Coefficient of variation

Description

Extract and return a data frame with the columns that include only numeric values

Usage

CV(x)

Arguments

Value

This function returns a vector with the coefficient of variance for the input vector,matrix or data frame.

Author(s)

Kleanthis Koupidis

Wuppertal Fiscal Data extracted from Open Spending API

Description

This dataset contains the budget of wuppertal for 2009 to 2020

• The product ID

• The account type

• The kind

• The year these amounts were measaured

• The amount

• The product area ID

• The product group ID

• The product

• The product area

• The product group

Format

A data frame with the previous characteristics as columns

Source

http://next.openspending.org/api/3/cubes/4b6d969e07ef7a86aa54e539fc127a14:wuppertalhaushalt/facts

Wuppertal Fiscal Data extracted from Open Spending API

Description

This dataset contains the budget of wuppertal for 2009 to 2020

• The product ID

• The account type

• The kind

• The year these amounts were measaured

• The amount

• The product area ID

• The product group ID

• The product

• The product area

• The product group

Format

A link with the json format data

Source

http://next.openspending.org/api/3/cubes/4b6d969e07ef7a86aa54e539fc127a14:wuppertalhaushalt/facts

Group and compare summaries statistics to a data frame

Description

Extract and return a data frame with the columns that include only numeric values

Usage

compare.stats(df, group_var, values, m_functions)

Arguments

Value

This function returns a data frame with the selected group_vars and the result of m_functions applied in the selected values.

Author(s)

Kleanthis Koupidis

Calculation of some Descriptive Tasks

Description

The function calculates the basic descriptive measures, the correlation and the boxplot parameters of all the numerical variables and the frequencies of all the nominal variables.

Usage

ds.analysis(data, c.out = 1.5, box.width = 0.15, outliers = TRUE, hist.class = "Sturges", 
corr.method = "pearson", fr.select = NULL, tojson = FALSE)

Arguments

Details

This function returns a list with the basic statistics, the parameters needed to visualize a boxplot and a histogram, it also provides the frequencies of non numerical data of the input dataset and the correlation coefficient. The input of this function can be a matrix or data frame.

Value

A list or json file with the following components:

• descriptives The descriptive measures

• boxplot The statistics of the boxplot

• histogram The histogram parameters

• frequencies The frequencies and the relative frequencies of factors/characters of the input dataset

• correlation The correlation coefficient

Author(s)

Kleanthis Koupidis, Charalampos Bratsas

See Also

open_spending.ds

Examples

# iris data frame as input with the default parameters
ds.analysis(iris)

# using iris data frame with different parameters
ds.analysis(iris, c.out = 1, box.width = 0.20, outliers = TRUE, tojson = TRUE)

# using iris data frame with different parameters 
# fr.select parameter specified as Species
ds.analysis(iris, c.out = 1, outliers = FALSE, fr.select = "Species", tojson = TRUE)

# OpenBudgets.eu Dataset Example:
ds.analysis(Wuppertal_df, c.out = 2, box.width = 0.15, 
outliers = FALSE, tojson = FALSE)
                

Boxplot Parameters of a numeric vector

Description

This function calculates the statistical measures needed to visualize the boxplot of a numeric vector.

Usage

ds.box(x, c = 1.5, c.width = 0.15 , out = TRUE, tojson = FALSE)

Arguments

Details

This function returns a list with the parameters needed to visualize a boxplot.

Value

Returns a list or a json file with the following components:

• lo.whisker The extreme of the lower whisker

• lo.hinge The lower "hinge"

• median The median

• up.hinge The upper "hinge"

• up.whisker The extreme of the upper whisker

• box.width The width of the box (default is 0.15 times the square root of the size of the vector)

• lo.out The values of any data points which lie below the extreme of the lower whisker

• up.out The values of any data points which lie above the extreme of the upper whisker

• n The non-NA observations of the vector

Author(s)

Kleanthis Koupidis, Charalampos Bratsas

See Also

ds.analysis, open_spending.ds

Examples

# with vector as an input and the default parameters
vec <- as.vector(iris$Sepal.Width)
ds.box(vec)

# with vector as an input and the different parameters
vec <- as.vector(iris$Sepal.Width)
ds.box(vec, c = 3, c.width = 0.20 , out = FALSE, tojson = FALSE)

# OpenBudgets.eu Dataset Example:
amounts <- as.vector(Wuppertal_df$Amount)
ds.box(amounts, c = 1.5, c.width = 0.20, out = TRUE)

Boxplot Parameters of a matrix or data frame

Description

This function calculates the statistics of the boxplot for the input matrix or data frame.

Usage

ds.boxplot(data, out.level = 1.5, width = 0.15 , outl = TRUE, tojson = FALSE)

Arguments

Details

This function returns as a list object the statistical parameters needed to visualize boxplot.

