Original cars dataset
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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")Original cars dataset
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")
Simulated cars dataset
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")
Original mtcars dataset
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")
Simulated iris dataset
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)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")
Simulated iris dataset (rounded)
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
#> id B1 W1a W1b
#> 1 S01 B1a NA NA
#> 2 S02 B1a NA NA
#> 3 S03 B1a NA NA
#> 4 S04 B1a -0.8758 NA
#> 5 S05 B1a 0.2793 NA
#> 6 S06 B1a 0.4628 NA
#> 7 S07 B1a -0.1168 -0.6680
#> 8 S08 B1a 1.3445 2.3040
#> 9 S09 B1a -1.2677 0.5574
#> 10 S10 B1a -0.7126 0.0918
#> 11 S11 B1b -0.3961 0.8502
#> 12 S12 B1b -1.1536 -0.1801
#> 13 S13 B1b -0.2153 1.0887
#> 14 S14 B1b -0.4237 0.9481
#> 15 S15 B1b -0.0572 0.6367
#> 16 S16 B1b -0.1273 -1.4733
#> 17 S17 B1b 0.2121 0.6901
#> 18 S18 B1b -0.2040 -1.0106
#> 19 S19 B1b -1.1489 -0.5013
#> 20 S20 B1b 0.8759 1.4761The 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)| B1 | W1a | W1b | n |
|---|---|---|---|
| B1a | NA | NA | 0.31 |
| B1a | not NA | NA | 0.31 |
| B1a | not NA | not NA | 0.38 |
| B1b | not NA | not NA | 1.00 |
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