Package 'CornerstoneR' reference manual

Title:	Collection of Scripts for Interface Between 'Cornerstone' and 'R'
Description:	Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'. The desktop application 'Cornerstone' (<https://www.camline.com/en/products/cornerstone/cornerstone-core.html>) is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster. The engineers incorporate intensified hands-on statistics into their projects. They benefit from an intuitive and uniquely designed graphical Workmap concept: you design experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon, immediately, interactively, and without any programming. While 'Cornerstone's' interface to the statistical programming language 'R' has been available since version 6.0, the latest interface with 'R' is even much more efficient. 'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the standard 'Cornerstone' GUI. Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R' routines, immediately and without the need to deal with programming code. Additionally, your 'R' programming team develops corresponding 'R' packages detached from 'Cornerstone' in their favorite 'R' environment. Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel (<https://www.youtube.com/watch?v=HEQHwq_laXU>) (available in German).
Authors:	Gerrith Djaja [aut, cre], Dirk Surmann [aut]
Maintainer:	Gerrith Djaja <[email protected]>
License:	GPL-3
Version:	2.0.2
Built:	2024-12-14 06:50:48 UTC
Source:	CRAN

CornerstoneR: Collection of Scripts for Interface Between 'Cornerstone' and 'R'

Description

Collection of generic 'R' scripts which enable you to use existing 'R' routines in 'Cornerstone'.

The desktop application 'Cornerstone' (<https://www.camline.com/en/products/cornerstone/cornerstone-core.html>) is a data analysis software provided by 'camLine' that empowers engineering teams to find solutions even faster. The engineers incorporate intensified hands-on statistics into their projects. They benefit from an intuitive and uniquely designed graphical Workmap concept: you design experiments (DoE) and explore data, analyze dependencies, and find answers you can act upon, immediately, interactively, and without any programming.

While 'Cornerstone's' interface to the statistical programming language 'R' has been available since version 6.0, the latest interface with 'R' is even much more efficient. 'Cornerstone' release 7.1.1 allows you to integrate user defined 'R' packages directly into the standard 'Cornerstone' GUI. Your engineering team stays in 'Cornerstone's' graphical working environment and can apply 'R' routines, immediately and without the need to deal with programming code. Additionally, your 'R' programming team develops corresponding 'R' packages detached from 'Cornerstone' in their favorite 'R' environment.

Learn how to use 'R' packages in 'Cornerstone' 7.1.1 on 'camLineTV' YouTube channel (<https://www.youtube.com/watch?v=HEQHwq_laXU>) (available in German).

Author(s)

Maintainer: Gerrith Djaja [email protected]

Authors:

Dirk Surmann [email protected] (ORCID)

Data from carstats

Description

Dataset of different cars and various values.

Format

A data.table object with 406 observations and 9 variables. The variables and their scale of measurement are as follows:

Model: nominal
Origin: nominal
MPG: interval
Cylinders: ordinal
Displacement: interval
Horsepower: interval
Weight: interval
Acceleration: interval
Model.Year: interval

Source

Cornerstone sample dataset

Fit Function to Data via Nonlinear Regression

Description

Fit predefined functions to data via nonlinear least squares using Levenberg-Marquardt algorithm via nlsLM.

Usage

fitFunction(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  groups = cs.in.groupvars(),
  auxs = cs.in.auxiliaries(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)
fitFunction(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  groups = cs.in.groupvars(),
  auxs = cs.in.auxiliaries(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`resps`	[`character`] Character vector of response variables.
`groups`	[`character`] Character vector of group variables.
`auxs`	[`character`] Character vector of auxiliary variables.
`scriptvars`	[`list`] Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `nls` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Details

