split_train_test splits data into two data frames for validation of models. One data frame is meant for model training ("train") and the other is meant to assess model performance ("test"). The distribution of outcome will be preserved acrosss the train and test datasets. Additionally, if there are groups in the dataset, you can keep all observations within a in the same train/test dataset by passing the name of the group column to grouping_col; this is useful, for example, when there are multiple observations per patient, and you want to keep each patient within one dataset.

split_train_test(d, outcome, percent_train = 0.8, seed, grouping_col)

## Arguments

d Data frame Target column, unquoted. Split will be stratified across this variable Proportion of rows in d to put into training. Default is 0.8 Optional, if provided the function will return the same split each time it is called column name that specifies grouping. Individuals in the same group are in the same training/test set.

## Value

A list of two data frames with names train and test

## Details

This function wraps caret::createDataPartition. If outcome is a factor then the test/training porportions are stratified. Otherwise they are randomly selected.

If the grouping_col is given, then the groups are divided into the test/ training porportions.

## Examples

split_train_test(mtcars, am, .9)#> $train #> mpg cyl disp hp drat wt qsec vs am gear carb #> Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 #> Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 #> Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 #> Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 #> Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 #> Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 #> Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 #> Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 #> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 #> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 #> Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 #> Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 #> AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 #> Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 #> Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 #> Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 #> Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 #> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 #> Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 #> Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 #> Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 #> #>$test
#>                    mpg cyl disp  hp drat   wt  qsec vs am gear carb
#> Datsun 710        22.8   4  108  93 3.85 2.32 18.61  1  1    4    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.44 17.02  0  0    3    2
#> Ferrari Dino      19.7   6  145 175 3.62 2.77 15.50  0  1    5    6
#>

# Below is an additional example of grouping. Grouping is where individuals
# in the same group are in the same training/test set. Here we group on car
# owners. Owners will be in the same training/test set.
library(dplyr)

mtcars %>%
mutate(owner = rep(letters[1:16], each = 2)) %>%
split_train_test(., am, grouping_col = owner)#> $train #> mpg cyl disp hp drat wt qsec vs am gear carb owner #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 a #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 a #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 b #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 b #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 c #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 c #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 d #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 d #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 e #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 e #> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 f #> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 f #> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 h #> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 h #> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 i #> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 i #> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 j #> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 j #> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 m #> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 m #> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 n #> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 n #> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 o #> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 o #> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 p #> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 p #> #>$test
#>     mpg cyl  disp  hp drat    wt  qsec vs am gear carb owner
#> 13 17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3     g
#> 14 15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3     g
#> 21 21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1     k
#> 22 15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2     k
#> 23 15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2     l
#> 24 13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4     l
#>