Package 'ggESDA'

Title: Exploratory Symbolic Data Analysis with 'ggplot2'
Description: Implements an extension of 'ggplot2' and visualizes the symbolic data with multiple plot which can be adjusted by more general and flexible input arguments. It also provides a function to transform the classical data to symbolic data by both clustering algorithm and customized method.
Authors: Bo-Syue Jiang
Maintainer: Bo-Syue Jiang <[email protected]>
License: GPL (>= 2)
Version: 0.2.0
Built: 2025-02-23 03:49:31 UTC
Source: https://github.com/kiangkiangkiang/ggesda

Help Index

AbaloneIdt data example

Description

AbaloneIdt interval data example.

Usage

data(AbaloneIdt)

Format

An object of class data.frame (inherits from symbolic_tbl) with 24 rows and 7 columns.

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(AbaloneIdt)
ggInterval_index(AbaloneIdt, aes(x = Length))

BLOOD data example

Description

BLOOD interval data example.

Usage

data(BLOOD)

Format

An object of class tbl_df (inherits from tbl, data.frame, symbolic_tbl) with 14 rows and 3 columns.

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(BLOOD)
ggInterval_minmax(BLOOD, aes(x = Hematocrit))

Cardiological data example

Description

Cardiological interval data example.

Usage

data(Cardiological)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 11 rows and 3 columns.

Source

https://CRAN.R-project.org/package=RSDA

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(Cardiological)
ggInterval_index(Cardiological, aes(x = Syst))

Cardiological data example

Description

Cardiological interval data example.

Usage

data(Cardiological2)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 15 rows and 3 columns.

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(Cardiological2)
ggInterval_index(Cardiological2, aes(x = Syst))

Convert classical data frame into a symbolic data.

Description

A function for converting a classical data, which may present as a data frame or a matrix with one entry one value, into a symbolic data,which is shown as a interval or a set in an entry. Object after converting is ggESDA class containing interval data and raw data(if it exist) and typically statistics.

Usage

classic2sym(data=NULL,groupby = "kmeans",k=5,minData=NULL,maxData=NULL,
modalData = NULL)

Arguments

data

A classical data frame that you want to be converted into a interval data
groupby

A way to aggregate. It can be either a clustering method or a variable name which exist in input data (necessary factor type) . Default "kmeans".
k

A number of group,which is used by clustering. Default k = 5.
minData

if choose groupby parameter as 'customize',user need to define which data is min data or max data.
maxData

if choose groupby parameter as 'customize',user need to define which data is min data or max data.
modalData

list, each cell of list contain a set of column index of its modal multi-valued data of the input data. the value of it is a proportion presentation, and sum of each row in these column must be equal to 1. ex 0,1,0 or 0.2,0.3,0.5. the input type of modalData for example is modalData[[1]] = c(2, 3), modalData[[2]] = c(7:10), that 2, 3, 7, 8, 9, 10 columns are modal type of the data. Note: the option is only valid when groupby == "customize".

Value

classic2sym returns an object of class "ggESDA",which have a interval data and others as follows.

  • intervalData - The Interval data after converting also known as a RSDA object.

  • rawData - Classical data that user input.

  • clusterResult - Cluster results .If the groupby method is a clustering method then it will exist.

  • statisticsDF - A list contains data frame including some typically statistics in each group.

Examples

#classical data to symbolic data
classic2sym(iris)
classic2sym(mtcars, groupby = "kmeans", k = 10)
classic2sym(iris, groupby = "hclust", k = 7)
classic2sym(iris, groupby = "Species")

x1<-runif(10, -30, -10)
y1<-runif(10, -10, 30)
x2<-runif(10, -5, 5)
y2<-runif(10, 10, 50)
x3<-runif(10, -50, 30)
y3<-runif(10, 31, 60)

d<-data.frame(min1=x1,max1=y1,min2=x2,max2=y2,min3=x3,max3=y3)
classic2sym(d,groupby="customize",minData=d[,c(1,3,5)],maxData=d[,c(2,4,6)])
classic2sym(d,groupby="customize",minData=d$min1,maxData=d$min2)