The following script variables are summarized in scriptvars list:

math.fun: [character(1)]
Function selection for fitting data. It is possible to choose a predefined model, or compose a model manually by selecting User Defined.
Default is User Defined
preds.frml: [character(1)]
Required if math.fun is set to User Defined. Valid R formula for the right hand side (predictors) of the model equation.
resp.frml: [character(1)]
Required if math.fun is set to User Defined. Valid R formula for the left hand side (response) of the model equation.
limits: [character(1)]
Optional if math.fun is set to User Defined. Specifies minimum and maximum value for function math.fun as a comma separated list of min and max. It is possible to assign variables, e.g. min=a, which need start values in start.vals, as well as real numbers, e.g. min=4.5, with a period as decimal separator.
start.vals: [character(1)]
Required if math.fun is set to User Defined. Specify starting values for all terms of the right hand side as a comma separated list with a period as decimal separator.
weights: [character(1)]
Select a weighting variable from the auxiliary variables.
max.iter: Maximum number of iterations. For details see link[minpack.lm]{nls.lm.control}
max.ftol: Maximum relative error desired in the sum of squares. If 0, the default is used. For details see link[minpack.lm]{nls.lm.control}

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame objects:

`coeff`	Estimated coefficients and standard errors for each group. Convergence information is available for every group (for details see `link[minpack.lm]{nls.lm}`).
`vcov`	Variance-Covariance matrix of the main coefficients for the fitted model of each group (for details see `link[stats]{vcov}`).
`predictions`	Dataset to brush with predictions and residuals added to original values and groups, if available.

Examples

# Generate data from logistic function:
fun = function(x, a, b, c, d, sigma = 1) {
  a+(b-a) / (1+exp(-d*(x-c))) + rnorm(length(x), sd = sigma)
  }
library(data.table)
dt = data.table(  x1 = sample(seq(-10, 10, length.out = 100))
                  , group1 = sample(x = c("A", "B"), replace = TRUE, size = 100)
                  )
dt[group1 == "A", y1 := fun(x1, 1, 10, 1, 0.6, 0.1)]
dt[group1 == "B", y1 := fun(x1, 8, 2, -1, 0.3, 0.1)]
# Set script variables
scriptvars = list(math.fun = "Logistic", resp.frml = "", preds.frml = "", limits = ""
                  , start.vals = "", weights = "", max.iter = 50, max.ftol = 0
                  )
# Fit the logistic function:
res = fitFunction(dt, "x1", "y1", "group1", character(0), scriptvars, TRUE)
# Show estimated coefficients:
res$coeff
# Variance-Covariance matrix:
res$vcov
# Plot fitted vs. residuals:
plot(res$predictions$Fitted, res$predictions$Residuals)
# Generate data from logistic function:
fun = function(x, a, b, c, d, sigma = 1) {
  a+(b-a) / (1+exp(-d*(x-c))) + rnorm(length(x), sd = sigma)
  }
library(data.table)
dt = data.table(  x1 = sample(seq(-10, 10, length.out = 100))
                  , group1 = sample(x = c("A", "B"), replace = TRUE, size = 100)
                  )
dt[group1 == "A", y1 := fun(x1, 1, 10, 1, 0.6, 0.1)]
dt[group1 == "B", y1 := fun(x1, 8, 2, -1, 0.3, 0.1)]
# Set script variables
scriptvars = list(math.fun = "Logistic", resp.frml = "", preds.frml = "", limits = ""
                  , start.vals = "", weights = "", max.iter = 50, max.ftol = 0
                  )
# Fit the logistic function:
res = fitFunction(dt, "x1", "y1", "group1", character(0), scriptvars, TRUE)
# Show estimated coefficients:
res$coeff
# Variance-Covariance matrix:
res$vcov
# Plot fitted vs. residuals:
plot(res$predictions$Fitted, res$predictions$Residuals)

Local Interface Functions

Description

CS-R interface functions are defined in package namespace via this file. Each function overwrites itself with the corresponding counterpart defined in the global environment from CS.

