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03_analyze_data.R
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#!/usr/local/bin/Rscript
################################################################################
### Analyze trimmed data
##
## Created on: 2017-12-15
## Author: Kazuki Yoshida
################################################################################
###
### Capture data filename argument
################################################################################
## Specify data file as the first argument
data_file_name <- commandArgs(trailingOnly = TRUE)[1]
## Specify the core count as the second argument
n_cores <- as.numeric(commandArgs(trailingOnly = TRUE)[2])
## Execution not allowed without data file
stopifnot(!is.na(data_file_name))
## Execution not allowed without n_cores
stopifnot(!is.na(n_cores))
## Check it is a scenario file
if (!grepl("scenario_prepared", data_file_name)) {
stop("Not a prepared scenario file")
}
###
### Prepare environment
################################################################################
## sink() if being run non-interactively
if (sink.number() != 0) {sink()}
.scriptFileName. <- gsub("^--file=", "", Filter(function(x) {grepl("^--file=", x)}, commandArgs()))
if (length(.scriptFileName.) == 1) {
## Include data file name
sink(file = paste0("./log/",
basename(.scriptFileName.),
"_",
## Data file name without folder or extension
tools::file_path_sans_ext(basename(data_file_name)),
".txt"), split = TRUE)
options(width = 100)
}
## Record start time
start_time <- Sys.time()
cat("### Started ", as.character(start_time), "\n")
## Configure parallelization
## Parallel backend for foreach (also loads foreach and parallel; includes doMC)
library(doParallel)
## Reproducible parallelization
library(doRNG)
## Used by parallel::mclapply() as default
options(mc.cores = n_cores)
## Used by doParallel as default
options(cores = n_cores)
## Register doParallel as the parallel backend for foreach
## http://stackoverflow.com/questions/28989855/the-difference-between-domc-and-doparallel-in-r
doParallel::registerDoParallel(cores = n_cores)
## Report multicore use
cat("### Using", foreach::getDoParWorkers(), "cores\n")
cat("### Using", foreach::getDoParName(), "as backend\n")
## Load packages
library(tidyverse)
library(datagen3)
library(trim3)
cat("
###
### Load data file
################################################################################\n")
## Load
load(data_file_name)
cat("### data_file_name\n", data_file_name, "\n")
cat("### scenario_count\n", scenario_count, "\n")
cat("### scenario_description\n", scenario_description, "\n")
cat("### part_count\n", part_count, "\n")
cat("### R\n", R, "\n")
cat("
###
### Analyze data
################################################################################\n")
## Prepare data for analysis readiness
lst_iter <- foreach::foreach(i = seq_along(lst_iter)) %dorng% {
cat("### Iteration", i, "\n")
## Data analysis step
nested_df <- lst_iter[[i]] %>%
group_by(trim_method_name, thres) %>%
mutate(
## Record number kept after trimming
n_kept = as.numeric(map(trimmed_data, nrow)),
## List column holding tables
n_kept_group = map(trimmed_data, function(df) {
table(df$A)
}),
## Extract group sizes
n0_kept = map_dbl(n_kept_group, magrittr::extract, 1),
n1_kept = map_dbl(n_kept_group, magrittr::extract, 2),
n2_kept = map_dbl(n_kept_group, magrittr::extract, 3),
## Analyze and return coef/vcov only
trimmed_data = map(trimmed_data, function(data) {
## Create an augmented counterfactual dataset.
data_aug <- augment_counterfactuals(data = data,
outcome_name = "Y",
counter_names = c("pYA0","pYA1","pYA2"),
A_name = "A",
A_levels = c(0,1,2))
df_outcome <- analyze_outcome_glm(data = data,
formula = Y ~ factor(A),
family = poisson(link = "log"),
data_aug = data_aug)
df_outcome
})) %>%
## Drop list column holding tables
select(-n_kept_group)
nested_df
}
cat("
###
### Save data
################################################################################\n")
## New file name
new_data_file_name <- gsub("prepared", "analyzed", data_file_name)
cat("### Saving", new_data_file_name,"\n")
## Save everything as a new file
save(scenario, R, scenario_count, scenario_description, part_count, lst_iter,
file = new_data_file_name)
################################################################################
cat("
###
### Record package versions etc
################################################################################\n")
print(sessionInfo())
## Record execution time
end_time <- Sys.time()
cat("\n### Started ", as.character(start_time), "\n")
cat("### Finished ", as.character(end_time), "\n")
print(end_time - start_time)
## Stop sinking to a file if active
if (sink.number() != 0) {sink()}