#example for build modal data
#for the first modal data proportion
a1 <- runif(10, 0,0.4) %>% round(digits = 1)
a2 <- runif(10, 0,0.4) %>% round(digits = 1)

#for the second modal data proportion
b1 <- runif(10, 0,0.4) %>% round(digits = 1)
b2 <- runif(10, 0,0.4) %>% round(digits = 1)

#for interval-valued data
c1 <- runif(10, 10, 20) %>% round(digits = 0)
c2 <- runif(10, -50, -10) %>% round(digits = 0)

#build simulated data
d <- data.frame(a1 = a1, a2 = a2, a3 = 1-(a1+a2),
                c1 = c1, c2 = c2,
                b1 = b1, b2 = b2, b3 = 1-(b1+b2))

#transformation
classic2sym(d, groupby = "customize",
            minData = d$c2,
            maxData = d$c1,
            modalData = list(1:3, 6:8))#two modal data

#extract the data
symObj<-classic2sym(iris)
symObj$intervalData       #interval data
symObj$rawData            #raw data
symObj$clusterResult      #cluster result
symObj$statisticsDF       #statistics

Generic function for the correlation

Description

This function compute the symbolic correlation

Usage

cor(x, ...)

## Default S3 method:
cor(
  x,
  y = NULL,
  use = "everything",
  method = c("pearson", "kendall", "spearman"),
  ...
)

## S3 method for class 'symbolic_tbl'
cor(x, ...)

## S3 method for class 'symbolic_interval'
cor(x, y, method = c("centers", "B", "BD", "BG"), ...)

Arguments

x

First symbolic variables.
...

As in R cor function.
y

Second symbolic variables.
use

an optional character string giving a method for computing correlation in the presence of missing values. This must be (an abbreviation of) one of the strings 'everything', 'all.obs', 'complete.obs', 'na.or.complete', or 'pairwise.complete.obs'.
method

The method to be use.

Value

Return a real number.

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Generic function for the covariance

Description

This function compute the symbolic covariance.

Usage

cov(x, ...)

## Default S3 method:
cov(
  x,
  y = NULL,
  use = "everything",
  method = c("pearson", "kendall", "spearman"),
  ...
)

## S3 method for class 'symbolic_tbl'
cov(x, ...)

## S3 method for class 'symbolic_interval'
cov(x, y = NULL, method = c("centers", "B", "BD", "BG"), na.rm = FALSE, ...)

Arguments

x

First symbolic variables.
...

As in R cov function.
y

Second symbolic variables.
use

an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings 'everything', 'all.obs', 'complete.obs', 'na.or.complete', or 'pairwise.complete.obs'.
method

The method to be use.
na.rm

As in R cov function.

Value

Return a real number.

Author(s)

Oldemar Rodriguez Rojas

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Rodriguez, O. (2000). Classification et Modeles Lineaires en Analyse des Donnees Symboliques. Ph.D. Thesis, Paris IX-Dauphine University.

Environment data example

Description

Environment interval and modal data example.

Usage

data(Environment)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 14 rows and 17 columns.

Examples

data(Environment)
ggInterval_radar(Environment,
 plotPartial = 2,
 showLegend = FALSE,
 base_circle = TRUE,
 base_lty = 2,
 addText = FALSE)

Face Data Example

Description

Symbolic data matrix with all the variables of interval type.

Usage

data('facedata')

Format

$I;AD;AD;$I;BC;BC;.........

