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DescriptiveStats.OBeu v1.2.1 release on CRAN

DescriptiveStats.OBeu v1.2.1 release on CRAN

We are very pleased to announce DescriptiveStats.OBeu v1.2.1 on CRAN!

DescriptiveStats.OBeu is used on OpenBudgets.eu data mininig tool platform 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 datasets on OpenBudgets.eu are described with the OpenBudgets.eu data model. Detailed documentation about OpenBudgets.eu data model can be found here

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 OpenBudgets.eu data model.

First install DescriptiveStats.OBeu

install.packages(DescriptiveStats.OBeu) # or# alternatively install the development version from github
devtools::install_github("okgreece/DescriptiveStats.OBeu")

and load the library

library(DescriptiveStats.OBeu)

Descriptive Statistics on OpenBudgets.eu tool platform

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.

open_spending.ds input
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.

Output

The output is a list in json format divided into four components of parameters and results with the first subcomponents.

Here is a sort description of these components:

open_spending.ds return
Function Output Description

statistics

  • Min
  • Max
  • Range
  • Mean
  • Median
  • Quantiles
  • Variance
  • StandardDeviation
  • Skewness
  • Kurtosis
  • The minimum observed value of the input data
  • The maximum observed value of the input data
  • The difference between maximum and minimum
  • The average value of the input data
  • The median value of the input data
  • The 25%, 75% percentiles
  • The variance of the input data
  • The standard deviation of the input data
  • The Skewness of the input data
  • The Kurtosis of the input data

boxplot

  • lo.whisker
  • lo.hinge
  • median
  • up.hinge
  • up.whisker
  • box.width
  • lo.out
  • up.out
  • n
  • Lower horizontal line out of the box
  • Lower horizontal line of the box
  • Horizontal line in the box
  • Upper horizontal line of the box
  • Upper horizontal line out of the box
  • The box width of each variable
  • Lower outliers
  • Upper outliers
  • The number of non-NA observations

histogram

  • cuts
  • counts
  • mean
  • median
  • The boundaries of the histogram classes
  • The frequency of each histogram class
  • The average value of the input vector
  • The median value of the input data

frequencies

  • Variable name
  • frequencies
  • “_row"
  • relative.frequencies
  • The name of the calculated variable
  • The frequency value
  • Name of the categories of the variable
  • Relative frequency values

correlation

  • Variable name
  • Correlation value
  • “_row"
  • The name of the calculated variable
  • The correlation value
  • The corresponding correlation variable

Examples

The dataset we use is available as json link in DescriptiveStats.OBeu package and represents the budget for Wuppertal for 2009 to 2020.

In R environment

open_spending.ds function’s input are data as json link and described with OpenBudgets.eu data model.

