--- title: "Iteration Examples" author: "Calvin K.F. Lee, Nicholas J. Murray" date: "2018-10-25" output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Iteration Examples} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- 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. ```{r global_options, include=FALSE} knitr::opts_chunk$set(eval = FALSE) ``` ## Loading packages ```{r} library(redlistr) library(stringr) ``` ## Preparing workspace and variables ```{r} # Example directory input_dir <- # Path to folder with tif files out_dir <- "C:/Users/Username/Desktop" # List all files within input_dir that ends with .tif input_list <- list.files(input_dir, pattern = '.tif$') # 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. ```{r} # 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. ```{r} 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 ```{r} library(redlistr) library(stringr) ``` ## Preparing workspace and variables ```{r} # 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. ```{r} # 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. ```{r} 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.