HUS1;$I;168.86;172.84;$I;58.55;63.39;.........
HUS2;$I;169.85;175.03;$I;60.21;64.38;.........
HUS3;$I;168.76;175.15;$I;61.4;63.51;.........
INC1;$I;155.26;160.45;$I;53.15;60.21;.........
INC2;$I;156.26;161.31;$I;51.09;60.07;.........
INC3;$I;154.47;160.31;$I;55.08;59.03;.........
ISA1;$I;164;168;$I;55.01;60.03;.........
ISA2;$I;163;170;$I;54.04;59;.........
ISA3;$I;164.01;169.01;$I;55;59.01;.........
JPL1;$I;167.11;171.19;$I;61.03;65.01;.........
JPL2;$I;169.14;173.18;$I;60.07;65.07;.........
JPL3;$I;169.03;170.11;$I;59.01;65.01;.........
KHA1;$I;149.34;155.54;$I;54.15;59.14;.........
KHA2;$I;149.34;155.32;$I;52.04;58.22;.........
KHA3;$I;150.33;157.26;$I;52.09;60.21;.........
LOT1;$I;152.64;157.62;$I;51.35;56.22;.........
LOT2;$I;154.64;157.62;$I;52.24;56.32;.........
LOT3;$I;154.83;157.81;$I;50.36;55.23;.........
PHI1;$I;163.08;167.07;$I;66.03;68.07;.........
PHI2;$I;164;168.03;$I;65.03;68.12;.........
PHI3;$I;161.01;167;$I;64.07;69.01;.........
ROM1;$I;167.15;171.24;$I;64.07;68.07;.........
ROM2;$I;168.15;172.14;$I;63.13;68.07;.........
ROM3;$I;167.11;171.19;$I;63.13;68.03;.........

Source

https://CRAN.R-project.org/package=RSDA

References

Billard L. and Diday E. (2006). Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

## Not run: 
data(facedata)
ggInterval_hist(facedata, aes(x = AD))

## End(Not run)

A symbolic object by R6 class for interval analysis and ggplot

Description

This is an object that will be used to make a ggplot object.A ggESDA object contains both classic data that user have and interval data which we transform.More over,some basic statistics from row data will also be recorded in this object,and the interval data which is from RSDA transformation will still contain RSDA properties.

Public fields

rawData

the data from user.

statisticsDF

contains min max mean median dataframe for each group of symbolic data

intervalData

interval data from RSDA type

clusterResult

clustering result

Methods

Public methods

Method new()

initialize all data, check whether satisfy theirs form

Usage
ggESDA$new(
  rawData = NULL,
  statisticsDF = NULL,
  intervalData = NULL,
  clusterResult = NULL
)
Arguments
rawData

Classical data frame.

statisticsDF

Data frame contained the statistic of raw data.

intervalData

Interval-valued data frame.

clusterResult

The clustering result from classical data drame to interval-valued data frame.

Method clone()

The objects of this class are cloneable with this method.

Usage
ggESDA$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

visualize a 2-dimension histogram by symbolic data with ggplot package.

Description

Visualize the two continuous variable distribution by dividing both the x axis and y axis into bins,and calculating the frequency of observation interval in each bin.

Usage

ggInterval_2Dhist(data = NULL,mapping = aes(NULL)
,xBins = 14,yBins=16,removeZero = FALSE,
addFreq = TRUE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame,which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
xBins

x axis bins, which mean how many partials x variable will be separate into.
yBins

y axis bins.It is the same as xBins.
removeZero

whether remove data whose frequency is equal to zero
addFreq

where add frequency text in each cells.

Value

Return a ggplot2 object.

Examples

ggInterval_2Dhist(oils, aes(x = GRA, y = FRE),
  xBins = 5, yBins = 5)

2-Dimension histogram matrix

Description

Visualize the all continuous variable distribution by dividing both the x axis and y axis into bins,and calculating the frequency of observation interval in each bin.Eventually show it by a matrix plot. Note: this function will automatically filter out the discrete variables,and plot all continuous in input data, so it can not be necessary that give the particularly variables in aes such like (aes(x = x, y = y)). It isn't also recommended to deal with too many variables because the big O in calculating full matrix will be too large.

Usage

ggInterval_2DhistMatrix(data = NULL,mapping = aes(NULL)
,xBins = 8,yBins=8,removeZero = FALSE,
addFreq = TRUE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame, which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
xBins

x axis bins,which mean how many bins x variable will be separate into
yBins

y axis bins. It is the same as xBins
removeZero

whether remove data whose frequency is equal to zero
addFreq

where add frequency text in each cells.

Value

Return a plot with ggplot2 object

Examples

ggInterval_2DhistMatrix(oils, xBins = 5, yBins = 5)

3D scatter plot for interval data

Description

Visualize the three continuous variable distribution by collecting all vertices in each interval to form a shape of cube.Also show the difference between each group.

