Type: Package
Title: Bivariate Correlations Calculation and Visualization
Version: 1.3.0
Date: 2020-07-23
Description: Compute and visualize using the 'visNetwork' package all the bivariate correlations of a dataframe. Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau, distance correlation, maximal information coefficient and equal-freq discretization-based maximal normalized mutual information) are used according to the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).
License: MIT + file LICENSE
URL: https://github.com/sambaala/linkspotter
BugReports: https://github.com/sambaala/linkspotter/issues
Encoding: UTF-8
LazyData: true
Depends: R (≥ 3.2.0)
Imports: shiny, visNetwork, infotheo, minerva, energy, mclust, rAmCharts, pbapply, ggplot2, dplyr, tidyr, shinybusy
Collate: 'linkspotterComplete.R' 'clusterVariables.R' 'linkspotterDurationEstimator.R' 'linkspotterGraph.R' 'linkspotterGraphOnMatrix.R' 'linkspotterOnFile.R' 'linkspotterUI.R' 'corCouplesToMatrix.R' 'matrixToCorCouples.R' 'maxNMI.R' 'BeEFdiscretization_numfact.R' 'BeEFdiscretization_numnum.R' 'multiBivariateCorrelation.R' 'NormalizedMI.R' 'createShinyAppFolder.R' 'EFdiscretization.R' 'non_informative_var.R'
RoxygenNote: 7.1.1
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-07-23 10:22:57 UTC; XXGB1668
Author: Alassane Samba [aut, cre], Orange [cph]
Maintainer: Alassane Samba <alassane.samba@orange.com>
Repository: CRAN
Date/Publication: 2020-07-23 10:40:02 UTC

BeEF: Best Equal-Frequency discretization

Description

Discretize a quantitative variable by optimizing the obtained the Normalized Mutual Information with a target qualitative variable

Usage

BeEFdiscretization.numfact(
  continuousY,
  factorX,
  includeNA = T,
  showProgress = F
)

Arguments

continuousY

a vector of numeric.

factorX

a vector of factor.

includeNA

a boolean. TRUE to include NA value as a factor level.

showProgress

a boolean to decide whether to show the progress bar.

Value

a factor.

Examples

# calculate a correlation dataframe
data(iris)
discreteSepalLength=BeEFdiscretization.numfact(continuousY=iris$Sepal.Length,factorX=iris$Species)
summary(discreteSepalLength)

BeEF: Best Equal-Frequency discretization (for a couple of quantitative variables)

Description

Discretize two quantitative variables by optimizing the obtained the Normalized Mutual Information

Usage

BeEFdiscretization.numnum(
  continuousX,
  continuousY,
  maxNbBins = 100,
  includeNA = T,
  showProgress = F
)

Arguments

continuousX

a vector of numeric.

continuousY

a vector of numeric.

maxNbBins

an integer corresponding to the number of bins limitation (for computation time limitation), maxNbBins=100 by default.

includeNA

a boolean. TRUE to include NA value as a factor level.

showProgress

a boolean to decide whether to show the progress bar.

Value

a list of two factors.

Examples

# calculate a correlation dataframe
data(iris)
disc=BeEFdiscretization.numnum(iris$Sepal.Length,iris$Sepal.Width)
summary(disc$x)
summary(disc$y)

EF: Equal-Frequency discretization

Description

Discretize a quantitative variable with equal frequency binning if possible

Usage

EFdiscretization(continuousX, nX, nbdigitsX = 3)

Arguments

continuousX

a vector of numeric.

nX

an integer corresponding to the desired number of intervals.

nbdigitsX

number of significant digits to use in constructing levels. Default is 3.

Value

a factor.

Examples

data(iris)
disc.Sepal.Length=EFdiscretization(iris$Sepal.Length,5)
summary(disc.Sepal.Length)

Maximal Normalized Mutual Information (MaxNMI) function for 2 categorical variables

Description

Calculate the MaxNMI relationship measurement for 2 categorical variables

Usage

NormalizedMI(x, y, includeNA = T)

Arguments

x

a vector of factor.

y

a vector of factor.

includeNA

a boolean. TRUE to include NA value as a factor level.

