diff --git a/R/data.R b/R/data.R
index c19cae0..bf924bf 100644
--- a/R/data.R
+++ b/R/data.R
@@ -1,20 +1,3 @@
-#' UNHCR data for 2017
-#'
-#' The dataset contains data about UNHCR's populations of concern for the year 2017.
-#'
-#' @format A data frame with 11237 observations and the following 6 variables:
-#'   \describe{
-#'     \item{\code{country_origin}}{Country of origin of population}
-#'     \item{\code{country_residence}}{Country / territory of asylum/residence of population}
-#'     \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
-#'     Returned IDPs, Stateless persons, Others of concern.}
-#'     \item{\code{value}}{Number of people concerned}
-#'     \item{\code{continent_residence}}{Continent of origin of population}
-#'     \item{\code{continent_origin}}{Continent of residence of population}
-#'   }
-#' @source UNHCR (The UN Refugee Agency) (\url{https://www.unhcr.org/})
-"unhcr_popstats_2017"
-
 
 #' UNHCR data by continent of origin
 #'
@@ -24,7 +7,7 @@
 #'   \describe{
 #'     \item{\code{year}}{Year concerned.}
 #'     \item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
-#'     Returned IDPs, Stateless persons, Others of concern.}    
+#'     Returned IDPs, Stateless persons, Others of concern.}
 #'     \item{\code{continent_origin}}{Continent of residence of population.}
 #'     \item{\code{n}}{Number of people concerned.}
 #'   }
@@ -40,7 +23,7 @@
 #' @format A data frame with 120 observations and the following 3 variables:
 #'   \describe{
 #'     \item{\code{date}}{date.}
-#'     \item{\code{type}}{Type of data : realized or forecast.}    
+#'     \item{\code{type}}{Type of data : realized or forecast.}
 #'     \item{\code{value}}{Value in giga-watt per hour.}
 #'   }
 #' @source Rte (Electricity Transmission Network in France) (\url{https://data.rte-france.com/})
@@ -53,7 +36,7 @@
 #' @format A data frame with 60 observations and the following 5 variables:
 #'   \describe{
 #'     \item{\code{datetime}}{Timestamp.}
-#'     \item{\code{open}}{Open value.}    
+#'     \item{\code{open}}{Open value.}
 #'     \item{\code{high}}{Highest value.}
 #'     \item{\code{low}}{Lowest value.}
 #'     \item{\code{close}}{Close value.}
@@ -63,14 +46,14 @@
 
 
 #' @title Paris Climate
-#' 
+#'
 #' @description Average temperature and precipitation in Paris for the period 1971-2000.
 #'
 #'
 #' @format A data frame with 12 observations and the following 3 variables:
 #'   \describe{
 #'     \item{\code{month}}{Month}
-#'     \item{\code{temperature}}{Temperature (in degree celsius).}    
+#'     \item{\code{temperature}}{Temperature (in degree celsius).}
 #'     \item{\code{precipitation}}{Precipitation (in mm).}
 #'   }
 #' @source Wikipedia (\url{https://fr.wikipedia.org/wiki/Climat_de_Paris})
diff --git a/data/unhcr_popstats_2017.rda b/data/unhcr_popstats_2017.rda
deleted file mode 100644
index 4f508b2..0000000
Binary files a/data/unhcr_popstats_2017.rda and /dev/null differ
diff --git a/examples/add-lines.R b/examples/add-lines.R
index 734ff3b..77eec94 100644
--- a/examples/add-lines.R
+++ b/examples/add-lines.R
@@ -2,34 +2,38 @@
 library(apexcharter)
 
 # On a column chart
-apex(
-  data = table(unhcr_popstats_2017$continent_residence),
-  aes(Var1, Freq),
-  "column"
-) %>% 
-  add_hline(value = 2100)
+unhcr_ts %>%
+  subset(year == 2017 & population_type == "Asylum-seekers") %>%
+  apex(
+    aes(continent_origin, n),
+    "column"
+  ) %>%
+  add_hline(value = 5e5)
 
