Dynamic Variables for List Key

Trying to learn R, and I’m getting an idea of lists. I have a function I’m calling:

try_dives <- function(fns, r) {
    D1 = function(r) round(r ** 2 + 100, 3)
    dn = function(r) round(r + 100, 3)

    for (fn in fns) {
        if (fn == "D1") {
            print(list('D1' = sapply(r, D1)))
        }
        # ... if / else ladder for D1, D2 ... dn
        else if (fn == 'dn') {
            print(list('dn' = sapply(r, dn)))
        }
    }
}

And call it as

try_dives(c('D1', 'D2', 'dn'), 100:200)

This, to me seems real inefficient, I would think I could just set the list variable

list(fn = sapply(r, fn)) and call the appropriate function where fn == 'D1', ...etc...

>Solution :

If the functions are defined outside, we can call those functions in the main function by looping over the fns with lapply and then call the function on r with match.fun and return the output as a named list by naming the list with setNames

try_dives <- function(fns, r) {
    setNames(lapply(fns, function(fn) match.fun(fn)(r)), fns)


}

-testing

D1 <- function(r) round(r ** 2 + 100, 3)
dn <- function(r) round(r + 100, 3)
try_dives(c("D1", "dn"), 100:200)
$D1
  [1] 10100 10301 10504 10709 10916 11125 11336 11549 11764 11981 12200 12421 12644 12869 13096 13325 13556 13789 14024
 [20] 14261 14500 14741 14984 15229 15476 15725 15976 16229 16484 16741 17000 17261 17524 17789 18056 18325 18596 18869
 [39] 19144 19421 19700 19981 20264 20549 20836 21125 21416 21709 22004 22301 22600 22901 23204 23509 23816 24125 24436
 [58] 24749 25064 25381 25700 26021 26344 26669 26996 27325 27656 27989 28324 28661 29000 29341 29684 30029 30376 30725
 [77] 31076 31429 31784 32141 32500 32861 33224 33589 33956 34325 34696 35069 35444 35821 36200 36581 36964 37349 37736
 [96] 38125 38516 38909 39304 39701 40100

$dn
  [1] 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228
 [30] 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257
 [59] 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286
 [88] 287 288 289 290 291 292 293 294 295 296 297 298 299 300

Or another option is to create a named list` of functions within the function and then subset the function list based on the input names passed in ‘fns’ and apply over the vector

try_dives <- function(fns, r) {
    fnlist <- list(D1 = function(r) round(r ** 2 + 100, 3), 
          dn = function(r) round(r + 100, 3))
  lapply(fnlist[fns], \(fn) fn(r))


}

-testing

> try_dives(c("D1", "dn"), 100:200)
$D1
  [1] 10100 10301 10504 10709 10916 11125 11336 11549 11764 11981 12200 12421 12644 12869 13096 13325 13556 13789 14024
 [20] 14261 14500 14741 14984 15229 15476 15725 15976 16229 16484 16741 17000 17261 17524 17789 18056 18325 18596 18869
 [39] 19144 19421 19700 19981 20264 20549 20836 21125 21416 21709 22004 22301 22600 22901 23204 23509 23816 24125 24436
 [58] 24749 25064 25381 25700 26021 26344 26669 26996 27325 27656 27989 28324 28661 29000 29341 29684 30029 30376 30725
 [77] 31076 31429 31784 32141 32500 32861 33224 33589 33956 34325 34696 35069 35444 35821 36200 36581 36964 37349 37736
 [96] 38125 38516 38909 39304 39701 40100

$dn
  [1] 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228
 [30] 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257
 [59] 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286
 [88] 287 288 289 290 291 292 293 294 295 296 297 298 299 300