Value

a double between 0 and 1 corresponding to the MaxNMI.

Examples

# calculate a correlation dataframe
data(iris)
discreteSepalLength=BeEFdiscretization.numfact(continuousY=iris$Sepal.Length,factorX=iris$Species)
NormalizedMI(iris$Species,discreteSepalLength)

Variable clustering (using Normal Mixture Modeling for Model-Based Clustering : mclust)

Description

Computation of a variable clustering on a correlation matrix.

Usage

clusterVariables(corMatrix, nbCluster = 1:9)

Arguments

corMatrix

a dataframe corresponding to a correlation matrix

nbCluster

an integer or a vector of integers corresponding to the preferred number of cluster for the unsupervised learning.

Value

a dataframe: the first column contains the variable names, the second column the index of the cluster they are affected to.

Examples

# calculate a correlation dataframe
data(iris)
corDF <- multiBivariateCorrelation(dataset = iris, corMethods = "MaxNMI")
# tranform to correlation matrix
corMatrix <- corCouplesToMatrix(x1_x2_val = corDF[,c('X1','X2',"MaxNMI")])
# perform the clustering
corGroups <- clusterVariables(corMatrix = corMatrix, nbCluster = 3)
print(corGroups)

Couples to matrix

Description

Transform a 2 column correlation dataframe into a correlation matrix

Usage

corCouplesToMatrix(x1_x2_val)

Arguments

x1_x2_val

a specific dataframe containing correlations values resulting from the function multiBivariateCorrelation() and containing only one coefficient type.

Value

a dataframe corresponding to a correlation matrix.

Examples

# calculate a correlation dataframe
data(iris)
corDF<-multiBivariateCorrelation(dataset = iris, corMethods = "MaxNMI")
corMatrix<-corCouplesToMatrix(x1_x2_val = corDF[,c('X1','X2',"MaxNMI")])
print(corMatrix)
corCouples<-matrixToCorCouples(corMatrix,coefName="pearson")
print(corCouples)

Ready-for-deployment shiny app folder creation

Description

This function creates a shiny app folder containing a shiny app object directly readable by a shiny-server.

Usage

createShinyAppFolder(linkspotterObject, folderName)

Arguments

linkspotterObject

a linkspotter object, resulting from linkspotterComplete() or linkspotterOnFile() functions.

folderName

a character string corresponding to the name of the shiny app folder to create.

Examples

data(iris)
lsOutputIris<-linkspotterComplete(iris)
tmpShinyFolder<-tempdir()
createShinyAppFolder(lsOutputIris,
folderName=file.path(tmpShinyFolder,"myIrisLinkspotterShinyApp1")
)
## Not run: 
# launch the shiny app
shiny::runApp(tmpShinyFolder)

## End(Not run)

Is a vector an non informative variable

Description

This function determines if a given vector of numeric or factor is a non informative variable or not.

Usage

is.not.informative.variable(x, includeNA = T)

Arguments

x

a vector of numeric or factor.

includeNA

a boolean. TRUE to include NA value as a factor level.

Examples

data(iris)
is.not.informative.variable(iris$Sepal.Length)

Linkspotter complete runner

Description

Computation of correlation matrices, variable clustering and the customizable user interface to visualize them using a graph together with variables distributions and cross plots.

Usage

linkspotterComplete(
  dataset,
  targetVar = NULL,
  corMethods = c("pearson", "spearman", "kendall", "mic", "MaxNMI"),
  maxNbBins = 100,
  defaultMinCor = 0.3,
  defaultCorMethod = corMethods[length(corMethods)],
  clusteringCorMethod = defaultCorMethod,
  nbCluster = 1:9,
  printInfo = T,
  appTitle = "Linkspotter",
  htmlTop = "",
  htmlBottom = ""
)

Arguments

dataset

the dataframe which variables bivariate correlations are to be analyzed.

