom but there is no upper limit. In order to add a geom to a plot, the + operator is employed. A list of available geometric objects can be obtained by typing geom_<tab> in Rstudio. The following command can also be used which will open a Help window.

help.search("geom_", package = "ggplot2")

ggplot2 in the tidyverse

ggplot2 is now part of the tidyverse. We have installed tidyverse in a previous workshop so you will not need to install it again.

We can now load the ggplot2 library with the commands:

Code

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

In order to work with ggplot2, we must have a data frame or a tibble containing our data. We need to specify the aesthetics or how the columns of our data frame can be translated into positions, colours, sizes, and shapes of graphical elements.

Let’s start with the rangitikei data we used in an earlier workshop

Code

rangitikei <- read.csv(
  "../data/rangitikei.csv",
  header=TRUE, 
  row.names = 1
  )

Code

ggplot(rangitikei) +
  aes(x = vehicle, y = people) +
  geom_point()

The aes part defines the “aesthetics”, which is how columns of the dataframe map to graphical attributes such as x and y position, colour, size, etc. An aesthetic can be either numeric or categorical and an appropriate scale will be used. After this, we add layers of graphics called geom_s. geom_point layer is employed to map x and y and we need not specify all the options for geom_point.

Exercise 2.1

Why does the following give an error and how would you fix it?

Code

ggplot(data = rangitikei) + 
  geom_point()

Adding layers

ggplot works by adding layers to your plots. Your plots must include aes() and a geom_; after that we can add extras.

We can add a title using labs() or ggtitle() functions. Try-

Code

ggplot(rangitikei) +
  aes(x = vehicle, y = people, color = river) +
  geom_point() + 
  ggtitle("No. of people vs No. of vehicles")

Code

ggplot(rangitikei)+
  aes(x = vehicle, y = people) +
  geom_point() + 
  labs(title = "No. of people vs No. of vehicles")

Note that labs() allows captions and subtitles. Check out ?labs() for all the options.

Exercise 2.2

Remake the above graph, adjusting the x axis to say Vehicles using the labs() function.

Code

# your code goes here

geom_smooth is additionally used to show trends.

Code

ggplot(rangitikei) +
  aes(x = vehicle, y = people) +
  geom_point() + 
  geom_smooth()

`geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Exercise 2.3

Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

Code

ggplot(rangitikei) +
  aes(x = vehicle, y = people) +
  geom_point()+
  geom_smooth(se = FALSE)

Similar to geom_smooth, a variety of other geoms are available. For example, geom_boxplot():

Code

ggplot(rangitikei) + 
  aes(x = factor(wind), y = people) +
  geom_boxplot()

You can add additional information to your graph by using multiple geoms. Each geom accepts a particular set of mappings; for example geom_text() accepts a labels mapping. Try-

Code

ggplot(rangitikei) +
  aes(x = vehicle, y = people) +
  geom_point() + 
  geom_text(aes(label = w.e), 
            size = 5)

Exercise 2.4

Will these two graphs look different? Why/why not? What happens when you run the code?

Code

ggplot(
  data = rangitikei,
  mapping = aes(x = vehicle, y = people)
) +
  geom_point() +
  geom_smooth()

ggplot() +
  geom_point(
    data = rangitikei,
    mapping = aes(x = vehicle, y = people)
  ) +
  geom_smooth(
    data = rangitikei,
    mapping = aes(x = vehicle, y = people)
  )

Explore multiple geoms and thing about how different types of data might be displayed in each.

