Package 'ggInterval' reference manual

Title:	Visualizing Interval-Valued Data Using 'ggplot2'
Description:	Extends 'ggplot2' for visualizing interval-valued data with scatter plots, histograms, index plots, boxplots, radar plots, PCA displays, and correlation heatmaps. The package also converts classical data tables into interval-valued data using clustering algorithms or user-defined groupings.
Authors:	Bo-Syue Jiang [aut], Han-Ming Wu [cre]
Maintainer:	Han-Ming Wu <[email protected]>
License:	GPL (>= 2)
Version:	0.2.5
Built:	2026-05-28 06:41:36 UTC
Source:	https://github.com/hanmingwu1103/gginterval

abalone.i data example

Description

abalone.i interval data example.

Usage

data(abalone.i)
data(abalone.i)

Format

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

Source

Adapted from MAINT.Data::AbaloneIdt; the underlying Abalone data are from the UCI Machine Learning Repository.

Examples

data(abalone.i)
ggInterval_indexplot(abalone.i, aes(x = Length))
data(abalone.i)
ggInterval_indexplot(abalone.i, aes(x = Length))

blood.i data example

Description

blood.i interval data example.

Usage

data(blood.i)
data(blood.i)

Format

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

Source

Adapted from HistDAWass::BLOOD; see https://CRAN.R-project.org/package=HistDAWass.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Examples

data(blood.i)
ggInterval_MMplot(blood.i, aes(x = Hematocrit))
data(blood.i)
ggInterval_MMplot(blood.i, aes(x = Hematocrit))

Cardiological data example

Description

Cardiological interval data example.

Usage

data(Cardiological)
data(Cardiological)

Format

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

Source

Adapted from RSDA::Cardiological; see https://CRAN.R-project.org/package=RSDA.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Examples

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

Cardiological data example

Description

Cardiological interval data example.

Usage

data(Cardiological2)
data(Cardiological2)

Format

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

Source

Adapted from RSDA::Cardiological2; see https://CRAN.R-project.org/package=RSDA.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Examples

data(Cardiological2)
ggInterval_indexplot(Cardiological2, aes(x = Syst))
data(Cardiological2)
ggInterval_indexplot(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 object, which is represented as an 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)
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 interval-valued 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 "ggInterval",which has interval-valued data and related outputs 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
#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"), ...)
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 use.

Details

Supported interval-valued methods are:

"centers": correlation of interval centers.
"B": Billard correlation.
"BD": Billard-Diday correlation.
"BG": Bertrand-Goupil correlation.

For "B", "BD", and "BG", the denominator uses the corresponding method-matched standard deviation.

Value

Return a real number in [-1, 1].

References

Bertrand, Patrice and Goupil, Francoise (2000). Descriptive Statistics for Symbolic Data. In Hans-Hermann Bock and Edwin Diday (eds.), Analysis of Symbolic Data, pp. 106–124. Berlin and Heidelberg: Springer.

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Billard, Lynne (2008). Sample covariance functions for complex quantitative data. In Proceedings of the World IASC Conference, pp. 157–163, Yokohama, Japan.

Rodriguez-Rojas, Oldemar (2000). Classification et modeles lineaires en analyse des donnees symboliques. PhD thesis, Universite Paris IX Dauphine.

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, ...)
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 use.

na.rm

As in R cov function.

Details

Supported interval-valued methods are:

"centers": covariance of interval centers.
"B": Billard covariance.
"BD": Billard-Diday covariance.
"BG": Bertrand-Goupil covariance.

Value

Return a real number.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Billard, Lynne (2008). Sample covariance functions for complex quantitative data. In Proceedings of the World IASC Conference, pp. 157–163, Yokohama, Japan.

Rodriguez-Rojas, Oldemar (2000). Classification et modeles lineaires en analyse des donnees symboliques. PhD thesis, Universite Paris IX Dauphine.

Environment data example

Description

Environment interval and modal data example.

Usage

data(Environment)
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)
data(Environment)
ggInterval_radarplot(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')
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

Adapted from RSDA::facedata; see https://CRAN.R-project.org/package=RSDA.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Examples


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

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

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

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

deep: Whether to make a deep clone.

Visualize a 2-dimension histogram for interval-valued data

Description

Visualize the joint distribution of two continuous interval-valued variables by dividing the x axis and y axis into rectangles and calculating the frequency of each observation interval in every rectangle.

