• Creating Functions in R
  • Function with Multiple Arguments

Last updated: 2024-12-30

Checks: 7 0

Knit directory: R_tutorial/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20241223)

The command set.seed(20241223) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 96baf68

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 96baf68. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Unstaged changes:
    Modified:   analysis/index.Rmd

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/Functions.Rmd) and HTML (docs/Functions.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 96baf68 Ohm-Np 2024-12-30 wflow_publish("analysis/Functions.Rmd")

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.


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] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       httr_1.4.7        cli_3.6.3         knitr_1.48       
 [5] rlang_1.1.4       xfun_0.47         stringi_1.8.4     processx_3.8.4   
 [9] promises_1.3.0    jsonlite_1.8.8    glue_1.7.0        rprojroot_2.0.4  
[13] git2r_0.33.0      htmltools_0.5.8.1 httpuv_1.6.15     ps_1.8.1         
[17] sass_0.4.9        fansi_1.0.6       rmarkdown_2.28    jquerylib_0.1.4  
[21] tibble_3.2.1      evaluate_0.24.0   fastmap_1.2.0     yaml_2.3.10      
[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       
[37] pillar_1.9.0      callr_3.7.6       magrittr_2.0.3    bslib_0.8.0      
[41] tools_4.4.0       cachem_1.1.0      getPass_0.2-4