This vignette provides some tips for the most common customisations of graphics produced by plot.incidence. Our graphics use ggplot2, which is a distinct graphical system from base graphics. If you want advanced customisation of your incidence plots, we recommend following an introduction to ggplot2.
# Example data: simulated Ebola outbreak
This example uses the simulated Ebola Virus Disease (EVD) outbreak from the package outbreaks: ebola.sim.clean.
First, we load the data:
library(outbreaks)
library(ggplot2)
library(incidence)
onset <- ebola.sim.clean$linelist$date.of.onset
class(onset)
#> [1] "Date"
head(onset)
#> [1] "2014-04-07" "2014-04-15" "2014-04-21" "2014-04-27" "2014-04-26"
#> [6] "2014-04-25"We compute the daily incidence:
i <- incidence(onset, interval = 7)
i
#> <incidence object>
#> [5829 cases from days 2014-04-07 to 2015-04-27]
#>
#> $counts: matrix with 56 rows and 1 columns
#> $n: 5829 cases in total
#> $dates: 56 dates marking the left-side of bins
#> $interval: 7 days
#> $timespan: 386 days
i.sex <- incidence(onset, interval = 7, group = ebola.sim.clean$linelist$gender)
i.sex
#> <incidence object>
#> [5829 cases from days 2014-04-07 to 2015-04-27]
#> [2 groups: f, m]
#>
#> $counts: matrix with 56 rows and 2 columns
#> $n: 5829 cases in total
#> $dates: 56 dates marking the left-side of bins
#> $interval: 7 days
#> $timespan: 386 days
i.hosp <- incidence(onset, interval = 7, group = ebola.sim.clean$linelist$hospital)
i.hosp
#> <incidence object>
#> [5829 cases from days 2014-04-07 to 2015-04-27]
#> [6 groups: Connaught Hospital, Military Hospital, NA, Princess Christian Maternity Hospital (PCMH), Rokupa Hospital, other]
#>
#> $counts: matrix with 56 rows and 6 columns
#> $n: 5829 cases in total
#> $dates: 56 dates marking the left-side of bins
#> $interval: 7 days
#> $timespan: 386 days
# The plot.incidence function
When calling plot on an incidence object, the function plot.incidence is implicitely used. To access its documentation, use ?plot.incidence. In this section, we illustrate existing customizations.
By default, the function uses grey for single time series, and colors from the color palette pal1 when incidence is computed by groups:
plot(i)
plot(i.sex)
plot(i.hosp)
However, some of these defaults can be altered through the various arguments of the function:
args(incidence:::plot.incidence)
#> function (x, ..., fit = NULL, stack = is.null(fit), color = "black",
#> border = NA, col_pal = pal1, alpha = 0.7, xlab = "", ylab = NULL)
#> NULLA color palette is a function which outputs a specified number of colors. By default, the color used in incidence is called pal1. It’s behaviour is different from usual palettes, in the sense that the first 4 colours are not interpolated:
par(mfrow=c(3,1), mar=c(4,2,1,1))
barplot(1:2, col = pal1(2))
barplot(1:4, col = pal1(4))
barplot(1:20, col = pal1(20))
This pallette also has a light and a dark version:
par(mfrow=c(3,1))
barplot(1:20, col = pal1dark(20), main = "palette: pal1dark")
barplot(1:20, col = pal1(20), main = "palette: pal1")
barplot(1:20, col = pal1light(20), main = "palette: pal1light")
Other color palettes can be provided via col_pal. Various palettes are part of the base R distribution, and many more are provided in additional packages. We provide a couple of examples:
plot(i.hosp, col_pal = rainbow)
plot(i.sex, col_pal = cm.colors)
Colors can be specified manually using the argument color; note that whenever incidence is computed by groups, the number of colors must match the number of groups, otherwise color is ignored.
plot(i, color = "darkred")
plot(i.sex, color = c(m = "orange2", f = "purple3"))
plot(i.hosp,
color = c("#ac3973", "#6666ff", "white", "white", "white", "white"))
# Useful ggplot2 tweaks
Numerous tweaks for ggplot2 are documented online. In the following, we merely provide a few useful tips in the context of incidence.
By default, the dates indicated on the x-axis of an incidence plot may not have the suitable format. The package scales can be used to change the way dates are labelled (see ?strptime for possible formats):
library(scales)
plot(i) +
scale_x_date(labels = date_format("%d %b %Y"))
And for another example, with a subset of the data (first 50 days), using more detailed dates and rotating the annotations:
plot(i[1:50]) +
scale_x_date(labels = date_format("%a %d %B %Y")) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size=12))
Note that you can save customisations for later use:
rotate.big <- theme(axis.text.x = element_text(angle = 45, hjust = 1, size=12))The last example above illustrates that it can be useful to have denser annotations of the x-axis, especially over short time periods. Here, we provide an example where we try to zoom on the peak of the epidemic, using the data by hospital:
plot(i.hosp)
Let us look at the data 40 days before and after the 1st of October:
period <- as.Date("2014-10-01") + c(-40, 40)
i.zoom <- subset(i.hosp, from=period[1], to = period[2])
detailed.x <- scale_x_date(labels = date_format("%a %d %B %Y"),
date_breaks = "2 weeks",
date_minor_breaks="week")
plot(i.zoom, border="black") + detailed.x + rotate.big
The previous plot has a fairly large legend which we may want to move around. Let us save the plot as a new object p and customize the legend:
p <- plot(i.zoom, border="black") + detailed.x + rotate.big
p + theme(axis.text.x = element_text(angle = 45, hjust = 1, size=12),
legend.position="top", legend.direction="horizontal",
legend.title = element_blank())