Value

Returns a list with the extracted components of ds.box for each variable/column of the input data.

Author(s)

Aikaterini Chatzopoulou, Kleanthis Koupidis

See Also

ds.box, ds.analysis, open_spending.ds

Examples

# with matrix as an input and the default parameters
Matrix <- cbind(Uni05 = (1:200)/21, Norm = rnorm(200),
         `5T` = rt(200, df = 5), Gam2 = rgamma(200, shape = 2))
ds.boxplot(Matrix, out.level = 1.5, width = 0.15 , outl = TRUE, tojson = FALSE)

# iris data frame as an input, different parameters and json output
ds.boxplot(iris, out.level = 2, width = 0.25 , outl = FALSE, tojson = TRUE)

# OpenBudgets.eu Dataset Example:
ds.boxplot(Wuppertal_df$Amount, out.level = 2.5, width = 0.15, 
outl = TRUE, tojson = FALSE)
       

Correlation Coefficient of a dataframe

Description

This functions calculates the correlation coefficient of the input vectors, matrix or data frame. By default, the correlation coefficient of pearson is computed.

Usage

ds.correlation(x, y = NULL, cor.method = "pearson", tojson = FALSE)

Arguments

Details

This function returns an upper triangle matrix with the correlation coefficients of the input data. The correlation coefficient of pearson is computed, by default. Other options are "kendall" or "spearman".

Author(s)

Aikaterini Chatzopoulou, Kleanthis Koupidis, Charalampos Bratsas

See Also

ds.analysis, open_spending.ds

Examples

# iris data frame as an input and the default parameters
ds.correlation(iris, cor.method = "pearson", tojson = FALSE)

# with matrix as an input , different parameters and json output
Matrix <- cbind(Uni05 = (1:200)/21, Norm = rnorm(200),
         `5T` = rt(200, df = 5), Gam2 = rgamma(200, shape = 2))
ds.correlation(Matrix, cor.method = "kendall", tojson = TRUE)

Barplot parameters

Description

This function calculates the frequencies and the relative frequencies of factors/characters of the input dataset.

Usage

ds.frequency(data, select = NULL, tojson = FALSE)

Arguments

Details

This function returns a list with the frequencies and relative frequencies of factors/characters of the input dataset.

Author(s)

Kleanthis Koupidis, Charalampos Bratsas

See Also

ds.analysis, open_spending.ds

Examples

# iris data frame as an input and a selected column to calculate its frequencies
ds.frequency(iris, select = "Species", tojson = FALSE)

# iris data frame as an input without a selected column and json output
ds.frequency(iris, tojson = TRUE)

# OpenBudgets.eu Dataset Example:
ds.frequency(Wuppertal_df, select = "Produkt", tojson = FALSE)

Histogram breaks and frequencies

Description

This function computes the histogram parameters of the numeric input vector. The default for breaks is the value resulted from Sturges algorithm.

Usage

ds.hist(x, breaks = "Sturges", tojson = FALSE)

Arguments

Details

The possible values for breaks are Sturges see nclass.Sturges, Scott see nclass.scott and FD or Freedman Diaconis nclass.FD which are in package grDevices.

Value

A list or json file with the following components:

• cuts The boundaries of the histogram classes

• density The density of each histogram class

• normal.curve.x Abscissa of the normal curve

• normal.curve.y Ordinate of the normal curve

• fit.line.x Abscissa of the data density curve

• fit.line.y Ordinate of the data density curve

• mean The average value of the input vector

• median The median value of the input data

Author(s)

Kleanthis Koupidis, Charalampos Bratsas

See Also

ds.analysis, open_spending.ds

Examples

# with a vector as an input and the defaults parameters
vec <- as.vector(iris$Sepal.Width)
ds.hist(vec)

# OpenBudgets.eu Dataset Example:
ds.hist(Wuppertal_df$Amount, tojson = TRUE)

Calculation of Kurtosis

Description

This function calculates kurtosis of the input vector, matrix or data frame.

Usage

ds.kurtosis(x, tojson = FALSE)

Arguments

Details

This function returns the kurtosis, based on a scaled version of the fourth moment, of numbers of the input data.

Author(s)

Aikaterini Chatzopoulou, Charalampos Bratsas

See Also

ds.skewness, ds.statistics, ds.analysis, open_spending.ds

Examples

# with a matrix as an input
Matrix <- cbind(Uni05 = (1:200)/21, Norm = rnorm(200),
        `5T` = rt(200, df = 5), Gam2 = rgamma(200, shape = 2))
ds.kurtosis(Matrix, tojson = FALSE)

# with iris data frame as an input
ds.kurtosis(iris, tojson = FALSE)

# with a vector as an input and json output
vec <- as.vector(iris$Sepal.Width)
ds.kurtosis(vec, tojson = TRUE)

# OpenBudgets.eu Dataset Example:
ds.kurtosis(Wuppertal_df, tojson = FALSE)

Calculation of Skewness

Description

This function calculates skewness of the input vector, matrix or data frame.