Usage

invokeFromR()

cs.in.auxiliaries(quote = FALSE)

cs.in.brushed()

cs.in.dataset()

cs.in.excluded()

cs.in.groupvars(quote = FALSE)

cs.in.predictors(quote = FALSE)

cs.in.responses(quote = FALSE)

cs.in.Robject(name = NA)

cs.in.scriptvars(name = NA)

cs.in.subsets()

cs.in.subsets.current()

cs.quote(x)

cs.out.dataset(data, name = NA, brush = FALSE)

cs.out.emf(name = NULL, width = 10, height = 10)

cs.out.png(name = NULL, width = 480, height = 480)

cs.out.Robject(R_object, name = NA)
invokeFromR()

cs.in.auxiliaries(quote = FALSE)

cs.in.brushed()

cs.in.dataset()

cs.in.excluded()

cs.in.groupvars(quote = FALSE)

cs.in.predictors(quote = FALSE)

cs.in.responses(quote = FALSE)

cs.in.Robject(name = NA)

cs.in.scriptvars(name = NA)

cs.in.subsets()

cs.in.subsets.current()

cs.quote(x)

cs.out.dataset(data, name = NA, brush = FALSE)

cs.out.emf(name = NULL, width = 10, height = 10)

cs.out.png(name = NULL, width = 480, height = 480)

cs.out.Robject(R_object, name = NA)

Arguments

`quote`	[`logical(1)`] Quote all variables to cover invalid names. Use `make.names` as an alternative.
`name`	[`character(1)`] Name for output to Cornerstone.
`x`	[`character(1)`] String to check for invalid characters related to `make.names`. Add backticks, if necessary.
`data`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`brush`	[`logical(1)`] Brushing of output dataset in Cornerstone across the R object.
`width`	[`numeric(1)`] Width of exported plotting object. See `pdf`.
`height`	[`numeric(1)`] Height of exported plotting object. See `pdf`.
`R_object`	[`list`] List of exported R objects to Cornerstone.

Match Nearest Neighbor Between Two Datasets

Description

Match the nearest neighbor from a redirected Cornerstone Robject dataset (redirectDataset) to corresponding selected predictor variables. Predictor variables from both datasets are supposed to be numeric to apply the Euclidean distance calculated by dist2. The function returns a dataset with the nearest neighbor to every observation, matched by the predictor variables. Available response, group, and auxiliary variables from the redirected datasets are passed through, as well as, selected auxiliary variables. The calculated distance is attached.

Usage

matchNearestNeighbor(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  auxs = cs.in.auxiliaries(),
  robject = cs.in.Robject(),
  return.results = FALSE
)
matchNearestNeighbor(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  auxs = cs.in.auxiliaries(),
  robject = cs.in.Robject(),
  return.results = FALSE
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`auxs`	[`character`] Character vector of auxiliary variables.
`robject`	[`list`] Named list of one `redirectDataset` object(s) set via Cornerstone menu "Input R Objects".
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame objects:

`nearest.neighbor`	Matched nearest neighbor which consists of predictor and available response, group, and auxiliary variables. The calculated distance is attached to this dataset.
`runtimes`	Run times for every input R object.

Mosaic Plot

Description

Plots (extended) mosaic displays via mosaic. The last response variable is highlighted. A high-dimensional contingency table is calculated via structable from the given dataset. Flat contingency table splits predictors horizontally and optional responses vertically.

Usage

mosaicPlot(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  return.results = FALSE,
  ...
)
mosaicPlot(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`resps`	[`character`] Character vector of response variables.
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `mosaic` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame objects:

long.contingency

Contingency table in long format.

Examples

# Draw mosaic plot from 'titanic' data:
mosaicPlot(titanic, c("Class", "Age", "Sex", "Survived"))
res = mosaicPlot(titanic, c("Class", "Age"), c("Sex", "Survived"), return.results = TRUE)
print(res)
# Draw mosaic plot from 'titanic' data:
mosaicPlot(titanic, c("Class", "Age", "Sex", "Survived"))
res = mosaicPlot(titanic, c("Class", "Age"), c("Sex", "Survived"), return.results = TRUE)
print(res)

Random Forest

Description

Random Forest via ranger. Predicts response variables or brushed set of rows from predictor variables, using Random Forest classification or regression.

