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
geom_epicurve(): A ggplot geom for plotting
epicurves
stat_bin_date() for date interval (week,
month etc.) based binning of case numbers with perfect alignment with
i.e. reporting week.scale_y_cases_5er() for better (case) count
axis breaks and positioning.geom_vline_year(), which automatically
detects the turn of the year(s) from the date or datetime axis and draws
a vertical line.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).
scale_y_discrete_reverse() which reverses the
order of the categorical scale.theme_mod_ functions for ggplot2 theme
modifications:
theme_mod_legend_position() etc. to adjust the legend
positions.theme_mod_rotate_x_axis_labels() etc. for rotating x
axis labels.theme_mod_remove_minor_grid() etc. to remove the minor
grid lines (x, y or both) or all grind lines.Additional utilities: geometric_mean(),
expand_counts(), and more
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()
library(ggplot2)
library(dplyr)
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 = NULL) +
scale_x_date(date_labels = "%b'%y") +
theme_bw() +
theme_mod_legend_position(position.inside = c(0.2, 0.8))
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggsurveillance)
# Transform to long format
linelist_hospital_outbreak |>
pivot_longer(
cols = starts_with("ward"),
names_to = c(".value", "num"),
names_pattern = "ward_(name|start_of_stay|end_of_stay)_([0-9]+)",
values_drop_na = TRUE
) -> df_stays_long
linelist_hospital_outbreak |>
pivot_longer(cols = starts_with("pathogen"), values_to = "date") -> df_detections_long
# Plot
ggplot(df_stays_long) +
geom_epigantt(aes(y = Patient, xmin = start_of_stay, xmax = end_of_stay, color = name)) +
geom_point(aes(y = Patient, x = date, shape = "Date of pathogen detection"), data = df_detections_long) +
scale_y_discrete_reverse() +
theme_bw() +
theme_mod_legend_bottom()