Demo_Manuscript/4_Summary_Stats.R

In PAHPLabResearch/FLASH: FLASH: Free Living Activity Study for Health

# Setup -------------------------------------------------------------------

  rm(list = ls()) # Clear memory
  library(magrittr) # Attach magrittr package (makes code more readable)
  source("Demo_Manuscript/zz_merge_helpers.R") # Load code/variables
  source("Demo_Manuscript/zz_summary_helpers.R") # Load code/variables
  source("Demo_Manuscript/zz_load_d.R") # Load data into object called `d`

# Demographic Summaries ---------------------------------------------------

  list(
    F = subset(d, sex == "F"),
    M = subset(d, sex == "M"),
    Total = d
  ) %>%
  mapply(
    demo_summarize,
    ., names(.), SIMPLIFY = FALSE
  ) %>%
  Reduce(merge, .) %>%
  data.table::fwrite("Demo_Manuscript/output/demographics.csv")

# Means -------------------------------------------------------------------

  paste(d$sex, d$age) %>%
  split(d, .) %>%
  c(
    total = list(d), sex = split(d, d$sex),
    age = split(d, d$age), .
  ) %>%
  {mapply(
    get_means, d = ., description = get_names(.), SIMPLIFY = FALSE
  )} %>%
  c(make.row.names = FALSE) %>%
  do.call(rbind, .) %T>%
  saveRDS("Demo_Manuscript/output/means.rds") %>%
  .[.$variable == "sum_string", setdiff(names(.), "variable")] %>%
  data.table::fwrite("Demo_Manuscript/output/bare_means.csv")

# Correlations ------------------------------------------------------------

  names(d) %>%
  .[grepl("kcal", .)] %>%
  d[ ,.] %>%
  cor(.) %T>%
  print(.) %>%
  as.vector(.) %>%
  .[.!=1] %T>%
  {cat("\n")} %>%
  range(.)

# MAPE --------------------------------------------------------------------

  paste(d$sex, d$age) %>%
  split(d, .) %>%
  c(
    total = list(d), sex = split(d, d$sex),
    age = split(d, d$age), .
  ) %>%
  {mapply(
    get_mape, d = ., description = get_names(.), SIMPLIFY = FALSE
  )} %>%
  c(make.row.names = FALSE) %>%
  do.call(rbind, .) %>%
  data.table::fwrite("Demo_Manuscript/output/mape.csv")