Package 'discoveR' reference manual

Title:	Exploratory Data Analysis System
Description:	Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.
Authors:	Oldemar Rodriguez [aut, cre], Diego Jiménez [aut]
Maintainer:	Oldemar Rodriguez <[email protected]>
License:	GPL (>=2)
Version:	3.1.5
Built:	2024-11-21 04:36:19 UTC
Source:	https://github.com/PROMiDAT/discoveR

Calculate inter-class inertia

Description

Calculate inter-class inertia

Usage

BP(DF, clusters)
BP(DF, clusters)

Arguments

`DF`	a data.frame object.
`clusters`	a vector specifying the cluster of each individual.

Value

numeric

Author(s)

Diego Jimenez <[email protected]>

Examples

m <- hclust(dist(iris[, -5]))
BP(iris[, -5], cutree(m, 3))

m <- hclust(dist(iris[, -5]))
BP(iris[, -5], cutree(m, 3))

Calculation of the center of clusters

Description

Calculation of the center of clusters

Usage

calc.centros(data, clusters)
calc.centros(data, clusters)

Arguments

`data`	a data.frame object.
`clusters`	a vector specifying the cluster of each individual.

Value

list

Author(s)

Diego Jimenez <[email protected]>

Examples

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
calc.centros(iris[, -5], clusters)

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
calc.centros(iris[, -5], clusters)

Exploratory Data Analysis System

Description

Performs an exploratory data analysis through a 'shiny' interface. It includes basic methods such as the mean, median, mode, normality test, among others. It also includes clustering techniques such as Principal Components Analysis, Hierarchical Clustering and the K-Means Method.

Details

Package:	discoveR
Type:	Package
Version:	3.1.5
Date:	2023-03-28
License:	GPL (>=2)

Author(s)

Maintainer: Oldemar Rodriguez Rojas <[email protected]>

Oldemar Rodriguez Rojas <[email protected]>
Diego Jiménez Alvarado

AFC biplot

Description

AFC biplot

Usage

e_afcbi(
  modelo,
  axes = c(1, 2),
  colorRow = "steelblue",
  colorCol = "forestgreen",
  cos2Row = 0,
  cos2Col = 0,
  colorRowCos = "firebrick",
  colorColCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcbi(
  modelo,
  axes = c(1, 2),
  colorRow = "steelblue",
  colorCol = "forestgreen",
  cos2Row = 0,
  cos2Col = 0,
  colorRowCos = "firebrick",
  colorColCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class CA [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorRow`	a color for the individuals well represented.
`colorCol`	a color for the variables well represented.
`cos2Row`	a numeric value from 0 to 1 specifying the quality of the individuals.
`cos2Col`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorRowCos`	a color for the individuals badly represented.
`colorColCos`	a color for the variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples


  p <- FactoMineR::CA(iris[, -5], graph = FALSE)
  e_afcbi(p)


p <- FactoMineR::CA(iris[, -5], graph = FALSE)
  e_afcbi(p)

AFC biplot in 3D

Description

AFC biplot in 3D

Usage

e_afcbi_3D(
  modelo,
  axes = c(1, 2, 3),
  colorRow = "steelblue",
  colorCol = "forestgreen",
  cos2Row = 0,
  cos2Col = 0,
  colorRowCos = "firebrick",
  colorColCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcbi_3D(
  modelo,
  axes = c(1, 2, 3),
  colorRow = "steelblue",
  colorCol = "forestgreen",
  cos2Row = 0,
  cos2Col = 0,
  colorRowCos = "firebrick",
  colorColCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class CA [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorRow`	a color for the individuals well represented.
`colorCol`	a color for the variables well represented.
`cos2Row`	a numeric value from 0 to 1 specifying the quality of the individuals.
`cos2Col`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorRowCos`	a color for individuals badly represented.
`colorColCos`	a color for variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples


  p <- FactoMineR::CA(iris[, -5], graph = FALSE)
  e_afcbi_3D(p)


p <- FactoMineR::CA(iris[, -5], graph = FALSE)
  e_afcbi_3D(p)