Usage

randomForest(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  brush = cs.in.brushed(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)
randomForest(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  brush = cs.in.brushed(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`resps`	[`character`] Character vector of response variables.
`brush`	[`logical`] Logical vector of length `nrow(dataset)`. Flags brushed rows in Cornerstone.
`scriptvars`	[`list`] Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `ranger` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Details

The following script variables are summarized in scriptvars list:

brush.pred: [logical(1)]
Use brush vector as additional predictor.
Default is FALSE.
use.rows: [character(1)]
Rows to use in model fit. Possible values are all, non-brushed, or brushed.
Default is all.
num.trees: [integer(1)]
Number of trees to fit in ranger.
Default is 500.
importance.mode: [character(1)]
Variable importance mode. For details see ranger.
Default is permutation.
respect.unordered.factors: [character(1)]
Handling of unordered factor covariates. For details see ranger.
Default is NULL.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame objects:

`statistics`	General statistics about the random forest.
`importances`	Variable importance of prediction variables in descending order of importance (most important first)
`predictions`	Dataset to brush with predicted values for `dataset`. The original input and other columns can be added to this dataset through the menu `Columns -> Add from Parent ...`.
`confusion`	For categorical response variables or brush state only. A table with counts of each distinct combination of predicted and actual values.
`rgobjects`	List of `ranger.forest` objects with fitted random forests.

Examples

# Fit random forest to iris data:
res = randomForest(iris, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), "Species"
                   , scriptvars = list(brush.pred = FALSE, use.rows = "all", num.trees = 500
                                       , importance.mode = "permutation"
                                       , respect.unordered.factors = "ignore"
                                       )
                   , brush = rep(FALSE, nrow(iris)), return.results = TRUE
                   )
# Show general statistics:
res$statistics
# Prediction
randomForestPredict(iris[, 1:4], c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
                    , robject = res$rgobjects
                    , return.results = TRUE
                    )
# Fit random forest to iris data:
res = randomForest(iris, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"), "Species"
                   , scriptvars = list(brush.pred = FALSE, use.rows = "all", num.trees = 500
                                       , importance.mode = "permutation"
                                       , respect.unordered.factors = "ignore"
                                       )
                   , brush = rep(FALSE, nrow(iris)), return.results = TRUE
                   )
# Show general statistics:
res$statistics
# Prediction
randomForestPredict(iris[, 1:4], c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")
                    , robject = res$rgobjects
                    , return.results = TRUE
                    )

Random Forest Prediction

Description

Random Forest prediction via predict.ranger. Predicts response variables from predictor variables, using ranger objects. All ranger objects have to work on the same set of prediction variables. These variables are exactly available in the prediction dataset. A response is not necessary, it will be predicted via this function.

Usage

randomForestPredict(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  robject = cs.in.Robject(),
  return.results = FALSE,
  ...
)
randomForestPredict(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  robject = cs.in.Robject(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`robject`	[`list`] Named list of `ranger` object(s) set via Cornerstone menu "Input R Objects".
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `ranger` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame objects:

predictions

Dataset to brush with predicted values for dataset. The original input and other columns can be added to this dataset through the menu Columns -> Add from Parent ....

Redirect Dataset

Description

Redirect input dataset to an output R object.

Usage

redirectDataset(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  groups = cs.in.groupvars(),
  auxs = cs.in.auxiliaries(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE
)
redirectDataset(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  groups = cs.in.groupvars(),
  auxs = cs.in.auxiliaries(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`resps`	[`character`] Character vector of response variables.
`groups`	[`character`] Character vector of group variables.
`auxs`	[`character`] Character vector of auxiliary variables.
`scriptvars`	[`list`] Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.

Details

The following script variables are summarized in scriptvars list:

remove.pattern: [character(1)]
The given pattern is removed in all variable names via gsub. Leading and / or trailing whitespaces are removed using trimws. Default is "".

