• Creating Functions in R
  • Function with Multiple Arguments
  • Downloading Geospatial Data in R
    • Using the download.file() Function
    • Automating Data Download

Last updated: 2024-12-31

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Functions are blocks of code that perform a specific task in R. They allow us to encapsulate logic and reuse it across different parts of the code, making our scripts more modular, efficient, and easy to debug. Functions are one of the most powerful tools in R and are commonly used in data analysis, machine learning, and statistical programming.

By using functions, we can:

  • Avoid repetitive code
  • Break complex tasks into smaller, manageable pieces
  • Improve readability and maintainability of your code

Creating Functions in R

In R, a function is created using the function() keyword. The basic syntax for defining a function is:

my_function <- function(arg1, arg2) {
  # function body
  result <- arg1 + arg2
  return(result)
}

Here’s what each part means:

  • my_function: The name of the function.
  • function(arg1, arg2): The definition of the function with its arguments.
  • {}: The body of the function where the logic is written.
  • return(result): The value that is returned by the function.

Example:

# A simple function to add two numbers
add_numbers <- function(a, b) {
  sum <- a + b
  return(sum)
}

# Call the function
add_numbers(5, 3)
[1] 8

Function with Multiple Arguments

Functions can have multiple arguments, and you can pass values in the form of position or by explicitly naming the arguments when calling the function.

# A function to calculate the area of a rectangle
rectangle_area <- function(length, width = 2) {  # Default value for width
  area <- length * width
  return(area)
}

# Call the function with default width
rectangle_area(5)
[1] 10
# Call the function with a specific width
rectangle_area(5, 3)
[1] 15

In the above example, the argument width has a default value of 2. If you don’t provide a value, R will use this default.

Downloading Geospatial Data in R

In geospatial analysis, it is often necessary to obtain datasets from online sources for analysis. R provides a straightforward way to automate the process of downloading data from URLs and saving it to a specific path on your local machine. This can save time, ensure consistency, and allow seamless integration of data acquisition into oour analysis workflows.

Using the download.file() Function

# URL of the GADM data of San Marino
url <- "https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_SMR.gpkg"

# Destination path to save the file
# provide the path where you want to save the geopackage and also change the name of the file
destfile <- "data/downloads/gadm41_SMR.gpkg"

# Download the file
download.file(url,
              destfile)

Automating Data Download

To simplify the process, we can wrap this functionality into a custom function that takes a URL and destination path as arguments. This can be particularly useful when managing multiple datasets.

Download one file at a time

In this subsection, we will create a function to download country-specific GeoPackage data from the GADM website.

get_gadm <- function(iso3 = NULL,
                     path = NULL) {
      
      # Construct the URL of the GADM data
      url <- 
        paste0("https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_", iso3, ".gpkg")
      # Destination path to save the file
      destfile <- 
        paste0(path, "/", iso3, ".gpkg")
      # Download the file
      download.file(url,
                    destfile)
      # print message
      message("Download process completed.")
}

Now, we created a function named get_gadm() that takes the ISO3 code of the country of interest and the destination file path as arguments. We will use this function to download the GeoPackage containing the country polygon. Please ensure that you provide a valid ISO3 code and a proper file path to download the polygons; otherwise, a download error will occur.

# Example 1: Luxembourg
iso3 = "LUX"
path = "data/downloads/"
get_gadm(iso3, path)
Download process completed.
# Example 2: Cyprus
get_gadm(iso3 = "CYP",
         path = "data/downloads/")
Download process completed.

Multiple files downloading simultaneously

In this subsection, we will create a function to download GADM data for multiple countries simultaneously.

gadm_downloader <- function(iso3 = NULL,
                            path = NULL) {
  
  # Loop through each ISO3 code
  for (code in iso3) {
    
    # Construct the URL
    url <- paste0("https://geodata.ucdavis.edu/gadm/gadm4.1/gpkg/gadm41_", code, ".gpkg")
    # Destination path to save the file
    destfile <- 
      file.path(path, paste0(code, ".gpkg"))
    
    # Download the file
    message("Downloading GADM data for country: ", code)
    
    tryCatch({
      
      download.file(url, destfile)
      message("File for ", code, " downloaded successfully.")
      
    }, 
    error = function(e) {
      message("Failed to download file for ", code, ": ", e$message)
    })
  }
  
  message("Download process completed.")
}

Key Features of the Updated Function:

  • Handles Multiple Countries: Accepts a vector of ISO3 codes and processes them iteratively.
  • Error Handling: Uses tryCatch to gracefully handle download failures and continue with the next country.
  • Informative Messages: Provides feedback during the download process for each country.
# Example 3: Cyprus, San Marino, Luxembourg
# Define ISO3 codes and path
iso3_codes <- 
  c("CYP", "LUX", "SMR")
# Define output file path
output_path <- 
  "data/downloads/"

# Call the function
gadm_downloader(iso3 = iso3_codes,
                path = output_path)
Downloading GADM data for country: CYP
File for CYP downloaded successfully.
Downloading GADM data for country: LUX
File for LUX downloaded successfully.
Downloading GADM data for country: SMR
File for SMR downloaded successfully.
Download process completed.

Advantages of Automating Downloads:

  • Efficiency: Automates the tedious process of manually downloading files.
  • Reproducibility: Ensures that analyses can be reproduced with the same datasets.
  • Scalability: Handles multiple datasets with ease, particularly useful in batch processing.

By leveraging these techniques, we can integrate data acquisition seamlessly into our R scripts, ensuring an efficient and organized workflow for geospatial projects.


sessionInfo()
R version 4.4.0 (2024-04-24 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
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[3] LC_MONETARY=English_Germany.utf8 LC_NUMERIC=C                    
[5] LC_TIME=English_Germany.utf8    

time zone: Europe/Berlin
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
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[25] lifecycle_1.0.4   whisker_0.4.1     stringr_1.5.1     compiler_4.4.0   
[29] fs_1.6.4          pkgconfig_2.0.3   Rcpp_1.0.13       rstudioapi_0.16.0
[33] later_1.3.2       digest_0.6.36     R6_2.5.1          utf8_1.2.4       
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[41] tools_4.4.0       cachem_1.1.0      getPass_0.2-4