targetVar

a vector of character strings corresponding to the names of the target variables. If not NULL, correlation coefficients are computed only with that target variables.

corMethods

a vector of correlation coefficients to compute. The available coefficients are the following : c("pearson","spearman","kendall","mic","distCor","MaxNMI"). It is not case sensitive and still work if only the beginning of the word is put (e.g. pears).

maxNbBins

an integer used if corMethods include 'MaxNMI'. It corresponds to the number of bins limitation (for computation time limitation), maxNbBins=100 by default.

defaultMinCor

a double between 0 and 1. It is the minimal correlation absolute value to consider for the first graph plot.

defaultCorMethod

a string. One of "pearson","spearman","kendall","mic", "distCor" or "MaxNMI". It is the correlation coefficient to consider for the first graph plot.

clusteringCorMethod

a string. One of "pearson","spearman","kendall","mic", "distCor" or "MaxNMI". It is the correlation coefficient to consider for the variables clustering.

nbCluster

an integer. It is the number of clusters to compute.

printInfo

a boolean indicating whether to print on the console some information about the dataset and the estimated computation time.

appTitle

a string taken as the title of the user interface.

htmlTop

a character string that enable to customize your shiny app by adding an HTML code in the HEAD tag.

htmlBottom

a character string that enable to customize your shiny app by adding an HTML code at the end of the BODY tag.

Value

a list containing all the material enabling to analyze correlations:

Examples

# run linkspotter on iris example data
data(iris)
lsOutputIris<-linkspotterComplete(iris)
summary(lsOutputIris)
## Not run: 
# launch the UI
lsOutputIris$launchShiny(option=list(port=8000))

## End(Not run)

Linkspotter graph runner

Description

plot the Linkspotter graph

Usage

linkspotterGraph(
  corDF,
  variablesClustering = NULL,
  minCor = 0.3,
  corMethod = colnames(corDF)[-c(1:3, ncol(corDF))][length(colnames(corDF)[-c(1:3,
    ncol(corDF))])],
  smoothEdges = T,
  dynamicNodes = F,
  colorEdgesByCorDirection = F
)

Arguments

corDF

a specific dataframe containing correlations values resulting from the function multiBivariateCorrelation()

variablesClustering

a specific dataframe containing the output of the variable clustering resulting from the function clusterVariables()

minCor

a double between 0 and 1. It is the minimal correlation absolute value to consider for the first graph plot.

corMethod

a string. One of "pearson","spearman","kendall","mic", "distCor" or "MaxNMI". It is the correlation coefficient to consider for the first graph plot.

smoothEdges

a boolean. TRUE to let the edges be smooth.

dynamicNodes

a boolean. TRUE to let the graph re-organize itself after any movement.

colorEdgesByCorDirection

a boolean. TRUE to get the edges colored according to the correlation direction (positive-> blue, negative->red or NA->grey).

Value

a visNetwork object corresponding to a dynamic graph for the correlation matrix visualization.

Examples

# calculate a correlation dataframe
data(iris)
corDF=multiBivariateCorrelation(dataset = iris)
corMatrix=corCouplesToMatrix(x1_x2_val = corDF[,c('X1','X2',"spearman")])
corGroups=clusterVariables(corMatrix = corMatrix, nbCluster = 3)
# launch the graph
linkspotterGraph(corDF=corDF, variablesClustering=corGroups, minCor=0.3,
corMethod='spearman', colorEdgesByCorDirection=TRUE)

Linkspotter graph on matrix

Description

Plot the Linkspotter graph from a correlation matrix.