Usage

ggInterval_3Dscatter(data = NULL,mapping = aes(NULL),scale=FALSE)

Arguments

data

A ggSDA object. It can also be either RSDA object or classical data frame, which will be automatically convert to ggSDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
scale

A boolean variable,TRUE, standardlize data. FALSE, not standardlize. If variance is too large(or small) or the difference between two variables are too large,it will be distortion or unseeable,which may happen in different units or others. So, a standardlize way is necessary.

Value

Return a ggplot2 object (It will still be 2-Dimension).

Examples

ggInterval_3Dscatter(facedata[1:5, ], aes(x = BC, y = EH, z = GH))

A interval Box plot

Description

Visualize the one continuous variable distribution by box represented by multiple rectangles.

Usage

ggInterval_boxplot(data = NULL,mapping = aes(NULL),plotAll=FALSE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame, which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
plotAll

booleans, if TRUE, plot all variable together

Value

Return a ggplot2 object.

Examples

p<-ggInterval_boxplot(iris,aes(iris$Petal.Length))
p
p+scale_fill_manual(values = c("red","yellow",
    "green","blue","black"),
    labels=c("0%","25%","50%","75%","100%"),
    name="quantile")

mydata<-ggESDA::facedata
ggInterval_boxplot(mydata,aes(AD,col="black",alpha=0.5))

myMtcars<-classic2sym(mtcars)
myMtcars<-myMtcars$intervalData
ggInterval_boxplot(myMtcars,aes(disp))

Figure with x-axis = center y-axis = range

Description

Visualize the relation between center and range.

Usage

ggInterval_centerRange(data = NULL,mapping = aes(NULL),plotAll=FALSE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame,which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
plotAll

booleans, if TRUE, plot all variable together

Value

Return a ggplot2 object.

Examples

ggInterval_centerRange(iris,aes(iris$Sepal.Length))

mydata<-ggESDA::facedata
ggInterval_centerRange(mydata,aes(AD,col="blue",pch=2))

Histogram for symbolic data with equal-bin or unequal-bin.

Description

Visualize the continuous variable distribution by dividing the x axis into bins,and calculating the frequency of observation interval in each bin.

Usage

ggInterval_hist(data = NULL,mapping = aes(NULL),method="equal-bin",bins=10,
 plotAll = FALSE, position = "identity", alpha = 0.5)

Arguments

data

A ggESDA object.It can also be either RSDA object or classical data frame,which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
method

It can be equal-bin(default) or unequal-bin.Enqual-bin means the width in histogram is equal, which represent all intervals divided have the same range. unequal-bin means the range of intervals are not the same,and it can be more general on data. Thus, the bins of unequal-bin method depends on the data, and the argument "bins" will be unused.
bins

x axis bins,which mean how many partials the variable
plotAll

boolean, whether plot all variables, default FALSE. will be separate into.
position

"stack" or "identity"
alpha

fill alpha

Value

Return a ggplot2 object.

Examples

ggInterval_hist(mtcars,aes(x=wt))

ggInterval_hist(iris,aes(iris$Petal.Length,col="blue",alpha=0.2,
   fill="red"),bins=30)


d<-data.frame(x=rnorm(1000,0,1))
p<-ggInterval_hist(d,aes(x=x),bins=40,method="equal-bin")$plot
p

p+scale_fill_manual(values=rainbow(40))+labs(title="myNorm")

Plot the range of each observations

Description

Visualize the range of the variables of each observations by using a kind of margin bar that indicate the minimal and maximal of observations.

Usage

ggInterval_index(data = NULL,mapping = aes(NULL),
plotAll = FALSE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame,which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
plotAll

plot all variables

Value

Return a ggplot2 object.

Examples

#the observations show on the y-axis .values on x-axis
ggInterval_index(iris,aes(x=iris$Sepal.Length))

#change above axis
ggInterval_index(mtcars,aes(y=disp,col="red",fill="grey"))

#symbolic data
mydata <- ggESDA::facedata
ggInterval_index(mydata,aes(x=3:13,y=AD))

An index plot presented by color image for interval data.

Description

Visualize the range of the variables of each observations by using color image.The index image replace margin bar by color,thus it will be more visible for data.

