diff --git a/R/plotting.R b/R/plotting.R
index 932590a..a22a841 100644
--- a/R/plotting.R
+++ b/R/plotting.R
@@ -9,7 +9,10 @@
 ##' The colour in each cell corresponds to:
 ##' * for `plot_immunity()`: the proportion of each cohort that is
 ##'   immune, therefore varying between 0 and 1.
-##' * for `plot_pop_size()`: the size of each cohort.
+##' * for `plot_pop_size(rel = FALSE)`: the absolute size of each cohort.
+##' * for `plot_pop_size(rel = TRUE)`: the cohort size divided by the
+##'   maximum total population size (aggregated across all cohorts)
+##'   reached at any point in time for each region in question.
 ##'
 ##' As the returned object is a regular `ggplot` object, it can be
 ##' further modified with the ususal `ggplot2` syntax.
@@ -19,7 +22,7 @@
 ##'     [popim_population()].
 ##' @param rel logical to indicate whether to use relative or absolute
 ##'     population size in `plot_pop_size()`. Defaults to FALSE
-##'     (plotting absolute population size).
+##'     (plotting absolute population size). 
 ##' @param cols vector of 2 colours to be used to generate the
 ##'     (continuous) colour palette for plotting. Defaults to
 ##'     `c("whitesmoke", "midnightblue")`.
@@ -42,12 +45,37 @@ NULL
 ##' ## update the population immunity based on the vaccination activities:
 ##' pop <- apply_vacc(pop, vacc)
 ##'
+##' ## plot the population immunity by age and time:
+##' plot_immunity(pop)
+##'
+##' ## adding some population size manually:
+##' ##  adding some population size manually:
+##' pop$pop_size <- pop$cohort - 1990
+##'
 ##' ## plot the population size by age and time:
 ##' plot_pop_size(pop)
 ##'
-##' ## plot the population immunity by age and time:
+##' ##-----------------------------------------------------------------------
+##' ## setting up a population with multiple regions:
+##' pop <- popim_population(region = c("A", "B"),
+##'                         year_min = 2000, year_max = 2005,
+##'                         age_min = 0, age_max = 10)
+##' pop$pop_size <- pop$cohort - 1990
+##' pop$pop_size[pop$region == "A"] <- 5 * pop$pop_size[pop$region == "A"]
+##'
+##' ## adding some vaccination activities:
+##' vacc <- popim_vacc_activities(region = c("A", "A", "B"),
+##'                               year = c(2001, 2002, 2003),
+##'                               age_first = c(0,0,0), age_last = c(0,0,10),
+##'                               coverage = 0.8, doses = NA,
+##'                               targeting = "random")
+##' pop <- apply_vacc(pop, vacc)
+##'
 ##' plot_immunity(pop)
 ##'
+##' plot_pop_size(pop)
+##' plot_pop_size(pop, rel = TRUE)
+##'
 plot_immunity <- function(pop, cols = c("whitesmoke", "midnightblue")) {
 
     assert_population(pop)
diff --git a/man/plotting.Rd b/man/plotting.Rd
index 8160808..5e9a6a4 100644
--- a/man/plotting.Rd
+++ b/man/plotting.Rd
@@ -39,7 +39,10 @@ The colour in each cell corresponds to:
 \itemize{
 \item for \code{plot_immunity()}: the proportion of each cohort that is
 immune, therefore varying between 0 and 1.
-\item for \code{plot_pop_size()}: the size of each cohort.
+\item for \code{plot_pop_size(rel = FALSE)}: the absolute size of each cohort.
+\item for \code{plot_pop_size(rel = TRUE)}: the cohort size divided by the
+maximum total population size (aggregated across all cohorts)
+reached at any point in time for each region in question.
 }
 
 As the returned object is a regular \code{ggplot} object, it can be
@@ -57,12 +60,37 @@ vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002),
 ## update the population immunity based on the vaccination activities:
 pop <- apply_vacc(pop, vacc)
 
+## plot the population immunity by age and time:
+plot_immunity(pop)
+
+## adding some population size manually:
+##  adding some population size manually:
+pop$pop_size <- pop$cohort - 1990
+
 ## plot the population size by age and time:
 plot_pop_size(pop)
 
-## plot the population immunity by age and time:
+##-----------------------------------------------------------------------
+## setting up a population with multiple regions:
+pop <- popim_population(region = c("A", "B"),
+                        year_min = 2000, year_max = 2005,
+                        age_min = 0, age_max = 10)
+pop$pop_size <- pop$cohort - 1990
+pop$pop_size[pop$region == "A"] <- 5 * pop$pop_size[pop$region == "A"]
+
+## adding some vaccination activities:
+vacc <- popim_vacc_activities(region = c("A", "A", "B"),
+                              year = c(2001, 2002, 2003),
+                              age_first = c(0,0,0), age_last = c(0,0,10),
+                              coverage = 0.8, doses = NA,
+                              targeting = "random")
+pop <- apply_vacc(pop, vacc)
+
 plot_immunity(pop)
 
+plot_pop_size(pop)
+plot_pop_size(pop, rel = TRUE)
+
 }
 \author{
 Tini Garske