cleanup

worldbank · Dec 7, 2023 · 6a2f042 · 6a2f042
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -8,23 +8,23 @@ Authors@R:
 Description: Create geographically referenced rasters of NASA Black Marble nighttime lights data <https://blackmarble.gsfc.nasa.gov/>.
 License: MIT + file LICENSE
 Encoding: UTF-8
-VignetteBuilder: utils
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.1
 URL: https://worldbank.github.io/blackmarbler/
 BugReports: https://github.com/worldbank/blackmarbler/issues
 Imports:
   readr,
+  hdf5r,
   dplyr,
   purrr,
   lubridate,
   raster,
   sf,
   exactextractr,
   stringr,
-  hdf5r,
   httr
 Suggests: 
   geodata,
   ggplot2,
-  tidyr
+  tidyr,
+  knitr
diff --git a/NAMESPACE b/NAMESPACE
@@ -7,8 +7,9 @@ import(exactextractr)
 import(hdf5r)
 import(httr)
 import(lubridate)
-import(purrr)
-import(raster, except = c(union, select, intersect, origin, tail, head))
+import(purrr, except = c(flatten_df, values))
+import(raster, except = c(union, select, intersect, origin, tail, head, values))
+
 import(readr)
 import(sf)
 import(stringr)
diff --git a/R/blackmarbler.R b/R/blackmarbler.R
@@ -98,15 +98,21 @@ file_to_raster <- function(f,
       if(variable %in% c("DNB_BRDF-Corrected_NTL",
                          "Gap_Filled_DNB_BRDF-Corrected_NTL",
                          "Latest_High_Quality_Retrieval")){
+
         for(val in quality_flag_rm){ # out[qf %in% quality_flag_rm] doesn't work, so loop
           out[qf == val] <- NA
         }
       }
     }
 
+    # Above doesn't fully capture
+    if(variable %in% "Latest_High_Quality_Retrieval"){
+      out[out == 255] <- NA
+    }
+
     #### Monthly/Annually
   } else{
-
+    
     lat <- h5_data[["HDFEOS/GRIDS/VIIRS_Grid_DNB_2d/Data Fields/lat"]][]
     lon <- h5_data[["HDFEOS/GRIDS/VIIRS_Grid_DNB_2d/Data Fields/lon"]][]
 
@@ -275,10 +281,10 @@ create_dataset_name_df <- function(product_id,
   }
 
   #### Create data
-  files_df <- map2_dfr(param_df$year,
-                       param_df$day,
-                       read_bm_csv,
-                       product_id)
+  files_df <- purrr::map2_dfr(param_df$year,
+                              param_df$day,
+                              read_bm_csv,
+                              product_id)
 
   return(files_df)
 }
@@ -301,11 +307,11 @@ download_raster <- function(file_name,
   download_path <- file.path(temp_dir, file_name)
 
   if(quiet == FALSE) message(paste0("Processing: ", file_name))
-
-  response <- GET(url, 
-                  add_headers(headers), 
-                  write_disk(download_path, overwrite = TRUE),
-                  progress())
+  
+  response <- httr::GET(url, 
+                        add_headers(headers), 
+                        write_disk(download_path, overwrite = TRUE),
+                        progress())
 
   if(response$status_code != 200){
     message("Error in downloading data")
@@ -660,17 +666,17 @@ bm_extract <- function(roi_sf,
 #'
 #' @export
 #'
-#' @import purrr
-#' @import stringr
+#' @import readr
 #' @import hdf5r
 #' @import dplyr
 #' @import sf
-#' @import lubridate
-#' @import readr
 #' @import exactextractr
+#' @import stringr
 #' @import httr
-#' @rawNamespace import(raster, except = c(union, select, intersect, origin, tail, head))
-
+#' @import lubridate
+#' @rawNamespace import(purrr, except = c(flatten_df, values))
+#' @rawNamespace import(raster, except = c(union, select, intersect, origin, tail, head, values))
+#' 
 # @rawNamespace import(utils, except = c(stack, unstack))
 bm_raster <- function(roi_sf,
                       product_id,

diff --git a/man/bm_tiles_sf.Rd b/man/bm_tiles_sf.Rd
diff --git a/vignettes/assess-quality.Rmd b/vignettes/assess-quality.Rmd
@@ -45,7 +45,7 @@ This page illustrates how to examine the quality of nighttime lights data.
 
 We first load packages and obtain a polygon for a region of interest; for this example, we use Switzerland. 
 