Usage

ggInterval_2Dhist(data = NULL, mapping = aes(NULL),
method = "equal-bin", xBins = 14, yBins = 16,
display = "p", palette = "Blues", direction = 1, tau = 0,
removeZero = FALSE, cell_labels = FALSE, label_rule = "above-mean",
addFreq = NULL)
ggInterval_2Dhist(data = NULL, mapping = aes(NULL),
method = "equal-bin", xBins = 14, yBins = 16,
display = "p", palette = "Blues", direction = 1, tau = 0,
removeZero = FALSE, cell_labels = FALSE, label_rule = "above-mean",
addFreq = NULL)

Arguments

data

A ggInterval object. It can also be either an RSDA object or a classical data frame, which will be automatically converted to ggInterval 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

Histogram partition method. Use "equal-bin" for equally spaced bins or "unequal-bin" for non-equidistant bins defined by the observed interval endpoints.

xBins

Number of x-axis bins used when method = "equal-bin".

yBins

Number of y-axis bins used when method = "equal-bin".

display

Metric shown in the cells. Use "p" for relative frequency, "f" for weighted frequency, or "h" for density.

palette

ColorBrewer palette passed to scale_fill_distiller().

direction

Direction passed to scale_fill_distiller().

tau

Non-negative tolerance used when method = "unequal-bin". Consecutive breakpoints whose gaps are smaller than tau are merged.

removeZero

Whether remove data whose frequency is equal to zero.

cell_labels

Logical. If TRUE, add frequency labels to cells.

label_rule

Rule used when cell_labels = TRUE. The default "above-mean" shows only cells whose displayed values exceed the mean cell value. Alternatives are "nonzero" and "all".

addFreq

Deprecated alias for cell_labels.

Value

Return a list containing a ggplot2 object and the corresponding frequency table.

Examples

ggInterval_2Dhist(oils, aes(x = GRA, y = FRE),
  xBins = 5, yBins = 5,
  display = "p",
  palette = "Blues",
  cell_labels = TRUE)

ggInterval_2Dhist(oils, aes(x = GRA, y = FRE),
  method = "unequal-bin",
  display = "p",
  palette = "Blues",
  tau = 0.5)

ggInterval_2Dhist(oils, aes(x = GRA, y = FRE),
  xBins = 5, yBins = 5,
  display = "p",
  palette = "Blues",
  cell_labels = TRUE)

ggInterval_2Dhist(oils, aes(x = GRA, y = FRE),
  method = "unequal-bin",
  display = "p",
  palette = "Blues",
  tau = 0.5)

2-Dimension histogram matrix

Description

Visualize all continuous interval-valued variables with a matrix of 2D histograms. Each off-diagonal panel shows a 2D histogram for a pair of variables, and each diagonal panel displays the variable name.

Usage

ggInterval_2DhistMatrix(data = NULL, mapping = aes(NULL),
method = "equal-bin", xBins = 8, yBins = 8,
display = "p", palette = "Blues", direction = 1, tau = 0,
removeZero = FALSE, cell_labels = FALSE, label_rule = "above-mean",
addFreq = NULL)
ggInterval_2DhistMatrix(data = NULL, mapping = aes(NULL),
method = "equal-bin", xBins = 8, yBins = 8,
display = "p", palette = "Blues", direction = 1, tau = 0,
removeZero = FALSE, cell_labels = FALSE, label_rule = "above-mean",
addFreq = NULL)

Arguments

data

A ggInterval object. It can also be either an RSDA object or a classical data frame, which will be automatically converted to ggInterval 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. It is the same as the mapping of ggplot2. This function ignores x and y mappings and plots all continuous interval-valued variables.

method

Histogram partition method. Use "equal-bin" for equally spaced bins or "unequal-bin" for non-equidistant bins defined by the observed interval endpoints.

xBins

Number of x-axis bins used when method = "equal-bin".

yBins

Number of y-axis bins used when method = "equal-bin".

display

Metric shown in the cells. Use "p" for relative frequency, "f" for weighted frequency, or "h" for density.

palette

ColorBrewer palette passed to scale_fill_distiller().

direction

Direction passed to scale_fill_distiller().

tau

Non-negative tolerance used when method = "unequal-bin". Consecutive breakpoints whose gaps are smaller than tau are merged.

removeZero

Whether remove cells whose frequency is equal to zero.

cell_labels

Logical. If TRUE, add cell labels.

label_rule

Rule used when cell_labels = TRUE. The default "above-mean" shows only cells whose displayed values exceed the mean value within the matrix. Alternatives are "nonzero" and "all".

addFreq

Deprecated alias for cell_labels.

Value

Return a ggplot2 object.