Panels

The faceting option allows a collection of small plots with the same scales. Try-

Code

ggplot(rangitikei) +
  aes(x=vehicle, y=people) + 
  geom_point() +
  facet_wrap(~ river)

Faceting is the ggplot2 option to create separate graphs for subsets of data. ggplot2 offers two functions for creating small multiples:

facet_wrap(): define subsets as the levels of a single grouping variable
facet_grid(): define subsets as the crossing of two grouping variables

The following arguments are common to most scales in ggplot2:

name: the first argument gives the axis or legend title
limits: the minimum and maximum of the scale
breaks: the points along the scale where labels should appear
labels: the labels that appear at each break

Adjusting scales

Specific scale functions may have additional arguments. Some of the available Scales are:

Scale	Examples
`scale_color_`	`scale_color_discrete`
`scale_fill_`	`scale_fill_continuous`
`scale_size_`	`scale_size_manual`
	`scale_size_discrete`

`scale_shape_`	`scale_shape_discrete`
	`scale_shape_manual`
`scale_linetype_`	`scale_linetype_discrete`

`scale_x_`	`scale_x_continuous`
	`scale_x_log`
	`scale_x_date`
`scale_y_`	`scale_y_reverse`
	`scale_y_discrete`
	`scale_y_datetime`

In RStudio, we can type scale_ followed by TAB to get the whole list of available scales.

Try-

Code

ggplot(rangitikei) + 
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point() + 
  scale_x_continuous(name = "No. of Vehicles") + 
  scale_y_continuous(name = "No. of people") + 
  scale_color_discrete(name = "Temperature")

The other coding option is shown below:

Code

ggplot(rangitikei) +
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point() + 
  xlab("No. of Vehicles") + 
  ylab("No. of people") + 
  labs(colour="Temperature")

Note that a desired graph can be obtained in more than one way.

In both of the above graphs adding color adds a third layer of data that can be plotted.

Themes

Themes can help adjust different pieces of your graphs to make them publication or presentation ready. Here you can adjust font, size, axis features, etc.

The ggplot2 theme system handles plot elements (not data based) such as

Axis labels
Plot background
Facet label background
Legend appearance

Built-in themes include:

theme_gray() (default)
theme_bw()
theme_minimal()
theme_classic()

Code

p1 <- ggplot(rangitikei) + 
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point()

Note that the graph is assigned an object name p1 and nothing will be printed unless we then print the object p1. Here, the plot is assigned to an object to make applying and comparing different themes simple.

Code

p1 <- ggplot(rangitikei) + 
  aes(x = vehicle, y = people, color = factor(temp)) + 
  geom_point()

p1

Try-

Code

p1 + theme_light()

Code

p1 + theme_bw()

Specific theme elements can be overridden using theme(). For example:

Code

p1 + theme_minimal() +
  theme(text = element_text(color = "red"))

All theme options can be seen with ?theme.

To specify a theme for a whole document, use

Code

theme_set(theme_minimal())

A cheat sheet for ggplot2 is available at https://www.rstudio.com/resources/cheatsheets/ (optional to download). There are many other packages which incorporate ggplot2 based graphs or dependent on it.

Multiple plots

Sometimes you want to create a figure with multiple plots. Rather than trying to combine the data and then faceting, you may need to save each as an object and combine with a helper library. The library patchwork allows complex composition arbitrary plots, which are not produced using the faceting option. Try

Code

library(patchwork)

p1 <- qplot(people, data = rangitikei, geom = "dotplot") # change this to a ggplot

Warning: `qplot()` was deprecated in ggplot2 3.4.0.

Code

p2 <- qplot(people, data = rangitikei, geom = "boxplot")
p3 <- ggplot(rangitikei, aes(x = vehicle, y = people)) + geom_point()

(p1 + p2) / p3 + 
  plot_annotation("My title", caption = "My caption")

Bin width defaults to 1/30 of the range of the data. Pick better value with
`binwidth`.

Have a quick google of the library ggpubr for the function ggarrange as another option for combining plots.

Dataset `Prestige`

As you work through this workshop, you can copy the code and paste it into a code chunk. Write notes and observations to your self as you go.

We will be using a well-known dataset called Prestige from the car R package. This dataset deals with prestige ratings of Canadian occupations. The Prestige dataset has 102 rows and 6 columns. Each row (or ‘observation’) is an occupation.