AFC plot of variables

Description

AFC plot of variables

Usage

e_afccol(
  modelo,
  axes = c(1, 2),
  colorCol = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)
e_afccol(
  modelo,
  axes = c(1, 2),
  colorCol = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)

Arguments

`modelo`	an object of class CA [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorCol`	a color for the variables well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorCos`	a color for the variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afccol(p)

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afccol(p)

AFC plot of variables in 3D

Description

AFC plot of variables in 3D

Usage

e_afccol_3D(
  modelo,
  axes = c(1, 2, 3),
  colorCol = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)
e_afccol_3D(
  modelo,
  axes = c(1, 2, 3),
  colorCol = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)

Arguments

`modelo`	an object of class CA [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorCol`	a color for the variables well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorCos`	a color for variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afccol_3D(p)

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afccol_3D(p)

AFCM biplot

Description

AFCM biplot

Usage

e_afcmbi(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcmbi(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`colorVar`	a color for the variables well represented.
`cos2Ind`	a numeric value from 0 to 1 specifying the quality of the individuals.
`cos2Var`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorIndCos`	a color for the individuals badly represented.
`colorVarCos`	a color for the variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmbi(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmbi(p)

AFCM biplot in 3D

Description

AFCM biplot in 3D

Usage

e_afcmbi_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcmbi_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`colorVar`	a color for the variables well represented.
`cos2Ind`	a numeric value from 0 to 1 specifying the quality of the individuals.
`cos2Var`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorIndCos`	a color for individuals badly represented.
`colorVarCos`	a color for variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmbi_3D(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmbi_3D(p)

AFCM plot of categories

Description

AFCM plot of categories

Usage

e_afcmcat(
  modelo,
  axes = c(1, 2),
  colorCat = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)
e_afcmcat(
  modelo,
  axes = c(1, 2),
  colorCat = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorCat`	a color for the categories well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the categories.
`colorCos`	a color for the categories badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmcat(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmcat(p)

AFCM plot of categories in 3D

Description

AFCM plot of categories in 3D

Usage

e_afcmcat_3D(
  modelo,
  axes = c(1, 2, 3),
  colorCat = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)
e_afcmcat_3D(
  modelo,
  axes = c(1, 2, 3),
  colorCat = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorCat`	a color for the categories well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the categories.
`colorCos`	a color for categories badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmcat_3D(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmcat_3D(p)

AFCM plot of individuals

Description

AFCM plot of individuals

Usage

e_afcmind(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcmind(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the individuals.
`colorCos`	a color for individuals badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmind(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmind(p)

AFCM plot of individuals in 3D

Description

AFCM plot of individuals in 3D

Usage

e_afcmind_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcmind_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the individuals.
`colorCos`	a color for individuals badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmind_3D(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmind_3D(p)

AFCM plot of variables

Description

AFCM plot of variables

Usage

e_afcmvar(modelo, axes = c(1, 2), colorVar = "forestgreen")
e_afcmvar(modelo, axes = c(1, 2), colorVar = "forestgreen")

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorVar`	a color for the variables.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmvar(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmvar(p)

AFCM plot of variables in 3D

Description

AFCM plot of variables in 3D

Usage

e_afcmvar_3D(modelo, axes = c(1, 2, 3), colorVar = "forestgreen")
e_afcmvar_3D(modelo, axes = c(1, 2, 3), colorVar = "forestgreen")

Arguments

`modelo`	an object of class AFCM [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorVar`	a color for the variables well represented.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmvar_3D(p)

data("poison", package = "FactoMineR")
poison.active <- poison[1:55, 5:15]
p <- FactoMineR::MCA(poison.active, graph = FALSE)
e_afcmvar_3D(p)

AFC plot of individuals

Description

AFC plot of individuals

Usage

e_afcrow(
  modelo,
  axes = c(1, 2),
  colorRow = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcrow(
  modelo,
  axes = c(1, 2),
  colorRow = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class CA [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorRow`	a color for the individuals well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the individuals.
`colorCos`	a color for individuals badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afcrow(p)

