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Initialize the Analysis Results Dataset

Source: R/ards.R
init_ards.Rd

A function to initialize the Analysis Results Dataset (ARDS). This function will first create a data template in the desired structure, and then populate common values across the dataset from that template. These common values will be repeated on each row of the analysis data frame for subsequent inserts from the add_ards function.

Usage

init_ards(
  studyid = NA,
  tableid = NA,
  adsns = NA,
  population = NA,
  time = NA,
  where = NA,
  reset = TRUE
)

Arguments

studyid

The study for which the analysis was performed. This parameter is optional.

tableid

A table identifier to use for the results. This value identifies the table within the study. Optional string value.

adsns

A vector of source dataset names. This parameter is used to identify the input data for the analysis. This parameter is optional.

population

A description of the analysis population. This parameter is used to identify the population for analysis. This parameter is optional.

time

A description of the time frame used in the analysis. For example, in a clinical study, the "time" value may identify the visit on which the analysis is based.

where

An optional description of the criteria used to subset the data for analysis.

reset

If true, clears out the existing ARDS dataset and replaces with an empty template. Otherwise, just assign new parameter values to the existing template. The default value is TRUE, meaning the ARDS in memory will be cleared every time init_ards is called. If you wish to assign new initialization values, but keep appending to the existing ARDS dataset, set this parameter to FALSE. This feature is used when you are creating two different tables in the same program.

Value

The initialized analysis dataset.

See also

Other ards: add_ards(), get_ards()

Examples

library(ards)
library(dplyr)

# Initialize the ARDS
# - These values will be common through the 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 per row
add_ards(analdf, 
         statvars = c("n", "mean", "std", "min", "max"),
         anal_var = "mpg", trtvar = "cyl")


# Get the ARDS
ards <- get_ards() 

# Uncomment to view ards
# View(ards)