Usage

ds.skewness(x, tojson = FALSE)

Arguments

Details

This function returns the skewness, also known as Pearson's moment coefficient of skewness, of numbers of the input data.

Author(s)

Aikaterini Chatzopoulou

See Also

ds.kurtosis, ds.statistics, ds.analysis, open_spending.ds

Examples

# with a matrix as an input
Matrix <- cbind(Uni05 = (1:200)/21, Norm = rnorm(200),
        `5T` = rt(200, df = 5), Gam2 = rgamma(200, shape = 2))
ds.skewness(Matrix, tojson = FALSE)

# with iris data frame as an input
ds.skewness(iris, tojson = FALSE)

# with a vector as an input and json output
vec <- as.vector(iris$Sepal.Width)
ds.skewness(vec, tojson = TRUE)

# OpenBudgets.eu Dataset Example:
ds.skewness(Wuppertal_df, tojson = FALSE)

Calculation of the Statistic Measures

Description

This function calculates the basic descriptive measures of the input dataset.

Usage

ds.statistics(data, tojson = FALSE)

Arguments

Details

This function returns the following values of the input data: minimum, maximum, range, mean, median, first and third quantiles, variance, standart deviation, skewness and kurtosis.

Value

A list or json file with the following components:

• Min The minimum observed value of the input data

• Max The maximum observed value of the input data

• Range The range, defined as the difference of the maximum and the minimum value.

• Mean The average value of the input data

• Median The median value of the input data

• Quantiles The 25% and 75% percentiles

• Variance The variance of the input data

• Standard Deviation The standard deviation of the input data

• Skewness The Skewness of the input data

• Kurtosis The Kurtosis of the input data

Author(s)

Aikaterini Chatzopoulou, Kleanthis Koupidis, Charalampos Bratsas

See Also

open_spending.ds

Examples

# with matrix as an input and json outpout
Matrix <- cbind(Uni05 = (1:200)/21, Norm = rnorm(200),
        `5T` = rt(200, df = 5), Gam2 = rgamma(200, shape = 2))
ds.statistics(Matrix, tojson = TRUE)

# with vector as an input
vec <- as.vector(iris$Sepal.Width)
ds.statistics(vec, tojson = FALSE)

# with iris data frame as an input
ds.statistics(iris, tojson = FALSE)

# OpenBudgets.eu Dataset Example:
ds.statistics(Wuppertal_df$Amount, tojson = TRUE)

Multiple replacement

Description

Extract and return a data frame with the columns that include only numeric values

Usage

multisub(pattern, replacement, x, ...)

Arguments

Value

This function returns a character vector with the replacements.

Author(s)

Kleanthis Koupidis

Select the numeric columns of a given dataset

Description

Extract and return a data frame with the columns that include only numeric values

Usage

nums(data)

Arguments

Value

This function returns a data frame with the numeric columns of the input dataset.

Author(s)

Kleanthis Koupidis

Examples

# with data frame as input
nums(iris)

# with vector as input
vec <- as.vector(iris$Sepal.Width)
nums(vec)

# with matrix as input
Matrix <- cbind(Uni05 = (1:200)/21, Norm = rnorm(200),
        `5T` = rt(200, df = 5), Gam2 = rgamma(200, shape = 2))
nums(Matrix)

# OpenBudgets.eu Dataset Example:
head(nums(Wuppertal_df))

Read and Calculate the Basic Information for Basic Descriptive Tasks from Open Spending and Rudolf APIs.

Description

Extract and analyze the input data provided from Open Spending API of OpenBudgets.eu, using the ds.analysis function.

Usage

open_spending.ds(json_data, dimensions = NULL, amounts = NULL, 
measured.dimensions = NULL, coef.outl = 1.5, box.outliers = TRUE, 
box.wdth = 0.15, cor.method = "pearson", freq.select = NULL)

Arguments

Details

This function is used to read data in json format from Open Spending and Rudolf APIs., in order to implement some basic descriptive tasks through ds.analysis function.

Value

A json string with the resulted parameters of the ds.analysis function.

Author(s)

Kleanthis Koupidis

See Also

ds.analysis

Examples

# OpenBudgets.eu Dataset Example:
# open_spending.ds(json_data = Wuppertal_openspending, 
  #    dimensions ="functional_classification_3.Produktgruppe|date_2.Year",
  #    amounts = "Amount")
                

Sample data from Open Spending

Description

Sample data of Revised Budget phase amounts

• The year (2016) of the recorded approved budget phase amounts

• The revised budget phase amounts of 2016

• The original amounts of this year

• The functional classification description

• The functional classification code

Format

A link with the json format data

Source

http://next.openspending.org/