Basic plotting

We will start by showing you how to build a basic plot with theggplot() function. The ggplot() function creates a blank plot to which you add different functions to build your data visualisation using the + operator.

There are two main arguments to the ggplot() function, these are:

• data: the name of the data frame we are working with
• mapping: the columns of the data frame we are plotting. This is created using the aes() command.

aes() stands for “aesthetic” and is used to specify what we want to show in the plot. We will start by looking at plots of simple time trends using the malaria incidence data for all ages. First, we filter the pf_incidence_national data frame forage_group == "total".

total_incidence <- 
filter(pf_incidence_national, age_group == 'total')

We then start with the ggplot command. We want to use the total incidence dataset and wish to plot the year on the x-axis and the incidence on the y-axis so the command is as follows:

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))

Scatter plots & line graphs

This wont yet produce a figure as we need to tell ggplot what sort of figure we would like this to be. We will start by making a scatter plot using geom_point(). We add layers to the ggplot object using the + operator.

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_point()

We can then control how the points look by adding commands to thegeom_point() function, such as the size, colour and transparency (alpha) of the points.

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_point(size=5, colour = "blue", alpha = 0.5)

We can also change the type of the point by using theshape = command.

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_point(size=4, colour = 'blue', alpha = 0.5, shape = 18)

# Available shapes (don't run)
shapes

The general structure of the ggplot() command remains similar for different plots.This means that it is easy to change the type of an established plot, or create a different plot using a similar code structure.

We can change the type of plot by replacing geom_point() with other options such as a line graph: geom_line(), or smoothed conditional means: geom_smooth().

# Line plot
ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_line(size=2, colour = "red", alpha = 0.5)

# Smoothed line of fit
ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_smooth(colour = "yellow")

The size, colour and alpha commands are similarly used to control the appearance of the line but instead of using shape = to control the appearance of the points we can change the style of the line in a line plot by specifying linetype =

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_line(size=2, colour = "red", alpha = 0.5, linetype = 'dashed')

# Available line types (not run)
lty

Additionally, you can layer the types of plots together, e.g. plotting the points and lines on the same graph.

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_line(size=1, colour = "red", alpha = 0.5)+
geom_point(size=5, colour = 'blue', shape = 18)

Task 6

• Import the dataset pf_incidence_national we created in the demo from the data folder and filter to just contain data for under 5’s
• Create a line graph plotting the incidence of malaria (y-axis) by date (x-axis)
• Change this to be a green dotted line and increase the line thickness

Solution

  pf_incidence_national <- read_csv("outputs/pf_incidence_national.csv")  %>% 
filter(age_group=="u5") 

ggplot(pf_incidence_national)+
geom_line(mapping = aes(x = date_tested, y = incidence), color =  'green', size = 2)

Themes

Further commands can then be added to the ggplot() object to control the appearance of the plot. We can label the axis and add a title usinglabs(), and can select different themes for the background appearance, such as the black and white theme: theme_bw(), or the dark theme: theme_dark().

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_point(size=2, colour = "blue")+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()

We can control the x and y axis limits by using xlim() or ylim(). As our x-axis is a date we can control this using the command scale_x_date(). Otherwise if this was a continuous variable we could use scale_x_continous() to control the scale. This command has additional options to control where you want the breaks in the axis to be and to alter the data labels

# Option 1 using xlim() and ylim()
ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_point(size=5, colour = 'blue')+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
ylim(0,1000)

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_point(size=5, colour = 'blue')+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
scale_x_date(limit=c(as.Date("2018-01-01"),as.Date("2018-06-01")),
date_breaks = "1 month", date_labels = "%b")

Furthermore, we can control the text of the axis and title within thetheme() command. theme() provides a large amount of control over the appearance of the plot. Common examples of what we can do within this command is rotating the x-axis text using the option angle =, changing the font size usingsize = and the font type using face =.

