removing examples for now

DESCRIPTION

@@ -18,9 +18,9 @@ Imports:
     stringr,
     vegan,
     tidyr,
-    tidyverse,
     geometry,
     sf,
+    ausplotsR,
     terra
 Suggests: 
     testthat (>= 3.0.0)

NAMESPACE

@@ -6,6 +6,7 @@ export(create_masked_raster)
 export(create_multiband_image)
 export(extract_pixel_values)
 export(find_optimum_thresholds)
+import(ausplotsR)
 import(data.table)
 import(dplyr)
 import(geometry)
@@ -15,6 +16,5 @@ import(sf)
 import(stringr)
 import(terra)
 import(tidyr)
-import(tidyverse)
 import(tools)
 import(vegan)

R/calculate_field_diversity.R

@@ -2,11 +2,6 @@
 #' @description Takes ausplot survey data, calculates field diversity for each site
 #' @param survey_data a df obtained from ausplotsR package with survey data for appropriate sites
 #' @return a list, containing 'field diversity' - values of species richness, shannon's index, simpsons index, exponential shannon's, inverse simpson, pielou's evenness, and 'community matrices'
-#' ausplotdata <- get_ausplots(veg.vouchers = T, veg.PI = T)
-#' twentyfour_survey_data <- veg$veg.PI %>%
-#'  left_join(veg$site.info %>% dplyr::select(site_unique, visit_start_date), by = "site_unique") %>%
-#'  filter(substr(visit_start_date, 1, 4) == '2024')
-#'  twentyfour_diversity <- calculate_field_diversity(twentyfour_survey_data)
 #' @export
 #' @import dplyr
 #' @import tidyr

R/calculate_spectral_metrics.R

@@ -6,16 +6,6 @@
 #' @param min_points if rarefaction = T, the minimum number of pixels for any aoi - to standardise uneven number of pixels across different sites
 #' @param n if rarefaction = T, number of subset permutations
 #' @return a dataframe containing spectral metrics for each aois within each site/raster
-#' @example set.seed(123)
-#' df <- data.frame(
-#' site_name = rep(c("site_one", "site_two", "site_three", "site_four"), each = 5000),
-#' aoi_id = 1,
-#' blue = runif(20000, min = 0, max = 1),
-#' green = runif(20000, min = 0, max = 1),
-#' red = runif(20000, min = 0, max = 1),
-#' red_edge = runif(20000, min = 0, max = 1),
-#' nir = runif(20000, min = 0, max = 1))
-#' pixelvalues <- calculate_spectral_metrics(df, wavelengths = c('blue','green','red','red_edge','nir'), rarefaction = T, min_points = 5000, n = 999)
 #' @export
 #' @import dplyr
 #' @import data.table

R/create_masked_raster.R

@@ -9,14 +9,6 @@
 #' @param red_band_index layer number for red band
 #' @param nir_band_index layer number for nir band
 #' @return A masked raster image, saved in the output directory
-#' @examples
-#'input_folder <- 'doc/multiband_image'
-#'create_masked_raster(input_folder,
-#'output_dir = 'tempdir()',
-#'ndvi_threshold = 0.02,
-#'nir_threshold = 0.04,
-#'red_band_index = 3,
-#'NIR_band_index = 5)
 #' @export
 #' @import terra
 #' @import raster
@@ -32,7 +24,7 @@
 create_masked_raster <- function(input, output_dir,
                                  ndvi_threshold = NULL, nir_threshold = NULL,
                                  ndvi_threshold_df = NULL, nir_threshold_df = NULL,
-                                 red_band_index = 3, NIR_band_index = 5) {
+                                 red_band_index = 3, nir_band_index = 5) {
 
   if (dir.exists(input)) {
     # list all ENVI or TIF files in the directory
@@ -83,7 +75,7 @@ create_masked_raster <- function(input, output_dir,
 
     # identify the bands for Red and NIR
     red <- raster_data[[red_band_index]]
-    nir <- raster_data[[NIR_band_index]]
+    nir <- raster_data[[nir_band_index]]
 
     # calculate NDVI
     ndvi <- (nir - red) / (nir + red)
@@ -102,6 +94,4 @@ create_masked_raster <- function(input, output_dir,
   }
 }
 
-library(tools)
-create_masked_raster('C:/Users/adele/Documents/fg_spectral_diversity/data_out/combined_rasters/2024/NSABHC0009_combined_image.tif', 'test', ndvi_threshold = 0.02, nir_threshold = 0.04)
 

R/create_multiband_image.R

@@ -6,17 +6,6 @@
 #' @param desired_band_order Order of files to be combined. should be in wavelength order. provide vector of file basenames in correct order
 #' @param output_dir Folder to store combined tif file
 #' @return  combined tif file
-#' @examples
-#' input_dir <- tempdir()
-#' for (i in 1:3) {
-#'   band <- rast(ncol=10, nrow=10, vals = runif(100, 0, 1))
-#'   writeRaster(band, filename = file.path(input_dir, paste0("band", i, ".tif")), overwrite = TRUE)
-#' }
-#' output_dir <- file.path(tempdir(), "output_dir")
-#' if (!dir.exists(output_dir)) {
-#'   dir.create(output_dir, recursive = TRUE)
-#' }
-#' create_multiband_image(input_dir, c('band1','band2','band3'), output_dir)
 #' @export
 #' @import tools
 #' @import terra
@@ -61,4 +50,3 @@ create_multiband_image <- function(input_dir, desired_band_order, output_dir){
   }
 }
 
-print(input_dir)

R/extract_pixel_values.R

@@ -4,10 +4,6 @@
 #' @param aoi_files area of interest file - shapefile containing one or more site polygons for each raster
 #' @param wavelength_names wavelength names for each band, must match order of stacked layers
 #' @return a df with pixel values for each of the image layers
-#' @examples
-#' raster_files <- list.files('doc/multiband_image', pattern = '.tif$', full.names = TRUE)
-#' aoi_files <- list.files('doc/fishnet', pattern = '.shp$', full.names = TRUE)
-#' pixelvalues <- extract_pixel_values(raster_files, aoi_files, c('blue','green','red','red_edge','nir'))
 #' @export
 #' @import raster
 #' @import dplyr

R/find_optimum_thresholds.R

@@ -6,13 +6,9 @@
 #' @param class_col name of column where object class are stored
 #' @param class_value value/s of class that should be masked - e.g. for ndvi, 'non-veg' or c('bare_ground','rocks'), for nir, 'shadow'
 #' @return a df with optimum thresholds for each site
-#' @examples
-#' ndvi_values <- data.frame(site = rep(c("site_one", "site_two"), each = 100),
-#` point = rep(1:100, times = 2),
-#` ndvi = runif(200, min = -1, max = 1),
-#` class = sample(rep(c('veg', 'non-veg'), each = 100)))
-#' ndvi_thresholds <- find_optimum_thresholds(ndvi_values, class = 'class', value = 'nir', site = 'site', class_value = 'non-veg')
 #' @export
+#' @examples
+#' rnorm(500)
 #' @import pROC
 #' @import dplyr