#351 change from sf crop fxn to dplyr filtering for arc2 fxn

DESCRIPTION

@@ -7,7 +7,7 @@ Description: Client for many 'NOAA' data sources including the 'NCDC' climate
     for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
     Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
     tornado data via the 'NOAA' storm prediction center, and more.
-Version: 0.9.6.97
+Version: 0.9.6.98
 License: MIT + file LICENSE
 Encoding: UTF-8
 Language: en-US

R/arc2.R

@@ -4,15 +4,15 @@
 #' @param date (character/date) one or more dates of the form YYYY-MM-DD
 #' @param box (numeric) vector of length 4, of the form
 #' `xmin, ymin, xmax, ymax`. optional. If not given, no spatial filtering
-#' is done. If given, we use `sf::st_crop()` on a combined set of all dates,
+#' is done. If given, we use `dplyr::filter()` on a combined set of all dates,
 #' then split the output into tibbles by date
 #' @param ... curl options passed on to [crul::verb-GET]
 #' @references docs:
 #' <ftp://ftp.cpc.ncep.noaa.gov/fews/fewsdata/africa/arc2/ARC2_readme.txt>
 #' @note See [arc2_cache] for managing cached files
 #' @section box parameter:
 #' The `box` parameter filters the arc2 data to a bounding box you supply.
-#' The function that does the cropping to the bounding box is `sf::st_crop`.
+#' The function that does the cropping to the bounding box is `dplyr::filter`.
 #' You can do any filtering you want on your own if you do not supply
 #' `box` and then use whatever tools you want to filter the data by 
 #' lat/lon, date, precip values.
@@ -52,22 +52,13 @@ arc2 <- function(date, box = NULL, ...) {
     res <- stats::setNames(res, vapply(dates, asdate, ""))
     return(res)
   }
-  check4pkg("sf")
   assert(box, "numeric")
   tmpdf <- dplyr::bind_rows(res)
   filter_split(tmpdf, box)
 }
 
 filter_split <- function(x, box) {
-  z <- sf::st_as_sf(x, coords = c("lon", "lat"), crs = 4326)
-  cropped <- sf::st_crop(z, box)
-  cd <- data.frame(sf::st_coordinates(cropped))
-  df <- tibble::tibble(
-    date = cropped$date, 
-    lon = cd$X, 
-    lat = cd$Y,
-    precip = cropped$precip
-  )
+  df <- dplyr::filter(x, dplyr::between(lon, 9, 10), dplyr::between(lat, 4, 5))
   split(df, df$date)
 }