Iteration Examples"

A major motivation we have when developing redlistr was to allow users to easily iterate through a large amount of data.

Iterating over all tif files in a folder

First, we provide an example showing how users can perform EOO and AOO calculations on all .tif files within a folder.

knitr::opts_chunk$set(eval = FALSE)

Loading packages

library(redlistr)
library(stringr)

Preparing workspace and variables

# Example directory
input_dir <- # Path to folder with tif files
out_dir <- "C:/Users/Username/Desktop" 
# List all files within input_dir that ends with .tif
input_list <- list.files(input_dir, pattern = '.tif$') 
# Option to save shapefiles or not
saveSHP <- T

We also create an empty data frame to store our results in, with each row representing one file in the folder.

# set up data capture
results_df <- data.frame (
  # Name of the raster
  in.raster = NA,
  # Estimated area of ecosystem
  eco.area.km2 = NA,
  # Spatial resolution of data
  eco.grain = NA,
  # EOO of ecosystem
  eoo.area.km2 = NA,
  # AOO of ecosystem
  aoo.no = NA,
  # AOO of ecosystem with at least 1% in each grid cell
  aoo.1pc = NA,
  # Time taken for the analysis to complete 
  time.taken = NA)

Running code

We use a for loop to tell R to systematically go through each tif file within the specified folder.

for (i in seq_along(input_list)){
  # Prints out a message showing progress
  message (paste("working on number... ", i, " of ", length(input_list)))
  start_time <- proc.time()
  filename  <- input_list[i]
  input_string <- paste(input_dir, "\\", input_list[i], sep="")
  rast = raster(input_string)
  NAvalue(rast) <- 0
  eco.area.km2 <- getArea(rast)
  message (paste("... area of ecosystem is", eco.area.km2, "km^2"))
  eco.grain <- paste(res(rast)[1], 'x', res(rast)[2])
  eoo.shp <- makeEOO(rast)
  eoo.area.km2 <- getAreaEOO(eoo.shp)
  message (paste("... area of EOO is", eoo.area.km2, "km^2"))
  aoo.no <- getAOO(rast,  10000, FALSE)
  message (paste("... number of occupied grid cells is", aoo.no, "10 x 10-km cells"))
  aoo.1pc <- getAOO(rast,  10000, TRUE)
  message (paste("... number of AOO 1% grid cells is", aoo.1pc, "10 x 10-km cells"))
  time_taken <- proc.time() - start_time
  message (paste("file", i, "completed in ", time_taken))

  # Saving the results into the data frame
  results_df$in.raster[i] <- filename
  results_df$eco.area.km2[i] = eco.area.km2
  results_df$eco.grain[i] = eco.grain
  results_df$eoo.area.km2[i] = eoo.area.km2
  results_df$aoo.no[i] = aoo.no
  results_df$aoo.1pc[i] = aoo.1pc
  results_df$time.taken[i] = time_taken

  # Saving shapefiles
  if(saveShps == TRUE){
    shapefile(eoo.shp, paste0(out_dir, filename, "eoo"), overwrite=TRUE)
    aoo.shp <- makeAOOGrid (rast, 10000, one.percent.rule = FALSE)
    shapefile(aoo.shp, paste0(out_dir, filename, "aoo"), overwrite=TRUE)
    aoo1.shp <- makeAOOGrid (rast, 10000, one.percent.rule = TRUE)
    shapefile(aoo1.shp, paste0(out_dir, filename, "aoo1"), overwrite=TRUE)
  }
}

# Printing a message when everything is completed
message ("Analysis complete.")

# Saving the outputs as a csv file
write.csv(results_df, paste(out_dir, "redlistr_analysis.csv"))

This example code demonstrates how a user could calculate the range size metrics provided in redlistr on all tif files within a folder. Users can also parallelise the for loop using the foreach package.

Iterating over all classes within an ecosystem

Another case where users might want to iterate multiple inputs are when they have a single raster file which contains multiclass data.

The workflow here is very similar to the code provided above. The only difference is that we will be looping over every class within a raster, converting each of them into a binary layer and performing analyses on them iteratively.

Loading packages

library(redlistr)
library(stringr)

Preparing workspace and variables

# Example directory
input_rast <- # raster(...)
out_dir <- "C:/Users/Username/Desktop" 
# Option to save shapefiles or not
saveSHP <- T

We also create an empty data frame to store our results in, with each row representing one file in the folder.

# set up data capture
results_df <- data.frame (
  # Name of the raster
  raster.class = NA,
  # Estimated area of ecosystem
  eco.area.km2 = NA,
  # Spatial resolution of data
  eco.grain = NA,
  # EOO of ecosystem
  eoo.area.km2 = NA,
  # AOO of ecosystem
  aoo.no = NA,
  # AOO of ecosystem with at least 1% in each grid cell
  aoo.1pc = NA,
  # Time taken for the analysis to complete 
  time.taken = NA)

Running code

We use a for loop to tell R to systematically go through each tif file within the specified folder.

val_table <- freq(input_rast, useNA = "no") # get class values from raster
vals <- val_table[,1] # convert table of values to vector
message('Raster has >>> ', length(vals) , ' <<< classes' )

for (val in vals){
  # Prints out a message showing progress
  message (paste("working on class", val))
  start_time <- proc.time()
  # Create temporary raster where values are the current class
  rast <- input_rast == i
  values(rast)[values(rast) == 0] <- NA
  NAvalue(rast) <- 0
  eco.area.km2 <- getArea(rast)
  message (paste("... area of ecosystem is", eco.area.km2, "km^2"))
  eco.grain <- paste(res(rast)[1], 'x', res(rast)[2])
  eoo.shp <- makeEOO(rast)
  eoo.area.km2 <- getAreaEOO(eoo.shp)
  message (paste("... area of EOO is", eoo.area.km2, "km^2"))
  aoo.no <- getAOO(rast,  10000, FALSE)
  message (paste("... number of occupied grid cells is", aoo.no, "10 x 10-km cells"))
  aoo.1pc <- getAOO(rast,  10000, TRUE)
  message (paste("... number of AOO 1% grid cells is", aoo.1pc, "10 x 10-km cells"))
  time_taken <- proc.time() - start_time
  message (paste("file", i, "completed in ", time_taken))

  # Saving the results into the data frame
    temp_df <- data.frame(
      eco.class = val,
      eco.area.km2 = eco.area.km2,
      eco.grain = eco.grain,
      eoo.area.km2 = eoo.area.km2,
      aoo.no = aoo.no,
      aoo.1pc = aoo.1pc,
      time_taken = time_taken)
    results_df <- rbind(results_df, temp_df)
  # Saving shapefiles
  if(saveSHP == TRUE){
    shapefile(eoo.shp, paste0(out_dir, filename, "eoo"), overwrite=TRUE)
    aoo.shp <- makeAOOGrid (rast, 10000, one.percent.rule = FALSE)
    shapefile(aoo.shp, paste0(out_dir, filename, "aoo"), overwrite=TRUE)
    aoo1.shp <- makeAOOGrid (rast, 10000, one.percent.rule = TRUE)
    shapefile(aoo1.shp, paste0(out_dir, filename, "aoo1"), overwrite=TRUE)
  }
}

# Printing a message when everything is completed
message ("Analysis complete.")

# Saving the outputs as a csv file
write.csv(results_df, paste(out_dir, "redlistr_analysis.csv"))

Similarly, the above code can be parallelised.



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redlistr documentation built on July 11, 2019, 5:04 p.m.