analysis/mcdb_pipeline.R

In diazrenata/scadsanalysis: What the Package Does (One Line, Title Case)

library(drake)
library(scadsanalysis)
library(ggplot2)

expose_imports("scadsanalysis")

datasets <- "mcdb"

sites_list <- list_sites("mcdb")
ndraws = 4000
#sites_list <- sites_list[1:30, ]
set.seed(1980)


all <- drake_plan(
  dat = target(load_dataset(dataset_name = d),
               transform = map(
                 d = !!datasets
               ),
               hpc = F
  ),
  dat_s = target(add_singletons_dataset(dat),
                 transform = map(dat),
                 hpc = F),
  mamm_p = target(readRDS(here::here("analysis", "masterp_mamm.Rds")),
                  hpc = F),
  fs = target(sample_fs_wrapper(dataset = dat_s_dat_mcdb, site_name = s, singletonsyn = singletons, n_samples = ndraws, p_table = mamm_p, seed = !!sample.int(10^6, size = 1)),
              transform = cross(s = !!sites_list$site,
                                singletons = !!c(TRUE, FALSE))),
  # fs_diffs = target(get_fs_diffs(fs),
  #                   transform = map(fs)),
  fs_pc = target(compare_props_fs(fs),
                 transform = map(fs)),
  di = target(add_dis(fs, props_comparison = fs_pc),
              transform = map(fs, fs_pc)),
  di_obs = target(pull_di(di),
                  transform = map(di)),
  di_obs_s = target(dplyr::bind_rows(di_obs),
                    transform = combine(di_obs, .by = singletons)),
  all_di_obs = target(dplyr::bind_rows(di_obs_s_TRUE, di_obs_s_FALSE)),
  cts = target(po_central_tendency(fs, fs_pc),
               transform = map(fs, fs_pc)),
  all_cts = target(dplyr::bind_rows(cts),
                   transform = combine(cts))
)

## Set up the cache and config
db <- DBI::dbConnect(RSQLite::SQLite(), here::here("analysis", "drake", "drake-cache-mcdb.sqlite"))
cache <- storr::storr_dbi("datatable", "keystable", db)
cache$del(key = "lock", namespace = "session")
#
# ## Run the pipeline
# nodename <- Sys.info()["nodename"]
# if(grepl("ufhpc", nodename)) {
#  print("I know I am on the HiPerGator!")
#  library(clustermq)
#  options(clustermq.scheduler = "slurm", clustermq.template = here::here("slurm_clustermq.tmpl"))
#  ## Run the pipeline parallelized for HiPerGator
#  make(all,
#       force = TRUE,
#       cache = cache,
#       cache_log_file = here::here("analysis", "drake", "cache_log_mcdb.txt"),
#       verbose = 1,
#       parallelism = "clustermq",
#       jobs = 20,
#       caching = "master",
#      memory_strategy = "autoclean",
#      garbage_collection = TRUE) # Important for DBI caches!
# } else {
  library(clustermq)
  options(clustermq.scheduler = "multicore")
  # Run the pipeline on multiple local cores
  system.time(make(all, cache = cache, cache_log_file = here::here("analysis", "drake", "cache_log_mcdb.txt"), verbose = 1, memory_strategy = "autoclean"))
#}

DBI::dbDisconnect(db)
rm(cache)

print("Completed OK")