Abalone Dataset

Description

A interval-valued data set containing 24 units, created from from the Abalone dataset (UCI Machine Learning Repository), after aggregating by sex and age.

Usage

data(Abalone)

Format

An object of class data.frame with 24 rows and 14 columns.

References

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

Examples

data(Abalone)

Abalone iGAP format Dataset

Description

A interval-valued data set containing 24 units, created from from the Abalone dataset (UCI Machine Learning Repository), after aggregating by sex and age.

Usage

data(Abalone.iGAP)

Format

An object of class data.frame 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(Abalone.iGAP)

Cars Interval Dataset

Description

Cars Interval Dataset generated from Cars dataset. This data set consist of the intervals for four characteristics (Price, EngineCapacity, TopSpeed and Acceleration) of 27 cars models partitioned into four different classes (Utilitarian, Berlina, Sportive and Luxury).

Usage

data(Cars.int)

Format

A data frame containing 27 observations on 5 variables, the first five with the interval characteristics for 27 car models, the last one a factor indicating the model class.

Source

https://CRAN.R-project.org/package=MAINT.Data

Examples

data(Cars.int)

China Temperatures Interval Dataset

Description

China Temperatures Interval Dataset generated from ChinaTemp dataset. This data set consist of the intervals of observed temperatures (Celsius scale) in each of the four quarters, Q_1 to Q_4, of the years 1974 to 1988 in 60 chinese meteorologic stations; one outlier observation (YinChuan_1982) has been discarded. The 60 stations belong to different regions in China, which therefore define a partition of the 899 stations-year combinations.

Usage

data(ChinaTemp.int)

Format

A data frame containing 899 observations on 5 variables, the first four with the temperatures by quarter in the 899 stations-year combinations, the last one a factor indicating the geographic region of each station.

Source

https://CRAN.R-project.org/package=MAINT.Data

Examples

data(ChinaTemp.int)

Face iGAP format Dataset

Description

Symbolic data matrix with all the variables of interval type.

Usage

data(Face.iGAP)

Format

An object of class data.frame with 27 rows and 6 columns.

References

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

Examples

data(Face.iGAP)

Loans by purpose: Interval Dataset

Description

Loans by purpose interval dataset generated from LoansbyPurpose dataset. This data set consist of the lower and upper bounds of the intervals for four interval characteristics of the loans aggregated by their purpose. The original microdata is available at the Kaggle Data Science platform and consists of 887 383 loan records characterized by 75 descriptors. Among the large set of variables available, we focus on borrowers' income and account and loan information aggregated by the 14 loan purposes, wich are considered as the units of interest.

Usage

data(LoansbyPurpose.int)

Format

A data frame containing 14 observations on the following 4 variables:

• ln-inc: The current loan purpose of natural logarithm of the self-reported annual income provided by the borrower during registration

• ln-revolbal: The current loan purpose of natural logarithm of the total credit revolving balance

• open-acc: The current loan purpose of the number of open credit lines in the borrower's credit file

• total-acc: The current loan purpose, of the total number of credit lines currently in the borrower's credit file

Source

https://CRAN.R-project.org/package=MAINT.Data

Examples

data(LoansbyPurpose.int)

MM to iGAP

Description

To convert MM format to iGAP format.

Usage

MM_to_iGAP(data)

Arguments

Value

Return a dataframe with the iGAP format.

Examples

data(Face.iGAP)
Face <- iGAP_to_MM(Face.iGAP, 1:6)
MM_to_iGAP(Face)

RSDA Format

Description

This function changes the format of the data to conform to RSDA format.

Usage

RSDA_format(data, sym_type1 = NULL, location = NULL, sym_type2 = NULL, var = NULL)

Arguments

Value

Return a dataframe with a label added to the previous column of symbolic variable.

Examples

data("mushroom")
mushroom.set <- set_variable_format(data = mushroom, location = 8, var = "Species")
mushroom.tmp <- RSDA_format(data = mushroom.set, sym_type1 = c("I", "S"),
                            location = c(25, 31), sym_type2 = c("S", "I", "I"),
                            var = c("Species", "Stipe.Length_min", "Stipe.Thickness_min"))

RSDA to MM

Description

To convert RSDA format interval dataframe to MM format.

Usage

RSDA_to_MM(data, RSDA)

Arguments

Value

Return a dataframe with the MM format.

Examples

data(mushroom.int)
RSDA_to_MM(mushroom.int, RSDA = FALSE)

RSDA to iGAP

Description

To convert RSDA format interval dataframe to iGAP format.

Usage

RSDA_to_iGAP(data)

Arguments

Value

Return a dataframe with the iGAP format.

Examples

data(mushroom.int)
RSDA_to_iGAP(mushroom.int)

SODAS to MM

Description

To convert SODAS format interval dataframe to the MM format.

