Mutate Data Frames with Random Variates

Work within the 'dplyr' workflow to add random variates to your data frame. Variates can be added at any level of an existing column. Also, bounds can be specified for simulated variates.

dmutate

Mutate a data.frame, adding random variates.

library(dplyr)
library(dmutate)

Univariate examples

Some variables to use in formulae:

low_wt <- 70
high_wt <- 90
mu_wt <- 80
sd <- 60
p.female <- 0.24

Use mutate_random to implement formulae in data frame. We can put bounds on any simulated variable

data.frame(ID = 1:10) %>% 
  mutate_random(WT[low_wt, high_wt] ~ rnorm(mu_wt, sd))

.    ID       WT
. 1   1 87.28619
. 2   2 74.41711
. 3   3 80.72592
. 4   4 89.63599
. 5   5 72.58696
. 6   6 86.46300
. 7   7 80.68241
. 8   8 84.47482
. 9   9 78.47417
. 10 10 88.54634

We can simulate from any probability distribution in R

data.frame(ID = 1:10) %>% mutate_random(X ~ rcauchy(0, 0.5))

.    ID           X
. 1   1  3.49562264
. 2   2 -3.87879719
. 3   3 -0.20956182
. 4   4 -0.33935718
. 5   5 -0.02254644
. 6   6 -0.28627923
. 7   7  0.66041153
. 8   8 -0.37298713
. 9   9  0.61288903
. 10 10  1.22101132

We can add the variate at any level

data.frame(ID = 1:10) %>%
  mutate(GROUP = ID%%2) %>%
  mutate_random(STUDY_RE ~ rnorm(50,sqrt(50))|GROUP)

.    ID GROUP STUDY_RE
. 1   1     1 43.69961
. 2   2     0 52.61525
. 3   3     1 43.69961
. 4   4     0 52.61525
. 5   5     1 43.69961
. 6   6     0 52.61525
. 7   7     1 43.69961
. 8   8     0 52.61525
. 9   9     1 43.69961
. 10 10     0 52.61525

Simulate multivariate normal with bounds

mu <- c(2, 200)
Sigma <- diag(c(10, 1000))
XY <- X[0,] + Y[200, 300] ~ rmvnorm(mu, Sigma)

The object

XY

. X[0, ] + Y[200, 300] ~ rmvnorm(mu, Sigma)

Simulate

data.frame(ID = 1:10000) %>%
  mutate_random(XY) %>% 
  summary()

.        ID              X                   Y        
.  Min.   :    1   Min.   :2.347e-04   Min.   :200.0  
.  1st Qu.: 2501   1st Qu.:1.598e+00   1st Qu.:210.0  
.  Median : 5000   Median :3.089e+00   Median :221.2  
.  Mean   : 5000   Mean   :3.412e+00   Mean   :225.0  
.  3rd Qu.: 7500   3rd Qu.:4.840e+00   3rd Qu.:236.4  
.  Max.   :10000   Max.   :1.414e+01   Max.   :299.8

An extended example

data.frame(ID = 1:10) %>%
  mutate(GROUP = ID%%2) %>%
  mutate_random(WT[low_wt, high_wt] ~ rnorm(mu_wt, 1)) %>%
  mutate_random(STUDY_RE ~ rnorm(0, sqrt(50)) | GROUP) %>%
  mutate_random(SEX ~ rbinomial(p.female)) %>%
  mutate_random(sigma ~ rgamma(1,1)) %>%
  mutate_random(kappa ~ rgamma(1,1) | GROUP) %>% 
  signif(3)

.    ID GROUP   WT STUDY_RE SEX sigma kappa
. 1   1     1 78.2    -4.42   1 0.741  2.41
. 2   2     0 82.6     3.85   0 0.185  2.77
. 3   3     1 79.8    -4.42   1 0.649  2.41
. 4   4     0 80.8     3.85   1 0.623  2.77
. 5   5     1 79.9    -4.42   0 0.264  2.41
. 6   6     0 79.9     3.85   0 0.596  2.77
. 7   7     1 78.7    -4.42   0 0.534  2.41
. 8   8     0 81.5     3.85   0 1.290  2.77
. 9   9     1 80.2    -4.42   0 0.180  2.41
. 10 10     0 79.1     3.85   0 0.496  2.77

Create formulae with expr to calculate new columns in the data.frame using dplyr::mutate

We can easily save formulae to R variables. We collect formulae together into sets called covset. For better control for where objects are found, we can specify an environment where objects can be found.

a <- X ~ rnorm(50,3)
b <- Y ~ expr(X/2 + c)
d <- A+B ~ rlmvnorm(log(c(20,80)), diag(c(0.2,0.2)))
cov1 <- covset(a, b, d)
e <- list(c = 3)

Notice that b has function expr. This assigns the column named Y (in this case) to the result of evaluating the expression in the data frame using dplyr::dmutate.

.data <- data.frame(ID = 1:3)

mutate_random(.data, cov1, envir = e) %>% signif(3)

.   ID    X    Y     A     B
. 1  1 49.7 27.9 12.70  45.8
. 2  2 46.1 26.0 26.10 121.0
. 3  3 53.3 29.6  6.65  57.4