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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:
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] 8Functions 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] 15In the above example, the argument width has a default value of 2. If you don’t provide a value, R will use this default.
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.
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)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.
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.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:
# 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: CYPFile for CYP downloaded successfully.Downloading GADM data for country: LUXFile for LUX downloaded successfully.Downloading GADM data for country: SMRFile for SMR downloaded successfully.Download process completed.Advantages of Automating Downloads:
By leveraging these techniques, we can integrate data acquisition seamlessly into our R scripts, ensuring an efficient and organized workflow for geospatial projects.
In this section, we will create a function that computes zonal
statistics based on the provided Area of Interest (AOI) and raster data.
The zonal_operation function calculates zonal statistics
for a given raster (spatial dataset) within a defined Area of Interest
(AOI). It uses the idcol field to rasterize a polygon and
then computes statistical summaries (like mean, sum, etc.) within each
zone of the AOI.
zonal_operation <- function(aoi = NULL,
idcol = NULL,
rast = NULL,
opn = "mean") {
# Convert AOI (sf) to SpatVector
aoi_v <- vect(aoi)
# Crop the raster to the AOI extent
cropped_raster <- crop(rast, aoi_v)
# Mask the raster using the AOI
masked_raster <- mask(cropped_raster, aoi_v)
# Rasterize the polygon using the ID column
rasterized_aoi <- rasterize(aoi_v, masked_raster, field = idcol)
# Compute zonal statistics
zonal_stats <- zonal(masked_raster, rasterized_aoi, fun = opn, na.rm = TRUE)
# Return zonal statistics as a data frame
return(zonal_stats)
}The function above takes four parameters:
aoi: The Area of Interest, typically a spatial polygon
(e.g., a shapefile or spatial feature object).idcol: The name of the column in the AOI data that will
be used to create zones (for rasterization).rast: The raster dataset on which zonal statistics will
be computed.opn: The operation to compute (e.g., “mean”, “sum”,
etc.) over each zone. Default is “mean”.As an example, let’s compute the total population in the year 2020 for different regions of Kanchanpur district.
# Load required libraries
library(sf)
library(terra)
# Load AOI polygon
zones <- read_sf("data/vector/kanchanpur.gpkg")
# Load population count raster from year 2020
raster_data <- rast("data/raster/popCount_2020.tif")
# Set unique ID for the zonal operation
unique_ID <- "NAME"
# Operation to compute
method <- "sum"
# Call the function
zonal_operation(aoi = zones,
idcol = unique_ID,
rast = raster_data,
opn = method) NAME npl_ppp_2020_UNadj
1 BaisiBichawa 37900.0425
2 Beldandi 46565.3564
3 Chandani 72813.0097
4 Daijee 49223.9277
5 Dekhatbhuli 52221.7364
6 Dodhara 64436.5646
7 Jhalari 40823.8799
8 Kalika 97216.4754
9 Krishnapur 36368.5505
10 Laxmipur 277108.9353
11 MahendranagarN.P. 51835.0291
12 Parasan 38199.8677
13 Pipaladi 46773.8070
14 RaikawarBichawa 44571.7505
15 RampurBilaspur 48838.7719
16 RauteliBichawa 11581.3578
17 Royal Shuklaphanta 131.6414
18 Shankarpur 19211.7471
19 Sreepur 56755.7132
20 Suda 58257.6391
21 Tribhuwanbasti 37573.9453In conclusion, mastering functions in R is essential for writing efficient, reusable, and organized code. Functions allow you to streamline complex tasks, avoid redundancy, and improve the readability and maintainability of your scripts. By understanding how to define and use functions, you can perform a wide range of data manipulation, statistical analysis, and visualization tasks more effectively. With the ability to build custom functions and leverage the vast array of built-in functions in R, you can significantly enhance your data analysis workflow, making your code more modular and adaptable to different challenges. Functions are a powerful tool that, once mastered, will elevate your R programming skills to the next level.
For this tutorial, this concludes the coverage of functions in R. If you would like to explore additional functions, examples, or need clarification on any of the steps covered, please visit the GitHub repository: R_tutorial and feel free to open an issue.
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:
[1] LC_COLLATE=English_Germany.utf8 LC_CTYPE=English_Germany.utf8
[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] terra_1.8-5 sf_1.0-19 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] jsonlite_1.8.8 compiler_4.4.0 promises_1.3.0 Rcpp_1.0.13
[5] stringr_1.5.1 git2r_0.33.0 callr_3.7.6 later_1.3.2
[9] jquerylib_0.1.4 yaml_2.3.10 fastmap_1.2.0 R6_2.5.1
[13] classInt_0.4-10 knitr_1.48 tibble_3.2.1 units_0.8-5
[17] rprojroot_2.0.4 DBI_1.2.3 bslib_0.8.0 pillar_1.9.0
[21] rlang_1.1.4 utf8_1.2.4 cachem_1.1.0 stringi_1.8.4
[25] httpuv_1.6.15 xfun_0.47 getPass_0.2-4 fs_1.6.4
[29] sass_0.4.9 cli_3.6.3 magrittr_2.0.3 class_7.3-22
[33] ps_1.8.1 grid_4.4.0 digest_0.6.36 processx_3.8.4
[37] rstudioapi_0.16.0 lifecycle_1.0.4 vctrs_0.6.5 KernSmooth_2.23-22
[41] proxy_0.4-27 evaluate_0.24.0 glue_1.7.0 whisker_0.4.1
[45] codetools_0.2-20 e1071_1.7-16 fansi_1.0.6 rmarkdown_2.28
[49] httr_1.4.7 tools_4.4.0 pkgconfig_2.0.3 htmltools_0.5.8.1