Examples

ggInterval_2DhistMatrix(
  oils,
  xBins = 5,
  yBins = 5,
  display = "p",
  palette = "Blues",
  cell_labels = TRUE
)

ggInterval_2DhistMatrix(
  oils,
  method = "unequal-bin",
  display = "p",
  palette = "Blues",
  tau = 0.5
)

ggInterval_2DhistMatrix(
  oils,
  xBins = 5,
  yBins = 5,
  display = "p",
  palette = "Blues",
  cell_labels = TRUE
)

ggInterval_2DhistMatrix(
  oils,
  method = "unequal-bin",
  display = "p",
  palette = "Blues",
  tau = 0.5
)

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)
ggInterval_3Dscatterplot(data = NULL,mapping = aes(NULL),scale=FALSE)

Arguments

data

A ggInterval object. It can also be either an RSDA object or a classical data frame, which will be automatically converted to ggInterval 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 standardizes the data and FALSE leaves the data unchanged. If variance is too large(or small) or the difference between two variables are too large,it will be distorted or difficult to see, which may happen when variables are measured in different units.

Value

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

Examples

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

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

A interval Box plot

Description

Visualize the one continuous variable distribution using one of three interval-aware boxplot styles.

Usage

ggInterval_boxplot(data = NULL,mapping = aes(NULL),plotAll=FALSE,
width_type = "violin-like")
ggInterval_boxplot(data = NULL,mapping = aes(NULL),plotAll=FALSE,
width_type = "violin-like")

Arguments

data

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

width_type

Box-width style. Use "violin-like" (default) to let the rectangle widths reflect local interval concentration, "quantile-depth" for progressively narrower quantile boxes, or "side-by-side" for conventional boxplots of the lower and upper bounds shown next to each other.

Value

Return a ggplot2 object.

Examples

mydata <- ggInterval::facedata
ggInterval_boxplot(mydata, aes(x = AD))
ggInterval_boxplot(mydata, aes(x = AD), width_type = "quantile-depth")
ggInterval_boxplot(mydata, plotAll = TRUE, width_type = "side-by-side")

mydata <- ggInterval::facedata
ggInterval_boxplot(mydata, aes(x = AD))
ggInterval_boxplot(mydata, aes(x = AD), width_type = "quantile-depth")
ggInterval_boxplot(mydata, plotAll = TRUE, width_type = "side-by-side")

Correlation heatmap for interval-valued data

Description

Visualize pairwise symbolic correlations between interval-valued variables with a heatmap. This plot is especially useful for summarizing multivariate dependence structures before more formal modeling or dimension reduction.

Usage

ggInterval_corrplot(
  data = NULL,
  method = c("centers", "B", "BD", "BG"),
  triangle = c("lower", "upper", "full"),
  showValues = TRUE,
  digits = 2,
  showLegend = TRUE
)
ggInterval_corrplot(
  data = NULL,
  method = c("centers", "B", "BD", "BG"),
  triangle = c("lower", "upper", "full"),
  showValues = TRUE,
  digits = 2,
  showLegend = TRUE
)

Arguments

data

A ggInterval object. It can also be either an RSDA object or a classical data frame, which will be automatically converted to ggInterval data.

method

Correlation method for interval-valued variables. It must be one of "centers" (interval centers), "B" (Billard), "BD" (Billard-Diday), or "BG" (Bertrand-Goupil).

triangle

Which part of the symmetric correlation matrix to display. It can be "lower", "upper", or "full".

showValues

Logical. If TRUE, print the correlation values in each cell.

digits

Number of digits used when showValues = TRUE.

showLegend

Logical. If TRUE, display the fill legend.

Value

Return a ggplot2 object.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Billard, Lynne (2008). Sample covariance functions for complex quantitative data. In Proceedings of the World IASC Conference, pp. 157–163, Yokohama, Japan.

Examples

ggInterval_corrplot(facedata)

ggInterval_corrplot(facedata, method = "BG", triangle = "full")
ggInterval_corrplot(facedata)

ggInterval_corrplot(facedata, method = "BG", triangle = "full")

Center-range plot for interval data

Description

Visualize the relation between interval centers and ranges, with mean reference lines and an optional ellipse overlay.

Usage

ggInterval_CRplot(data = NULL,mapping = aes(NULL),plotAll=FALSE,
                         addEllipse = TRUE,
                         ellipseFill = "blue",
                         ellipseAlpha = 0.3)
ggInterval_CRplot(data = NULL,mapping = aes(NULL),plotAll=FALSE,
                         addEllipse = TRUE,
                         ellipseFill = "blue",
                         ellipseAlpha = 0.3)

Arguments

data

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

addEllipse

logical, if TRUE (default), add a shaded ellipse layer.

ellipseFill

fill color of the ellipse layer. Default is "blue".

ellipseAlpha

alpha level of the ellipse layer. Default is 0.3.

