ggsurveillance is an R package with helpful tools and ggplot extensions for epidemiology, especially infectious disease surveillance and outbreak investigation. All functions provide tidy functional interfaces for easy integration with the tidyverse. For documentation and vignettes see: ggsurveillance.biostats.dev
This packages provides:
-
geom_epicurve()
: A ggplot geom for plotting epicurves- including
state_bin_date()
for date interval (week, month etc.) based binning of case numbers with perfect alignment with i.e. reporting week. - including
scale_y_cases_5er()
for better (case) count axis breaks and positioning. - including
geom_vline_year()
, which automatically detects the turn of the year(s) from the date or datetime axis and draws a vertical line.
- including
align_dates_seasonal()
: Align surveillance data for seasonal plots (e.g. flu season).create_agegroups()
: Create reproducible age groups with highly customizable labels.geom_epigantt()
: A geom for epigantt plots. Helpful to visualize overlapping time intervals for contact tracing (i.e. hospital outbreaks). (Beta)and more:
geometric_mean()
,expand_counts()
,
Creating Epicurves
library(ggplot2)
library(tidyr)
library(outbreaks)
library(ggsurveillance)
sars_canada_2003 |> #SARS dataset from outbreaks
pivot_longer(starts_with("cases"),
names_prefix = "cases_",
names_to = "origin") |>
ggplot(aes(x = date, weight = value, fill = origin)) +
geom_epicurve(date_resolution = "week") +
scale_x_date(date_labels = "W%V'%g", date_breaks = "2 weeks") +
scale_y_cases_5er() +
scale_fill_brewer(type = "qual", palette = 6) +
theme_classic()
Align surveillance data for seasonal comparison
library(ggplot2)
library(ggsurveillance)
influenza_germany |>
filter(AgeGroup == "00+") |>
align_dates_seasonal(dates_from = ReportingWeek,
date_resolution = "isoweek",
start = 28) -> df_flu_aligned
ggplot(df_flu_aligned, aes(x = date_aligned, y = Incidence)) +
stat_summary(
aes(linetype = "Historical Median (Min-Max)"), data = . %>% filter(!current_season),
fun.data = median_hilow, geom = "ribbon", alpha = 0.3) +
stat_summary(
aes(linetype = "Historical Median (Min-Max)"), data = . %>% filter(!current_season),
fun = median, geom = "line") +
geom_line(
aes(linetype = "2024/25"), data = . %>% filter(current_season), colour = "dodgerblue4", linewidth = 2) +
labs(linetype = "") +
scale_x_date(date_labels = "%b'%y") +
theme_bw() +
theme(legend.position = c(0.2,0.8))