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afcrow(p)

AFC plot of individuals in 3D

Description

AFC plot of individuals in 3D

Usage

e_afcrow_3D(
  modelo,
  axes = c(1, 2, 3),
  colorRow = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)
e_afcrow_3D(
  modelo,
  axes = c(1, 2, 3),
  colorRow = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = T
)

Arguments

`modelo`	an object of class CA [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorRow`	a color for the individuals well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the individuals.
`colorCos`	a color for individuals badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afcrow_3D(p)

p <- FactoMineR::CA(iris[, -5], graph = FALSE)
e_afcrow_3D(p)

Balloonplot

Description

Balloonplot

Usage

e_balloon(datos)
e_balloon(datos)

Arguments

datos

a data frame object.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

e_balloon(iris)

e_balloon(iris)

Barplot for categoric variable by clusters.

Description

Barplot for categoric variable by clusters.

Usage

e_cat(clusters, var, colores = NULL, escalar = T)
e_cat(clusters, var, colores = NULL, escalar = T)

Arguments

`clusters`	a vector specifying the cluster of each individual.
`var`	a factor column of a data.frame.
`colores`	a vector of color for each cluster.
`escalar`	a boolean value specifying if use percentage or real values.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
e_cat(clusters, iris[, 5], colores = c("steelblue", "pink", "forestgreen"))

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
e_cat(clusters, iris[, 5], colores = c("steelblue", "pink", "forestgreen"))

Horizontal representation for centers of clusters.

Description

Horizontal representation for centers of clusters.

Usage

e_horiz(centros, colores = NULL)
e_horiz(centros, colores = NULL)

Arguments

`centros`	a data.frame object with the centers of the clusters.
`colores`	a vector of color for each cluster.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
c <- calc.centros(iris[, -5], clusters)
e_horiz(c$real, c("steelblue", "pink", "forestgreen"))

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
c <- calc.centros(iris[, -5], clusters)
e_horiz(c$real, c("steelblue", "pink", "forestgreen"))

Inertia plot of clusterization

Description

Inertia plot of clusterization

Usage

e_inercia(
  data,
  titulos = c("Inercia", "Inercia Inter-Clase", "Inercia Inter-Clase")
)
e_inercia(
  data,
  titulos = c("Inercia", "Inercia Inter-Clase", "Inercia Inter-Clase")
)

Arguments

`data`	a data.frame object with the inertia values.
`titulos`	a character vector of length 3 specifying the titles to use on legend.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Jambu Elbow plot

Description

Jambu Elbow plot

Usage

e_jambu(data, max.clusters)
e_jambu(data, max.clusters)

Arguments

`data`	a data.frame object.
`max.clusters`	a numeric value specifying the number of times to generate the model.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

e_jambu(iris[, -5], 10)

e_jambu(iris[, -5], 10)

PCA plot of individuals colored by clusters

Description

PCA plot of individuals colored by clusters

Usage

e_mapa(pca.model, clusters, colores = NULL, ejes = c(1, 2), etq = F)
e_mapa(pca.model, clusters, colores = NULL, ejes = c(1, 2), etq = F)

Arguments

`pca.model`	an object of class PCA [FactoMineR].
`clusters`	a vector specifying the cluster of each individual.
`colores`	a vector of color for each cluster.
`ejes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
clusters <- factor(kmeans(iris[, -5], 3)$cluster)
e_mapa(p, clusters, c("steelblue", "pink", "forestgreen"), etq = FALSE)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
clusters <- factor(kmeans(iris[, -5], 3)$cluster)
e_mapa(p, clusters, c("steelblue", "pink", "forestgreen"), etq = FALSE)

PCA plot of individuals colored by clusters

Description

PCA plot of individuals colored by clusters

Usage

e_mapa_3D(pca.model, clusters, colores = NULL, ejes = c(1, 2, 3), etq = F)
e_mapa_3D(pca.model, clusters, colores = NULL, ejes = c(1, 2, 3), etq = F)

