Looping across multiple variables and parameters using map() and mutate()

Question

I\'m having trouble figuring out how to effective map across multiple parameters and variables within a tbl to generate new variables.

In the \"real\" version, I basica

Answer 1

Consider a vectorized approach (forgive me for non-tidyverse data wrangling) where all new columns can be handled in one call. Use seed(888) before random data to reproduce output:

f1 <- function(df, vars) {
  df[paste0("d.", vars)] <- df$p * df[vars] * df$c
  return(df)
}

newpracdf <- f1(pracdf, c("low.a","high.a","med.a","med.b","low.b","high.b"))

Output

# # A tibble: 26 x 15
#       ID        p      med.a      med.b          c      low.a     low.b    high.a    high.b    d.low.a   d.high.a    d.med.a    d.med.b    d.low.b   d.high.b
#                                                                                   
#  1     a 122.9573 0.65746601 0.43123587 0.81314570 0.52597281 0.3449887 0.7889592 0.5174830  52.587917  78.881876  65.734897  43.115909  34.492727  51.739091
#  2     b 412.0127 0.19793909 0.77148952 0.26039116 0.15835127 0.6171916 0.2375269 0.9257874  16.988630  25.482945  21.235787  82.768834  66.215068  99.322601
#  3     c 155.1248 0.30834064 0.99850558 0.57853922 0.24667251 0.7988045 0.3700088 1.1982067  22.137823  33.206735  27.672279  89.611689  71.689351 107.534027
#  4     d 715.3769 0.85517040 0.81715464 0.84196723 0.68413632 0.6537237 1.0262045 0.9805856 412.071636 618.107455 515.089546 492.191742 393.753393 590.630090
#  5     e 790.5284 0.12617255 0.59290522 0.54879020 0.10093804 0.4743242 0.1514071 0.7114863  43.790379  65.685568  54.737973 257.222588 205.778070 308.667105
#  6     f 193.6968 0.15173488 0.93054996 0.08587380 0.12138791 0.7444400 0.1820819 1.1166600   2.019104   3.028655   2.523879  15.478286  12.382629  18.573943
#  7     g 451.6000 0.88123996 0.62858787 0.12546384 0.70499197 0.5028703 1.0574880 0.7543054  39.944473  59.916709  49.930591  35.615457  28.492365  42.738548
#  8     h 342.3741 0.09952918 0.56932309 0.10980862 0.07962334 0.4554585 0.1194350 0.6831877   2.993489   4.490234   3.741861  21.404056  17.123245  25.684867
#  9     i 143.9489 0.42407685 0.94929822 0.02754267 0.33926148 0.7594386 0.5088922 1.1391579   1.345083   2.017624   1.681353   3.763718   3.010975   4.516462
# 10     j 911.8069 0.25822441 0.08934875 0.55244369 0.20657953 0.0714790 0.3098693 0.1072185 104.058645 156.087967 130.073306  45.006930  36.005544  54.008316
# # ... with 16 more rows