| Type: | Package |
| Title: | Cluster Analysis 'OpenBudgets.eu' |
| Version: | 1.2.3 |
| Date: | 2019-12-17 |
| Description: | Estimate and return the needed parameters for visualisations designed for 'OpenBudgets' http://openbudgets.eu/ data. Calculate cluster analysis measures in Budget data of municipalities across Europe, according to the 'OpenBudgets' data model. It involves a set of techniques and algorithms used to find and divide the data into groups of similar observations. Also, can be used generally to extract visualisation parameters convert them to 'JSON' format and use them as input in a different graphical interface. |
| Maintainer: | Kleanthis Koupidis <koupidis@okfn.gr> |
| URL: | https://github.com/okgreece/Cluster.OBeu |
| BugReports: | https://github.com/okgreece/Cluster.OBeu/issues |
| License: | GPL-2 | file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | TRUE |
| Imports: | car, cluster, clValid, data.tree, dendextend, graphics, jsonlite, mclust, methods, RCurl, reshape, reshape2, stringr, utils |
| RoxygenNote: | 7.0.0 |
| Depends: | R (≥ 3.5.0) |
| Suggests: | knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2019-12-17 13:21:57 UTC; kleanthis-okfngr |
| Author: | Kleanthis Koupidis [aut, cre], Charalampos Bratsas [aut], Jaroslav Kuchar [ctb] |
| Repository: | CRAN |
| Date/Publication: | 2019-12-17 14:20:07 UTC |
city data example
Description
This dataset is an example data frame of the budget phase data
Administrative_Unit
Approved
Draft
Executed
Revised
Format
A data frame with the previous characteristics as columns
Cluster analusis
Description
Clustering Analysis for OBEU datasets.
Usage
cl.analysis(cl.data, cl_feature = NULL, amount = NULL, cl.aggregate = "sum",
cl.meth = NULL, clust.numb = NULL, dist = "euclidean", tojson = FALSE)
Arguments
cl.data |
The input data |
cl_feature |
The feature to be clustered (nominal variables) |
amount |
The numeric variables |
cl.aggregate |
Select a different aggregation in case of filtering the input data |
cl.meth |
The clustering method algorithm |
clust.numb |
The number of clusters |
dist |
The distance metric |
tojson |
If TRUE the results are returned in json format, default returns a list |
Details
There are different clustering models to be selected through an evaluation process. The user should define the cl_feature, cl.aggregate and amount parameters to form the structure of cluster data. The clustering algorithm, the number of clusters and the distance metric of the clustering model are set to the best selection using internal and stability measures. The end user can also interact with the cluster analysis and these parameters by specifying the cl.method, cl.num and cl.dist parameters respectively.
Value
The final returns are the parameters needed for visualizing the cluster data depending on the selected algorithm and the specification parameters, as long as some comparison measure matrices.
cluster.method - Label of the clustering algorithm
raw.data - Input data
data.pca - The principal components to visualize the input data
modelparam - The results of this parameter depend of the selected clustering model
compare - Clustering measures
Author(s)
Kleanthis Koupidis, Jaroslav Kuchar
See Also
cl.features, clValid, diana, agnes,
pam, clara, fanny, Mclust
Examples
cl.analysis(city_data, cl.meth = "pam", clust.numb = 3)
Clustering features
Description
Select clustering characteristic to form the clustering data
Usage
cl.features(data, features = NULL, amounts = NULL, aggregate = "sum", tojson = FALSE )
Arguments
data |
The input data |
features |
The clustering features |
amounts |
The amount measures of the dataset |
aggregate |
The function to aggregate |
tojson |
If TRUE the results are returned in json format, default returns a list |
Details
This function adapts the dataset according to the selected dimension of the dataset and the aggregation function.
Value
This function returns the dataset for cluster analysis adapted to the desired features.
Author(s)
Kleanthis Koupidis
See Also
Examples
cl.features(city_data, features = 'Administrative_Unit')
# works also for other datasets
cl.features(iris, features = 'Species')
Clustering model plotting
Description
cl.plot function plots the clustering model constructed by the cl.analysis function.
Usage
cl.plot(clustering.model, parameters = list())
Arguments
clustering.model |
Object returned by the |
parameters |
List of parameters to indicate plotting of ellipses or convex hulls. Default values: |
Author(s)
Jaroslav Kuchar <https://github.com/jaroslav-kuchar>
See Also
Examples
inputs.clustering <- cl.analysis(city_data, cl.meth="pam", clust.numb=2)
cl.plot(inputs.clustering, parameters = list(ellipses=TRUE))
Extract the proposed clustering method and the number of clusters from clvalid method
Description
Extract the most frequent
Usage
cl.summary(clv)
Arguments
clv |
A clValid object |
Details
This function returns the proposed method or number of clusters or both according to the majority clustering indices of a clValid process
Value
A value that indicates the proposed method and number of clusters.
Author(s)
Kleanthis Koupidis
Convex hull points
Description
Computes points to plot a convex hull for each cluster of the clustering model
Usage
convex.hulls(clustering.model, data.pca)
Arguments
clustering.model |
Object returned by the |
data.pca |
data as result of the |
Value
List of vectors with points for each convex hull.
Ellipse points
Description
Computes points to plot an ellipse for each cluster of the clustering model
Usage
ellipses(clustering.model, data.pca)
Arguments
clustering.model |
Object returned by the |
data.pca |
data as result of the |
Value
List of vectors with points for each ellipse.
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
data |
The input data frame, matrix |
Value
This function returns a data frame with the numeric columns of the input dataset.
Author(s)
Kleanthis Koupidis
Examples
nums(city_data)
Read and Calculate the Basic Information for Cluster Analysis Tasks from Open Spending API
Description
Extract and analyze the input data provided from Open Spending API, using the cl.analysis function.
Usage
open_spending.cl(json_data, dimensions=NULL, amounts=NULL, measured.dimensions=NULL,
cl.aggregate="sum", cl.method=NULL, cl.num=NULL, cl.dist="euclidean")
Arguments
json_data |
The json string, URL or file from Open Spending API |
dimensions |
The dimensions/feature of the input data |
amounts |
The measures of the input data |
measured.dimensions |
The dimensions to which correspond amount/numeric variables |
cl.aggregate |
Aggregate function of the input data |
cl.method |
The clustering algorithm |
cl.num |
The number of clusters |
cl.dist |
The distance metric |
Details
This function is used to read data in json format from Open Spending API, in order to implement
cluster analysis through cl.analysis function.
Value
A json string with the resulted parameters of the cl.analysis function.
Author(s)
Kleanthis Koupidis