Value

Return a ggplot2 object.

Examples

mydata <- ggInterval::facedata
ggInterval_CRplot(mydata, aes(x = AD, col = "blue", shape = 2))
ggInterval_CRplot(mydata, plotAll = TRUE)
ggInterval_CRplot(mydata, plotAll = TRUE, addEllipse = FALSE)
mydata <- ggInterval::facedata
ggInterval_CRplot(mydata, aes(x = AD, col = "blue", shape = 2))
ggInterval_CRplot(mydata, plotAll = TRUE)
ggInterval_CRplot(mydata, plotAll = TRUE, addEllipse = FALSE)

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

Arguments

data

A ggInterval object.It can also be either RSDA object or classical data frame,which will be automatically convert to ggInterval 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". Equal-bin means the histogram uses equally spaced intervals. Unequal-bin means the bin widths are determined by the data, so the argument bins is unused in that case.

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

An object of class ggInterval_hist_result. A direct call displays the histogram, and the returned object stores the ggplot object in $plot together with the accompanying frequency tables.

Examples

mydata <- ggInterval::facedata
ggInterval_hist(mydata, aes(x = AD), bins = 10)

hist_obj <- ggInterval_hist(mydata, plotAll = TRUE, bins = 10)
hist_obj
hist_obj$`Table AD`

ggInterval_hist(mydata, aes(x = AD), method = "unequal-bin")

mydata <- ggInterval::facedata
ggInterval_hist(mydata, aes(x = AD), bins = 10)

hist_obj <- ggInterval_hist(mydata, plotAll = TRUE, bins = 10)
hist_obj
hist_obj$`Table AD`

ggInterval_hist(mydata, aes(x = AD), method = "unequal-bin")

Index image plot for interval-valued data

Description

Visualize interval-valued observations with color strips. For a single variable, the display acts as a color-based analogue of an index plot. When plotAll = TRUE, the function produces a multivariate image plot across all continuous interval-valued variables.

Usage

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

Arguments

data

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

Logical. If TRUE, the color scale is applied separately within each variable (column condition). If FALSE, one common color scale is applied to the whole matrix (matrix condition).

full_strip

Logical. If TRUE, each strip spans the full plotting width. If FALSE, strip widths reflect the interval ranges.

plotAll

Logical. If TRUE, produce a multivariate image plot for all continuous interval-valued variables. The default is FALSE.

Value

Return a ggplot2 object.

Examples

mydata <- ggInterval::facedata
p <- ggInterval_indexImage(mydata, aes(x = AD),
  full_strip = TRUE, column_condition = TRUE)
# Recommend adding coord_flip() to make the single-variable display clearer.
p + coord_flip()
ggInterval_indexImage(
  mydata,
  plotAll = TRUE,
  full_strip = TRUE,
  column_condition = FALSE
) +
  scale_colour_distiller(palette = "Blues", direction = 1)
ggInterval_indexImage(mydata, plotAll = TRUE, full_strip = FALSE)

mydata <- ggInterval::facedata
p <- ggInterval_indexImage(mydata, aes(x = AD),
  full_strip = TRUE, column_condition = TRUE)
# Recommend adding coord_flip() to make the single-variable display clearer.
p + coord_flip()
ggInterval_indexImage(
  mydata,
  plotAll = TRUE,
  full_strip = TRUE,
  column_condition = FALSE
) +
  scale_colour_distiller(palette = "Blues", direction = 1)
ggInterval_indexImage(mydata, plotAll = TRUE, full_strip = FALSE)

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, row_order = "o", user_order = NULL, labels = FALSE)
ggInterval_indexplot(data = NULL,mapping = aes(NULL),
plotAll = FALSE, row_order = "o", user_order = NULL, labels = FALSE)

Arguments

data

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

row_order

Row ordering used when plotAll = TRUE: "o" for the original order, "c" for ascending interval centers within each variable panel, "r" for ascending interval ranges within each variable panel, and "u" for a user-defined permutation supplied through user_order.

user_order

User-defined row permutation used when row_order = "u" and plotAll = TRUE. Supply either an integer permutation of 1:nrow(data) or a character permutation of the row names.

labels

Logical. When TRUE and plotAll = TRUE, repeat the row labels inside each variable panel. This is especially useful for ordered displays with row_order = "c" or "r". For ordered displays, the outer left-side row labels are suppressed by default.