Usage

ggInterval_indexImage(data = NULL,mapping = aes(NULL),
column_condition=TRUE,full_strip=FALSE, plotAll = FALSE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame,which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
column_condition

Boolean variables, which mean the color present by column condition (if TRUE) or matrix condition (if FALSE)
full_strip

Boolean variables, which mean the strip present in full figure-width (if TRUE) or only in its variable values(if FALSE).
plotAll

Boolean, which determine if the heatmap type for visualizing full variables is used. default FALSE.

Value

Return a ggplot2 object.

Examples

d<-data.frame(qq=rnorm(1000,0,1))
ggInterval_indexImage(d,aes(qq))

mydata<-ggESDA::facedata
p<-ggInterval_indexImage(mydata,aes(AD),full_strip=TRUE,column_condition = TRUE)
#Recommend to add coord_flip() to make the plot more visible
p+coord_flip()

myIris<-classic2sym(iris,groupby="Species")
myIris<-myIris$intervalData
p<-ggInterval_indexImage(myIris,aes(myIris$Petal.Length),full_strip=FALSE,column_condition=TRUE)
p

ggInterval_indexImage(mtcars,aes(disp))+labs(x="anything")

A min-max plot for interval data

Description

Visualize the range of the variables of each observations by marking minimal and maximal point.

Usage

ggInterval_minmax(data = NULL,mapping = aes(NULL),
          scaleXY = "local",plotAll=FALSE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame,which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
scaleXY

default "local", which means limits of x-axis and y-axis depend on their own variable. "global" means limits of them depend on all variables that user input.
plotAll

booleans, if TRUE, plot all variable together

Value

Return a ggplot2 object.

Examples

ggInterval_minmax(mtcars,aes(disp))

mydata2<-ggESDA::Cardiological
ggInterval_minmax(mydata2,aes(mydata2$Pulse,size=3))

d<-mapply(c(10,20,40,80,160),c(20,40,80,160,320),FUN=runif,n=1000)
d<-data.frame(qq=matrix(d,ncol=1))
ggInterval_minmax(d,aes(qq))

myIris<-classic2sym(iris,groupby="Species")
myIris<-myIris$intervalData
ggInterval_minmax(myIris,aes(myIris$Petal.Length))+
   theme_classic()

Vertice-PCA for interval data

Description

ggInterval_PCA performs a principal components analysis on the given numeric interval data and returns the results like princomp, ggplot object and a interval scores.

Usage

ggInterval_PCA(data = NULL,mapping = aes(NULL),plot=TRUE,
                      concepts_group=NULL, poly = FALSE, adjust = TRUE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame, which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping. It is the same as the mapping of ggplot2.
plot

Boolean variable,Auto plot (if TRUE).It can also plot by its inner object
concepts_group

color with each group of concept
poly

if plot a poly result
adjust

adjust sign of the principal component

Value

A ggplot object for PC1,PC2,and a interval scores and others.

  • scores_interval - The interval scores after PCA.

  • ggplotPCA - a ggplot object with x-axis and y-axis are PC1 and PC2.

  • others - others are the returns values of princomp.

Examples

ggInterval_PCA(iris)

mydata2<-ggESDA::Cardiological
ggInterval_PCA(mydata2,aes(col="red",alpha=0.2))

d<-mapply(c(10,20,40,80,160),c(20,40,80,160,320),FUN=runif,n=1000)
d<-data.frame(qq=matrix(d,ncol=4))
ggInterval_PCA(d)

myIris<-classic2sym(iris,"Species")
p<-ggInterval_PCA(myIris,plot=FALSE)
p$ggplotPCA
p$scores_interval

A interval Radar plot

Description

Using ggplot2 package to make a radar plot with multiple variables.Each variables contains min values and max values as a symbolic data.

