pointvis: Point Data Visualization

#' Clustering of Point Data
#'
#' This function allows you to visualize an input point data file (as a CSV) on top of a basemap, as point clusters. As one zooms in, specific data points are revealed with their particular locations. You may additionally select a field to use for popups on the points, so the map becomes interactive. NOTE: the fields for latitude and longitude (coordinates to plot the points) must be labeled as "lat" and "lon" in your input CSV file (without the quotation marks)
#' @param dataset Path to CSV file used as input
#' @param field Optional field that will enable informational popups upon hover (note that if no field is desired as input, an empty set of quotations must be specified (e.g., " "))
#' @keywords point clustering, data visualization
#' @examples
#' showClusters(dataset = "../data/food.csv", field = STORE_NAME)
#' @return A map of the points clustered based on proximity
#' @export
showClusters <- function(dataset, field)
{
  ## Load necessary libraries (just in case!)
  library(leaflet)
  library(leaflet.extras)
  library(rMaps)
  library(sp)
  library(magrittr)
  library(ggmap)
  library(dplyr)

  ## Load file from function call (path, separated by a comma because it should be a CSV file)
  ct <- read.csv(dataset, sep = ",")

  ## Add leaflet tiles (basemap)
  map <- leaflet(ct) %>% addTiles('http://korona.geog.uni-heidelberg.de/tiles/roads/x={x}&y={y}&z={z}', attribution = 'OpenStreetMap')

  ## Calculate mean for x and y, so we know where to center the map in the next step - attribute to the chosen lat/long columns from CSV file (i.e., 'lat' and 'lon')
  meanX <- mean(ct$lat)
  meanY <- mean(ct$lon)

  ## Center map and set zoom based on lat/long center means (from previous step)
  map %>% setView(meanX, meanY, zoom = 10)

  ## Push point data from file, and set option for point data visualization (i.e., marker clustering). Set label field for popups
  leaflet(ct) %>% addTiles('http://korona.geog.uni-heidelberg.de/tiles/roads/x={x}&y={y}&z={z}', attribution = 'OpenStreetMap') %>%
    addMarkers(clusterOptions = markerClusterOptions(), label = ~as.character(STORE_NAME))
}


# showClusters(dataset = "data/food.csv", field = )  ## not working with field parameter yet...But will be fixed in the future! :)