Value

Return a ggplot2 object.

Examples

mydata <- ggInterval::facedata
Subjects <- substr(rownames(mydata), 1, 3)
ggInterval_indexplot(mydata, aes(x = AD))
ggInterval_indexplot(mydata, aes(x = AD, fill = Subjects))
ggInterval_indexplot(mydata, plotAll = TRUE, row_order = "c")
custom_order <- c(19:21, 22:24, 1:18, 25:27)
ggInterval_indexplot(mydata["AD"], plotAll = TRUE,
                     row_order = "u", user_order = custom_order)
ggInterval_indexplot(mydata, plotAll = TRUE, row_order = "c", labels = TRUE)

mydata <- ggInterval::facedata
Subjects <- substr(rownames(mydata), 1, 3)
ggInterval_indexplot(mydata, aes(x = AD))
ggInterval_indexplot(mydata, aes(x = AD, fill = Subjects))
ggInterval_indexplot(mydata, plotAll = TRUE, row_order = "c")
custom_order <- c(19:21, 22:24, 1:18, 25:27)
ggInterval_indexplot(mydata["AD"], plotAll = TRUE,
                     row_order = "u", user_order = custom_order)
ggInterval_indexplot(mydata, plotAll = TRUE, row_order = "c", labels = TRUE)

Interval-valued line plot

Description

Visualize interval-valued data along an ordered horizontal axis. A line can connect the centers of intervals at each position, with crossbars or errorbars indicating the interval range. When the horizontal axis is time, the display becomes an interval-valued time-series plot.

Usage

ggInterval_lineplot(data = NULL, mapping = aes(NULL),
barWidth = 0.5, add_line = TRUE)

ggInterval_tsplot(data = NULL, mapping = aes(NULL),
barWidth = 0.5, add_line = TRUE)
ggInterval_lineplot(data = NULL, mapping = aes(NULL),
barWidth = 0.5, add_line = TRUE)

ggInterval_tsplot(data = NULL, mapping = aes(NULL),
barWidth = 0.5, add_line = TRUE)

Arguments

data

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

mapping

Set of aesthetic mappings created by aes() or aes_(). Must include x for the ordered variable and y for the interval variable. Optional aesthetics include group and fill for multiple line series. It is the same as the mapping of ggplot2.

barWidth

The width of the crossbar or errorbar indicating the interval range at each x position. Default is 0.5.

add_line

Logical; if TRUE (default), connect interval centers with a line. Set FALSE when adding a customized line layer with geom_line(...).

Value

Return a ggplot2 object.

Examples


if (requireNamespace("TTR", quietly = TRUE)) {
  data("ttrc", package = "TTR")
  stock.data <- subset(
    ttrc[, c("Date", "Close", "Low", "High")],
    format(Date, "%Y-%m") %in% c("1985-01", "1985-02", "1985-03")
  )
  stock.data$Month <- factor(
    month.abb[as.integer(format(stock.data$Date, "%m"))],
    levels = month.abb[1:3]
  )
  stock.data$Day <- as.integer(format(stock.data$Date, "%d"))
  stock.i <- classic2sym(
    stock.data,
    groupby = "customize",
    minData = stock.data$Low,
    maxData = stock.data$High
  )
  ggInterval_lineplot(stock.i, aes(y = V1, x = Day), barWidth = 0.6) +
    geom_point(aes(y = Close), shape = 21, fill = "#D95F02",
               color = "black", size = 1.6, stroke = 0.2) +
    coord_cartesian(xlim = c(1, 31), expand = FALSE) +
    facet_wrap(~Month, ncol = 1, scales = "free_y") +
    scale_x_continuous(breaks = c(1, 8, 15, 22, 29)) +
    labs(x = "Day of month", y = "Price") +
    ggthemes::theme_economist() +
    theme(strip.text = element_text(face = "bold"))
}

if (requireNamespace("TTR", quietly = TRUE)) {
  data("ttrc", package = "TTR")
  stock.data <- subset(
    ttrc[, c("Date", "Close", "Low", "High")],
    format(Date, "%Y-%m") %in% c("1985-01", "1985-02", "1985-03")
  )
  stock.data$Month <- factor(
    month.abb[as.integer(format(stock.data$Date, "%m"))],
    levels = month.abb[1:3]
  )
  stock.data$Day <- as.integer(format(stock.data$Date, "%d"))
  stock.i <- classic2sym(
    stock.data,
    groupby = "customize",
    minData = stock.data$Low,
    maxData = stock.data$High
  )
  ggInterval_lineplot(stock.i, aes(y = V1, x = Day), barWidth = 0.6) +
    geom_point(aes(y = Close), shape = 21, fill = "#D95F02",
               color = "black", size = 1.6, stroke = 0.2) +
    coord_cartesian(xlim = c(1, 31), expand = FALSE) +
    facet_wrap(~Month, ncol = 1, scales = "free_y") +
    scale_x_continuous(breaks = c(1, 8, 15, 22, 29)) +
    labs(x = "Day of month", y = "Price") +
    ggthemes::theme_economist() +
    theme(strip.text = element_text(face = "bold"))
}

