| Title: | Creates Analysis Results Datasets |
|---|---|
| Description: | Contains functions to help create an Analysis Results Dataset. The dataset follows industry recommended structure. The dataset can be created in multiple passes, using different data frames as input. Analysis Results Datasets are used in the pharmaceutical and biotech industries to capture analysis in a common tabular data structure. |
| Authors: | David Bosak [aut, cre], Kevin Kramer [ctb], Jack Fuller [ctb], Matt Baldwin [ctb] |
| Maintainer: | David Bosak <[email protected]> |
| License: | CC0 |
| Version: | 0.1.1 |
| Built: | 2024-11-04 05:34:42 UTC |
| Source: | https://github.com/dbosak01/ards |
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.
add_ards( data, statvars, statdesc = NULL, byvars = NULL, trtvar = NULL, paramcd = NULL, anal_var = NULL, anal_val = NULL )add_ards( data, statvars, statdesc = NULL, byvars = NULL, trtvar = NULL, paramcd = NULL, anal_var = NULL, anal_val = NULL )
data |
The input data to create analysis results for. This parameter is required. |
statvars |
A vector of column names that identify the desired results. Statvar columns must be numeric. This parameter is required. |
statdesc |
A vector of values or a column name that identifies a description for each statvar. If passed as a vector of values, the number of values should correspond to the number of 'statvar' variables. |
byvars |
A vector of column names to use for by variables. |
trtvar |
A column name to use for the treatment variable. |
paramcd |
A character string that describes the analysis parameter code or column name that contains the parameter code. If supplied as a column name, the function will populate the 'paramcd' column in the ARDS with the value of the 'paramcd' column. |
anal_var |
A column name for the analysis variable or a string that identifies the analysis variable. |
anal_val |
The analysis variable value. Can be identified by a column name or a vector of values. By default, the analysis values will be taken from the values of the variable passed in 'anal_var'. This parameter exists so that you may pass in the values from a different variable, if desired. |
The input data frame, unaltered.
Other ards:
get_ards(),
init_ards()
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)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)
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.
get_ards()get_ards()
A data frame of the current analysis results.
Other ards:
add_ards(),
init_ards()
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)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)
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.
init_ards( studyid = NA, tableid = NA, adsns = NA, population = NA, time = NA, where = NA, reset = TRUE )init_ards( studyid = NA, tableid = NA, adsns = NA, population = NA, time = NA, where = NA, reset = TRUE )
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
|
The initialized analysis dataset.
Other ards:
add_ards(),
get_ards()
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)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)