This data frame contains the following columns:

education - Average education of occupational incumbents, years, in 1971.
income - Average income of incumbents, dollars, in 1971.
women - Percentage of incumbents who are women.
prestige - Pineo-Porter prestige score for occupation, from a social survey conducted in the mid-1960s.
census - Canadian Census occupational code.
type - Type of occupation. A factor with levels: bc, Blue Collar; prof, Professional, Managerial, and Technical; wc, White Collar. (includes four missing values).

First we’ll load the data. The dataset sits in the car package, so you need to load the car package first.

Code

library(car)
data(Prestige)

Exercise 2.5

Draw a bar chart for type. These plots show the count or relative frequency of blue collar (bc), professional (prof), and white collar (wc) professions in the dataset.

Code

# your code goes here

Exercise 2.6

Draw a histogram of prestige.

Code

# your code goes here

Below demonstrates the flexibility of ggplot code. You can specify the data argument by piping it into ggplot, or by putting it as an argument to ggplot or a geom_. Likewise, the mapping or aes information, which determines which variables are used where, can be added as an extra line or specified inside the ggplot or geom_ function.

Code

Prestige |> 
  ggplot() +
  aes(x = prestige) +
  geom_histogram()

Now, this histogram, where the number of bins has been chosen for us, looks a bit “spiky” to my eye. You can control the number of bins by adding an argument bins = 10.

Code

Prestige |> 
  ggplot() +
  aes(x = prestige) +
  geom_histogram(bins=10)

ggplot is very flexible as to where you put the data and the aes information; all of these methods give the same result.

Code

Prestige |> 
  ggplot() +
  aes(x = prestige) +
  geom_histogram(bins=10)

Code

ggplot(
  data = Prestige,
  mapping = aes(x = prestige)
  ) +
  geom_histogram(bins=10)

Code

ggplot(Prestige) +
  aes(x = prestige) +
  geom_histogram(bins=10)

Code

ggplot() +
  geom_histogram(
    data = Prestige,
    mapping = aes(x = prestige),
    bins = 10
    )

Code

# or
# hist(Prestige$prestige)

Now let’s display the prestige scores for each profession as a dot plot.

Note that I’m including the code-chunk option #| fig-height: 12 at the beginning of my code chunk so that the plot is big enough to show all the professions without overlap.

Code

Prestige |>
  ggplot() + 
  aes(x = rownames(Prestige), y = prestige) +
  geom_point() +
  coord_flip()

What a mess!

We can tidy it up by ordering the professions on the plot according to prestige. First, we move the professions from rownames to a variable. Then, we fct_reorder the professions using the prestige scores. Then, the resulting data gets piped into ggplot.

Code

Prestige |> 
  rownames_to_column(var = "profession") |> 
  mutate(
    profession = fct_reorder(profession, prestige)
    ) |>
  ggplot() + 
  aes(x = profession, y = prestige, colour = type) +
  geom_point() +
  coord_flip()

Exercise 2.7

Obtain some summary statistics for prestige. There are a few options for this.

Code

# your code goes here

Make a summary dataset, average variable for each type of occupation.

Code

# your code goes here

Exercise 2.8

Make a boxplot of prestige ~ type:

Code

# your code goes here

Exercise 2.9

Obtain the Empirical Cumulative Distribution Function (ECDF) graphs of prestige ~ type:

Code

Prestige |> 
  ggplot() + 
  aes(prestige, colour=type) +
  stat_ecdf()

Code

Prestige |> 
  ggplot() + 
  aes(prestige) +
  stat_ecdf() + 
  facet_wrap(~type)

Code

Prestige |> 
  ggplot() + 
  aes(
    x = prestige, # these aes settings are used
    col = type    # by both geoms
    ) +
  geom_density(
    aes(fill = type), # the 'fill' aes goes here because 
    alpha = .2        # geom_rug doesn't use 'fill'
    ) +
  geom_rug()

With which plot – the ECDF or the density plot – is it easier to compare the distributions of prestige scores among these groups?

