R/geospark.R

In datagistips/geosparklines: Create Geographic Sparklines

#' Generates Spatial Sparklines as Simple Feature Collection (sfc)
#'
#' @param coords Points coordinates
#' @param my_stats Values associated to points as a column-wise (long) format. For instance, one column = one date
#' @param width Total width of the line
#' @param height Total height of the line
#' @param direction Direction of lines, upward (north), downward (south), left (west) or right (east). For instance, "ne" will generate upward right lines, "sw" will generated downward left lines.
#' @param mode Normal or Log transformation mode
#' @details
#' This function calculates sparklines, based on statistical data, for instance Time-Series
#' You can choose width, height of the line, but also direction of the lines.
#' The Log transformation helps having smoother lines when having an exponential evolution.
#' You can pilot the rendering of individual lines with finer details, with `singleGeospark`
#' @seealso [geosparklines::singleGeospark()], `browseVignettes("geosparklines")`
#' @return returns a geometry collection (sfc)
#' @return returns a geometry collection (sfc)
#' @import magrittr sf dplyr
#' @examples
#' library(readr)
#' library(sf)
#' f <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")
#' geosparks <- geospark(f[, c('Long', 'Lat')], f[, 5:ncol(f)], width=20, height=20, mode = 'log')
#' plot(geosparks)
#' st_geometry(f) <- geosparks
#' @export

geospark <- function(coords, my_stats, width, height, direction = "ne", mode = "normal") {

  max_value <- max(my_stats)

  out <- lapply(1:nrow(coords), function(x) {
    v <- my_stats[x, ] %>% as.numeric
    my_coords <- coords[x, ] %>% as.matrix
    singleGeospark(my_coords,
                 v,
                 max_value,
                 width, height, direction,
                 mode)
  })

  res <- st_sfc(out)
  return(res)
}