Adds data to an Analysis Results Dataset

Description

The add_ards function dumps data from an input analysis dataset to the ARDS dataset. The function is designed to be pipe-friendly, and will return the input dataset unaltered. The parameters on the function define how to extract the desired data from the analysis dataset. The "statvars" parameter defines which columns contain desired analysis results. The values in these columns will be used to populate the "statval" variable in the output dataset. Other parameters are used to define identifying information for the statistics values, and are optional.

The add_ards function should be called immediately after any calculations, while the analysis results are still in numeric form. This recommendation is to ensure that the ARDS will contain full precision of the analysis values. Once the analysis values are dumped into the ARDS, you may proceed to transform and format your analysis data, without affecting the values captured in the ARDS.

Usage

add_ards(
  data,
  statvars,
  statdesc = NULL,
  byvars = NULL,
  trtvar = NULL,
  paramcd = NULL,
  anal_var = NULL,
  anal_val = NULL
)

Arguments

Value

The input data frame, unaltered.

See Also

Other ards: get_ards(), init_ards()

Examples

library(ards)
library(dplyr)

# Initialize the ARDS
init_ards(studyid = "MTCARS",
          tableid = "01", adsns = "mtcars",
          population = "all cars",
          time = "1973")

# Perform analysis on MPG
# - Add to ARDS from within continuous variable pipeline
mpgdf <- mtcars |> 
  select(cyl, mpg) |> 
  group_by(cyl) |> 
  summarize(n = n(),
            mean = mean(mpg),
            std = sd(mpg),
            min = min(mpg),
            max = max(mpg)) |> 
  add_ards(statvars = c("n", "mean", "std", "min", "max"),
         anal_var = "mpg", trtvar = "cyl")
            
# Perform analysis on GEAR
# - Add to ARDS from within categorical variable pipeline
geardf <- mtcars |> 
  mutate(denom = n()) |> 
  select(cyl, gear, denom) |> 
  group_by(cyl, gear) |> 
  summarize(cnt = n(), 
            denom = max(denom)) |>
  mutate(pct = cnt / denom * 100) |> 
  add_ards(statvars = c("cnt", "pct", "denom"),
         anal_var = "gear", trtvar = "cyl")

# Get the ARDS
ards <- get_ards() 

# Uncomment to view ards
# View(ards)

ards: A package for creating Analysis Results Datasets

Description

An Analysis Results Dataset (ARDS) is commonly used in the pharma-biotech industry to capture the results of an analysis in a tabular data structure. The ards package helps create the ARDS. ards functions can be called from inside a report program or a data preparation program. The functions use a "bucketing" approach, whereby data can be added to the ARDS in multiple calls and from multiple intermediate data sets.

Details

The ards package allows you to easily create an ARDS dataset in a standard clinical reporting or data preparation program. The ards package contains only three functions, and each will be used to create the ARDS dataset. The functions will be called in the following order, and for the described purpose:

• init_ards: Initialize an ARDS dataset

• add_ards: Add data to an ARDS dataset

• get_ards: Extract the completed ARDS

Click on the links above for more information about each function.

The following figure describes the structure of the ARDS dataset. This structure is recommended by CDISC.

Returns the current Analysis Results Dataset

Description

The get_ards function returns the current state of the Analysis Results Dataset (ARDS) as an R data frame. This data frame may be saved to disk, saved in a database, or examined from code. The function takes no parameters.

Usage

get_ards()

Value

A data frame of the current analysis results.

See Also

Other ards: add_ards(), init_ards()

Examples

library(ards)
library(dplyr)

# Initialize the ARDS
# - These values will be common through the dataset
init_ards(studyid = "IRIS",
          tableid = "01", adsns = "iris",
          population = "all flowers",
          time = "1973")

# Perform analysis on Petal.Length
# - Using Species as a by-group
analdf1 <- iris |> 
  select(Petal.Length, Species) |> 
  group_by(Species) |> 
  summarize(n = n(),
            mean = mean(Petal.Length),
            std = sd(Petal.Length),
            min = min(Petal.Length),
            max = max(Petal.Length)) |> 
  add_ards(statvars = c("n", "mean", "std", "min", "max"),
           statdesc = c("Count", "Mean", "STD", "Minimum", "Maximum"),
           anal_var = "Petal.Length", trtvar = "Species")
           
# Perform analysis on Petal.Width
# - Using Species as a by-group
analdf2 <- iris |> 
  select(Petal.Width, Species) |> 
  group_by(Species) |> 
  summarize(n = n(),
            mean = mean(Petal.Width),
            std = sd(Petal.Width),
            min = min(Petal.Width),
            max = max(Petal.Width)) |> 
  add_ards(statvars = c("n", "mean", "std", "min", "max"),
           statdesc = c("Count", "Mean", "STD", "Minimum", "Maximum"),
           anal_var = "Petal.Width", trtvar = "Species")

# Get the ARDS
ards <- get_ards() 

# Uncomment to view ards
# View(ards)

Initialize the Analysis Results Dataset

Description

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

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)