dependencies

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: blackmarbler
 Title: Black Marble Data and Statistics
-Version: 0.1.0
+Version: 0.1.1
 Authors@R: 
     c(person("Robert", "Marty", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-3164-3813")),
@@ -27,5 +27,4 @@ Imports:
 Suggests: 
   geodata,
   ggplot2,
-  tidyr,
   knitr

NAMESPACE

@@ -13,4 +13,4 @@ import(raster, except = c(union, select, intersect, origin, tail, head, values))
 import(readr)
 import(sf)
 import(stringr)
-import(tidyr)
+import(tidyr, except = c(extract))

R/blackmarbler.R

@@ -397,6 +397,18 @@ define_date_name <- function(date_i, product_id){
   return(date_name_i)
 }
 
+
+count_n_obs <- function(values, coverage_fraction) {
+  ## Function to count observations, for exact_extract
+
+  orig_vars <- names(values)
+
+  values %>%
+    dplyr::mutate(across(orig_vars, ~ as.numeric(!is.na(.)) )) %>%
+    dplyr::summarise(across(orig_vars, sum, .names = "n_non_na_pixels.{.col}"),
+                     across(orig_vars, ~length(.), .names = "n_pixels.{.col}"))
+}
+
 #' Extract and Aggregate Black Marble Data
 #'
 #' Extract and aggregate nighttime lights data from [NASA Black Marble data](https://blackmarble.gsfc.nasa.gov/)
@@ -547,18 +559,6 @@ bm_extract <- function(roi_sf,
                              NArule = NArule)
 
     #### Extract
-
-    ## Function to count observations
-    count_n_obs <- function(values, coverage_fraction) {
-
-      orig_vars <- names(values)
-
-      values %>%
-        dplyr::mutate(across(orig_vars, ~ as.numeric(!is.na(.)) )) %>%
-        dplyr::summarise(across(orig_vars, sum, .names = "n_non_na_pixels.{.col}"),
-                         across(orig_vars, ~length(.), .names = "n_pixels.{.col}"))
-    }
-
     roi_df <- roi_sf %>% st_drop_geometry()
     roi_df$date <- NULL
 
@@ -567,7 +567,7 @@ bm_extract <- function(roi_sf,
       tidyr::pivot_longer(cols = -c(names(roi_df)),
                           names_to = c(".value", "date"),
                           names_sep = "\\.t") %>%
-      dplyr::mutate(prop_non_na_pixels = n_non_na_pixels / n_pixels)
+      dplyr::mutate(prop_non_na_pixels = .data$n_non_na_pixels / .data$n_pixels)
 
     ntl_df <- exact_extract(bm_r, roi_sf, aggregation_fun) %>%
       tidyr::pivot_longer(cols = everything(),
@@ -804,7 +804,7 @@ bm_extract <- function(roi_sf,
 #' @import stringr
 #' @import httr
 #' @import lubridate
-#' @import tidyr
+#' @rawNamespace import(tidyr, except = c(extract))
 #' @rawNamespace import(purrr, except = c(flatten_df, values))
 #' @rawNamespace import(raster, except = c(union, select, intersect, origin, tail, head, values))
 #' 

readme_figures/figures_for_readme.R

@@ -7,6 +7,7 @@ if(F){
   library(ggplot2)
   library(dplyr)
   library(sf)
+  library(raster)
 
   # Setup ------------------------------------------------------------------------
   bearer <- read_csv("~/Desktop/bearer_bm.csv") %>%
@@ -32,8 +33,6 @@ if(F){
   names(r_df) <- c("value", "x", "y")
 
   ## Transform NTL
-  r_df$value[r_df$value <= 1] <- 0
-
   r_df$value_adj <- log(r_df$value+1)
 
   ##### Map 
@@ -44,7 +43,7 @@ if(F){
     scale_fill_gradient2(low = "black",
                          mid = "yellow",
                          high = "red",
-                         midpoint = 4.5) +
+                         midpoint = 3.1) +
     labs(title = "Nighttime Lights: October 2021") +
     coord_quickmap() + 
     theme_void() +
@@ -58,7 +57,7 @@ if(F){
   # Extract timeseries -----------------------------------------------------------
   ntl_df <- bm_extract(roi_sf = roi_sf,
                        product_id = "VNP46A4",
-                       date = 2012:2021,
+                       date = 2012:2022,
                        bearer = bearer,
                        aggregation_fun = c("mean"))