 
 # On a scatter chart
 apex(
-  data = iris,
-  aes(Sepal.Length, Sepal.Width),
+  data = cars,
+  aes(speed, dist),
   "scatter"
-) %>% 
-  add_hline(value = mean(iris$Sepal.Width)) %>% 
-  add_vline(value = mean(iris$Sepal.Length))
+) %>%
+  add_hline(value = mean(cars$dist)) %>%
+  add_vline(value = mean(cars$speed))
 
 # With labels
 apex(
-  data = iris,
-  aes(Sepal.Length, Sepal.Width),
+  data = cars,
+  aes(speed, dist),
   "scatter"
-) %>% 
+) %>%
   add_hline(
-    value = mean(iris$Sepal.Width),
-    label = "Mean of Sepal.Width"
-  ) %>% 
+    value = mean(cars$dist),
+    label = "Mean of dist"
+  ) %>%
   add_vline(
-    value = mean(iris$Sepal.Length),
-    label = "Mean of Sepal.Length"
+    value = mean(cars$speed),
+    label = label(
+      text = "Mean of speed",
+      borderColor = "red"
+    )
   )
diff --git a/man/add-vh-lines.Rd b/man/add-vh-lines.Rd
index 35ed9c0..7e1ec4b 100644
--- a/man/add-vh-lines.Rd
+++ b/man/add-vh-lines.Rd
@@ -38,35 +38,39 @@ Add horizontal or vertical line
 library(apexcharter)
 
 # On a column chart
-apex(
-  data = table(unhcr_popstats_2017$continent_residence),
-  aes(Var1, Freq),
-  "column"
-) \%>\% 
-  add_hline(value = 2100)
+unhcr_ts \%>\%
+  subset(year == 2017 & population_type == "Asylum-seekers") \%>\%
+  apex(
+    aes(continent_origin, n),
+    "column"
+  ) \%>\%
+  add_hline(value = 5e5)
 
 
 # On a scatter chart
 apex(
-  data = iris,
-  aes(Sepal.Length, Sepal.Width),
+  data = cars,
+  aes(speed, dist),
   "scatter"
-) \%>\% 
-  add_hline(value = mean(iris$Sepal.Width)) \%>\% 
-  add_vline(value = mean(iris$Sepal.Length))
+) \%>\%
+  add_hline(value = mean(cars$dist)) \%>\%
+  add_vline(value = mean(cars$speed))
 
 # With labels
 apex(
-  data = iris,
-  aes(Sepal.Length, Sepal.Width),
+  data = cars,
+  aes(speed, dist),
   "scatter"
-) \%>\% 
+) \%>\%
   add_hline(
-    value = mean(iris$Sepal.Width),
-    label = "Mean of Sepal.Width"
-  ) \%>\% 
+    value = mean(cars$dist),
+    label = "Mean of dist"
+  ) \%>\%
   add_vline(
-    value = mean(iris$Sepal.Length),
-    label = "Mean of Sepal.Length"
+    value = mean(cars$speed),
+    label = label(
+      text = "Mean of speed",
+      borderColor = "red"
+    )
   )
 }
diff --git a/man/unhcr_popstats_2017.Rd b/man/unhcr_popstats_2017.Rd
deleted file mode 100644
index b953aba..0000000
--- a/man/unhcr_popstats_2017.Rd
+++ /dev/null
@@ -1,28 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{unhcr_popstats_2017}
-\alias{unhcr_popstats_2017}
-\title{UNHCR data for 2017}
-\format{
-A data frame with 11237 observations and the following 6 variables:
-\describe{
-\item{\code{country_origin}}{Country of origin of population}
-\item{\code{country_residence}}{Country / territory of asylum/residence of population}
-\item{\code{population_type}}{Populations of concern : Refugees, Asylum-seekers, Internally displaced persons (IDPs), Returned refugees,
-Returned IDPs, Stateless persons, Others of concern.}
-\item{\code{value}}{Number of people concerned}
-\item{\code{continent_residence}}{Continent of origin of population}
-\item{\code{continent_origin}}{Continent of residence of population}
-}
-}
-\source{
-UNHCR (The UN Refugee Agency) (\url{https://www.unhcr.org/})
-}
-\usage{
-unhcr_popstats_2017
-}
-\description{
-The dataset contains data about UNHCR's populations of concern for the year 2017.
-}
-\keyword{datasets}