## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  library(dplyr)
#  library(ards)
#  
#  # Initialize the ARDS
#  # - These values will be repeated on all rows in the ARDS dataset
#  init_ards(studyid = "MTCARS",
#            tableid = "01", adsns = "mtcars",
#            population = "all cars",
#            time = "1973")
#  
#  # Perform analysis on MPG
#  # - Using cylinders as a by group
#  analdf <- mtcars |>
#    select(cyl, mpg) |>
#    group_by(cyl) |>
#    summarize(n = n(),
#              mean = mean(mpg),
#              std = sd(mpg),
#              min = min(mpg),
#              max = max(mpg))
#  
#  # View analysis data
#  analdf
#  #     cyl     n  mean   std   min   max
#  #   <dbl> <int> <dbl> <dbl> <dbl> <dbl>
#  # 1     4    11  26.7  4.51  21.4  33.9
#  # 2     6     7  19.7  1.45  17.8  21.4
#  # 3     8    14  15.1  2.56  10.4  19.2
#  
#  # Add analysis data to ARDS
#  # - These values will be unique for each row in the ARDS dataset
#  add_ards(analdf,
#           statvars = c("n", "mean", "std", "min", "max"),
#           anal_var = "mpg", trtvar = "cyl")
#  
#  
#  # Get the ARDS
#  # - Remove by-variables to make the ARDS dataset easier to read
#  ards <- get_ards() |> select(-starts_with("by"))
#  
#  # Uncomment to view ards
#  # View(ards)
#  

## ----eval=FALSE, echo=TRUE----------------------------------------------------
#  # Restore to wide format
#  res <- restore_ards(ards)
#  
#  # View results
#  res
#  # $mpg
#  #   cyl anal_var  n     mean      std  min  max
#  # 1   4      mpg 11 26.66364 4.509828 21.4 33.9
#  # 2   6      mpg  7 19.74286 1.453567 17.8 21.4
#  # 3   8      mpg 14 15.10000 2.560048 10.4 19.2
#