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

Arguments

data

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

mydata <- ggInterval::facedata
ggInterval_MMplot(mydata, aes(x = AD))
ggInterval_MMplot(mydata, aes(x = AD, size = 3))
ggInterval_MMplot(mydata, plotAll = TRUE, scaleXY = "global") +
  theme_classic()
mydata <- ggInterval::facedata
ggInterval_MMplot(mydata, aes(x = AD))
ggInterval_MMplot(mydata, aes(x = AD, size = 3))
ggInterval_MMplot(mydata, plotAll = TRUE, scaleXY = "global") +
  theme_classic()

Vertices PCA for interval data

Description

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

Usage

ggInterval_PCA(data = NULL,mapping = aes(NULL),plot=TRUE,
                      concepts_group=NULL, poly = FALSE, adjust = TRUE,
                      showLabels = TRUE, labelSize = 3,
                      checkOverlap = FALSE)
ggInterval_PCA(data = NULL,mapping = aes(NULL),plot=TRUE,
                      concepts_group=NULL, poly = FALSE, adjust = TRUE,
                      showLabels = TRUE, labelSize = 3,
                      checkOverlap = FALSE)

Arguments

data

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

showLabels

Logical. If TRUE, label interval scores in the plot.

labelSize

Numeric text size used when showLabels = TRUE.

checkOverlap

Logical. Passed to geom_text() to suppress overlapping labels when needed.

Value

A ggplot object for PC1, PC2, and interval scores with related outputs.

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


Subjects <- substr(rownames(facedata), 1, 3)
p <- ggInterval_PCA(facedata, plot = FALSE, concepts_group = Subjects)
p$ggplotPCA
p$scores_interval
ggInterval_PCA(facedata, poly = TRUE, concepts_group = Subjects)


Subjects <- substr(rownames(facedata), 1, 3)
p <- ggInterval_PCA(facedata, plot = FALSE, concepts_group = Subjects)
p$ggplotPCA
p$scores_interval
ggInterval_PCA(facedata, poly = TRUE, concepts_group = Subjects)

Radar plot for symbolic data

Description

Visualize interval-valued and mixed symbolic data with a radar-style display. Interval-valued variables are represented through their lower and upper bounds, while modal multivalued variables can be shown with stacked bars. When type = "quantile", the function summarizes all observations of each interval-valued variable through nested empirical quantile bands.

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)
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

data

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

layerNumber

Number of concentric guide layers in the radar display.

inOneFig

Logical. If TRUE, draw all selected observations in one figure. Otherwise, generate separate figures for the selected observations.

showLegend

Logical. Whether to show the legend.

showXYLabs

Logical. Whether to show the x- and y-axis labels.

plotPartial

Numeric vector giving the row indices to plot. If NULL, all observations are used. This argument is ignored when type = "quantile" because the quantile plot summarizes all observations.

alpha

Alpha transparency for filled elements.

base_circle

Logical. If TRUE, add inner guide circles.

base_lty

Line type used in the base figure.

addText

Logical. Whether to add interval-valued labels to the plot.

type

Radar representation. Use "default" for polygon intervals, "rect" for rectangle intervals, or "quantile" for the quantile-radar display.

quantileNum

Number of quantile layers when type = "quantile".

Drift

Drift term controlling where the radar values begin.

addText_modal

Logical. Whether to add labels for modal multivalued variables.

addText_modal.p

Logical. Whether to add modal percentages.

