R/utils.R

In thierrygosselin/assigner: Assignment Analysis with RADseq Data

# assigner_function_header -----------------------------------------------------
#' @title assigner_function_header
#' @description Generate function header
#' @rdname assigner_function_header
#' @keywords internal
#' @export
assigner_function_header <- function(f.name = NULL, start = TRUE, verbose = TRUE) {
  if (is.null(f.name)) invisible(NULL)
  if (start) {
    if (verbose) {
      cat("################################################################################\n")
      cat(paste0(stringi::stri_pad_both(str = paste0(" assigner::", f.name, " "), width = 80L, pad = "#"), "\n"))
      cat("################################################################################\n")
    }
  } else {
    if (verbose) {
      cat(paste0(stringi::stri_pad_both(str = paste0(" completed ", f.name, " "), width = 80L, pad = "#"), "\n"))
    }
  }
}# End assigner_function_header


# Pipe operator ----------------------------------------------------------------
#' @title Forward-pipe operator
#' @description magrittr forward-pipe operator
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
NULL

# Exposition pipe-operator -----------------------------------------------------
#' @title Exposition pipe-operator
#' @description magrittr Exposition pipe-operator
#' @name %$%
#' @rdname Exposition_pipe_operator
#' @keywords internal
#' @export
#' @importFrom magrittr %$%
#' @usage lhs \%$\% rhs
NULL

# compound assignment pipe operator --------------------------------------------
#' @title compound assignment pipe operator
#' @description magrittr compound assignment pipe operator
#' @name %<>%
#' @rdname compound_assignment_pipe_operator
#' @keywords internal
#' @export
#' @importFrom magrittr %<>%
#' @usage lhs \%<>\% rhs
NULL

# dplyr n ----------------------------------------------------------------------
# The number of observations in the current group.
#' @title The number of observations in the current group.
#' @description Check dplyr
#' @name n
#' @rdname n
#' @keywords internal
#' @export
#' @importFrom dplyr n
#' @usage n()
NULL

# subsampling_data --------------------------------------------------------------
#' @title subsampling data
#' @description subsampling data
#' @rdname subsampling_data
#' @export
#' @keywords internal


subsampling_data <- function(
  iteration.subsample = 1,
  strata = NULL,
  subsample = NULL,
  random.seed = NULL
) {
  # message(paste0("Creating data subsample: ", iteration.subsample))
  if (is.null(subsample)) {
    subsample.select <- dplyr::mutate(strata, SUBSAMPLE = iteration.subsample)
  } else {

    # Set seed for sampling reproducibility
    if (is.null(random.seed)) {
      random.seed <- sample(x = 1:1000000, size = 1)
    }
    set.seed(random.seed)

    if (subsample > 1) {# integer
      subsample.select <- strata %>%
        dplyr::group_by(STRATA_SEQ) %>%
        dplyr::sample_n(tbl = ., size = subsample, replace = FALSE) %>%
        dplyr::ungroup(.)# sampling individuals for each pop
    }

    subsample.select %<>%
      dplyr::mutate(
        SUBSAMPLE = iteration.subsample,
        RANDOM_SEED_NUMBER = random.seed
      ) %>%
      dplyr::arrange(STRATA_SEQ, ID_SEQ) %>%
      dplyr::ungroup(.)
  }
  return(subsample.select)
} # End subsampling function



# import_subsamples ------------------------------------------------------------
# Write a dataframe containing all the subsample individual assignment

#' @name import_subsamples
#' @title Import individual's assignment results of different subsample folder.
#' @description This function will import all the individual's assignment results
#' of different subsample folder into R.
#' @param dir.path The path to the directory containing the subsample folders.
#' @param imputations (logical) Was the data imputed or not.
#' Default: \code{imputations = FALSE}
#' @return A data frame of all the individual's assignment, with iterations and subsample.

