Setting function parameters for debugging

Utility function for setting function parameters to the global environment to assist debugging.

I tend to write a lot of functions that create specific graphics implemented with ggplot2. Although I try to pick graphic parameters (e.g. colors, text size, etc.) that are reasonable, I will typically define all relevant aesthetics as parameters to my function. As a result, my functions tend to have a lot of parameters. When I need to debug the function I need to have all those parameters set in the global environment which usually requires me highlighting each assignment and running it. This function automates this process. You can pass any function and it will attempt to set parameters to the given environment (the global environment by default). It will return a data frame with a column indicating if the variable was set and the value. This is useful to know what parameters don’t have a default value that need to be set yourself.

#' Set function parameters to an environment.
#'
#' This function is designed to help debug functions. It will attempt to set all
#' the default parameter values to the specified environment (global environment
#' by default). This is useful for when you want to execute code within the 
#' function definition interactively but need the parameters set in the current 
#' environment.
#'
#' **Warning:** This function will modify the global environment and therefore 
#' violates CRAN policy
#' ["Packages should not modify the global environment (user’s workspace)"]
#' (https://cran.r-project.org/web/packages/policies.html#Source-packages).
#'
#' @param FUN the function to assign parameters to an environment.
#' @param envir the environment to assign the variables to. Defaults to the 
#'        global environment.
#' @param verbose whether to return the data frame invisibly or to print the results.
#' @return a data frame where row names correspond to the parameter name with 
#'        two columns: `set` which is logical indicating if the variable was set 
#'        and `value` with a character representation of the variable value.
set_function_params <- function(FUN, envir = globalenv(), verbose = interactive()) {
    params <- formals(FUN)
    params_set <- data.frame(row.names = names(params),
                             set = rep(FALSE, length(params)),
                             value = rep(NA_character_, length(params)))
    for(param in names(params)) {
        value <- params[[param]]
        if(!missing(value)) {
            if(is.character(value)) {
                assign(param, value, envir = envir)
                params_set[param,]$value <- value
            } else {
                assign(param, eval(value), envir = envir)
                params_set[param,]$value <- eval(value)
            }
            params_set[param,]$set <- TRUE
        }
    }
    if(verbose) {
        return(params_set)
    } else {
        invisible(params_set)
    }
}

Very recently I was trying to debug a function that creates profile plots for cluster analysis (clav::profile_plot(), documentation). This function has 23 parameters! Setting these all manually is pretty tedious.

# List objects in the current environment
ls()

[1] "set_function_params"

# Call the function
param_set_result <- set_function_params(clav::profile_plot)

# Check to see if the parameters are actually set
ls()

 [1] "bonferroni"          "center_alpha"        "center_band"        
 [4] "center_fill"         "cluster_label_hjust" "color_palette"      
 [7] "hjust"               "label_clusters"      "label_means"        
[10] "label_outcome_means" "label_profile_means" "param_set_result"   
[13] "point_size"          "se_factor"           "set_function_params"
[16] "standardize"         "text_size"           "title"              
[19] "ylab"               

We can examine the data frame which gives a summary of the parameters set (or not).

param_set_result

                      set               value
df                  FALSE                <NA>
clusters            FALSE                <NA>
df_dep              FALSE                <NA>
standardize          TRUE                TRUE
bonferroni           TRUE                TRUE
label_means          TRUE                TRUE
label_profile_means  TRUE                TRUE
label_outcome_means  TRUE                TRUE
center_band          TRUE                0.25
center_fill          TRUE             #f0f9e8
center_alpha         TRUE                 0.1
text_size            TRUE                   4
hjust                TRUE                 0.5
point_size           TRUE                   2
se_factor            TRUE                1.96
color_palette        TRUE                   2
cluster_labels      FALSE                <NA>
cluster_order       FALSE                <NA>
label_clusters       TRUE                TRUE
cluster_label_x     FALSE                <NA>
cluster_label_hjust  TRUE                   5
ylab                 TRUE Mean Standard Score
title                TRUE    Cluster Profiles