The default simpr workflow is easy to understand but computationally inefficient. Although simpr prioritizes ease of use over computational speed, two things together can make simpr more efficient:

  1. Generating, fitting, and tidying simultaneously
  2. Parallel processing with the future package

Generating, fitting, and tidying simultaneously

Consider a standard simpr workflow:


specify(a = ~ rnorm(n),
        b = ~ a + rnorm(n)) %>% 
  define(n = c(100, 200)) %>% 
  generate(10) %>% 
  fit(lm = ~ lm(b ~ a)) %>% 
  tidy_fits()

An issue with this workflow is that it involves shuttling a lot of data around: the generate() step adds a list-column with simulated data to a tibble, which is then sent to fit(), which adds a list-column containing large model objects, and then these are all sent to tidy_fits() for extracting essential model statistics.

Instead, you can simply place the call to generate() later in the chain:

specify(a = ~ rnorm(n),
        b = ~ a + rnorm(n)) %>% 
  define(n = c(100, 200)) %>% 
  fit(lm = ~ lm(b ~ a)) %>% 
  tidy_fits() %>% 
  generate(10) 

This means that the data is generated, fit, and tidied all at once once you call generate(). This means that these steps can occur on a single parallel worker without pushing lots of data around.

Behind the scenes, before generate() is called, simpr simply stores successive commands in the simpr_spec object. When generate() is called, these successive commands are executed in order. Data munging, including with per_sim() or on the final tidied data, using dplyr or tidyr is also supported in this workflow. Below, data is specified, and the commands for reshaping, fitting, and tidying, and selecting columns from the tidied output are all written before generate() and are executed together:

specify(control = ~ rnorm(n, mean = 0),
        intervention_1 = ~ rnorm(n, mean = 0.2),
        intervention_2 = ~ rnorm(n, mean = 2)) %>% 
  define(n = c(6, 12)) %>% 
  per_sim() %>% 
  pivot_longer(cols = everything(),
               names_to = "group", 
               values_to = "response") %>% 
  fit(lm = ~ lm(response ~ group)) %>% 
  tidy_fits() %>% 
  select(.sim_id, n, term, estimate) %>% 
  generate(2) 
#> # A tibble: 12 × 4
#>    .sim_id     n term                estimate
#>      <int> <dbl> <chr>                  <dbl>
#>  1       1     6 (Intercept)           0.267 
#>  2       1     6 groupintervention_1  -0.528 
#>  3       1     6 groupintervention_2   1.96  
#>  4       2    12 (Intercept)           0.348 
#>  5       2    12 groupintervention_1  -0.174 
#>  6       2    12 groupintervention_2   1.30  
#>  7       3     6 (Intercept)           0.0552
#>  8       3     6 groupintervention_1   0.701 
#>  9       3     6 groupintervention_2   2.56  
#> 10       4    12 (Intercept)           0.0285
#> 11       4    12 groupintervention_1   0.157 
#> 12       4    12 groupintervention_2   1.72

Parallel processing with the future package

Changing the evaluation order makes little difference on its own, but combined with parallel processing can produce a speedup. simpr uses the furrr package, part of the futureverse suite of packages designed around the future package. These packages are designed to make parallel processing transparent as easy to use.

To use parallel processing with simpr, simply load the future package and declare your “plan” for code execution with future::plan(). The three most relevant plans are:

  1. sequential, the default R behavior using just one worker.
  2. multiprocess, a parallel processing approach.
  3. multicore, another parallel processing approach that can be faster than multiprocess, but which doesn’t work with R Studio and only works on Linux/Mac.

For both multiprocess and multicore plans, you must tell R how many cores you want to use. You can check how many your computer has available with future::availableCores().

The optimized version of the opening example, rewritten to both change execution order and use parallel processing:

library(future)

plan(multisession, availableCores() - 1)

specify(a = ~ rnorm(n),
        b = ~ a + rnorm(n)) %>% 
  define(n = c(100, 200)) %>% 
  fit(lm = ~ lm(b ~ a)) %>% 
  tidy_fits() %>% 
  generate(10) 

Results will vary. The parallel version for this small simulation actually takes longer, because there fixed costs in setting up the workers. The speed advantage will become more apparent for larger simulations with slow data-generation or data-fitting steps.