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Cluster analysis in OpenBudget.eu

Cluster.OBeu is used on OpenBudgets.eu data mininig tool platform with OpenCPU integration of R and JavaScript to estimate and return the necessary parameters for cluster analysis visualizations for budget or expenditure datasets of Municipality across Europe.

The vignette shows the way Cluster.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

Cluster analysis on OpenBudgets.eu platform

open_spending.cl is designed to estimate and return the clustering model measures of OpenBudgets.eu datasets.

The available clustering algorithms are hierarchical, kmeans from R base, pam, clara, fuzzy from cluster package and model based algorithms from mclust package. It can be used to find the appropriate clustering algorithm and/or the appropriate clustering number of the input data according to the internal and stability measures from clValid package.

The input data must be a JSON link according to 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. open_spending.cl estimates and returns the json data that are described with the OpenBudgets.eu data model, using cl.analysis function.

`open_spending.cl` input
Input	Description
json_data	The json string, URL or file from Open Spending API
dimensions	The dimensions of the input data
amounts	The amounts of the input data
measured.dimensions	The dimensions to which correspond amount/numeric variables
cl.aggregate	Aggregate function of the input data
cl.method	clustering algorithm
cl.num	Number of clusters
cl.dist	Distance metric

The following table shows a sort description of the open_spending.cl return components:

`open_spending.cl` return
Component	Description
cluster.method	Label of the clustering algorithm
raw.data	Input data
data.pca	Principal components
modelparam	Clustering model specifications
compare	Clustering measures

Examples

The dataset in the following example is being used in OpenBudgets.eu platform and concerns the income of Aragon. in 2007.

In R environment

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

aragon_income = "http://apps.openbudgets.eu/api/3/cubes/aragon-2007-income__3209b/aggregate?drilldown=fundingClassification.prefLabel%7CeconomicClassification.prefLabel&aggregates=amount.sum"

results = open_spending.cl(
  json_data =  aragon_income, 
  dimensions ="economicClassification.prefLabel",
  amounts = "amount.sum",
  measured.dimensions = "fundingClassification.prefLabel",
  cl.method="kmeans" 
  )
# Pretty output using prettify of jsonlite library
jsonlite::prettify(results)

In OpenCPU environment

Select library and function

Go to: yourserver/ocpu/test
Copy and paste the following function to the endpoint

../library/Cluster.OBeu/R/open_spending.cl
# library/ {name of the library} /R/ {function}

Select Method: Post

Add parameters

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

Define the input data:
- Param Name: json_data
- Param Value (URL of json data): "https://apps.openbudgets.eu//api/3/cubes/aragon-2007-income__3209b/aggregate?drilldown=fundingClassification.prefLabel%7CeconomicClassification.prefLabel&aggregates=amount.sum" (or any other json URL with the data)
Define the dimensions parameter:
- Param Name: dimensions
- Param Value: "economicClassification.prefLabel"
Define the amount parameter:
- Param Name: amounts
- Param Value: "amount.sum"
Define the measured dimension parameter:
- Param Name: measured.dimensions
- Param Value: "fundingClassification.prefLabel"

You add likewise further parameters and change the default parameters of cl.method, cl.num, cl.dist, see Cluster.OBeu reference manual for further details.

Ready! Click on Ajax request!

Results

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.

Further Details

Github

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.