mapzoom bugfix

.Rhistory

@@ -1,52 +1,3 @@
-readr::read_csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-03_Frances_Naomi/2023-02-21 categorical variables issue/count_elasmo/Gaus_Best_line_isl_grp.csv")
-# add check to see whether csv is categorical.
-if (class(tmp[, 1][[1]]) != "character") stop("csv is not a categorical/factorial variable")
-tmp <- tmp |>
-dplyr::mutate(ycentred = y - mean(y)) |>
-dplyr::rename(Category = tidyselect::last_col(offset = 2)) |> # no first_col option
-# alter axis titles
-dplyr::mutate(Category = dplyr::case_match(Category,
-"australes" ~ "Australes",
-# add more here. Or do this in csv
-.default = Category))
-tmp <-
-readr::read_csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-03_Frances_Naomi/2023-02-21 categorical variables issue/count_elasmo/Gaus_Best_line_isl_grp.csv")
-# add check to see whether csv is categorical.
-if (class(tmp[, 1][[1]]) != "character") stop("csv is not a categorical/factorial variable")
-tmp <- tmp |>
-dplyr::mutate(ycentred = y - mean(y)) |>
-dplyr::rename(Category = tidyselect::last_col(offset = 2)) |> # no first_col option
-# re-order the x axis for categorical variables so they are either
-# 1) in order from high to low or
-dplyr::arrange(ycentred)
-tmp <-
-readr::read_csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-03_Frances_Naomi/2023-02-21 categorical variables issue/count_elasmo/Gaus_Best_line_isl_grp.csv")
-# add check to see whether csv is categorical.
-if (class(tmp[, 1][[1]]) != "character") stop("csv is not a categorical/factorial variable")
-tmp <- tmp |>
-dplyr::mutate(ycentred = y - mean(y)) |>
-dplyr::rename(Category = tidyselect::last_col(offset = 2)) |> # no first_col option
-# re-order the x axis for categorical variables so they are either
-# 1) in order from high to low or
-dplyr::arrange(ycentred) |>
-dplyr::mutate(Category = ordered(Category, levels = Category))
-tmp <-
-readr::read_csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-03_Frances_Naomi/2023-02-21 categorical variables issue/count_elasmo/Gaus_Best_line_isl_grp.csv")
-# add check to see whether csv is categorical.
-if (class(tmp[, 1][[1]]) != "character") stop("csv is not a categorical/factorial variable")
-tmp <- tmp |>
-dplyr::mutate(ycentred = y - mean(y)) |>
-dplyr::rename(Category = tidyselect::last_col(offset = 2)) |> # no first_col option
-# re-order the x axis for categorical variables so they are either
-# 1) in order from high to low or
-dplyr::arrange(ycentred)
-tmp <-
-readr::read_csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-03_Frances_Naomi/2023-02-21 categorical variables issue/count_elasmo/Gaus_Best_line_isl_grp.csv")
-# add check to see whether csv is categorical.
-if (class(tmp[, 1][[1]]) != "character") stop("csv is not a categorical/factorial variable")
-tmp <- tmp |>
-dplyr::mutate(ycentred = y - mean(y)) |>
-dplyr::rename(Category = tidyselect::last_col(offset = 2)) |> # no first_col option
 # re-order the x axis for categorical variables so they are either
 # 1) in order from high to low or
 dplyr::arrange(ycentred) |>
@@ -510,3 +461,52 @@ alerts = FALSE,
 randomvar = TRUE,
 smooth = TRUE,
 savedir = "/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-12-06 Cat Wells/")
+library(gbm.auto)
+RSandVSsamples <- read.csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-12-06 Cat Wells/NarrowedVars_07272023_RSVS.csv")
+MatureRSGrid <- read.csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-12-06 Cat Wells/MatureRSGrid.csv")
+RSVScropmap <- sf::st_read(dsn = "/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-12-06 Cat Wells/Map/CroppedMap/Crop_Map.shp")