#' @export
#' @rdname import_subsamples

#' @examples
#' \dontrun{
#' subsamples.data <- import_subsamples(
#' dir.path = "assignment_analysis_method_random_imputations_rf_populations",
#' imputations = TRUE
#' )
#' }

#' @author Thierry Gosselin \email{thierrygosselin@@icloud.com}

import_subsamples <- function(dir.path, imputations = FALSE){

  if (missing(dir.path)) stop("dir.path argument missing")

  sampling.method.files <- list.files(path = dir.path, pattern = "assignment", full.names = FALSE)[1]
  sampling.method <- stringi::stri_detect_fixed(str = sampling.method.files, pattern = "ranked") # looks for ranked

  subsample.folders <- list.files(path = dir.path, pattern = "subsample_", full.names = FALSE)
  data <- list()
  for (i in subsample.folders) {
    sub.name <- stringi::stri_replace_all_fixed(str = i, pattern = "_", replacement = ".", vectorize_all = FALSE)
    if (!sampling.method) {
      if (imputations) {
        filename <- stringi::stri_join(dir.path, "/", i, "/","assignment.random.imputed.results.individuals.iterations.", sub.name, ".tsv")
      } else {
        filename <- stringi::stri_join(dir.path, "/", i, "/","assignment.random.no.imputation.results.individuals.iterations.", sub.name, ".tsv")
      }
    } else {
      if (imputations) {
        filename <- stringi::stri_join(dir.path, "/", i, "/","assignment.ranked.imputed.results.individuals.iterations.", sub.name, ".tsv")
      } else {
        filename <- stringi::stri_join(dir.path, "/", i, "/","assignment.ranked.no.imputation.results.individuals.iterations.", sub.name, ".tsv")
      }
    }
    subsample.data <- readr::read_tsv(file = filename, col_names = TRUE)
    # mutate(SUBSAMPLE = rep(i, n()))
    # filter (MISSING_DATA == 'no.imputation')
    data[[i]] <- subsample.data
  }
  data <- tibble::as_tibble(dplyr::bind_rows(data))
  return(data)
}#End import_subsamples

# import_subsamples_fst---------------------------------------------------------
# Write a dataframe containing all the subsample individual assignment

#' @name import_subsamples_fst
#' @title Import the fst ranking from all the subsample runs inside
#' an assignment folder.
#' @description This function will import all the fst ranking from all the
#' subsample runs inside an assignment folder.
#' @param dir.path The path to the directory containing the subsample folders.
#' @return A data frame of all the Fst and ranking.

#' @export
#' @rdname import_subsamples_fst
#' @examples
#' \dontrun{
#' subsamples.data <- import_subsamples_fst(
#' dir.path = "assignment_analysis_method_ranked_no_imputations_20160425@2321"
#' )
#' }

#' @author Thierry Gosselin \email{thierrygosselin@@icloud.com}

import_subsamples_fst <- function(dir.path){
  if (missing (dir.path)) stop("dir.path argument missing")

  # sampling.method <- stri_detect_fixed(str = dir.path, pattern = "ranked") # looks for ranked
  # if (sampling.method == FALSE) stop("This function doesn't work for markers sampled randomly")

  subsample.folders <- list.files(path = dir.path, pattern = "subsample_", full.names = FALSE)

  data.subsample <- list()
  for (i in subsample.folders) {
    fst.files.list <- list.files(path = stringi::stri_join(dir.path, "/", i), pattern = "fst.ranked", full.names = FALSE)
    data.fst <- list()
    for (j in fst.files.list) {
      fst.file <- readr::read_tsv(file = stringi::stri_join(dir.path, "/", i, "/", j), col_names = TRUE) %>%
        dplyr::mutate(
          SUBSAMPLE = rep(i, n()),
          ITERATIONS = rep(j, n())
        )
      data.fst[[j]] <- fst.file
    }
    data.fst <- tibble::as_tibble(dplyr::bind_rows(data.fst))
    data.subsample[[i]] <- data.fst
  }
  data <- tibble::as_tibble(dplyr::bind_rows(data.subsample))
  return(data)
}#End import_subsamples_fst


# GSI_BINARY_SUPPORT -----------------------------------------------------------


#' return the path where gsi_sim should be in the R system paths
#'
#' @keywords internal
#' @name gsi_sim_binary_path
#' @rdname gsi_sim_binary_path
#' @export
gsi_sim_binary_path <- function() {
  file.path(system.file(package = "assigner"), "bin", "gsi_sim")
}

#' return TRUE if gsi_sim exists where it should be
#' @keywords internal
#' @export
#' @name gsi_sim_exists
#' @rdname gsi_sim_exists
gsi_sim_exists <- function() {
  file.exists(gsi_sim_binary_path())
}


