diff --git a/R/indicator_native_range_year.R b/R/indicator_native_range_year.R
new file mode 100644
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+++ b/R/indicator_native_range_year.R
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+#' Create interactive plot for counts per native region and year of introduction
+#' 
+#' Based on \code{\link{countYearProvince}} plot from grofwild
+#' @param type character, native_range level of interest should be one of 
+#' \code{c("native_continent", "native_range")} 
+#' @inheritParams countYearProvince
+#' @return list with:
+#' \itemize{
+#' \item{'plot': }{plotly object, for a given specie the observed number 
+#' per year and per province is plotted in a stacked bar chart}
+#' \item{'data': }{data displayed in the plot, as data.frame with:
+#' \itemize{
+#' \item{'year': }{year at which the animal was introduced}
+#' \item{'nativeRange': }{native range of the introduced animal}
+#' \item{'aantal': }{counts of animals}
+#' }
+#' }
+#' }
+#' @import plotly
+#' @importFrom reshape2 melt
+#' @importFrom INBOtheme inbo.2015.colours
+#' @export
+countYearNativerange <- function(data, jaartallen = NULL, 
+    type = c("native_continent", "native_range"),
+    width = NULL, height = NULL) {
+  
+  type <- match.arg(type)
+  
+  if (is.null(jaartallen))
+    jaartallen <- sort(unique(data$first_observed))
+  
+  plotData <- data
+  plotData$locatie <- switch(type,
+      native_range = plotData$native_range,
+      native_continent = plotData$native_continent
+  )
+  
+  # Select data
+  plotData <- plotData[plotData$first_observed %in% jaartallen, c("first_observed", "locatie")]
+  plotData <- plotData[!is.na(plotData$first_observed) & !is.na(plotData$locatie), ]
+  
+  # Exclude unused provinces
+  plotData$locatie <- as.factor(plotData$locatie)    
+  plotData$locatie <- droplevels(plotData$locatie)
+  
+  # Summarize data per native_range and year
+  plotData$first_observed <- with(plotData, factor(first_observed, levels = 
+              min(jaartallen):max(jaartallen)))
+  
+  summaryData <- melt(table(plotData), id.vars = "first_observed")
+  
+  # Summarize data per year
+  totalCount <- table(plotData$first_observed)
+  
+  
+  # For optimal displaying in the plot
+  summaryData$locatie <- as.factor(summaryData$locatie)
+  summaryData$locatie <- factor(summaryData$locatie, levels = rev(levels(summaryData$locatie)))
+  summaryData$first_observed <- as.factor(summaryData$first_observed)
+  
+  colors <- rev(inbo.2015.colours(n = nlevels(summaryData$locatie)))
+  title <- yearToTitleString(year = c(jaartallen[1], tail(jaartallen, 1)), brackets = FALSE)
+  
+  # Create plot
+  pl <- plot_ly(data = summaryData, x = ~first_observed, y = ~value, color = ~locatie,
+          colors = colors, type = "bar",  width = width, height = height) %>%
+      layout(title = title,
+          xaxis = list(title = "Jaar"), 
+          yaxis = list(title = "Aantal", tickformat = ",d"),
+          margin = list(b = 80, t = 100), 
+          barmode = ifelse(nlevels(summaryData$first_observed) == 1, "group", "stack"),
+          annotations = list(x = levels(summaryData$first_observed), 
+              y = totalCount, 
+              text = paste(ifelse(nlevels(summaryData$first_observed) == 1, "totaal:", ""), ifelse(totalCount > 0, totalCount, "")),
+              xanchor = 'center', yanchor = 'bottom',
+              showarrow = FALSE),
+          showlegend = TRUE)  
+  
+  # To prevent warnings in UI
+  pl$elementId <- NULL
+  
+  # Change variable name
+  names(summaryData)[names(summaryData) == "value"] <- "aantal"
+  names(summaryData)[names(summaryData) == "first_observed"] <- "jaar"
+  names(summaryData)[names(summaryData) == "locatie"] <- "regio van oorsprong"
+    
+  return(list(plot = pl, data = summaryData))
+  
+}