Value

Logical [TRUE] invisibly and outputs to Cornerstone cs.out.Robject or, if return.results = TRUE, list of resulting data.frame objects and character(n) vectors:

`dataset`	Input dataset.
`predictors`	Vector of predictors.
`responses`	Vector of responses.
`groups`	Vector of groups.
`auxiliaries`	Vector of auxiliaries.

The list is wrapped in an additional list to get the same return value corresponding to cs.in.Robject.

Reshape Grouped Data to Long

Description

Reshaping grouped data via melt to 'long' format. The responses are merged in one column, with its column name in an additional column. This column is split into multiple columns, if a split character is given. All predictors are merged multiple times corresponding to the number or responses.

Usage

reshapeLong(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)
reshapeLong(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`resps`	[`character`] Character vector of response variables.
`scriptvars`	[`list`] Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `melt` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Details

One script variables is summarized in scriptvars list:

split: [character(1)]
Split character to split response names into multiple columns. Default is “_”.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame object:

reshapeLong

Dataset with reshaped data.

Examples

# Data to transform:
library(data.table)
dtTest = data.table(i_1 = c(1:4, NA, 5), i_2 = c(51, 61, NA , 71, 81, 91)
                    , f1 = factor(sample(c(letters[1:3], NA), 6, TRUE))
                    , f2 = factor(c("z", "a", "x", "c", "x", "x"), ordered = TRUE)
                    )
# Reshape to long format:
reshapeLong(dtTest, c("i_1", "i_2"), c("f1", "f2"), list(split = "_"), return.results = TRUE)
# Data to transform:
library(data.table)
dtTest = data.table(i_1 = c(1:4, NA, 5), i_2 = c(51, 61, NA , 71, 81, 91)
                    , f1 = factor(sample(c(letters[1:3], NA), 6, TRUE))
                    , f2 = factor(c("z", "a", "x", "c", "x", "x"), ordered = TRUE)
                    )
# Reshape to long format:
reshapeLong(dtTest, c("i_1", "i_2"), c("f1", "f2"), list(split = "_"), return.results = TRUE)

Transpose Data

Description

Transpose data via transpose. All predictors, responses, groups, and auxiliaries are transpose.

Usage

reshapeTranspose(
  dataset = cs.in.dataset(),
  groups = cs.in.groupvars(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)
reshapeTranspose(
  dataset = cs.in.dataset(),
  groups = cs.in.groupvars(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`groups`	[`character`] Character vector of group variables.
`scriptvars`	[`list`] Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `transpose` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Details

One script variables is summarized in scriptvars list:

split: [character(1)]
Split character to split response names into multiple columns. Default is “_”.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame object:

reshapeTranspose

Dataset with transposed data.

Examples

# Data to transform:
library(data.table)
dtTest = data.table(i_1 = c(1:4, NA, 5), i_2 = c(51, 61, NA , 71, 81, 91))
# Reshape to long format:
reshapeTranspose(dtTest, groups = character(0), list(convert.numeric = TRUE), return.results = TRUE)
# Data to transform:
library(data.table)
dtTest = data.table(i_1 = c(1:4, NA, 5), i_2 = c(51, 61, NA , 71, 81, 91))
# Reshape to long format:
reshapeTranspose(dtTest, groups = character(0), list(convert.numeric = TRUE), return.results = TRUE)

Reshape Grouped Data to Wide

Description

Reshaping grouped data via dcast to 'wide' format with rows for each unique combination of group variables. The response are arranged in separate columns for each datum in predictors. If a combination of groups identifies multiple rows, the number of rows in a group is returned to CS for the whole dataset instead of the response variable value.