+setwd("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-12-06 Cat Wells/")
+# but gbm.mapsf not outputting data, whereas gbm.map IS for b&w. Why?
+# Abundance_Preds_only.csv col order = lat lon pred. Is this way due to changed order in grids?
+# gbm.auto L1787 : gbm.mapsf(predabund = grids[c(gridslat, gridslon, predabund)],
+# gbm.mapsf function defaults:
+# gbm.mapsf <- function(
+#     predabund = NULL, # predicted abundance data frame produced by gbm.auto (Abundance_Preds_only.csv), with Latitude, Longitude, and Predicted Abundance columns.
+#     predabundlon = 2, # Longitude column number.
+#     predabundlat = 1, # Latitude column number.
+#     predabundpreds = 3,
+# Looks right
+predabund <- read.csv("/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-12-06 Cat Wells/CPUESpawnVS/Abundance_Preds_only.csv")
+gbm.auto::gbm.mapsf(predabund = predabund,
+# predabundlon = 2, # Longitude column number.
+# predabundlat = 1, # Latitude column number.
+# predabundpreds = 3, # Predicted abundance column number.
+# myLocation = NULL, # location for extents, format c(xmin, ymin, xmax, ymax).
+# trim = TRUE, # remove NA & 0 values and crop to remaining date extents? Default TRUE.
+# scale100 = FALSE, # scale Predicted Abundance to 100? Default FALSE.
+# gmapsAPI = NULL, # enter your Google maps API here, quoted character string
+# mapsource = "google", # Source for ggmap::get_map; uses Stamen as fallback if no Google Maps API present. Options: "google", "stamen", "gbm.basemap".
+# googlemap = TRUE, # If pulling basemap from Google maps, this sets expansion factors since
+# # Google Maps tiling zoom setup doesn't align to myLocation extents.
+# maptype = "satellite",
+# darkenproportion = 0, # amount to darken the basemap, 0-1.
+# mapzoom = 6, # google: 3 (continent) - 21 (building). stamen: 0-18
+shape = RSVScropmap, # If mapsource is "gbm.basemap", enter the full path to gbm.basemaps downloaded map, typically Crop_Map.shp, including the .shp.
+# expandfactor = 0, # extents expansion factor for basemap. default was 1.6
+# colourscale = "viridis", # Scale fill colour scheme to use, default "viridis", other option is "gradient".
+# colorscale = NULL, # Scale fill colour scheme to use, default NULL, populating this will overwrite colourscale.
+# heatcolours = c("white", "yellow", "orange","red", "brown4"), # Vector of colours if gradient selected for colourscale, defaults to heatmap theme.
+# colournumber = 8, # Number of colours to spread heatcolours over, if gradient selected for colourscale. Default 8.
+studyspecies = "TEST",
+# plottitle = paste0("Predicted abundance of ", studyspecies),
+# plotsubtitle = "CPUE", # data %>% distinct(ID) %>% nrow() # 13
+# legendtitle = "CPUE",
+# plotcaption = paste0("gbm.auto::gbm.map, ", lubridate::today()),
+# axisxlabel = "Longitude",
+# axisylabel = "Latitude",
+legendposition = c(0.05, 0.18),
+# fontsize = 12,
+# fontfamily = "Times New Roman",
+# filesavename = paste0(lubridate::today(), "_", studyspecies, "_", legendtitle, ".png"),
+savedir = "/home/simon/Documents/Si Work/PostDoc Work/Gbmauto help/2022-12-06 Cat Wells/CPUESpawnVS/gbmmapsftest"
+)

R/gbm.mapsf.R

@@ -241,7 +241,7 @@ gbm.mapsf <- function(
     myLocation <- c(xmin, ymin, xmax, ymax)
   }
 
-  if (mapsource == "google" & mapzoom == NULL) {
+  if (mapsource == "google" & is.null(mapzoom)) {
     # Created lookup table for degrees to mapzoom by eye at all zoom levels
     lonvec <- c(0.00042724609375,
                 0.0008544921875,