Text Templates in R Programming Language

R offers several packages for creating dynamic content or showing customized output to the user. While there isn’t a direct equivalent to Go’s text/template package, we can achieve similar functionality using R’s string manipulation and evaluation capabilities.

library(glue)

main <- function() {
  # We can create a string template using glue
  # Expressions inside {} will be evaluated and inserted
  t1 <- "Value is {.}\n"
  
  # We can use glue to insert values into the template
  cat(glue(t1, "some text"))
  cat(glue(t1, 5))
  cat(glue(t1, c("R", "Python", "Julia", "Scala")))
  
  # Helper function to create a template
  create <- function(name, t) {
    function(...) cat(glue(t, ...))
  }
  
  # If the data is a list, we can access its elements by name
  t2 <- create("t2", "Name: {.name}\n")
  
  t2(list(name = "Jane Doe"))
  
  # The same applies to named vectors
  t2(c(name = "Mickey Mouse"))
  
  # Conditional execution can be achieved using ifelse
  t3 <- create("t3", "{ifelse(nchar(.) > 0, 'yes', 'no')}\n")
  t3("not empty")
  t3("")
  
  # We can use lapply or sapply for iteration
  t4 <- create("t4", "Range: {paste(., collapse = ' ')}\n")
  t4(c("R", "Python", "Julia", "Scala"))
}

main()

To run the program, save it as templates.R and use Rscript:

$ Rscript templates.R
Value is some text
Value is 5
Value is c("R", "Python", "Julia", "Scala")
Name: Jane Doe
Name: Mickey Mouse
yes
no
Range: R Python Julia Scala

In this R version:

1. We use the glue package for string interpolation, which is similar to Go’s template syntax.

2. Instead of creating formal Template objects, we use functions that return formatted strings.

3. The create function returns a new function that applies the template to its arguments.

4. For conditional execution, we use R’s ifelse function instead of a template-specific syntax.

5. For iteration, we use R’s vector operations and paste function to join elements.

While this approach doesn’t provide all the features of Go’s templating system, it demonstrates how to achieve similar functionality in R using built-in features and common packages.