Exercise 2.10

Obtain the {0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95} quantiles of prestige:

Code

# your code goes here

Exercise 2.11

Obtain the scatter plot (with and without marginal boxplots) prestige vs. education : How can you describe the relationship implied by the pattern?

Code

# your code goes here

Exercise 2.12

Make a bubble or balloon plot prestige vs. education vs. income with income forming the bubble size.

Make a different scatter plot using the same three variables. Keep x = education, y = prestige but use a different option to illustrate the influence of income.

Code

# your code goes here

Exercise 2.13

Create prestige ~ education | type graphs. That is, prestige ~ education grouped by type as colours and/or panels.

Code

# your code goes here

Select answers: # Answer: Exercise 2.5 {-}

Code

p <- Prestige |> 
  ggplot() +
  aes(type) + 
  geom_bar()

p

Answer: Exercise 2.7a

Code

summary(Prestige)

   education          income          women           prestige    
 Min.   : 6.380   Min.   :  611   Min.   : 0.000   Min.   :14.80  
 1st Qu.: 8.445   1st Qu.: 4106   1st Qu.: 3.592   1st Qu.:35.23  
 Median :10.540   Median : 5930   Median :13.600   Median :43.60  
 Mean   :10.738   Mean   : 6798   Mean   :28.979   Mean   :46.83  
 3rd Qu.:12.648   3rd Qu.: 8187   3rd Qu.:52.203   3rd Qu.:59.27  
 Max.   :15.970   Max.   :25879   Max.   :97.510   Max.   :87.20  
     census       type   
 Min.   :1113   bc  :44  
 1st Qu.:3120   prof:31  
 Median :5135   wc  :23  
 Mean   :5402   NA's: 4  
 3rd Qu.:8312            
 Max.   :9517

Code

library(psych)

describe(Prestige)

          vars   n    mean      sd  median trimmed     mad     min      max
education    1 102   10.74    2.73   10.54   10.63    3.15    6.38    15.97
income       2 102 6797.90 4245.92 5930.50 6161.49 3060.83  611.00 25879.00
women        3 102   28.98   31.72   13.60   24.74   18.73    0.00    97.51
prestige     4 102   46.83   17.20   43.60   46.20   19.20   14.80    87.20
census       5 102 5401.77 2644.99 5135.00 5393.87 4097.91 1113.00  9517.00
type*        6  98    1.79    0.80    2.00    1.74    1.48    1.00     3.00
             range skew kurtosis     se
education     9.59 0.32    -1.03   0.27
income    25268.00 2.13     6.29 420.41
women        97.51 0.90    -0.68   3.14
prestige     72.40 0.33    -0.79   1.70
census     8404.00 0.11    -1.49 261.89
type*         2.00 0.40    -1.36   0.08

Code

describeBy(education + income + women + prestige ~ type, 
           data = Prestige)