Usage

linkspotterGraphOnMatrix(
  corMatrix,
  cluster = FALSE,
  variablesClustering = NULL,
  minCor = 0.3,
  corMethod = "Coef.",
  smoothEdges = T,
  dynamicNodes = F,
  colorEdgesByCorDirection = F
)

Arguments

corMatrix

a dataframe corresponding to a matrix of correlation or distance.

cluster

a boolean to decide if to cluster variables or an integer corresponding directly to the number of clusters to consider. If variablesClustering is filled, "cluster" parameter is ignored.

variablesClustering

a specific dataframe containing the output of the variable clustering resulting from the function clusterVariables()

minCor

a double between 0 and 1. It is the minimal correlation absolute value to consider for the first graph plot.

corMethod

a string. One of "pearson","spearman","kendall","mic", "distCor" or "MaxNMI". It is the correlation coefficient to consider for the first graph plot.

smoothEdges

a boolean. TRUE to let the edges be smooth.

dynamicNodes

a boolean. TRUE to let the graph re-organize itself after any movement.

colorEdgesByCorDirection

a boolean. TRUE to get the edges colored according to the correlation direction (positive-> blue, negative->red or NA->grey).

Value

a visNetwork object corresponding to a dynamic graph for the correlation matrix visualization.

Examples

# calculate a correlation dataframe
data(iris)
corDF=multiBivariateCorrelation(dataset = iris)
corMatrix=corCouplesToMatrix(x1_x2_val = corDF[,c('X1','X2',"pearson")])
# launch the graph
linkspotterGraphOnMatrix(corMatrix=corMatrix, minCor=0.3)

Process Linkspotter on an external file

Description

This function imports an external dataset, computes its correlation matrices, variable clustering and the customizable user interface to visualize them using a graph.

Usage

linkspotterOnFile(
  file,
  corMethods = c("pearson", "spearman", "kendall", "mic", "MaxNMI"),
  defaultMinCor = 0.3,
  defaultCorMethod = corMethods[length(corMethods)],
  clusteringCorMethod = corMethods[length(corMethods)],
  nbCluster = 1:9,
  printInfo = T,
  appTitle = "Linkspotter",
  htmlTop = "",
  htmlBottom = "",
  ...
)

Arguments

file

the file containing a structured dataset which the bivariate correlations are to be analyzed.

corMethods

a vector of correlation coefficients to compute. The available coefficients are the following : c("pearson","spearman","kendall","mic","distCor","MaxNMI"). It is not case sensitive and still work if only the beginning of the word is put (e.g. pears).

defaultMinCor

a double between 0 and 1. It is the minimal correlation absolute value to consider for the first graph plot.

defaultCorMethod

a string. One of "pearson","spearman","kendall","mic", "distCor" or "MaxNMI". It is the correlation coefficient to consider for the first graph plot.

clusteringCorMethod

a string. One of "pearson","spearman","kendall","mic", "distCor" or "MaxNMI". It is the correlation coefficient to consider for the variables clustering.

nbCluster

an integer. It is the number of clusters to compute.

printInfo

a boolean indicating whether to print on the console some information about the dataset and the estimated computation time.

appTitle

a string taken as the title of the user interface.

htmlTop

a character string that enable to customize your shiny app by adding an HTML code in the HEAD tag.

htmlBottom

a character string that enable to customize your shiny app by adding an HTML code at the end of the BODY tag.

...

Further arguments to be passed to the used read.csv function.

Value

a list containing all the material enabling to analyze correlations:

Examples

# run linkspotter on iris example data
data(iris)
tmpCSV<-tempfile(fileext = '.csv')
write.csv(iris, tmpCSV, row.names = FALSE)
lsOutputIrisFromFile<-linkspotterOnFile(file=tmpCSV)
summary(lsOutputIrisFromFile)
## Not run: 
# launch the UI
lsOutputIrisFromFile$launchShiny(options=list(port=8000))

## End(Not run)

Linkspotter user interface runner

Description

Build the Linkspotter user interface

Usage

linkspotterUI(
  dataset,
  corDF,
  variablesClustering = NULL,
  defaultMinCor = 0.3,
  appTitle = "Linkspotter",
  htmlTop = "",
  htmlBottom = "",
  ...
)

Arguments

dataset

the dataframe which variables bivariate correlations are contained in corDF

corDF

a specific dataframe containing correlations values resulting from the function multiBivariateCorrelation()

variablesClustering

a specific dataframe containing the output of the variable clustering resulting from the function clusterVariables()

defaultMinCor

a double between 0 and 1. It is the minimal correlation absolute value to consider for the first graph plot.

appTitle

a character string taken as the title of the user interface.

htmlTop

a character string that enable to customize your shiny app by adding an HTML code in the HEAD tag.

htmlBottom

a character string that enable to customize your shiny app by adding an HTML code at the end of the BODY tag.