Examples


# must specify plotPartial to the 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,
  type = "rect"
) +
  labs(title = "") +
  scale_fill_manual(values = c("gray50")) +
  scale_color_manual(values = c("gray50"))

ggInterval_radarplot(
  facedata,
  base_circle = FALSE,
  base_lty = 1,
  type = "quantile",
  quantileNum = 5,
  showLegend = TRUE,
  Drift = 0
) +
  scale_fill_brewer(palette = "Greys") +
  labs(title = "", fill = "Quantiles")


# must specify plotPartial to the 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,
  type = "rect"
) +
  labs(title = "") +
  scale_fill_manual(values = c("gray50")) +
  scale_color_manual(values = c("gray50"))

ggInterval_radarplot(
  facedata,
  base_circle = FALSE,
  base_lty = 1,
  type = "quantile",
  quantileNum = 5,
  showLegend = TRUE,
  Drift = 0
) +
  scale_fill_brewer(palette = "Greys") +
  labs(title = "", fill = "Quantiles")

Scatterplot matrix for interval-valued data

Description

Visualize all continuous interval-valued variables with a matrix of pairwise interval scatterplots. Each off-diagonal panel shows interval rectangles for one variable pair, whereas each diagonal panel displays the variable name. This function automatically filters out non-interval variables and plots all remaining continuous interval variables, so explicit x and y mappings are not required. It is not recommended to apply the function to too many variables because the full pairwise matrix becomes computationally expensive and visually crowded.

Usage

ggInterval_scatterMatrix(data = NULL, mapping = aes(NULL),
showLegend = FALSE, borderLinewidth = 0.08)
ggInterval_scatterMatrix(data = NULL, mapping = aes(NULL),
showLegend = FALSE, borderLinewidth = 0.08)

Arguments

data

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

borderLinewidth

Numeric border width used for the interval rectangles.

Value

Return a ggplot2 object.

Examples

mydata <- ggInterval::facedata
ggInterval_scatterMatrix(mydata[, 1:3])
ggInterval_scatterMatrix(
  mydata[, 1:3],
  aes(fill = "black", alpha = 0.2),
  borderLinewidth = 0.15
)
mydata <- ggInterval::facedata
ggInterval_scatterMatrix(mydata[, 1:3])
ggInterval_scatterMatrix(
  mydata[, 1:3],
  aes(fill = "black", alpha = 0.2),
  borderLinewidth = 0.15
)

Scatter plot for two continuous interval variables

Description

Visualize the distribution of two continuous interval-valued variables with rectangles whose widths and heights represent the corresponding intervals.

Usage

ggInterval_scatterplot(data = NULL,mapping = aes(NULL),
showLabels = TRUE, labelSize = 3,
labelPosition = "topright", labelNudgeX = 0, labelNudgeY = 0,
checkOverlap = FALSE, ...)
ggInterval_scatterplot(data = NULL,mapping = aes(NULL),
showLabels = TRUE, labelSize = 3,
labelPosition = "topright", labelNudgeX = 0, labelNudgeY = 0,
checkOverlap = FALSE, ...)

Arguments

data

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

showLabels

Logical. If TRUE, add row labels to the rectangles.

labelSize

Numeric text size used when showLabels = TRUE.

labelPosition

Character label position used when showLabels = TRUE. One of "topright", "topleft", "bottomright", or "bottomleft".

labelNudgeX

Numeric horizontal adjustment applied to label positions.

labelNudgeY

Numeric vertical adjustment applied to label positions.

checkOverlap

Logical. Passed to geom_text() to suppress overlapping labels when needed.

...

Others in ggplot2.

Value

Return a ggplot2 object.

Examples

Subjects <- substr(rownames(facedata), 1, 3)
ggInterval_scatterplot(facedata, aes(x = AD, y = BC))
ggInterval_scatterplot(
  facedata,
  aes(x = AD, y = BC, fill = Subjects),
  showLabels = TRUE,
  labelSize = 2.6,
  labelPosition = "topright",
  labelNudgeX = 0.8,
  labelNudgeY = 0.15,
  checkOverlap = TRUE,
  col = "black"
)
p <- ggInterval_scatterplot(facedata[1:10, ], aes(x = AD, y = BC, alpha = 0.2))
p + scale_fill_manual(
  labels = rownames(facedata)[1:10],
  values = rainbow(10),
  name = "Group"
)
Subjects <- substr(rownames(facedata), 1, 3)
ggInterval_scatterplot(facedata, aes(x = AD, y = BC))
ggInterval_scatterplot(
  facedata,
  aes(x = AD, y = BC, fill = Subjects),
  showLabels = TRUE,
  labelSize = 2.6,
  labelPosition = "topright",
  labelNudgeX = 0.8,
  labelNudgeY = 0.15,
  checkOverlap = TRUE,
  col = "black"
)
p <- ggInterval_scatterplot(facedata[1:10, ], aes(x = AD, y = BC, alpha = 0.2))
p + scale_fill_manual(
  labels = rownames(facedata)[1:10],
  values = rainbow(10),
  name = "Group"
)

iris.i data example

Description

iris.i interval data example.