ggplot(data = total_incidence, mapping = aes(x = date_tested, y = incidence))+
geom_point(size=5, colour = 'blue')+
labs(x = "Month",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
scale_x_date(date_breaks = "1 month", date_labels = "%b")+
theme(axis.text.x = element_text(angle = 90, hjust = 0.5, vjust = 0.5),
axis.title.x = element_text(size = 14,face = 'bold'),
axis.title.y = element_text(size = 14,face = 'bold'),
title = element_text(size = 16,face = 'bold'))

Subgroups and facets

We will now look visualising multiple data trends, for example if you had incidence data for multiple countries there are various ways you could show this data. Here we will use the datasetpf_incidence_national.csv we imported earlier, with incidence for different age groups.

Firstly, we can incorporate the trends for different age_groups on the same plot by using different colours. We use the colour argument inside the aes functions and assign it to the country variable. This results in ggplot plotting a line for each country in the dataset in a different colour, with a legend added to the plot.

ggplot(data = pf_incidence_national, mapping = aes(x = date_tested, y = incidence, colour = age_group))+
geom_line(size = 3)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018",
colour = 'Age group')+
theme_bw()

We can control the colours in the plots by using thescale_color_manual() function, and passing it the colours you wish to use. You can use either the names of colours or the HEX codes. We can also move the position of the legend to the bottom using the legend.position = "bottom" command withintheme() and change the title of the legend withinlabs().

ggplot(data = pf_incidence_national, mapping = aes(x = date_tested, y = incidence, colour = age_group))+
geom_line(size = 3)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018",
colour = 'Age group')+
theme_bw()+
theme(legend.position = 'bottom')+
scale_colour_manual(values = c('pink', 'yellow', 'purple'))

Another way to look at multiple data trends in one plot is to uses facets. This creates separate plots for each “facet”, as part of the overall plot.

ggplot(data = pf_incidence_national, mapping = aes(x = date_tested, y = incidence))+
geom_line(size=2, colour = "blue")+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
facet_wrap(~age_group)

There are various options in facet_wrap(). If you have largely varying scales between the facets you are plotting, you can plot these on different scales using either free_y = TRUE, orfree_x = TRUE as options inside offacet_wrap(). You can also specify the number of rows or columns you want in the plot, e.g. if you had 6 facets you could plot it as nrow = 2, ncol = 3 ornrow = 3, ncol = 2.

ggplot(data = pf_incidence_national, mapping = aes(x = date_tested, y = incidence))+
geom_line(size=2, colour = "blue")+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
facet_wrap(~age_group, scales = 'free_y', nrow = 3)

Task 7

• Using the pf_incidence_national dataset create scatter plots the number of confirmed malaria cases(y-axis) by date tested (x-axis), with facets for age group.
• Change the colour of the points based on the age group
• Change the axis labels and add a title to the plot
• Change the plot theme to black and white

Solution

  pf_incidence_national <- read_csv("outputs/pf_incidence_national.csv")

ggplot(pf_incidence_national)+
geom_line(mapping = aes(x = date_tested, y = incidence, color =  >age_group))+
facet_wrap(~age_group)+
labs(x = "Date", y = "Incidence per 1000 PYO", title = "Incidence >Rate")+
theme_bw()

Continuous colour scales

Similarly to using the colour option to plot your data by categorical variables, we can use this options to plot continuous data on a colour scale. For example, if we wanted colour the points on our plot of incidence by year based on the total population we can addcolour = population to the aesthetic command.

ggplot(total_incidence, aes(x = date_tested, y = incidence, colour = pop))+
geom_point(size=5)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()

There are then a few options for how we can control the colour scale of a continuous variable. Firstly we can use thescale_colour_gradient() command to specify the low and high colours of a gradient from which to build the colour ramp. Secondly ggpplot allows us to use colour scales from ColorBrewer (https://colorbrewer2.org). This has a wide range of sequential, diverging and qualitative colour schemes you can use for a rnage of data and projects. For continuous data this is specified using the command scale_colour_distiller() and using the optionpalette = to specify the colour palette to use.