Usage

SODAS_to_MM(XMLPath)

Arguments

Value

Return a dataframe with the MM format.

Examples

## Not run:
data(Abalone)

SODAS to iGAP

Description

To convert SODAS format interval dataframe to the iGAP format.

Usage

SODAS_to_iGAP(XMLPath)

Arguments

Value

Return a dataframe with the iGAP format.

Examples

## Not run:
data(Abalone)

Age-cholesterol-weight Interval-Valued Dataset

Description

Age-cholesterol-weight Interval-Valued Dataset.

Usage

data(age_cholesterol_weight.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 7 rows and 4 columns.

References

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

Examples

data(age_cholesterol_weight.int)

Airline Flights Dataset

Description

Airline Flights Dataset.

Usage

data(airline_flights)

Format

An object of class data.frame with 16 rows and 17 columns.

References

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

Examples

data(airline_flights)

Airline Flights Modal-Valued Dataset

Description

Airline Flights Modal-Valued Dataset.

Usage

data(airline_flights2)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 16 rows and 6 columns.

References

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

Examples

data(airline_flights2)

Baseball Interval-Valued Dataset

Description

Baseball Interval-Valued Dataset.

Usage

data(baseball.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 19 rows and 3 columns.

References

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

Examples

data(baseball.int)

Bird Interval-Valued Dataset

Description

Bird Interval-Valued Dataset.

Usage

data(bird.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 20 rows and 2 columns.

References

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

Examples

data(bird.int)

Blood Pressure Interval-Valued Dataset

Description

blood pressure Interval-Valued Dataset.

Usage

data(blood_pressure.int)

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(blood_pressure.int)

Car Interval-Valued Dataset

Description

Car Interval-Valued Dataset.

Usage

data(car.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 8 rows and 5 columns.

References

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

Examples

data(car.int)

clean_colnames

Description

This function is used to clean up variable names to conform to the RSDA format.

Usage

clean_colnames(data)

Arguments

Value

Data after cleaning variable names.

Examples

data(mushroom)
mushroom.clean <- clean_colnames(data = mushroom)

Crime demographics Dataset

Description

Crime demographics Dataset.

Usage

data(crime)

Format

An object of class data.frame with 15 rows and 7 columns.

References

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

Examples

data(crime)

Crime demographics Modal-Valued Dataset

Description

Crime demographics Modal-Valued Dataset.

Usage

data(crime2)

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(crime2)

Finance Interval-Valued Dataset

Description

Finance Interval-Valued Dataset.

Usage

data(finance.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 14 rows and 7 columns.

References

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

Examples

data(finance.int)

Fuel Consumption Dataset

Description

Fuel Consumption Dataset.

Usage

data(fuel_consumption)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 10 rows and 3 columns.

References

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

Examples

data(fuel_consumption)

Health Insurance Dataset

Description

Health Insurance Dataset.

Usage

data(health_insurance)

Format

An object of class data.frame with 51 rows and 30 columns.

References

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

Examples

data(health_insurance)

Health Insurance Modal-Valued Dataset

Description

Health Insurance Modal-Valued Dataset.

Usage

data(health_insurance2)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 6 rows and 6 columns.

References

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

Examples

data(health_insurance2)

Hierarchy Dataset

Description

Hierarchy Dataset.

Usage

data(hierarchy)

Format

An object of class data.frame with 20 rows and 6 columns.

References

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

Examples

data(hierarchy)

Hierarchy Interval-Valued Dataset

Description

Hierarchy Interval-Valued Dataset.

Usage

data(hierarchy.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 20 rows and 6 columns.

References

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

Examples

data(hierarchy.int)

Statistics for Histogram Data

Description

Functions to compute the mean, variance, covariance, and correlation of histogram-valued data.

Usage

hist_mean(x, var_name, method = "BG", ...)

hist_var(x, var_name, method = "BG", ...)

hist_cov(x, var_name1, var_name2, method = "BG")

hist_cor(x, var_name1, var_name2, method = "BG")

Arguments

Details

...

Value

A numeric value: the mean, variance, covariance, or correlation.

Author(s)

Po-Wei Chen, Han-Ming Wu

See Also

int_mean int_var int_cov int_cor

Examples

library(HistDAWass)

Horses Interval-Valued Dataset

Description

Horses Interval-Valued Dataset.

Usage

data(horses.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 8 rows and 7 columns.

References

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

Examples

data(horses.int)

iGAP to MM

Description

To convert iGAP format to MM format.

Usage

iGAP_to_MM(data, location)

Arguments

Value

Return a dataframe with the MM format.

Examples

data(Abalone.iGAP)
Abalone <- iGAP_to_MM(Abalone.iGAP, 1:7)

Statistics for Interval Data

Description

Functions to compute the mean, variance, covariance, and correlation of interval-valued data.

