DescriptiveStats.OBeu
is used on OpenBudgets.eu data mininig tool platform with OpenCPU integration of R and JavaScript to estimate central tendency and dispersion of numeric variables along with their distributions and correlations and the frequencies of categorical dimensions for budget or expenditure datasets of Municipality across Europe.
The vignette shows the way DescriptiveStats.OBeu
(in R and OpenCPU environment) is fitted with datasets of OpenBudgets.eu according to the OpenBudgets.eu data model. Detailed documentation about OpenBudgets.eu data model can be found here
The input and the resulted object are in json format.
First you have to load the library
open_spending.ds
is designed to estimate and return the basic descriptive measures, correlation, histogram and boxplot parameters of all the numerical variables and the frequencies of all the nominal variables of OpenBudgets.eu datasets.
The input data must be a JSON link that are described with the OpenBudgets.eu 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. 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.
Input  Description 

json_data  The json string, URL or file from Open Spending API 
dimensions  The dimensions of the input data 
amounts  The measures of the input data 
measured.dimensions  The dimensions to which correspond amount/numeric variables 
coef.outl  Determines the length of the “whiskers” plot. Default is 1.5. 
box.outliers  If TRUE the outliers will be computed at the selected “coef.outl” level 
box.width  The width level is determined 0.15 times the square root of the size of the input data. 
cor.method  The correlation coefficient method to compute: “pearson” (default),“kendall” or “spearman”. 
freq.select  One or more nominal variables to calculate their corresponding frequencies. 
DescriptiveStats.OBeu includes functions that automatically calculates the central tendency and spread measures, the boxplot, histogram and barplot visualization parameters and the correlation matrix of the input fiscal dataset.
The final returns are the parameters needed for forming summary tables of central tendency and dispersion measures and visualizing boxplot, histogram, barplot and correlation matrix of the input data.
Central Tendency Measures describe the central position of a distribution for a group of data. The basic measures are the mean and the median.
Dispersion measures describe how similar or varied the data are. The range, quartiles and the interquartile range, variance and standard deviation are measures of spread.
The output of this process is a list in json format divided into four components of parameters and results with the first subcomponents.
Here is a sort description of the outputs in each function:
Function  Output  Description 

statistics 


boxplot 


histogram 


frequencies 


correlation 


The dataset that is being used is available as json link in DescriptiveStats.OBeu package and represents the budget for Wuppertal for 2009 to 2020.
open_spending.ds
function’s input are data as json link and described with OpenBudgets.eu data model.
Go to: yourserver/ocpu/test
Copy and paste the following function to the endpoint
Post
Click add parameters every time you want to add a new parameters and values.
Define the input data:
json_data
"http://next.openspending.org/api/3/cubes/21260d070eb5d63a121ea4c400dafbbb:apbn_fungsi_2016/facts?pagesize=20"
(or any other json URL with the data)Define the dimensions parameter:
dimensions
"functional_classification_2.Functionfunctional_classification_2.Code"
Define the amount parameter:
amounts
"Revised"
You add likewise further parameters and change the default parameters of coef.outl
, box.outliers
, box.wdth
, cor.method
, see DesciptiveStats.OBeu reference manual for further details.
copy the /ocpu/tmp/{this_id_number}/R/.val (second on the right panel)
finally, paste yourserver/ocpu/tmp/{this_id_number}/R/.val
on a new tab.