#' return TRUE if gsi_sim is executable
#' @keywords internal
#' @export
#' @name gsi_sim_is_executable
#' @rdname gsi_sim_is_executable
gsi_sim_is_executable <- function() {
  NULL #incomplete
}


#' file path to be used in a call to gsi_sim.
#'
#' This version checks to make sure it is there and throws an
#' error with a suggestion of how to get it if it is not there.
#' @export
#' @keywords internal
#' @name gsi_sim_binary
#' @rdname gsi_sim_binary
gsi_sim_binary <- function() {
  if (!gsi_sim_exists()) stop("Can't find the gsi_sim executable where it was expected
                              at ", gsi_sim_binary_path(), ".
                              If you have internet access, you can install it
                              from within R by invoking the function \"install_gsi_sim()\"")

  # then I should check to make sure it is executable

  # if so, return the path
  gsi_sim_binary_path()

}


#' downloads gsi_sim that is appropriate for the operating system
#'
#' If the system is Mac, or Windows, this function will
#' download a precompiled binary from GitHub.  In other cases, or
#' if fromSource == TRUE, this function will attempt to download
#' the source code and compile the program from source and install
#' it.
#'
#' If this function fails, then you can just compile gsi_sim by
#' going to GITHUB_URL and compiling it yourself and naming the
#' executable gsi_sim and putting it at the location specified by the
#' function \code{\link{gsi_sim_binary_path}}.
#' @param commit  The full SHA-1 hash from GitHub from which to get
#' the binary or source
#' @param fromSource If TRUE, download source, even if a binary is available.
#' If FALSE, then it will download a precompiled binary, if available.  If a
#' binary is not available, then it will attempt to download the source.
#' @export
#' @name install_gsi_sim
#' @rdname install_gsi_sim
install_gsi_sim <- function(commit = "080f462c8eff035fa3e9f2fdce26c3ac013e208a", fromSource = FALSE) {

  # make a bin directory
  suppressWarnings(dir.create(file.path(system.file(package = "assigner"), "bin")))

  uname <- Sys.info()["sysname"]
  urlbase <- paste("https://github.com/eriqande/gsi_sim/blob/", commit,
                   "/gsi_sim-", sep = "")

  if (fromSource == FALSE) {
    if (uname == "Darwin") {
      url <- paste(urlbase, "Darwin", sep = "")
    }
    if (uname == "Windows") {
      url <- paste(urlbase, "MINGW32_NT-6.1", sep = "")
    }
    if (uname == "Darwin" || uname == "Windows") {
      message("Downloading file ", url)
      message("And copying to ", gsi_sim_binary_path())
      utils::download.file(url = url, destfile = gsi_sim_binary_path())
      Sys.chmod(gsi_sim_binary_path()) # make it executable
    }
    return(NULL)
  }

  if (uname == "Linux" || fromSource == TRUE) {  # in this case we will just compile from source
    td <- tempdir()

    message("Will be cloning gsi_sim repository to ", td)
    message("Removing any earlier instances of the repository in that temp directory")
    system(paste("cd", td, "; rm -r -f gsi_sim"))
    message("Cloning repository, dealing with submodules, compiling gsi_sim ")
    comm <- paste("cd ", td,
                  "&&  git clone https://github.com/eriqande/gsi_sim.git ",
                  "&&  cd gsi_sim ",
                  "&&  git checkout ", commit,
                  "&& git submodule init && git submodule update ",
                  "&& ./Compile_gsi_sim.sh ", sep = "")
    boing <- system(comm)
    if (boing != 0) {
      stop("Failed trying to clone and compile gsi_sim")
    } else {
      message("Apparently successful compiling gsi_sim.  Now copying to ", gsi_sim_binary_path())
      trycopy <- file.copy(from = paste(td, "/gsi_sim/gsi_sim-", uname, sep = ""),
                           to = gsi_sim_binary_path(),
                           overwrite = TRUE)
      if (trycopy == FALSE) stop("Apparently failed trying to copy ",
                                 paste(td, "/gsi_sim/gsi_sim-", uname, sep = ""),
                                 "to ",
                                 gsi_sim_binary_path())
    }
  }
}
# parallel_core_opt ------------------------------------------------------------
#' @title parallel_core_opt
#' @description Optimization of parallel core argument for radiator
#' @keywords internal
#' @export
parallel_core_opt <- function(parallel.core = NULL, max.core = NULL) {
  # strategy:
  # minimum of 1 core and a maximum of all the core available -2
  # even number of core
  # test
  # parallel.core <- 1
  # parallel.core <- 2
  # parallel.core <- 3
  # parallel.core <- 11
  # parallel.core <- 12
  # parallel.core <- 16
  # max.core <- 5
  # max.core <- 50
  # max.core <- NULL

