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_indexplot(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_MMplot(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_indexplot(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_indexplot(Cardiological2, aes(x = Syst))

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

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 ggInterval object will do for plot(ggplot) and RSDA's analysis.

Usage

RSDA2sym(data=NULL,rawData=NULL)

Arguments

Value

Return an object of class "ggInterval", 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<-ggInterval::Cardiological
mySym<-RSDA2sym(r)
mySym$intervalData

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 ggInterval 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

Value

classic2sym returns an object of class "ggInterval",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

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

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.

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

data(facedata)
ggInterval_hist(facedata, aes(x = AD))

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 ggInterval 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

Methods

Public methods

• ggInterval$new()

• ggInterval$clone()

Method new()

initialize all data, check whether satisfy theirs form

Usage

ggInterval$new(
  rawData = NULL,
  statisticsDF = NULL,
  intervalData = NULL,
  clusterResult = NULL
)

Method clone()

The objects of this class are cloneable with this method.

Usage

ggInterval$clone(deep = FALSE)

Arguments

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

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

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_3Dscatterplot(data = NULL,mapping = aes(NULL),scale=FALSE)

Arguments

Value

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

Examples

ggInterval_3Dscatterplot(facedata[1:5, ], aes(x = BC, y = EH, z = GH))

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

Description

Visualize the relation between center and range.

Usage

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

Arguments

Value

Return a ggplot2 object.

Examples

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

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

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

Arguments

Value

Return a ggplot2 object.

Examples

ggInterval_MMplot(mtcars,aes(disp))

mydata2<-ggInterval::Cardiological
ggInterval_MMplot(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_MMplot(d,aes(qq))

myIris<-classic2sym(iris,groupby="Species")
myIris<-myIris$intervalData
ggInterval_MMplot(myIris,aes(myIris$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

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<-ggInterval::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 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

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

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

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

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")

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

Value

Return a ggplot2 object.

Examples

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

mydata<-ggInterval::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")

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

Arguments

Value

Return a ggplot2 object.

Examples

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

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

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

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_radarplot(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

Examples

# must specify plotPartial to some certain rows you want to plot
Environment.n <- Environment[, 5:17]
ggInterval_radarplot(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_radarplot(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_scatterMatrix(data = NULL,mapping = aes(NULL), showLegend=FALSE)

Arguments

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_scatterMatrix(d)


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


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


mydata <- ggInterval::Cardiological
ggInterval_scatterMatrix(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_scatterplot(data = NULL,mapping = aes(NULL), ...)

Arguments

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_scatterplot(d,aes(a,b))


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


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


mydata <- ggInterval::facedata
p<-ggInterval_scatterplot(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_indexplot(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_indexplot(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_scatterplot(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_scatterplot(oils, aes(x = GRA, y = IOD))

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

Value

Return a scale ggInterval 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

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")