 Descriptive statistics by group 
type: bc
          vars  n    mean      sd  median trimmed     mad     min     max
education    1 44    8.36    1.16    8.35    8.32    1.14    6.38   10.93
income       2 44 5374.14 2004.33 5216.50 5338.56 2275.05 1656.00 8895.00
women        3 44   18.97   26.15    4.72   14.48    7.01    0.00   90.67
prestige     4 44   35.53   10.02   35.90   35.46   11.34   17.30   54.90
            range skew kurtosis     se
education    4.55 0.34    -0.76   0.18
income    7239.00 0.17    -1.00 302.16
women       90.67 1.36     0.51   3.94
prestige    37.60 0.05    -1.03   1.51
------------------------------------------------------------ 
type: prof
          vars  n     mean      sd  median trimmed     mad     min      max
education    1 31    14.08    1.39   14.44   14.16    1.22   11.09    15.97
income       2 31 10559.45 5422.82 8865.00 9700.04 3955.58 4614.00 25879.00
women        3 31    25.51   28.37   11.68   21.03   13.86    0.58    96.12
prestige     4 31    67.85    8.68   68.40   67.34    9.19   53.80    87.20
             range  skew kurtosis     se
education     4.88 -0.47    -0.93   0.25
income    21265.00  1.37     1.36 973.97
women        95.54  1.14    -0.04   5.09
prestige     33.40  0.36    -0.67   1.56
------------------------------------------------------------ 
type: wc
          vars  n    mean      sd  median trimmed     mad     min     max
education    1 23   11.02    0.92   11.13   11.03    0.68    9.17   12.79
income       2 23 5052.30 1944.32 4741.00 4960.53 2342.51 2448.00 8780.00
women        3 23   52.83   33.11   56.10   53.19   47.77    3.16   97.51
prestige     4 23   42.24    9.52   41.50   41.61    8.60   26.50   67.50
            range  skew kurtosis     se
education    3.62 -0.20    -0.27   0.19
income    6332.00  0.44    -1.18 405.42
women       94.35 -0.10    -1.58   6.90
prestige    41.00  0.63     0.18   1.98

Answer: Exercise 2.8

Code

Prestige |> 
  ggplot() +
  aes(y=prestige, x=type) +
  geom_boxplot()

Code

# as violin plots
Prestige |> 
  ggplot() +
  aes(y=prestige, x=type) +
  geom_violin()

Code

# Or put it all together
Prestige |> 
  ggplot() +
  aes(y=prestige, x=type) +
  geom_violin() + 
  geom_boxplot(col = 2, alpha = .2) +
  geom_jitter(alpha = .2, width = .2, height = 0, colour = 4)

Answer: Exercise 2.10

Code

pr <- c(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99)

Prestige |> 
  summarise(
    probs = pr,
    quants = quantile(prestige, pr)
    )

# or simply
quantile(Prestige$prestige, pr)

Answer: Exercise 2.11

Code

library(ggExtra)

p1 <- Prestige |> 
  ggplot() + 
  aes(x = education, y = prestige) +
  geom_point() + 
  geom_smooth(col = 2) + 
  geom_smooth(method = "lm", se = FALSE)

ggMarginal(p1, type="boxplot")

Code

library(car)

scatterplot(education ~ prestige, data = Prestige)

The later plot will show prediction interval ribbon while the first plot will show the confidence interval ribbon.

Answer: Exercise 2.12

Code

library(ggplot2)

Prestige |> 
  ggplot() + 
  aes(x = education, y = prestige, size = income) +
  geom_point()

Answer: Exercise 2.13

Code

Prestige |> 
  ggplot() + 
  aes(x = education, y = prestige, colour = type) +
  geom_point() + 
  facet_wrap(~ type)

Code

p <- Prestige |> 
  ggplot() + 
  aes(x = education, y = prestige, color = type) +
  geom_point()

p

More graphing examples are here (R code file).

References

Wilkinson, L. 2005. The Grammar of Graphics. Berlin, Heidelberg: Springer-Verlag.

Graphing with ggplot2

Grammar of Graphics

Aesthetic Mapping (aes)

Geometric Objects (geom)

ggplot2 in the tidyverse

Exercise 2.1

Adding layers

Exercise 2.2

Exercise 2.3

Exercise 2.4

Panels

Adjusting scales

Themes

Multiple plots

Dataset Prestige

Exercise 2.5

Exercise 2.6

Exercise 2.7

Exercise 2.8

Exercise 2.9

Exercise 2.10

Exercise 2.11

Exercise 2.12

Exercise 2.13

Answer: Exercise 2.7a

Answer: Exercise 2.8

Answer: Exercise 2.10

Answer: Exercise 2.11

Answer: Exercise 2.12

Answer: Exercise 2.13

References

Graphing with `ggplot2`

Aesthetic Mapping (`aes`)

Geometric Objects (`geom`)

Dataset `Prestige`