Usage

reshapeWide(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  groups = cs.in.groupvars(),
  auxs = cs.in.auxiliaries(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)
reshapeWide(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  groups = cs.in.groupvars(),
  auxs = cs.in.auxiliaries(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)

Arguments

`dataset`	[`data.frame`] Dataset with named columns. The names correspond to predictors and responses.
`preds`	[`character`] Character vector of predictor variables.
`resps`	[`character`] Character vector of response variables.
`groups`	[`character`] Character vector of group variables.
`auxs`	[`character`] Character vector of auxiliary variables.
`scriptvars`	[`list`] Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.
`return.results`	[`logical(1)`] If `FALSE` the function returns `TRUE` invisibly. If `TRUE`, it returns a `list` of results. Default is `FALSE`.
`...`	[ANY] Additional arguments to be passed to `dcast` . Please consider possible script variables (`scriptvars`) to prevent duplicates.

Details

One script variables is summarized in scriptvars list:

drop: [logical(1)]
Drop missing combinations (TRUE) or include all (FALSE). Default is TRUE.
For details see dcast.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame object:

reshapeWide

Dataset with reshaped data.

Examples

# Reshape dataset to wide format:
reshapeWide(Indometh, "time", "conc", "Subject", character(0)
            , list(drop = TRUE, aggr.fun = "mean"), return.results = TRUE
            )
# Reshape dataset to wide format:
reshapeWide(Indometh, "time", "conc", "Subject", character(0)
            , list(drop = TRUE, aggr.fun = "mean"), return.results = TRUE
            )

Voltage of 26 Different Runs

Description

The datasets contains 26 different runs in each case measuring the voltage over time. The S-shaped curves are different for each run.

Format

A data.table object with 7826 observations and 3 variables. The discrete variable 'FileName' has 26 levels corresponding to the file name of the source data from Cornerstone. The remaining other two variables are continuous.

Source

Cornerstone sample dataset

Show Versions of R and CornerstoneR

Description

Write the versions of R and CornerstoneR in a Cornerstone dataset.

Usage

showVersions(return.results = FALSE)
showVersions(return.results = FALSE)

Arguments

return.results

[logical(1)]
If FALSE the function returns TRUE invisibly. If TRUE, it returns a list of results. Default is FALSE.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame objects:

versions

Dataset with versions of R and CornerstoneR.

Examples

  res = showVersions(return.results = TRUE)
  res$versions
res = showVersions(return.results = TRUE)
  res$versions

Survival of Passengers on the Titanic

Description

This data set provides information on the fate of passengers on the fatal maiden voyage of the ocean liner Titanic, summarized according to economic status (Class), Age, Sex, and Survival.

Format

A data.table object with 2201 observations and 4 variables. The discrete variables and their levels are as follows:

Class: 1st, 2nd, 3rd, Crew
Age: Child, Adult
Sex: Male, Female
Survived: Yes, No

Source

Cornerstone sample dataset

References

Dawson, Robert J. MacG. (1995), The Unusual Episode Data Revisited. Journal of Statistics Education, 3. http://jse.amstat.org/v3n3/datasets.dawson.html

Package 'CornerstoneR'

Help Index

CornerstoneR: Collection of Scripts for Interface Between 'Cornerstone' and 'R'

Description

Author(s)

See Also

Data from carstats

Description

Format

Source

Fit Function to Data via Nonlinear Regression

Description

Usage

Arguments

Details

Value

Examples

Local Interface Functions

Description

Usage

Arguments

Match Nearest Neighbor Between Two Datasets

Description

Usage

Arguments

Value

Mosaic Plot

Description

Usage

Arguments

Value

Examples

Random Forest

Description

Usage

Arguments

Details

Value

See Also

Examples

Random Forest Prediction

Description

Usage

Arguments

Value

See Also

Redirect Dataset

Description

Usage

Arguments

Details

Value

Reshape Grouped Data to Long

Description

Usage

Arguments

Details

Value

Examples

Transpose Data

Description

Usage

Arguments

Details

Value

Examples

Reshape Grouped Data to Wide

Description

Usage

Arguments

Details

Value

Examples

Voltage of 26 Different Runs

Description

Format

Source

Show Versions of R and CornerstoneR

Description

Usage