Usage

ggInterval_radar(data=NULL,layerNumber=3,
inOneFig=TRUE,showLegend=TRUE,showXYLabs=FALSE,
plotPartial=NULL,
alpha=0.5,
base_circle=TRUE,
base_lty=2,
addText=TRUE,
type="default",
quantileNum=4,
Drift=0.5,
addText_modal=TRUE,
addText_modal.p=FALSE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame(not recommended),which will be automatically convert to ggESDA data.
layerNumber

number of layer of a concentric circle,usually to visuallize the reach of a observation in particularly variable.
inOneFig

whether plot all observations in one figure.if not, it will generate a new windows containing distinct observations.
showLegend

whether show the legend.
showXYLabs

whether show the x,y axis labels.
plotPartial

a numeric vector,which is the row index from the data.if it is not null, it will extract the row user deciding to draw a radar plot from original data.Notes : the data must be an interval data if the plotPartial is not null.
alpha

aesthetic alpha of fill color
base_circle

boolean, if true, it will generate inner circle.
base_lty

line type in base figure
addText

add the value of interval-valued variables in figure
type

different type of radar,it can be "default","rect","quantile"
quantileNum

if type == "quantile", it will provide the number of percentage
Drift

The drift term, which determines the radar values beginning.
addText_modal

add the factor of modal multi-valued variables in figure..
addText_modal.p

add the value of modal multi-valued variables in figure..

Examples

# must specify plotPartial to some certain rows you want to plot
Environment.n <- Environment[, 5:17]
ggInterval_radar(Environment.n,
                plotPartial = 2,
                showLegend = FALSE,
                base_circle = TRUE,
                base_lty = 2,
                addText = FALSE
) +
 labs(title = "") +
 scale_fill_manual(values = c("gray50")) +
 scale_color_manual(values = c("red"))

ggInterval_radar(Environment,
                plotPartial = 2,
                showLegend = FALSE,
                base_circle = FALSE,
                base_lty = 1,
                addText = TRUE
) +
 labs(title = "") +
 scale_fill_manual(values = c("gray50")) +
 scale_color_manual(values = c("gray50"))

scatter plot for all variable by interval data.

Description

Visualize the all continuous variable distribution by rectangle for both x-axis and y-axis with a matrix grid. Note: this function will automatically filter out the discrete variables,and plot all continuous in input data,so it can not be necessary that give the particularly variables in aes such like (aes(x = x, y = y)). It isn't also recommended to deal with too many variables because the big O in calculating full matrix will be too large.

Usage

ggInterval_scaMatrix(data = NULL,mapping = aes(NULL), showLegend=FALSE)

Arguments

data

A ggESDA object. It can also be either RSDA object or classical data frame,which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
showLegend

whether show the legend.

Value

Return a plot with no longer a ggplot2 object,instead of a marrangeGrob object.

Examples

a<-rnorm(1000,0,5)
b<-runif(1000,-20,-10)
c<-rgamma(1000,10,5)
d<-as.data.frame(cbind(norm=a,unif=b,gamma_10_5=c))
ggInterval_scaMatrix(d)


ggInterval_scaMatrix(mtcars[,c("mpg","wt","qsec")],
    aes(col="red",lty=2,fill="blue",alpha=0.3))


myIris <- classic2sym(iris,groupby = "Species")$intervalData
ggInterval_scaMatrix(myIris[,1:3])


mydata <- ggESDA::Cardiological
ggInterval_scaMatrix(mydata[,1:3],aes(fill="black",alpha=0.2))

scatter plot for two continuous interval data

Description

Visualize the twwo continuous variable distribution by rectangle and each of its width and heigth represents a interval of the data.

Usage

ggInterval_scatter(data = NULL,mapping = aes(NULL), ...)

Arguments

data

A ggESDA object.It can also be either RSDA object or classical data frame, which will be automatically convert to ggESDA data.
mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit. aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
...

Others in ggplot2.

Value

Return a ggplot2 object.

Examples

a<-rnorm(1000,0,5)
b<-runif(1000,-20,-10)
d<-as.data.frame(cbind(norm=a,unif=b))
ggInterval_scatter(d,aes(a,b))


ggInterval_scatter(mtcars[,c("mpg","wt","qsec")],
    aes(x=mpg,y=wt,
    col="red",lty=2,fill="blue",alpha=0.3))


myIris <- classic2sym(iris,groupby = "Species")$intervalData
p<-ggInterval_scatter(myIris,aes(myIris$Petal.Length,myIris$Petal.Width))
p
p+scale_fill_manual(labels=rownames(myIris),
                   values=c("red","blue","green"),
                   name="Group")


mydata <- ggESDA::facedata
p<-ggInterval_scatter(mydata[1:10,],aes(AD,BC,alpha=0.2))
p+scale_fill_manual(labels=rownames(mydata)[1:10],
                   values=rainbow(10),
                   name="Group")

iris.i data example

Description

iris.i interval data example.