Arguments

`pca.model`	an object of class PCA [FactoMineR].
`clusters`	a vector specifying the cluster of each individual.
`colores`	a vector of color for each cluster.
`ejes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
clusters <- factor(kmeans(iris[, -5], 3)$cluster)
e_mapa_3D(p, clusters, c("steelblue", "pink", "forestgreen"), etq = FALSE)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
clusters <- factor(kmeans(iris[, -5], 3)$cluster)
e_mapa_3D(p, clusters, c("steelblue", "pink", "forestgreen"), etq = FALSE)

PCA biplot

Description

PCA biplot

Usage

e_pcabi(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)
e_pcabi(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)

Arguments

`modelo`	an object of class PCA [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`colorVar`	a color for the variables well represented.
`cos2Ind`	a numeric value from 0 to 1 specifying the quality of the individuals.
`cos2Var`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorIndCos`	a color for the individuals badly represented.
`colorVarCos`	a color for the variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcabi(p)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcabi(p)

PCA biplot in 3D

Description

PCA biplot in 3D

Usage

e_pcabi_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)
e_pcabi_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  colorVar = "forestgreen",
  cos2Ind = 0,
  cos2Var = 0,
  colorIndCos = "firebrick",
  colorVarCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)

Arguments

`modelo`	an object of class PCA [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`colorVar`	a color for the variables well represented.
`cos2Ind`	a numeric value from 0 to 1 specifying the quality of the individuals.
`cos2Var`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorIndCos`	a color for individuals badly represented.
`colorVarCos`	a color for variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcabi_3D(p)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcabi_3D(p)

PCA plot of individuals

Description

PCA plot of individuals

Usage

e_pcaind(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)
e_pcaind(
  modelo,
  axes = c(1, 2),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)

Arguments

`modelo`	an object of class PCA [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the individuals.
`colorCos`	a color for individuals badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcaind(p)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcaind(p)

PCA plot of individuals in 3D

Description

PCA plot of individuals in 3D

Usage

e_pcaind_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)
e_pcaind_3D(
  modelo,
  axes = c(1, 2, 3),
  colorInd = "steelblue",
  cos2 = 0,
  colorCos = "firebrick",
  titulos = c("Bien Representados", "Mal Representados"),
  etq = F
)

Arguments

`modelo`	an object of class PCA [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorInd`	a color for the individuals well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the individuals.
`colorCos`	a color for individuals badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.
`etq`	a boolean, whether to add label to graph or not.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcaind_3D(p)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcaind_3D(p)

PCA plot of variables

Description

PCA plot of variables

Usage

e_pcavar(
  modelo,
  axes = c(1, 2),
  colorVar = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)
e_pcavar(
  modelo,
  axes = c(1, 2),
  colorVar = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)

Arguments

`modelo`	an object of class PCA [FactoMineR].
`axes`	a numeric vector of length 2 specifying the dimensions to be plotted.
`colorVar`	a color for the variables well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorCos`	a color for the variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcavar(p)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcavar(p)

PCA plot of variables in 3D

Description

PCA plot of variables in 3D

Usage

e_pcavar_3D(
  modelo,
  axes = c(1, 2, 3),
  colorVar = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)
e_pcavar_3D(
  modelo,
  axes = c(1, 2, 3),
  colorVar = "forestgreen",
  cos2 = 0,
  colorCos = "darkorchid",
  titulos = c("Bien Representados", "Mal Representados")
)

Arguments

`modelo`	an object of class PCA [FactoMineR].
`axes`	a numeric vector of length 3 specifying the dimensions to be plotted.
`colorVar`	a color for the variables well represented.
`cos2`	a numeric value from 0 to 1 specifying the quality of the variables.
`colorCos`	a color for variables badly represented.
`titulos`	a character vector of length 2 specifying the titles to use on legend.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcavar_3D(p)

p <- FactoMineR::PCA(iris[, -5], graph = FALSE)
e_pcavar_3D(p)

Radar representation for centers of clusters.

