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‎docs/antora/modules/ROOT/pages/user_manual.adoc‎

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image:user_manual/media/image20.png[image,width=601,height=362]
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== Sample application
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This chapter explains how to use sample applications in Excel, Python, R
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and Power BI that shows how TED data can be accessed in Cellar. Sample
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applications represent different ways to access TED notices in CELLAR
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and how they can be used for simple scenarios. As scenarios, each method
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will take over the list of European countries and shows them in one
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column.
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*Note:* Jupyter Notebook samples are explained with assumption that a
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code editor is already prepared. For example VS Code or Pycharm, or
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Jupyter server. Examples are explained using
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https://code.visualstudio.com/docs[[.underline]#Visual Studio Code#].
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=== Excel
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This chapter shows an example using Excel. Microsoft Excel is a
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spreadsheet developed by Microsoft through which we will use the
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interface to query CELLAR repository to see an example.
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[arabic]
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. Prepare link with necessary query:
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* Access:
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https://publications.europa.eu/webapi/rdf/sparql[[.underline]#https://publications.europa.eu/webapi/rdf/sparql#]
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* Insert query
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Example query:
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[source, sparql]
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prefix cdm: <http://publications.europa.eu/ontology/cdm#>
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select distinct ?Countries
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where {
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?Countries a cdm:country .
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}
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* Push the “Run Query” button
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image:user_manual/media/image6.png[image,width=407,height=466]
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* Copy entire link of the result
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image:user_manual/media/image47.png[image,width=601,height=281]
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[arabic, start=2]
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. Access result table via Excel:
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* Click on Data -> From Web button
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image:user_manual/media/image58.png[image,width=601,height=264]
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* In appeared window insert result link then press OK:
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image:user_manual/media/image88.png[image,width=601,height=320]
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* In result window select necessary table, press load:
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image:user_manual/media/image41.png[image,width=601,height=464]
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=== Jupyter Notebook - Python
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This chapter shows an example using the Jupyter Notebook in Python. The
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Jupyter Notebook is an application for creating and sharing
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computational documents. Python represents a programming language for
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writing computational documents. To realize the proposed scenario, it is
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necessary to install the special tools and use the Python code that will
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perform a query to the cellar and display the results in tabular
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form (xref:references[Working with Jupyter Notebook]).
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To run Python sample:
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[arabic]
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. Download Jupyter Notebook file:
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https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_python.ipynb[[.underline]#https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_python.ipynb#]
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[arabic, start=2]
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. Download & Install Python 3.8:
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[loweralpha]
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.. Windows 64bit:
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https://www.python.org/ftp/python/3.8.10/python-3.8.10-amd64.exe[[.underline]#download#]
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.. Windows 86bit:
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https://www.python.org/ftp/python/3.8.10/python-3.8.10.exe[[.underline]#download#]
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. Open Jupyter Notebook file with code editor (VS Code):
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. In code editor, select interpreter for Python that was installed
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Example in VS Code:
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image:user_manual/media/image8.png[image,width=617,height=204]
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[arabic, start=5]
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. Install dependencies:
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* Use OS command line and type:
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[source,python]
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pip install ipykernel sparqlwrapper pandas
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[arabic, start=6]
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. Run Cells (example in VS Code):
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image:user_manual/media/image62.png[image,width=601,height=84]
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If all works good, on last output cell we can see results:
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image:user_manual/media/image2.png[image,width=387,height=420]
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=== Jupyter Notebook - R
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This chapter shows an example using Jupyter Notebook in R language. R is
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software environment for statistical computing and graphic
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representation. In this case, R represents the programming language for
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writing notebook documents. To realize the proposed scenario, it is
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necessary to install the special tools (xref:references[Running R Jupyter Notebooks]) and
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use the R code that will perform a query (xref:references[SPARQL with R]) to
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the cellar and display the results in tabular form.
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To run R sample:
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[arabic]
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. Install & install R language:
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* Windows 64bit:
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https://cran.r-project.org/bin/windows/base/R-4.2.2-win.exe[[.underline]#download#]
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[arabic, start=2]
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. Download Jupyter Notebook file:
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https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_R.ipynb[[.underline]#https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_R.ipynb#]
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[arabic, start=3]
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. Download SPARQL package archive and put in same folder as Notebook file:
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https://cran.r-project.org/src/contrib/Archive/SPARQL/SPARQL_1.16.tar.gz[[.underline]#download SPARQL#]
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[arabic, start=4]
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. Install IRKernel:
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[loweralpha]
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.. Run R in OS command line and type:
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[source,bash]
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install.packages("IRkernel")
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install.packages('RCurl')
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install.packages('XML')
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[loweralpha, start=2]
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. In appeared window, select first mirror and press OK:
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image:user_manual/media/image45.png[image,width=240,height=309]
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[loweralpha, start=3]
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. After installation is completed, type:
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[source,bash]
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IRkernel::installspec()
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[arabic, start=5]
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. Open Jupyter Notebook file in code editor;
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. Select R interpreter;
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image:user_manual/media/image23.png[image,width=601,height=142]
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[arabic, start=7]
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. Run all cells
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image:user_manual/media/image90.png[image,width=502,height=100]
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[arabic, start=8]
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. After running, after last cell we can see results:
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image:user_manual/media/image80.png[image,width=487,height=411]
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== References [[references]]
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[arabic]
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. “Working with Jupyter Notebooks in Visual Studio Code.” n.d. Visual
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Studio Code.
