Extending base R graphs

Whilst the plots above are useful, we can (hopefully!) all see there are many issues that they present. Firstly, they are extremely visually unappealing! Secondly, there are also issues when it comes to lacks of graph titles and axis labels. Luckily, many of these problems can be solved by adding extra arguments to the graphs. Let’s use the final graph as an example.

#visually updating the graphs
#we separate these arguments onto different lines to aid reading
plot(synthetic_news_data$syst, synthetic_news_data$dias,
     main = "Relationship between systolic blood pressure and diastolic blood pressure", 
     xlab = "Systolic blood pressure (mmHg)",
     ylab = "Diastolic blood pressure (mmHg)",
     col = "darkblue") 

Figure 2: Bar plot with additional arguments to control appearance

I think this plot is a slight improvement, and I emphasise the word slight! You can see we use the argument main to specify the graph title, then the xlab and ylab arguments to create the x axis and y axis labels respectively and then use the col argument to specify the colour of points.

ggplot2

Whilst the above plots are useful, I do not find them visually appealing! They strike me as highly useful for diagnostic purposes and quickly understanding a new dataset, however I would not want to present such visualisations to colleagues or collaborators. Rather than using base R to generate such graphics, we could use the plotting package ggplot2. This package is part of the tidyverse series of packages and is the most widely used data visualisation package in the R community. This wide use, combined with the consistent syntax across many graph types, makes ggplot2 our recommendation for plotting in R.

As mentioned above, ggplot2 follows a consistent syntax when creating graphs. The basic structure of this is outlined below.

ggplot(data = data, aes(x = x, y = y)) +
  geom_(graph_type)
#this is the basic ggplot2 syntax

Hopefully this syntax is not to scary! We start by using the ggplot function and pass into it a data argument (usually a dataframe containing the columns you want to plot). We then use the aes (short for aesthetics) function where we can specify which variables to put on the X and/or Y axis of the plot. We then use the + symbol (similar to the pipe function from the magrittr package) to add geoms to our plot. These specify which sort of graph we want to be plotted.

Now we understand the basic syntax, lets create a basic histogram using ggplot2. We will use the age variable from the synthetic_news_data dataframe again.

#using ggplot to generate the graph
library(ggplot2)
ggplot(data = synthetic_news_data, aes(x = age)) +
  geom_histogram()

#this applies colours to our graph that can help those with colour blindness discern the variables
ggplot(synthetic_news_data, aes(age, fill = as.factor(male))) +
  geom_histogram(binwidth = 10) +
  scale_fill_viridis_d() 

Figure 3: A histogram of age separated by sex created using ggplot2

From this graph, we can see that generally the distributions are the same but there are fewer male participants than female at all ages. You may notice that I have added an extra line of code to this graph: scale_fill_viridis_d. This is a function that control the fill of the graph (the colour of the bars). In our example, we apply the viridis colour palette. This palette can be seen well by visually impaired individuals however many other colour schemes are available for you to choose from. the "_d" signifies we want the discrete colour palette (separate colours) rather than the continuous colour palette for continuous variables.

Now we have made our first ggplot2 graph, let’s make another.

#generates another graph within ggplot using the same data
ggplot(data = synthetic_news_data, aes(x = NEWS, fill = as.factor(NEWS))) +
  geom_bar() +
  scale_fill_viridis_d()

Figure 4: A bar chart created by ggplot2

Here we go, we now have a colour coded bar chart showing the number of participants who received each NEWS value. We used the viridis colour palette to colour the graph once again. Now lets take a look at the scatter plot from earlier.

#creating a scatter plot using ggplot
#the geom_hline and geom_vline functions create a horizontal and vertical line 
#these lines are at 120 and 80 (the generally accepted threshold for high blood pressure)
#the transparency of these lines was also set to 40% (alpha = 0.4) so they do not over power the graph
ggplot(data = synthetic_news_data, aes(x = syst, y = dias, color = as.factor(male))) +
  geom_point() +
  geom_vline(aes(xintercept = 120, alpha = 0.4)) +
  geom_hline(aes(yintercept = 80, alpha = 0.4))

Figure 5: ggplot2 scatter plot

As we can see, we can create a relatively complex graph with only a few simple lines of code! We start by creating the plot as we have before and setting the x, y and fill characteristics of the graph. We then specify geom_point as we want a scatter plot. Next, we use the geom_hline and geom_vline functions to create a horizontal and vertical line respectively. I put these lines at 120 and 80 (the generally accepted threshold for high blood pressure). I also set the transparency of these lines to 40% (alpha = 0.4) so they do not over power the graph.

Multiple plots in ggplot2

Another key benefit of ggplot2 over base R is the ability to combine graphs together. This can include something known as faceting, splitting a single graph into two based on a variable in the dataset or combining two graph types on the same set of axes.

Let’s first look at faceting.

#how to split the graph based on a grouping variable 
ggplot(data = synthetic_news_data, aes(x = syst, y = dias)) +
  geom_point() +
  facet_wrap(~male)

Figure 6: A ggplot2 scatter graph that has been faceted

And again, it really is that simple! We have now split our blood pressure graph to look at male and female participants separately. This was achieved by using the facet_wrap command and providing it the variable (in this case sex) to facet the plot by. This function is also incredibly useful when looking at data across time, or any other form of categorical variable for that matter.

Base or ggplot2?

In short, the answer is ggplot2! Its ease of use, combined with its greater adjustability and most importantly, the fact it utilises the same syntax for all graph types regardless of their complexity makes it an obvious choice for all R users. However, it is not quite that simple! Base R functions are useful especially when you want to examine your data quickly and get a rough idea of what is going on.