Description

Radar representation for centers of clusters.

Usage

e_radar(centros, colores = NULL)
e_radar(centros, colores = NULL)

Arguments

`centros`	a data.frame object with the centers of the clusters.
`colores`	a vector of color for each cluster.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
c <- calc.centros(iris[, -5], clusters)
e_radar(c$porcentual, c("steelblue", "pink", "forestgreen"))

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
c <- calc.centros(iris[, -5], clusters)
e_radar(c$porcentual, c("steelblue", "pink", "forestgreen"))

Silhouette plot

Description

Silhouette plot

Usage

e_silhouette(data, max.clusters)
e_silhouette(data, max.clusters)

Arguments

`data`	a data.frame object.
`max.clusters`	a numeric value specifying the number of times to generate the model.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

e_silhouette(iris[, -5], 10)

e_silhouette(iris[, -5], 10)

Vertical representation for centers of clusters.

Description

Vertical representation for centers of clusters.

Usage

e_vert(centros, colores = NULL)
e_vert(centros, colores = NULL)

Arguments

`centros`	a data.frame object with the centers of the clusters.
`colores`	a vector of color for each cluster.

Value

echarts4r plot

Author(s)

Diego Jimenez <[email protected]>

Examples

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
c <- calc.centros(iris[, -5], clusters)
e_vert(c$real, c("steelblue", "pink", "forestgreen"))

clusters <- factor(kmeans(iris[, -5], 3)$cluster)
c <- calc.centros(iris[, -5], clusters)
e_vert(c$real, c("steelblue", "pink", "forestgreen"))

Dendrogram plot

Description

Dendrogram plot

Usage

gg_dendrograma(model, k, colors = NULL)
gg_dendrograma(model, k, colors = NULL)

Arguments

`model`	an object of class hclust.
`k`	a vector specifying the cluster of each individual.
`colors`	a vector of color for each cluster.

Value

ggplot

Author(s)

Diego Jimenez <[email protected]>

Calculate total inertia

Description

Calculate total inertia

Usage

inercia.total(DF)
inercia.total(DF)

Arguments

`DF`	a data.frame object.

Value

numeric

Author(s)

Diego Jimenez <[email protected]>

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(...)
run_app(...)

Arguments

...

A series of options to be used inside the app.

Examples

if(interactive()) {
  run_app()
}


if(interactive()) {
  run_app()
}

Calculate intra-class inertia

Description

Calculate intra-class inertia

Usage

WP(DF, clusters)
WP(DF, clusters)

Arguments

`DF`	a data.frame object.
`clusters`	a vector specifying the cluster of each individual.

Value

numeric

Author(s)

Diego Jimenez <[email protected]>

Examples

m <- hclust(dist(iris[, -5]))
WP(iris[, -5], cutree(m, 3))

m <- hclust(dist(iris[, -5]))
WP(iris[, -5], cutree(m, 3))

Package 'discoveR'

Help Index

Calculate inter-class inertia

Description

Usage

Arguments

Value

Author(s)

Examples

Calculation of the center of clusters

Description

Usage

Arguments

Value

Author(s)

Examples

Exploratory Data Analysis System

Description

Details

Author(s)

AFC biplot

Description

Usage

Arguments

Value

Author(s)

Examples

AFC biplot in 3D

Description

Usage

Arguments

Value

Author(s)

Examples

AFC plot of variables

Description

Usage

Arguments

Value

Author(s)

Examples

AFC plot of variables in 3D

Description

Usage

Arguments

Value

Author(s)

Examples

AFCM biplot

Description

Usage

Arguments

Value

Author(s)

Examples

AFCM biplot in 3D

Description

Usage

Arguments

Value

Author(s)

Examples

AFCM plot of categories

Description

Usage

Arguments

Value

Author(s)

Examples

AFCM plot of categories in 3D

Description

Usage

Arguments

Value

Author(s)

Examples

AFCM plot of individuals

Description

Usage

Arguments