...

: arguments for 'shiny::shinyApp' function

Value

a 'shiny.appobj' object enable to deploy instantly the user interface for a customizable visualization.

Examples

# calculate a correlation dataframe
data(iris)
corDF=multiBivariateCorrelation(dataset = iris)
corMatrix=corCouplesToMatrix(x1_x2_val = corDF[,c('X1','X2',"MaxNMI")])
corGroups=clusterVariables(corMatrix = corMatrix, nbCluster = 3)
## Not run: 
# launch the UI
linkspotterUI(dataset=iris, corDF=corDF, variablesClustering=corGroups,
defaultMinCor=0.3,cappTitle="Linkspotter on iris data",
options = list(port=8000)
)

## End(Not run)

Matrix to couples

Description

Transform a correlation matrix into a correlation couples dataframe

Usage

matrixToCorCouples(matrix, coefName = "Coef.", sortByDescAbs = F)

Arguments

matrix

a dataframe corresponding to a matrix of correlation.

coefName

a string: the name of the coefficient the values of the matrix represent.

sortByDescAbs

a boolean to decide if to sort by descending absolute value of the coefficient.

Value

a dataframe corresponding to all correlation couples from the matrix.

Examples

# calculate a correlation dataframe
data(iris)
corDF<-multiBivariateCorrelation(dataset = iris)
corMatrix<-corCouplesToMatrix(x1_x2_val = corDF[,c('X1','X2',"pearson")])
print(corMatrix)
corCouples<-matrixToCorCouples(matrix = corMatrix,coefName="pearson")
print(corCouples)

Maximal Normalized Mutual Information (MaxNMI)

Description

Computes the MaxNMI between the two variables whatever their types, by discretizing using Best Equal-Frequency-based discretization (BeEF) if necessary.

Usage

maxNMI(x, y, includeNA = T, maxNbBins = 100, showProgress = F)

Arguments

x

a vector of numeric or factor.

y

a vector of numeric or factor.

includeNA

a boolean. TRUE to include NA value as a factor level.

maxNbBins

an integer corresponding to the number of bins limitation (for computation time limitation), maxNbBins=100 by default.

showProgress

a boolean to decide whether to show the progress bar.

Value

a double between 0 and 1 corresponding to the MaxNMI.

Examples

# calculate a correlation dataframe
data(iris)
maxNMI(iris$Sepal.Length,iris$Species)
maxNMI(iris$Sepal.Length,iris$Sepal.Width)

Calculation of all the bivariate correlations in a dataframe

Description

Computation of a correlation dataframe.

Usage

multiBivariateCorrelation(
  dataset,
  targetVar = NULL,
  corMethods = c("pearson", "spearman", "kendall", "mic", "MaxNMI"),
  maxNbBins = 100,
  showProgress = T
)

Arguments

dataset

the dataframe which variables bivariate correlations are to be analyzed.

targetVar

a vector of character strings corresponding to the names of the target variables. If not NULL, correlation coefficients are computed only with that target variables.

corMethods

a vector of correlation coefficients to compute. The available coefficients are the following : c("pearson","spearman","kendall","mic","distCor","MaxNMI"). It is not case sensitive and still work if only the beginning of the word is put (e.g. pears).

maxNbBins

an integer used if corMethods include 'MaxNMI'. It corresponds to the number of bins limitation (for computation time limitation), maxNbBins=100 by default.

showProgress

a boolean to decide whether to show the progress bar.

Value

a specific dataframe containing correlations values or each specified correlation coefficient.

Examples

# run linkspotter on iris example data
data(iris)
corDF<-multiBivariateCorrelation(iris)
print(corDF)