diff --git a/R/calc_new_immunity.R b/R/calc_new_immunity.R
index 26d314d..aa0547f 100644
--- a/R/calc_new_immunity.R
+++ b/R/calc_new_immunity.R
@@ -66,6 +66,21 @@ assert_valid_targeting <- function(x, name = deparse(substitute(x))) {
 ##' @return A dataframe containing the `popim_population` aggregated by age.
 ##' @author Tini Garske
 ##' @export
+##' @examples
+##' ## set up population and vaccination activities:
+##' pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005,
+##'                         age_min = 0, age_max = 10)
+##' vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002),
+##'                               age_first = 0, age_last = 0,
+##'                               coverage = 0.8, doses = NA,
+##'                               targeting = "random")
+##'
+##' ## update the population immunity based on the vaccination activities:
+##' pop <- apply_vacc(pop, vacc)
+##'
+##' ## calculate the population immunity aggregated across ages:
+##' pop_aggregated <- calc_pop_immunity(pop)
+##'
 calc_pop_immunity <- function(pop) {
 
     assert_population(pop)
diff --git a/R/plotting.R b/R/plotting.R
index 711dea5..a362e74 100644
--- a/R/plotting.R
+++ b/R/plotting.R
@@ -24,6 +24,24 @@ NULL
 ##' @rdname plotting
 ##' @export
 ##' @author Tini Garske
+##' @examples
+##' ## set up population and vaccination activities:
+##' pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005,
+##'                         age_min = 0, age_max = 10)
+##' vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002),
+##'                               age_first = 0, age_last = 0,
+##'                               coverage = 0.8, doses = NA,
+##'                               targeting = "random")
+##'
+##' ## update the population immunity based on the vaccination activities:
+##' pop <- apply_vacc(pop, vacc)
+##'
+##' ## plot the population size by age and time:
+##' plot_pop_size(pop)
+##'
+##' ## plot the population immunity by age and time:
+##' plot_immunity(pop)
+##'
 plot_immunity <- function(pop) {
 
     assert_population(pop)
diff --git a/R/popim_vacc_activities.R b/R/popim_vacc_activities.R
index bbfb8d7..71c347a 100644
--- a/R/popim_vacc_activities.R
+++ b/R/popim_vacc_activities.R
@@ -226,6 +226,21 @@ as_vacc_activities <- function(df) {
 ##'     updated to have both `doses` and `coverage` information.
 ##' @author Tini Garske
 ##' @export
+##' @examples
+##' ## set up some vaccination activities:
+##' vacc <- popim_vacc_activities(region = "UK", year = 2001:2003,
+##'                               age_first = 0, age_last = 0,
+##'                               coverage = c(0.8, 0.8, NA),
+##'                               doses = c(NA, NA, 60),
+##'                               targeting = "random")
+##' ## set up a population to which these activities shall apply:
+##' pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005,
+##'                         age_min = 0, age_max = 10)
+##' pop$pop_size <- 100 ## cohort size of 100 for all cohorts
+##'
+##' ## fill in missing coverage/doses information based on population size:
+##' vacc <- complete_vacc_activities(vacc, pop)
+##'
 complete_vacc_activities <- function(vacc, pop) {
     validate_vacc_activities(vacc)
     stopifnot(is_population(pop))
diff --git a/man/calc_pop_immunity.Rd b/man/calc_pop_immunity.Rd
index 2be538d..a4fa3fd 100644
--- a/man/calc_pop_immunity.Rd
+++ b/man/calc_pop_immunity.Rd
@@ -16,6 +16,22 @@ A dataframe containing the \code{popim_population} aggregated by age.
 \description{
 Calculate the overall population immunity (aggregating over age)
 from the supplied \code{popim_population} object.
+}
+\examples{
+## set up population and vaccination activities:
+pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005,
+                        age_min = 0, age_max = 10)
+vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002),
+                              age_first = 0, age_last = 0,
+                              coverage = 0.8, doses = NA,
+                              targeting = "random")
+
+## update the population immunity based on the vaccination activities:
+pop <- apply_vacc(pop, vacc)
+
+## calculate the population immunity aggregated across ages:
+pop_aggregated <- calc_pop_immunity(pop)
+
 }
 \author{
 Tini Garske
diff --git a/man/complete_vacc_activities.Rd b/man/complete_vacc_activities.Rd
index b87da57..6a97c49 100644
--- a/man/complete_vacc_activities.Rd
+++ b/man/complete_vacc_activities.Rd
@@ -23,6 +23,22 @@ using the target population size implied by the \code{popim_population}
 object supplied. If both coverage and doses are given for any
 activity, the function checks if they are consistent with the
 population size, and fails if there are any inconsistencies.
+}
+\examples{
+## set up some vaccination activities:
+vacc <- popim_vacc_activities(region = "UK", year = 2001:2003,
+                              age_first = 0, age_last = 0,
+                              coverage = c(0.8, 0.8, NA),
+                              doses = c(NA, NA, 60),
+                              targeting = "random")
+## set up a population to which these activities shall apply:
+pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005,
+                        age_min = 0, age_max = 10)
+pop$pop_size <- 100 ## cohort size of 100 for all cohorts
+
+## fill in missing coverage/doses information based on population size:
+vacc <- complete_vacc_activities(vacc, pop)
+
 }
 \author{
 Tini Garske
diff --git a/man/plotting.Rd b/man/plotting.Rd
index 897908a..25df68b 100644
--- a/man/plotting.Rd
+++ b/man/plotting.Rd
@@ -36,6 +36,25 @@ immune, therefore varying between 0 and 1.
 
 As the returned object is a regular \code{ggplot} object, it can be
 further modified with the ususal \code{ggplot2} syntax.
+}
+\examples{
+## set up population and vaccination activities:
+pop <- popim_population(region = "UK", year_min = 2000, year_max = 2005,
+                        age_min = 0, age_max = 10)
+vacc <- popim_vacc_activities(region = "UK", year = c(2001, 2002),
+                              age_first = 0, age_last = 0,
+                              coverage = 0.8, doses = NA,
+                              targeting = "random")
+
+## update the population immunity based on the vaccination activities:
+pop <- apply_vacc(pop, vacc)
+
+## plot the population size by age and time:
+plot_pop_size(pop)
+
+## plot the population immunity by age and time:
+plot_immunity(pop)
+
 }
 \author{
 Tini Garske