ggplot(total_incidence, aes(x = date_tested, y = incidence, colour = pop))+
geom_point(size=5)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
scale_colour_gradient(low = "yellow", high = "red")

ggplot(total_incidence, aes(x = date_tested, y = incidence, colour = pop))+
geom_point(size=5)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
scale_colour_distiller(palette = 'Greens')

An additional option for- continuous colour scales are the viridis scales. These are well designed colour scales which are excellent for graphs and maps. They are colourful, appear uniform in both colour and black-and-white and are easy to view for persons with common forms of colour blindness. There are various options withing this package and they can be used within ggplot through the commandscale_colour_viridis_c().

# Default viridis colour scale
ggplot(total_incidence, aes(x = date_tested, y = incidence, colour = pop))+
geom_point(size=5)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
scale_colour_viridis_c()

# Inferno option
ggplot(total_incidence, aes(x = date_tested, y = incidence, colour = pop))+
geom_point(size=5)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
scale_colour_viridis_c(option = 'inferno')

# Reversing the direction
ggplot(total_incidence, aes(x = date_tested, y = incidence, colour = pop))+
geom_point(size=5)+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018")+
theme_bw()+
scale_colour_viridis_c(option = 'inferno', direction = -1)

Finally, we can use ggplot to have plot different data on different layers of the plot. We do this by moving the data = andmapping = arguments from the ggpplot() to the specific layers such as geom_point(). Here we plot a scatter plot with different colours for each country, but we add a smoothed mean line for all of the data.

ggplot()+
geom_point(data = filter(pf_incidence_national, age_group != 'total'),
aes(x = date_tested, y = incidence, colour = age_group), size=4)+
geom_line(data = filter(pf_incidence_national, age_group == 'total'),
aes(x = date_tested, y = incidence), colour = 'grey', size = 2, linetype = 'dashed')+
labs(x = "Date",
y = "Incidence/100,000/month",
title = "Malaria incidence in 2018",
colour = 'Age group')+
theme_bw()

Box plots, bar charts and histograms

ggplot can also be used to create plots summarising the data and incorporating statistical transformations.

Box plots are a good way of summarising continuous data by discrete variables. For example, in this dataset we have the number of confirmed malaria cases in different districts for each month. Inggplot we can use box plots to easiy summarise and visualise these data.

As in the previous section, we provide ggplot() with the x and y variables in the aes() argument and R will calculate the size of the boxes and whiskers. Similarly, you can control the colour of the lines and the fill colour of the plot by using thecolour and fill arguments.

To look at these plots we will use the “pf_incidence” dataset we created earlier, with the confirmed cases, incidence, and population by date and district. We will subset this data to look at all ages initially. We introduced pipes earlier, these can also be used with ggplot. Dates in ggplot are treated as continuous variables to here we must specify group = date_tested to group the data by date.

filter(pf_incidence, age_group == 'total') %>% 
ggplot(aes(x = date_tested, y = conf, group=date_tested))+
geom_boxplot(colour = 'black', fill = 'dark red')+  
labs(x = "Date",
y = "Number of cases",
title = "Number of malaria cases by month")+
theme_bw()

Histograms are created using the geom_histogram() function. This splits the data into bins using stat_bins() and counts the number of occurrences in each bin. You can control the number of bins or the width of the bins using bins andbinwidth respectively. Options such as fill can also be used to control the fill colour of the bars, along with the additional options already discussed.

filter(pf_incidence, age_group == 'total') %>% 
ggplot(aes(x = conf))+
geom_histogram(bins = 20, fill = 'purple', colour = 'black')+
theme_bw()+
labs(x = "Number tested",
y = "Count",
title = "Histogram of malaria tests conducted")