Usage

data(iris.i)

Format

An object of class data.frame (inherits from symbolic_tbl) with 3 rows and 4 columns.

Examples

data(iris.i)
ggInterval_index(iris.i, aes(x = Sepal.Length))

mtcars.i data example

Description

mtcars.i interval and modal data example.

Usage

data(mtcars.i)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame, symbolic_tbl) with 5 rows and 11 columns.

Examples

data(mtcars.i)
ggInterval_index(mtcars.i, aes(x = mpg))

mushroom data example

Description

mushroom interval data example.

Usage

data(mushroom)

Format

An object of class tbl_df (inherits from tbl, data.frame, symbolic_tbl) with 23 rows and 3 columns.

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(mushroom)
ggInterval_scatter(mushroom, aes(x = Cap.Widths, y = Stipe.Lengths))

oils data example

Description

oils interval data example.

Usage

data(oils)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 8 rows and 4 columns.

References

Billard L. and Diday E. (2006).Symbolic data analysis: Conceptual statistics and data mining. Wiley, Chichester.

Examples

data(oils)
ggInterval_scatter(oils, aes(x = GRA, y = IOD))

RSDA object to symbolic object for ggplot

Description

It will be a good way to unify all symbolic data object in R that collects all useful symbolic analysis tools such like RSDA into the same class for management. In this way, user who wants to do some study in symbolic data will be more convenient for searching packages.Thus,RSDA2sym collecting RSDA object into ggESDA object will do for plot(ggplot) and RSDA's analysis.

Usage

RSDA2sym(data=NULL,rawData=NULL)

Arguments

data

an interval data, which may transfrom by RSDA::classic.to.sym .Note:data is a necessary parameter,and must have symbolic_tbl class.
rawData

rawData, which can be transformed to interval data, must be a data frame and match to data.

Value

Return an object of class "ggESDA", which have a interval data and others as follows.

  • intervalData - The Interval data after converting also known as a RSDA object.

  • rawData - Classical data that user input.

  • clusterResult - Cluster results .If the groupby method is a clustering method then it will exist.

  • statisticsDF - A list contains data frame including some typically statistics in each group.

#'

Examples

r<-ggESDA::Cardiological
mySym<-RSDA2sym(r)
mySym$intervalData

scale for symbolic data table

Description

scale for symbolic data table

Usage

scale(x, ...)

## Default S3 method:
scale(x, center = TRUE, scale = TRUE, ...)

## S3 method for class 'symbolic_tbl'
scale(x, ...)

## S3 method for class 'symbolic_interval'
scale(x, ...)

Arguments

x

A ggESDA object. It can also be either RSDA object or classical data frame, which will be automatically convert to ggESDA data.
...

Used by other R function.
center

same as base::scale, either a logical value or numeric-alike vector of length equal to the number of columns of x, where ‘numeric-alike’ means that as.numeric(.) will be applied successfully if is.numeric(.) is not true.
scale

same as base::scale, either a logical value or a numeric-alike vector of length equal to the number of columns of x.

Value

Return a scale ggESDA object.

Examples

#For all interval-valued
scale(facedata)

#For both interval-valued and modal multi-valued
scale(mtcars.i)

summary for symbolic data table

Description

summary for symbolic data table

Usage

summary(object, ...)

## Default S3 method:
summary(object, ...)

## S3 method for class 'symbolic_tbl'
summary(object, ...)

## S3 method for class 'symbolic_interval'
summary(object, ...)

## S3 method for class 'symbolic_modal'
summary(object, summary_fun = "mean", ...)

Arguments

object

an object for which a summary is desired.
...

additional arguments affecting the summary produced.
summary_fun

only works when the symbolic_modal class input, it determine which summary function be applied for each modal.

Value

Return a summary table.

Examples

#For all interval-valued
summary(facedata)

#For both interval-valued and modal multi-valued
summary(Environment)

summary(Environment$URBANICITY, summary_fun = "quantile")