How to generate random rows based on the properties of the respective columns?

[GreyMerchant]

Hello,

I am looking for a way to take a data.frame like mtcars and add new rows of data which are randomly generated with constraints. The constraints should be as follow, it should look at a column and determine the min and max of that column from the original entries then generate a random value within that range. For numbers with decimals it should allow for decimals and in case of whole numbers it should stay to whole numbers.

Any idea of how to go about this?

data <- mtcars

data
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

^{Created on 2021-10-05 by the reprex package (v2.0.0)}

[StatSteph]

What have you tried? I thought about this by writing a function to do it on one column and then go through all the columns as illustrated below:

library(tidyverse)

gen_column <- function(inp, N, dat){
# this function takes in a column name (inp), N, and a data.frame or tibble
# and returns a tibble with one column which is a random column
# generated from the min and max of the input column
  if (!(inp %in% names(dat))) stop("Not a valid column name")
  inp_v <- dat[, inp]
  if (!is.numeric(inp_v)) stop("Must be a numeric column")
  wn <- all(dplyr::near(inp_v, round(inp_v))) # check if whole number
  minval <- min(inp_v, na.rm=TRUE)
  maxval <- max(inp_v, na.rm=TRUE)
  if (!wn){
    tibble(!!inp:=runif(N, minval, maxval))
  } else(
    tibble(!!inp:=sample(minval:maxval, N, replace=TRUE))
  )
}

# map_dfc will return a tibble looping through each column
names(mtcars) %>% map_dfc(gen_column, N=5, dat=mtcars)
#> # A tibble: 5 x 11
#>     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <dbl> <int> <dbl> <int> <dbl> <dbl> <dbl> <int> <int> <int> <int>
#> 1  26.2     5 178.    333  4.29  2.70  15.4     0     1     5     2
#> 2  32.1     5 273.    161  4.28  1.68  20.2     0     1     3     4
#> 3  22.5     8  77.0    81  2.92  5.17  16.0     1     0     5     6
#> 4  23.9     6 217.    277  4.16  5.41  17.6     0     0     3     3
#> 5  16.2     4 339.     59  2.91  4.52  20.4     1     0     3     5

^{Created on 2021-10-05 by the reprex package (v2.0.1)}

[GreyMerchant]

Thanks for your response! I was trying something like this last night (still very raw):

library(tidyverse)


# 01) Determine min and max for all columns -------------------------------

mincars <- 
mtcars %>% sapply(max)

maxcars <- 
mtcars %>% sapply(min) 

# 2) Create the random values for each respective column" -----------------

output <- 
purrr::map2(.x = mincars, .y = maxcars, .f = function(x,y)
{
  sample(c(x:y),1)
 # if(x%%1==0){
 #  sample(c(x:y),1)
 # } else {
 #  sample(c(x:y),1)  
 # }

}
  ) %>% unlist()

# 3) Combine with the original --------------------------------------------

output_data <- mtcars %>% rbind(output)

# 4) WIP 

custom_mapper <- 
function(mincars,maxcars, repeter){
  purrr::map2(.x = mincars, .y = maxcars, .f = function(x,y)
  {
    sample(c(x:y),1)
    # if(x%%1==0){
    #  sample(c(x:y),1)
    # } else {
    #  sample(c(x:y),1)  
    # }
    
  }
  ) %>% rep( times = repeter) 
  
}


output <- 
custom_mapper(mincars,maxcars,5) %>% unlist() %>% 
  matrix( nrow = 5) %>% as.data.frame()

names(output) <- names(mtcars)

output_data <- mtcars %>% rbind(output)

I think the approaches are similar. I was going to see if the respective value had no remainder to determine whole or decimal number. I knew it likely will have to need some map function given we have multiple inputs. I just had to figure out how best to reduce my list into vector and into rows (your approach definitely seems a lot cleaner there!).