  # Add-ons options
  # to control the max and min number to use...

  if (is.null(parallel.core)) {
    parallel.core <- parallel::detectCores() - 2
  } else {
    parallel.core <- floor(parallel.core / 2) * 2
    parallel.core <- max(1, min(parallel.core, parallel::detectCores() - 2))
  }

  if (is.null(max.core)) {
    parallel.core.opt <- parallel.core
  } else {
    parallel.core.opt <- min(parallel.core, floor(max.core / 2) * 2)
  }
  return(parallel.core.opt)
}#End parallel_core_opt

# assigner_future: future and future.apply -------------------------------------
#' @name assigner_future
#' @title assigner parallel function
#' @description Updating assigner to use future
# @inheritParams future::plan
# @inheritParams future::availableCores
#' @inheritParams future.apply::future_apply
#' @rdname assigner_future
#' @export
#' @keywords internal
assigner_future <- function(
  .x,
  .f,
  flat.future = c("int", "chr", "dfr", "dfc", "walk", "drop"),
  split.vec = FALSE,
  split.with = NULL,
  split.chunks = 4L,
  parallel.core = parallel::detectCores() - 1,
  ...
) {
  opt.change <- getOption("width")
  options(width = 70)
  on.exit(options(width = opt.change), add = TRUE)
  on.exit(if (parallel.core > 1L) future::plan(strategy = "sequential"), add = TRUE)

  # argument for flattening the results
  flat.future <- match.arg(
    arg = flat.future,
    choices = c("int", "chr", "dfr", "dfc", "walk", "drop"),
    several.ok = FALSE
  )

  # splitting into chunks-------------------------------------------------------
  if (split.vec && is.null(split.with)) {
    # d: data, data length, data size
    # sv: split vector
    d <- .x
    df <- FALSE
    if (any(class(d) %in% c("tbl_df","tbl","data.frame"))) {
      d <- nrow(d)
      df <- TRUE
    }
    if (length(d) > 1L) d <- length(d)
    stopifnot(is.integer(d))
    sv <- as.integer(floor((split.chunks * (seq_len(d) - 1) / d) + 1))
    # sv <- as.integer(floor((parallel.core * cpu.rounds * (seq_len(d) - 1) / d) + 1))
    stopifnot(length(sv) == d)

    # split
    if (df) {
      .x$SPLIT_VEC <- sv
      .x %<>% dplyr::group_split(.tbl = ., "SPLIT_VEC", .keep = FALSE)
    } else {
      .x %<>% split(x = ., f = sv)
    }
  }
  if (!is.null(split.with)) {
    # check
    if (length(split.with) != 1 || !is.character(split.with)) {
      rlang::abort(message = "Contact author: problem with parallel computation")
    }
    .data <- NULL
    stopifnot(rlang::has_name(.x, split.with))
    if (split.vec) {
      sv <- dplyr::distinct(.x, .data[[split.with]])
      d <- nrow(sv)
      sv$SPLIT_VEC <- as.integer(floor((split.chunks * (seq_len(d) - 1) / d) + 1))
      .x %<>%
        dplyr::left_join(sv, by = split.with) %>%
        dplyr::group_split(.tbl = ., "SPLIT_VEC", .keep = FALSE)
    } else {
      .x %<>% dplyr::group_split(.tbl = ., .data[[split.with]], .keep = TRUE)
    }
  }



  if (parallel.core == 1L) {
    future::plan(strategy = "sequential")
  } else {
    parallel.core <- parallel_core_opt(parallel.core = parallel.core)
    lx <- length(.x)
    if (lx < parallel.core) {
      future::plan(strategy = "multisession", workers = lx)
    } else {
      future::plan(strategy = "multisession", workers = parallel.core)
    }
  }

  # .x <- future.apply::future_apply(X = .x, FUN = .f, ...)
  # capture dots
  # d <- rlang::dots_list(..., .ignore_empty = "all", .preserve_empty = TRUE, .homonyms = "first")
  # if (bind.rows) .x %<>% dplyr::bind_rows(.)