Usage

int_mean(x, var_name, method = "CM", ...)

int_var(x, var_name, method = "CM", ...)

int_cov(x, var_name1, var_name2, method = "CM", ...)

int_cor(x, var_name1, var_name2, method = "CM", ...)

Arguments

Details

...

Value

A numeric value: the mean, variance, covariance, or correlation.

Author(s)

Han-Ming Wu

See Also

int_mean int_var int_cov int_cor

Examples

data(mushroom.int)
int_mean(mushroom.int, var_name = "Pileus.Cap.Width")
int_mean(mushroom.int, var_name = 2:3)

var_name <- c("Stipe.Length", "Stipe.Thickness")
method <- c("CM", "FV", "EJD")
int_mean(mushroom.int, var_name, method)
int_var(mushroom.int, var_name, method)

var_name1 <- "Pileus.Cap.Width"
var_name2 <- c("Stipe.Length", "Stipe.Thickness")
method <- c("CM", "VM", "EJD", "GQ", "SPT")
int_cov(mushroom.int, var_name1, var_name2, method)
int_cor(mushroom.int, var_name1, var_name2, method)

Lack of information questionnaire interval dataset.

Description

Lack of information questionnaire interval dataset generated from lackinfo dataset. A dataset containing some biographical data and the responses to 5 items measuring the perception of lack of information in a questionnaire.

Usage

data(lackinfo.int)

Format

A data frame with 50 observations of the following 8 variables:

• id: identification number.

• sex: sex of the respondent (male or female).

• age: respondent's age (in years).

• item1: respondent's interval-valued answer to item 1.

• item2: respondent's interval-valued answer to item 2.

• item3: respondent's interval-valued answer to item 3.

• item4: respondent's interval-valued answer to item 4.

• item5: respondent's interval-valued answer to item 5.

Details

An educational innovation project was carried out for improving teaching-learning processes at the University of Oviedo (Spain) for the 2020/2021 academic year. A total of 50 students have been requested to answer an online questionnaire about some biographical data (sex and age) and their perception of lack of information by selecting the interval that best represents their level of agreement to the statements proposed in a interval-valued scale bounded between 1 and 7, where 1 represents the option 'strongly disagree' and 7 represents the option 'strongly agree'.

These are the 5 items used to measure the perception of lack of information:

• I1: I receive too little information from my classmates.

• I2: It is difficult to receive relevant information from my classmates.

• I3: It is difficult to receive relevant information from the teacher.

• I4: The amount of information I receive from my classmates is very low.

• I5: The amount of information I receive from the teacher is very low.

Source

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

Examples

data(lackinfo.int)

Mushroom Data Set

Description

The mushroom data set consists of a set of 23 species described by 3 interval variables. These mushroom species are members of the genus Agaricies. The specific variables and their values are extracted from the Fungi of California Species.

Usage

data(mushroom)

Format

A data frame with 23 observations and 5 variables named Species, Pileus Cap Width, Stipe Length, Stipe Thickness, and Edibility.

• Species: The class of mushroom.

• Pileus Cap Width: The pileus cap width of the mushroom.

• Stipe Length: The stipe length of the mushroom.

• Stipe Thickness: The stipe thickness of the mushroom.

• Edibility: The edibility of mushroom (U: unknown, Y: Yes, N: No, T: Toxic).

Source

Billard, L. and Diday, E. (2006) Symbolic Data Analysis: Conceptual Statistics and Data Mining John Wiley & Sons, Ltd.

References

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

Examples

data(mushroom)

Mushroom Interval Dataset

Description

Mushroom interval dataset generated from mushroom dataset. The mushroom data set consists of a set of 23 species described by 3 interval variables. These mushroom species are members of the genus Agaricies. The specific variables and their values are extracted from the Fungi of California Species.

Usage

data(mushroom.int)

Format

A data frame with 23 observations and 5 variables named Species, Pileus Cap Width, Stipe Length, Stipe Thickness, and Edibility.

• Species: The class of mushroom.

• Pileus Cap Width: The pileus cap width of the mushroom.

• Stipe Length: The stipe length of the mushroom.

• Stipe Thickness: The stipe thickness of the mushroom.

• Edibility: The edibility of mushroom (U: unknown, Y: Yes, N: No, T: Toxic).

Source

Billard, L. and Diday, E. (2006) Symbolic Data Analysis: Conceptual Statistics and Data Mining John Wiley & Sons, Ltd.

References

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

Examples

data(mushroom.int)

New York City flights Interval Dataset

Description

New York City flights interval dataset generated from nycflights dataset. A interval-valued data set containing 142 units and four interval-valued variables (dep_delay, arr_delay, air_time and distance), created from from the flights data set in the R package nycflights13 (on-time data for all flights that departed the JFK, LGA or EWR airports in 2013), after removing all rows with missing observations, and aggregating by month and carrier.

