R/gettotalcoveragetransactions.R

In chloeanalyst/coverageR: CoverageR - Calculate coverage of location sets.

#' CoverageR
#'
#' Calculates whether locations are within a defined radius.
#'
#'
#'
#' @param locations Data Frame of locations
#' @param lon Longitude for point of interest eg: The starting point
#' @param lat Latitude for point of interest eg: The starting point
#' @param distance Value for the size of the radius in KM eg: 1.4 or 3.0
#'
#' @author Chloe Morgan
#'
#' @import geosphere
#' @import purrr
#' @import dplyr
#'
#' @return Table containing coverage information.
#' @export
#'
#' @examples
#' \dontrun{
#'
#' Example One
#'
#'distances <- c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1)
#'walking_time <- function(i) {
#'  distance <- i
#'  print(i)
#'  output <- get.multi.coverage(hotel,stop_data,i)
#'  output <- output %>% cbind(stop_locations)
#'  output <- output %>% distinct
#'  output$distance <- distance
#'  return(output)
#'  }
#'
#'  result <- map_df(distances, walking_time)
#'
#'
#' }
#'


#                                                                                     Calculate the number of locations with a radius of a point.




get.total.coverage.transactions <- function(locations,lon,lat,distance) {

  output <- data.frame(locations,                                                       #Calculate the distance of the point of interest for each row in locations data frame.
                       coverage = distHaversine(
                         locations,
                         c (lon,lat)
                       ) / 1000 < distance)                    # Convert the distance to Kilometers.


  result <- table(output$coverage)                                                      # Extract the coverage results from the output.


  result$coverage_percentage <- round(output %>% filter(coverage == TRUE) %>% nrow() / nrow(hotel_locations) * 100,2)    # Calculate the percentage of locations within range of the point of interest.

  result$covered_locations <- output %>% filter(coverage == TRUE) %>% nrow()                 # Calculate the total number of locations that are within range of the point of interest.



  result$total_locations <- nrow(locations)                                             # The total number of locations in the data set.

  result$total_unique_locations <- nrow(unique(locations))

  combined <- cbind(hotel, covered = output[, 3])
  covered_transactions <- combined %>% filter( covered == TRUE ) %>% summarise( transactions = sum(transactions))
  total_transactions <- combined %>% summarise( transactions = sum(transactions))



  result$transactions <- covered_transactions$transactions
  result$total_transactions <- total_transactions$transactions

  result$percentage_transactions <- round(result$transactions / result$total_transactions * 100,2)


  result <- tibble(result$coverage_percentage,                                          # Create a tibble of results.
                   result$covered_locations,
                   result$total_locations,
                   result$total_unique_locations,
                   result$transactions,
                   result$total_transactions,
                   result$percentage_transactions)



  names(result) <- c("Coverage %",                                                      # Rename the columns.
                     "Count of Locations Covered",
                     "Total Locations",
                     "Total Unique Locations",
                     "Transactions Covered",
                     "Total Transactions",
                     "Transaction %")

  return(result)                                                                        # Prints the output when function is called.

}