Descriptive Statistics ''

Estimate and return the needed parameters for visualizations designed for '' <> datasets. Calculate descriptive statistical measures in budget data of municipalities across Europe, according to the '' data model. There are functions for measuring central tendency and dispersion of amount variables along with their distributions and correlations and the frequencies of categorical variables for a given dataset. Also, can be used generally to extract visualization parameters, convert them to 'JSON' format and use them as input in a different graphical interface.

This package can generally be used to extract visualization parameters convert them to JSON format and use them as input in a different graphical interface. Most functions can have general use out of the data model. You can see detailed information here. install.packages(DescriptiveStats.OBeu) # or # alternatively install the development version from github devtools::install_github("okgreece/DescriptiveStats.OBeu")

Load library DescriptiveStats.OBeu


Descriptive Statistics in a call

ds.analysis is used to estimate minimum, maximum, range, mean, median, first and third quantiles, variance, standart deviation, skewness and kurtosis, boxplot, histogram parameters needed for visualization of numeric variables and frequencies of factor variables of a given vector, matrix or data frame of data.

ds.analysis returns by default a list object, we set tojson parameter TRUE, outliers parameter FALSE, = "Produktbereich". Ξ€here is one numeric variable, correlation will be empty.

wuppertalanalysis = ds.analysis(Wuppertal_df,outliers=FALSE, = "Produktbereich", tojson=TRUE) # json string format
jsonlite::prettify(wuppertalanalysis) # use prettify of jsonlite library to add indentation to the returned JSON string

ds.analysis uses internally the functions ds.statistics,ds.hist,ds.boxplot,ds.correlation and ds.frequency. However, these functions can be used independently and depends on the user requirements (see package manual or vignettes).

Descriptive Statistics on platform

open_spending.ds is designed to estimate and return the basic descriptive measures, the correlation and the boxplot parameters of all the numerical variables and the frequencies of all factor variables of datasets.

The input data must be a JSON link according to the data model. There are different parameters that a user could specify, e.g. dimensions, measured.dimensions and amounts should be defined by the user, to form the dimensions of the dataset. Then the basic descriptive measures of tendency and spread, boxplot and histogram parameters are estimated in order to describe and visualize the distribution characteristics of the desired dataset.

open_spending.ds estimates and returns the json data that are described with the data model, using ds.analysis function.

descript = open_spending.ds(
  json_data =  Wuppertal_openspending, 
  dimensions ="functional_classification_3.Produktgruppe|date_2.Year",
  amounts = "Amount"
# Pretty output using prettify of jsonlite library
jsonlite::prettify(descript,indent = 2)


DescriptiveStats.OBeu v0.2.0

First stable version of the library.

DescriptiveStats.OBeu v1.2.0

Add openspending.ds function to be used in tools.

Reference manual

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1.3.1 by Kleanthis Koupidis, a month ago

Report a bug at

Browse source code at

Authors: Kleanthis Koupidis [aut, cre] , Aikaterini Chatzopoulou [aut] , Charalampos Bratsas [aut]

Documentation:   PDF Manual  

GPL-2 | file LICENSE license

Imports dplyr, graphics, grDevices, jsonlite, magrittr, RCurl, reshape, stats

Suggests curl, knitr, rmarkdown

See at CRAN