For bar plots the geom_bar()function is used. The default transformation is to count the number of rows for each category,stat = "bin". There are other transformations we can use, so if we want to make the heights of the bars to represent values in the data (provided by the assigning the y aesthetic), we usestat = identity. Here we plot a bar chart of the total number of malaria tests conducted each month in our routine data.

filter(pf_incidence, age_group == 'total') %>% 
ggplot(aes(x = date_tested, y = conf, group=date_tested))+
geom_bar(stat = "identity", fill = "blue")+
labs(x = "Number tested",
y = "Count",
title = "Bar chart of malaria tests conducted per month") +
theme_bw()

If we want to display different subgroups of data, such as number of tests by age group, on a bar plot we can either create a stacked bar plot with different colours representing the subgroups by using thefill = age_group command, or by creating a side-by-side bar plot by combining fill = age_group with the commandposition = "dodge".

# Stacked bar chart
filter(pf_incidence, age_group != 'total') %>% 
ggplot(mapping = aes(x = date_tested, y = conf, fill = age_group))+
geom_bar(stat = "identity")+
theme_bw()+
labs(x = "Number tested",
y = "Count",
title = "Bar chart of confirmed malaria cases per month, by age group",
fill = NULL)

# Side by side bar chart
filter(pf_incidence, age_group != 'total') %>% 
ggplot(mapping = aes(x = date_tested, y = conf, fill = age_group))+
geom_bar(stat = "identity", position = "dodge")+
theme_bw()+
labs(x = "Number tested",
y = "Count",
title = "Bar chart of confirmed malaria cases per month, by age group",
fill = NULL) 

This is just an introduction to what you can achieve for ggplot. For further guidance look at the help pages and the cheat-sheet available athttps://www.rstudio.com/resources/cheatsheets.

Saving plots

There are a couple of different ways you can save plots in R. Firstly you can save them by opening a png(), jpeg() or pdf() depending on the file type you want to save. These commands contain the output file path and the desired height and width of the figure (optional). It is then followed by the figure, and closing down the file with dev.off().

png("outputs/malaria_incidence_plot.png")
filter(pf_incidence, age_group != 'total') %>% 
ggplot(mapping = aes(x = date_tested, y = conf, fill = age_group))+
geom_bar(stat = "identity", position = "dodge")+
theme_bw()+
labs(x = "Number tested",
y = "Count",
title = "Bar chart of confirmed malaria cases per month, by age group",
fill = NULL) 
dev.off()

Another option for saving plots is using ggsave(). This takes the file name, including the extension, that you wish to save and the plot. Leaving the plot name empty will default to the last plot created. You can also include commands for the desired height and width of the figure if required. So saving the above plot would entail:

filter(pf_incidence, age_group != 'total') %>% 
ggplot(mapping = aes(x = date_tested, y = conf, fill = age_group))+
geom_bar(stat = "identity", position = "dodge")+
theme_bw()+
labs(x = "Number tested",
y = "Count",
title = "Bar chart of confirmed malaria cases per month, by age group",
fill = NULL) 
ggsave("outputs/malaria_incidence_plot.png")

Task 8

• Import the pf_incidence dataset we created in demo 2.
• Create a box plot of the confirmed malaria cases for children under 5 by month
• Using the pf_incidence dataset create a stacked bar chart of confirmed > malaria cases in children under 5 and over 5’s by month

Solution

  pf_incidence <- read_csv("outputs/pf_incidence.csv") %>% 
mutate(month = month(date_tested, label = TRUE, abbr = TRUE), year = year(date_tested)) 

pf_incidence %>% 
filter(age_group == "u5") %>% 
ggplot()+
geom_boxplot(mapping = aes(x = month, y = conf))+
labs(y = "Confirmed Cases")+
theme_bw()

ggplot(pf_incidence)+
geom_boxplot(mapping = aes(x = month, y = conf, fill = age_group))+
labs(y = "Confirmed Cases under 5")+
theme_bw()