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")
```

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")
```

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")
```

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 B1 30% of A1 values are missing and 60% of A2 values are missing. This is correlated so that there is a 100% chance that A2 is missing if A1 is. There is no missing data for condition B2.

```
data <- sim_design(2, 2, n = 10, plot = FALSE)
data$A1[1:3] <- NA
data$A2[1:6] <- NA
data
#> id B A1 A2
#> 1 S01 B1 NA NA
#> 2 S02 B1 NA NA
#> 3 S03 B1 NA NA
#> 4 S04 B1 -0.87577954 NA
#> 5 S05 B1 0.27931928 NA
#> 6 S06 B1 0.46277729 NA
#> 7 S07 B1 -0.11678369 -0.66795544
#> 8 S08 B1 1.34454748 2.30398889
#> 9 S09 B1 -1.26771349 0.55738252
#> 10 S10 B1 -0.71258675 0.09183975
#> 11 S11 B2 -0.39607245 0.85023624
#> 12 S12 B2 -1.15357665 -0.18013489
#> 13 S13 B2 -0.21533313 1.08873598
#> 14 S14 B2 -0.42370435 0.94812875
#> 15 S15 B2 -0.05717782 0.63667947
#> 16 S16 B2 -0.12733224 -1.47331528
#> 17 S17 B2 0.21208884 0.69005635
#> 18 S18 B2 -0.20401676 -1.01059002
#> 19 S19 B2 -1.14885697 -0.50127757
#> 20 S20 B2 0.87586766 1.47606428
```

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 = "B", n = 1000,
missing = TRUE)
```

```
#> # A tibble: 4 x 4
#> # Groups: B [2]
#> B A1 A2 n
#> <fct> <chr> <chr> <dbl>
#> 1 B1 NA NA 0.31
#> 2 B1 not NA NA 0.31
#> 3 B1 not NA not NA 0.38
#> 4 B2 not NA not NA 1
```