Wuppertal_openspending
## [1] "http://next.openspending.org/api/3/cubes/4b6d969e07ef7a86aa54e539fc127a14:wuppertalhaushalt/facts"
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)
## {##     "descriptives": {##         "Min": {
##             "Amount": [
## 533.21
## ]
## },
## "Max": {
## "Amount": [
## 2997043.49
## ]
## },
## "Range": {
## "Amount": [
## 2996510.28
## ]
## },
## "Mean": {
## "Amount": [
## 659132.4457
## ]
## },
## "Median": {
## "Amount": [
## 476400.565
## ]
## },
## "Quantiles": {
## "Amount": [
## 313924.26,
## 656962.815
## ]
## },
## "Variance": {
## "Amount": [
## 469375540712.697
## ]
## },
## "StandardDeviation": {
## "Amount": [
## 685109.8749
## ]
## },
## "Kurtosis": [
## 5.7675
## ],
## "Skewness": [
## 2.5221
## ]
## },
## "boxplot": {
## "Amount": {
## "lo.whisker": [
## 533.21
## ],
## "lo.hinge": [
## 306296.49
## ],
## "median": [
## 476400.565
## ],
## "up.hinge": [
## 658308.4
## ],
## "up.whisker": [
## 1185907.2
## ],
## "box.width": [
## 1.5
## ],
## "lo.out": [
##
## ],
## "up.out": [
## 2954238.51,
## 2979998.49,
## 2992244.95,
## 2916160.36,
## 2885816.5,
## 2997043.49,
## 2875275.56,
## 1252420.49,
## 1248584.45
## ],
## "n": [
## 100
## ]
## }
## },
## "histogram": {
## "Amount": {
## "cuts": [
## 0,
## 500000,
## 1000000,
## 1500000,
## 2000000,
## 2500000,
## 3000000
## ],
## "counts": [
## 54,
## 32,
## 7,
## 0,
## 0,
## 7
## ],
## "mean": [
## 659132.4457
## ],
## "median": [
## 476400.565
## ]
## }
## },
## "frequencies": {
## "frequencies": {
## "functional_classification_3.Produktgruppe": [
## {
## "Var1": "",
## "Freq": 2
## },
## {
## "Var1": "(entfallen in 2013) Geschäftsbereichsleitung GB 1.1 ",
## "Freq": 5
## },
## {
## "Var1": "Beschäftigtenvertretung",
## "Freq": 1
## },
## {
## "Var1": "Bezirksvertretungen",
## "Freq": 7
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 1",
## "Freq": 15
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 2.1",
## "Freq": 7
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 2.2",
## "Freq": 7
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 4",
## "Freq": 28
## },
## {
## "Var1": "Gleichstellung von Frau und Mann",
## "Freq": 7
## },
## {
## "Var1": "Politische Gremien",
## "Freq": 7
## },
## {
## "Var1": "Verwaltungsführung",
## "Freq": 14
## }
## ],
## "date_2.Year": [
## {
## "Var1": "2009",
## "Freq": 16
## },
## {
## "Var1": "2010",
## "Freq": 15
## },
## {
## "Var1": "2011",
## "Freq": 14
## },
## {
## "Var1": "2012",
## "Freq": 14
## },
## {
## "Var1": "2013",
## "Freq": 15
## },
## {
## "Var1": "2014",
## "Freq": 13
## },
## {
## "Var1": "2015",
## "Freq": 13
## }
## ]
## },
## "relative.frequencies": {
## "functional_classification_3.Produktgruppe": [
## {
## "Var1": "",
## "Freq": 0.02
## },
## {
## "Var1": "(entfallen in 2013) Geschäftsbereichsleitung GB 1.1 ",
## "Freq": 0.05
## },
## {
## "Var1": "Beschäftigtenvertretung",
## "Freq": 0.01
## },
## {
## "Var1": "Bezirksvertretungen",
## "Freq": 0.07
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 1",
## "Freq": 0.15
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 2.1",
## "Freq": 0.07
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 2.2",
## "Freq": 0.07
## },
## {
## "Var1": "Geschäftsbereichsleitung GB 4",
## "Freq": 0.28
## },
## {
## "Var1": "Gleichstellung von Frau und Mann",
## "Freq": 0.07
## },
## {
## "Var1": "Politische Gremien",
## "Freq": 0.07
## },
## {
## "Var1": "Verwaltungsführung",
## "Freq": 0.14
## }
## ],
## "date_2.Year": [
## {
## "Var1": "2009",
## "Freq": 0.16
## },
## {
## "Var1": "2010",
## "Freq": 0.15
## },
## {
## "Var1": "2011",
## "Freq": 0.14
## },
## {
## "Var1": "2012",
## "Freq": 0.14
## },
## {
## "Var1": "2013",
## "Freq": 0.15
## },
## {
## "Var1": "2014",
## "Freq": 0.13
## },
## {
## "Var1": "2015",
## "Freq": 0.13
## }
## ]
## }
## },
## "correlation": {
##
## }
## }
##

In OpenCPU environment

Select library and function

  1. Go to: yourserver/ocpu/test

  2. Copy and paste the following function to the endpoint

../library/DescriptiveStats.OBeu/R/open_spending.ds# library/ {name of the library} /R/ {function}
  1. Select Method: Post

Add parameters

Click add parameters every time you want to add a new parameters and values.

  1. Define the input data:

    • Param Name: json_data
    • Param Value (URL of json data): "http://next.openspending.org/api/3/cubes/21260d070eb5d63a121ea4c400dafbbb:apbn_fungsi_2016/facts?pagesize=20" (or any other json URL with the data)
  2. Define the dimensions parameter:

    • Param Name: dimensions
    • Param Value: "functional_classification_2.Function|functional_classification_2.Code"
  3. Define the amount parameter:

    • Param Name: amounts
    • Param Value: "Revised"

Likewise, you can add more parameters to change the defaults of coef.outl, box.outliers, box.wdth, cor.method, see DesciptiveStats.OBeu reference manual for further details.

  1. Ready! Click on Ajax request!

Results

  1. copy the /ocpu/tmp/{this_id_number}/R/.val (second on the right panel)

  2. finally, paste yourserver/ocpu/tmp/{this_id_number}/R/.val on a new tab.

CRAN

Please feel free to make questions, issue reports or pull requests.

Views: 81

Tags: cran, descriptive, greece, knowledge, open, openbudgets.eu, opencpu, statistics

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