run_specs following setup_specs

[akarlinsky]

For my analysis, there's a certain control variable which I want to be included in all specifications.
This is very easy to do after the fact by filtering for only rows where it appears:

 specr_results %>% 
  filter(str_detect(string = controls, pattern = fixed("important_control_var")))

When specr_results is small, this is ok. However, when specr_results is large, making it actually estimate the models without this crucial control variable is redundant. I thought to use setup_specs to setup my estimation tibble, filter that and then run_specs on that, thus reducing computation time immensely.

However, it seems that setup_specs is for illustration purposes only(?). There's no way that I see to pass on a setup_specs tibble to run_specs.

If I'm right, I suggest that you add an option to run_specs to get a setup_specs object as input OR to get x,y, controls etc.

P.S

Thanks for a great package!

[akarlinsky]

similar to #23

[akarlinsky]

For my purposes, I have the following, very hacky, solution:

run_specs2 <- function (df, 
                        spec_df, 
                        subsets = NULL, 
                        conf.level = 0.95, keep.results = FALSE) {

  specs <- spec_df
  if (!is.null(subsets)) {
    if (class(subsets) != "list") {
      wrong_class <- class(subsets)
      stop(glue("Subsets must be a 'list' and not a '{wrong_class}'."))
    }
    subsets <- map(subsets, as.character)
    df_list <- create_subsets(df, subsets)
    df_list[[length(df_list) + 1]] <- df %>% dplyr::mutate(filter = "all")
    if (length(subsets) > 1) {
      suppressMessages({
        df_comb <- subsets %>% cross %>% map(~create_subsets(subsets = .x, 
          df = df) %>% map(~dplyr::select(.x, -filter)) %>% 
          reduce(dplyr::inner_join) %>% dplyr::mutate(filter = paste(names(.x), 
          .x, collapse = " & ", sep = " = ")))
        df_all <- append(df_list, df_comb)
      })
    }
    else {
      df_all <- df_list
    }
    if (conf.level > 1 | conf.level < 0) {
      stop("The confidence level must be strictly greater than 0 and less than 1.")
    }
    map_df(df_all, ~run_spec(specs, .x, conf.level = conf.level, 
      keep.results = keep.results) %>% dplyr::mutate(subsets = unique(.x$filter)))
  }
  else {
    run_spec(specs, df, conf.level = conf.level, keep.results = keep.results) %>% 
      dplyr::mutate(subsets = "all")
  }
}

environment(run_specs2) <- asNamespace('specr')

I took the original code for run_specs, removed the x y controls model input and instead made a setup_specs output the input. A much better solution would be either user inputs x y controls model OR a setup_specs object.

[masurp]

A new version of specr, which I hope to push to CRAN soon will solve this. Give me a bit more time! ;)

[masurp]

The new development version 1.0.0 (which will be pushed to CRAN soon) implements such a framework, where you first setup specifications and then run them afterwards. Check the newly update website in this regard: https://masurp.github.io/specr/index.html