Usage

data(nycflights.int)

Format

FlightsDF

A data frame containing the original 327346 valid (i.e. with non missing values) flights from the nycflights13 package, described by the 4 variables: dep_delay, arr_delay, air_time and distance.

FlightsUnits

A factor with 327346 observations and 142 levels, indicating the month by carrier combination to which each orginal flight belongs to.

FlightsIdt

An IData object with 142 observations and 4 interval-valued variables, describing the intervals formed by agregating the FlightsDF microdata by the 0.05 and 0.95 quantiles of the subsamples formed by FlightsUnits factor.

Source

https://CRAN.R-project.org/package=MAINT.Data

References

Duarte Silva, A. P., Brito, P., Filzmoser, P., & Dias, J. G. (2021). MAINT. Data: Modelling and Analysing Interval Data in R. R Journal, 13(2).

Examples

data(nycflights.int)

Occupation Salaries Dataset

Description

Occupation Salaries Dataset.

Usage

data(occupations)

Format

An object of class data.frame with 9 rows and 11 columns.

References

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

Examples

data(occupations)

Occupation Salaries Modal-Valued Dataset

Description

Occupation Salaries Modal-Valued Dataset.

Usage

data(occupations)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 9 rows and 4 columns.

References

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

Examples

data(occupations2)

30 year trimmed mean daily temperatures interval dataset for the Ohio river basin.

Description

30 year trimmed mean daily temperatures interval dataset for the Ohio river basin generated from ohtemp dataset. Intervals are defined by the mean daily maximum and minimum temperatures for the Ohio river basin from January 1, 1988 - December 31, 2018. The 116 observations in this dataset all had at least 300 daily observations of temperature in at least 30 of the 31 considered years. The mean was calculated after trimming 10 influence of potential outliers.

Usage

data(ohtemp.int)

Format

A data frame with 161 rows and 7 variables:

• ID: The global historical climatological network (GHCN) station identifier

• NAME: The GHCN station name

• STATE: The two-digit designation for the state in which each station resides

• LATITUDE: Latitude coordinate position

• LONGITUDE: Longitude coordinate position

• ELEVATION: Elevation of the measurement location (meters)

• TEMPERATURE: The 30 year mean daily temperature (tenths of degrees Celsius)

Source

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

Examples

data(ohtemp.int)

Profession Work Salary Time Interval-Valued Dataset

Description

Profession Work Salary Time Interval-Valued Dataset.

Usage

data(profession.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 15 rows and 4 columns.

References

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

Examples

data(profession.int)

Set Variable Format

Description

This function changes the format of the set variables in the data to conform to the RSDA format.

Usage

set_variable_format(data, location, var)

Arguments

Value

Return a dataframe in which a set variable is converted to one-hot encoding.

Examples

data("mushroom")
mushroom.set <- set_variable_format(data = mushroom, location = 8, var = "Species")

Soccer bivar Interval Data Set

Description

Soccer bivar interval dataset generated from soccer.bivar dataset. A real interval-valued data set.

Usage

soccer.bivar.int

Format

A data frame with 20 rows and 3 variables:

• y: The response variable Y (weight)

• t1: The explanatory variable T1 (height)

• t2: The explanatory variable T2 (age)

Details

This data set concerns the record of the Weight (Y), Height (T1) and Age (T2) from 20 soccer teams of the premiere French championship.

Source

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

References

Lima Neto, E. A., Cordeiro, G. and De Carvalho, F.A.T. (2011). Bivariate symbolic regression models for interval-valued variables. Journal of Statistical Computation and Simulation (Print), 81, 1727–1744.

Examples

data(soccer.bivar.int)

Veterinary Interval-Valued Dataset

Description

Veterinary Interval-Valued Dataset.

Usage

data(veterinary.int)

Format

An object of class symbolic_tbl (inherits from tbl_df, tbl, data.frame) with 10 rows and 3 columns.

References

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

Examples

data(veterinary.int)

Write Symbolic Data Table

Description

This function write (save) a symbolic data table from a CSV data file.

Usage

write_csv_table(data, file, output)

Arguments

Value

Write in CSV file the symbolic data table.

Examples

data(mushroom)
mushroom.set <- set_variable_format(data = mushroom, location = 8, var = "Species")
mushroom.tmp <- RSDA_format(data = mushroom.set, sym_type1 = c("I", "S"),
                            location = c(25, 31), sym_type2 = c("S", "I", "I"),
                            var = c("Species", "Stipe.Length_min", "Stipe.Thickness_min"))
mushroom.clean <- clean_colnames(data = mushroom.tmp)
# We can save the file in CSV to RSDA format as follows:
write_csv_table(data = mushroom.clean, file = "mushroom_interval.csv", output = FALSE)