Usage

data(iris.i)
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))
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)
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))
data(mtcars.i)
ggInterval_indexplot(mtcars.i, aes(x = mpg))

mushroom data example

Description

mushroom interval data example.

Usage

data(mushroom)
data(mushroom)

Format

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

Source

Adapted from RSDA::mushroom; see https://CRAN.R-project.org/package=RSDA.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Examples

data(mushroom)

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

data(mushroom)

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

oils data example

Description

oils interval data example.

Usage

data(oils)
data(oils)

Format

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

Source

Adapted from RSDA::oils; see https://CRAN.R-project.org/package=RSDA.

References

Cazes, Pierre; Chouakria, Assia; Diday, Edwin; and Schektman, Youri (1997). Extension de l'analyse en composantes principales a des donnees de type intervalle. Revue de Statistique Appliquee, 45(3), 5–24.

Examples

data(oils)

ggInterval_scatterplot(oils, aes(x = GRA, y = IOD))

data(oils)

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

Usage

RSDA2sym(data=NULL,rawData=NULL)
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 "ggInterval", which has interval-valued data and related outputs 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

r<-ggInterval::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, ...)
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 ggInterval object. It can also be either RSDA object or classical data frame, which will be automatically convert to ggInterval 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 nmeric-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 ggInterval object.

Examples


#For all interval-valued
scale(facedata)

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

#For all interval-valued
scale(facedata)

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

Generic function for the standard deviation

Description

This function compute the symbolic standard deviation.

Usage

sd(x, ...)

## Default S3 method:
sd(x, na.rm = FALSE, ...)

## S3 method for class 'symbolic_tbl'
sd(
  x,
  method = c("billard", "centers", "interval", "B", "BD", "BG"),
  na.rm = FALSE,
  ...
)

## S3 method for class 'symbolic_interval'
sd(
  x,
  method = c("billard", "centers", "interval", "B", "BD", "BG"),
  na.rm = FALSE,
  ...
)
sd(x, ...)

## Default S3 method:
sd(x, na.rm = FALSE, ...)

## S3 method for class 'symbolic_tbl'
sd(
  x,
  method = c("billard", "centers", "interval", "B", "BD", "BG"),
  na.rm = FALSE,
  ...
)

## S3 method for class 'symbolic_interval'
sd(
  x,
  method = c("billard", "centers", "interval", "B", "BD", "BG"),
  na.rm = FALSE,
  ...
)

Arguments

x

First symbolic variables.

...

As in R sd function.

na.rm

As in R sd function.

method

The method to use.

Details

Supported interval-valued methods are:

"billard": standard deviation based on the Billard univariate variance formula.
"centers": standard deviation of interval centers.
"interval": interval-valued standard deviation obtained by standardizing lower and upper bounds separately.
"B": method-matched standard deviation defined by $\sqrt{C_B(X, X)}$ .
"BD": method-matched standard deviation defined by $\sqrt{C_{BD}(X, X)}$ .
"BG": method-matched standard deviation defined by $\sqrt{C_{BG}(X, X)}$ .

Value

Return a real number.

References

Billard, Lynne and Diday, Edwin (2006). Symbolic Data Analysis: Conceptual Statistics and Data Mining. Chichester, UK: John Wiley and Sons.

Billard, Lynne (2008). Sample covariance functions for complex quantitative data. In Proceedings of the World IASC Conference, pp. 157–163, Yokohama, Japan.

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", ...)
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")


#For all interval-valued
summary(facedata)

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

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

Package 'ggInterval'

Help Index

abalone.i data example

Description

Usage

Format

Source

Examples

blood.i data example

Description

Usage

Format

Source

References

Examples

Cardiological data example

Description

Usage

Format

Source

References

Examples

Cardiological data example

Description

Usage

Format

Source

References

Examples

Convert classical data frame into a symbolic data.

Description

Usage

Arguments

Value

Examples

Generic function for the correlation

Description

Usage

Arguments

Details

Value

References

Generic function for the covariance

Description

Usage

Arguments

Details

Value

References

Environment data example

Description

Usage

Format

Examples

Face Data Example

Description

Usage

Format

Source

References

Examples

A symbolic object by R6 class for interval analysis and ggplot

Description

Public fields

Methods

Public methods

Method new()

Usage

Method clone()

Usage

Arguments

Visualize a 2-dimension histogram for interval-valued data

Description

Usage

Arguments

Value

Examples

2-Dimension histogram matrix

Description

Usage

Method `new()`

Method `clone()`