Simulate from Existing Data
In faux: Simulation for Factorial Designs

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  out.width = "100%"
)
ggplot2::theme_set(ggplot2::theme_bw())
set.seed(8675309)

library(ggplot2)
library(dplyr)
library(tidyr)
library(faux)

The sim_df() function produces a data table with the same distributions and correlations as an existing data table. It simulates all numeric variables from a continuous normal distribution (for now).

For example, here is the relationship between speed and distance in the built-in dataset cars.

cars %>%
  ggplot(aes(speed, dist)) + 
  geom_point() +
  geom_smooth(method = "lm", formula = "y~x")

You can create a new sample with the same parameters and 500 rows with the code sim_df(cars, 500).

sim_df(cars, 500) %>%
  ggplot(aes(speed, dist)) + 
    geom_point() +
    geom_smooth(method = "lm", formula = "y~x")

Between-subject variables

You can also optionally add between-subject variables. For example, here is the relationship between horsepower (hp) and weight (wt) for automatic (am = 0) versus manual (am = 1) transmission in the built-in dataset mtcars.

mtcars %>%
  mutate(transmission = factor(am, labels = c("automatic", "manual"))) %>%
  ggplot(aes(hp, wt, color = transmission)) +
  geom_point() +
  geom_smooth(method = "lm", formula = "y~x")

And here is a new sample with 50 observations of each.

sim_df(mtcars, 50 , between = "am") %>%
  mutate(transmission = factor(am, labels = c("automatic", "manual"))) %>%
  ggplot(aes(hp, wt, color = transmission)) +
  geom_point() +
  geom_smooth(method = "lm", formula = "y~x")

Empirical

Set empirical = TRUE to return a data frame with exactly the same means, SDs, and correlations as the original dataset.

exact_mtcars <- sim_df(mtcars, 50, between = "am", empirical = TRUE)

Rounding

For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you'd need to do any rounding or truncating yourself.

sim_df(mtcars, 50, between = "am") %>%
  mutate(hp = round(hp),
         transmission = factor(am, labels = c("automatic", "manual"))) %>%
  ggplot(aes(hp, wt, color = transmission)) +
  geom_point() +
  geom_smooth(method = "lm", formula = "y~x")

Missing data

As of faux 0.0.1.8, if you want to simulate missing data, set missing = TRUE and sim_df will simulate missing data with the same joint probabilities as your data. In the dataset below, in condition B1a, 30% of W1a values are missing and 60% of W1b values are missing. This is correlated so that there is a 100% chance that W1b is missing if W1a is. There is no missing data for condition B1b.

data <- sim_design(2, 2, n = 10, plot = FALSE)
data$W1a[1:3] <- NA
data$W1b[1:6] <- NA
data

The simulated data will have the same pattern of missingness (sampled from the joint distribution, so it won't be exact).

simdat <- sim_df(data, between = "B1", n = 1000, 
                 missing = TRUE)

simdat %>%
  mutate(W1a = ifelse(is.na(W1a), "NA", "not NA"),
         W1b = ifelse(is.na(W1b), "NA", "not NA")) %>%
  count(B1, W1a, W1b) %>%
  group_by(B1) %>%
  mutate(n = round(n/sum(n), 2)) %>%
  knitr::kable()