-```{r}
+```{r, results='hide'}
 library(blackmarbler)
 library(geodata)
 library(sf)
@@ -56,6 +56,7 @@ library(exactextractr)
 library(lubridate)
 library(tidyr)
 library(geodata)
+library(knitr)
 
 bearer <- "BEARER-TOKEN-HERE"
 ```
@@ -77,7 +78,7 @@ Below shows an example examining quality for daily data (`VNP46A2`).
 
 We download data for January 1st, 2023. When the `variable` parameter is not specified, `bm_raster` creates a raster using the `Gap_Filled_DNB_BRDF-Corrected_NTL` variable for daily data.
 
-```{r}
+```{r, results='hide'}
 ntl_r <- bm_raster(roi_sf = roi_sf,
                    product_id = "VNP46A2",
                    date = "2023-01-01",
@@ -137,7 +138,7 @@ print(n_non_na_pixel_num / n_pixel_num)
 
 By default, the `bm_extract` function computes these values:
 
-```{r}
+```{r, results='hide'}
 ntl_df <- bm_extract(roi_sf = roi_sf,
                      product_id = "VNP46A2",
                    date = seq.Date(from = ymd("2023-01-01"),
@@ -184,7 +185,7 @@ For daily data, the quality values are:
 
 We can map quality by using the `Mandatory_Quality_Flag` variable.
 
-```{r}
+```{r, results='hide'}
 quality_r <- bm_raster(roi_sf = roi_sf,
                        product_id = "VNP46A2",
                        date = "2023-01-01",
@@ -229,7 +230,7 @@ ggplot() +
 
 The `quality_flag_rm` parameter determines which pixels are set to `NA` based on the quality indicator. By default, only pixels with a value of `255` are filtered out. However, if we only want data for good quality pixels, we can adjust the `quality_flag_rm` parameter.
 
-```{r}
+```{r, results='hide'}
 ntl_good_qual_r <- bm_raster(roi_sf = roi_sf,
                              product_id = "VNP46A2", 
                              date = "2023-01-01",
@@ -273,7 +274,7 @@ ggplot() +
 
 By default, the `bm_raster` function uses the `Gap_Filled_DNB_BRDF-Corrected_NTL` variable for daily data. Gap filling indicates that some poor quality pixels use data from a previous date; the `Latest_High_Quality_Retrieval` indicates the date the nighttime lights value came from.
 
-```{r}
+```{r, results='hide'}
 ntl_tmp_gap_r <- bm_raster(roi_sf = roi_sf,
                            product_id = "VNP46A2",
                            date = "2023-01-01",
@@ -290,8 +291,6 @@ ntl_tmp_gap_r <- ntl_tmp_gap_r |> mask(roi_sf)
 ntl_tmp_gap_df <- rasterToPoints(ntl_tmp_gap_r, spatial = TRUE) |> as.data.frame()
 names(ntl_tmp_gap_df) <- c("value", "x", "y")
 
-#ntl_tmp_gap_df$value[ntl_tmp_gap_df$value == 255] <- NA
-
 ##### Map 
 ggplot() +
   geom_raster(data = ntl_tmp_gap_df, 
@@ -311,7 +310,7 @@ ggplot() +
 
 Instead of using `Gap_Filled_DNB_BRDF-Corrected_NTL`, we could ignore gap filled observations---using the `DNB_BRDF-Corrected_NTL` variable. Here, we also remove poor quality pixels.
 
-```{r}
+```{r, results='hide'}
 ntl_r <- bm_raster(roi_sf = roi_sf,
                    product_id = "VNP46A2",
                    date = "2023-01-01",
@@ -359,7 +358,7 @@ Below shows an example examining quality for monthly data (`VNP46A3`). The same
 
 We download data for January 2023. When the `variable` parameter is not specified, `bm_raster` creates a raster using the `NearNadir_Composite_Snow_Free` variable for monthly and annual data---which is nighttime lights, removing effects from snow cover. 
 
-```{r}
+```{r, results='hide'}
 ntl_r <- bm_raster(roi_sf = roi_sf,
                    product_id = "VNP46A3", 
                    date = "2023-01-01",
@@ -402,7 +401,7 @@ ggplot() +
 
 Black Marble removes poor quality observations, such as pixels covered by clouds. To determine the number of observations used to generate nighttime light values for each pixel, we add `_Num` to the variable name.
 
-```{r}
+```{r, results='hide'}
 cf_r <- bm_raster(roi_sf = roi_sf,
                   product_id = "VNP46A3",
                   date = "2023-01-01",
@@ -451,7 +450,7 @@ For monthly and annual data, the quality values are:
 
 We can map quality by adding `_Quality` to the variable name.
 
-```{r}
+```{r, results='hide'}
 quality_r <- bm_raster(roi_sf = roi_sf,
                        product_id = "VNP46A3",
                        date = "2023-01-01",
@@ -496,7 +495,7 @@ ggplot() +
 
 The `quality_flag_rm` parameter determines which pixels are set to `NA` based on the quality indicator. By default, only pixels with a value of `255` are filtered out. However, if we also want to remove poor quality pixels, we can adjust the `quality_flag_rm` parameter.
 
-```{r}
+```{r, results='hide'}
 ntl_good_qual_r <- bm_raster(roi_sf = roi_sf,
                              product_id = "VNP46A3", 
                              date = "2023-01-01",

diff --git a/vignettes/assess-quality.html b/vignettes/assess-quality.html