  # Run the function in parallel and account for dots-dots-dots argument
  rad_map <- switch(flat.future,
                    int = {furrr::future_map_int},
                    chr = {furrr::future_map_chr},
                    dfr = {furrr::future_map_dfr},
                    dfc = {furrr::future_map_dfc},
                    walk = {furrr::future_walk},
                    drop = {furrr::future_map}
  )

  opts <- furrr::furrr_options(globals = FALSE, seed = TRUE)

  if (length(list(...)) == 0) {
    .x %<>% rad_map(.x = ., .f = .f, .options = opts)

  } else {
    .x %<>% rad_map(.x = ., .f = .f, ..., .options = opts)
  }
  return(.x)
}#End assigner_future


# PIVOT-GATHER-CAST ------------------------------------------------------------
# rationale for doing this is that i'm tired of using tidyverse or data.table semantics
# tidyr changed from gather/spread to pivot_ functions but their are still very slow compared
# to 1. the original gather/spread and data.table equivalent...

#' @title rad_long
#' @description Gather, melt and pivot_longer
#' @rdname rad_long
#' @keywords internal
#' @export

rad_long <- function(
  x,
  cols = NULL,
  measure_vars = NULL,
  names_to = NULL,
  values_to = NULL,
  variable_factor = TRUE,
  keep_rownames = FALSE,
  tidy = FALSE
){


  # tidyr
  if (tidy) {
    x %>%
      tidyr::pivot_longer(
        data = .,
        cols = -cols,
        names_to = names_to,
        values_to = values_to
      )
  } else {# data.table
    x %>%
      data.table::as.data.table(., keep.rownames = keep_rownames) %>%
      data.table::melt.data.table(
        data = .,
        id.vars = cols,
        measure.vars = measure_vars,
        variable.name = names_to,
        value.name = values_to,
        variable.factor = variable_factor
      ) %>%
      tibble::as_tibble(.)
  }
}#rad_long

#' @title rad_wide
#' @description Spread, dcast and pivot_wider
#' @rdname rad_wide
#' @keywords internal
#' @export
rad_wide <- function(
  x ,
  formula = NULL,
  names_from = NULL,
  values_from = NULL,
  sep = "_",
  fun_aggregate = NULL,
  values_fill = NULL,
  tidy = FALSE
){
  # tidyr
  if (tidy) {
    x %<>%
      tidyr::pivot_wider(
        data = .,
        names_from = names_from,
        values_from = values_from,
        values_fill = values_fill
      )
  } else {# data.table
    if (is.null(fun_aggregate)) {
      x  %>%
        data.table::as.data.table(.) %>%
        data.table::dcast.data.table(
          data = .,
          formula =  formula,
          value.var = values_from,
          sep = sep,
          fill = values_fill
        ) %>%
        tibble::as_tibble(.)
    } else {
      x  %>%
        data.table::as.data.table(.) %>%
        data.table::dcast.data.table(
          data = .,
          formula =  formula,
          value.var = values_from,
          sep = sep,
          fun.aggregate = fun_aggregate,
          fill = values_fill
        ) %>%
        tibble::as_tibble(.)
    }
  }
}#rad_wide


# assigner_clock ---------------------------------------------------------------
#' @title assigner_tic
#' @description assigner tictoc function
#' @rdname assigner_tic
#' @keywords internal
#' @export
assigner_tic <- function(timing = proc.time()) {
  invisible(timing)
}# End assigner_tic

#' @title assigner_toc
#' @description assigner tictoc function
#' @rdname assigner_toc
#' @keywords internal
#' @export
assigner_toc <- function(
  timing,
  end.message = "Computation time, overall:",
  verbose = TRUE
) {
  if (verbose) message("\n", end.message, " ", round((proc.time() - timing)[[3]]), " sec")
}# End assigner_toc