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https://code.visualstudio.com/docs/datascience/jupyter-notebooks[[.underline]#https://code.visualstudio.com/docs/datascience/jupyter-notebooks#]
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. “Running R Jupyter Notebooks in VS Code.” n.d. Practical Data Science.
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https://www.practicaldatascience.org/html/jupyter_r_notebooks.html[[.underline]#https://www.practicaldatascience.org/html/jupyter_r_notebooks.html#]
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. “SPARQL with R.” 2013. R-bloggers.
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https://www.r-bloggers.com/2013/01/sparql-with-r-in-less-than-5-minutes/[[.underline]#https://www.r-bloggers.com/2013/01/sparql-with-r-in-less-than-5-minutes/#]
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‎docs/antora/modules/ROOT/pages/using_procurement_data.adoc‎

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= Using procurement data
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This page explains how to use procurement data accessed from Cellar with Excel, Python, R
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and Power BI.
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There are different ways to access TED notices in CELLAR
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and use the data. As scenarios, each method presented in this page
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will take over the list of European countries and shows them in one
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column.
12+
13+
*Note:* Jupyter Notebook samples are explained with assumption that a
14+
code editor is already prepared. For example VS Code or Pycharm, or
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Jupyter server. Examples are explained using
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https://code.visualstudio.com/docs[[.underline]#Visual Studio Code#].
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=== Excel
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This chapter shows an example using Excel. Microsoft Excel is a
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spreadsheet developed by Microsoft through which we will use the
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interface to query CELLAR repository to see an example.
23+
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[arabic]
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. Prepare link with necessary query:
26+
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* Access:
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https://publications.europa.eu/webapi/rdf/sparql[[.underline]#https://publications.europa.eu/webapi/rdf/sparql#]
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* Insert query
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Example query:
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[source, sparql]
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prefix cdm: <http://publications.europa.eu/ontology/cdm#>
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select distinct ?Countries
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where {
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?Countries a cdm:country .
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}
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* Push the “Run Query” button
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image:user_manual/media/image6.png[image,width=407,height=466]
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* Copy entire link of the result
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image:user_manual/media/image47.png[image,width=601,height=281]
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[arabic, start=2]
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. Access result table via Excel:
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* Click on Data -> From Web button
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image:user_manual/media/image58.png[image,width=601,height=264]
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* In appeared window insert result link then press OK:
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image:user_manual/media/image88.png[image,width=601,height=320]
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* In result window select necessary table, press load:
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image:user_manual/media/image41.png[image,width=601,height=464]
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=== Jupyter Notebook - Python
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This chapter shows an example using the Jupyter Notebook in Python. The
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Jupyter Notebook is an application for creating and sharing
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computational documents. Python represents a programming language for
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writing computational documents. To realize the proposed scenario, it is
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necessary to install the special tools and use the Python code that will
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perform a query to the cellar and display the results in tabular
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form (xref:references[Working with Jupyter Notebook]).
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To run Python sample:
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[arabic]
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. Download Jupyter Notebook file:
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https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_python.ipynb[[.underline]#https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_python.ipynb#]
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[arabic, start=2]
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. Download & Install Python 3.8:
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[loweralpha]
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.. Windows 64bit:
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https://www.python.org/ftp/python/3.8.10/python-3.8.10-amd64.exe[[.underline]#download#]
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.. Windows 86bit:
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https://www.python.org/ftp/python/3.8.10/python-3.8.10.exe[[.underline]#download#]
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. Open Jupyter Notebook file with code editor (VS Code):
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. In code editor, select interpreter for Python that was installed
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Example in VS Code:
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image:user_manual/media/image8.png[image,width=617,height=204]
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[arabic, start=5]
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. Install dependencies:
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* Use OS command line and type:
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[source,python]
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pip install ipykernel sparqlwrapper pandas
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[arabic, start=6]
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. Run Cells (example in VS Code):
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image:user_manual/media/image62.png[image,width=601,height=84]
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If all works good, on last output cell we can see results:
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image:user_manual/media/image2.png[image,width=387,height=420]
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=== Jupyter Notebook - R
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This chapter shows an example using Jupyter Notebook in R language. R is
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software environment for statistical computing and graphic
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representation. In this case, R represents the programming language for
119+
writing notebook documents. To realize the proposed scenario, it is
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necessary to install the special tools (xref:references[Running R Jupyter Notebooks]) and
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use the R code that will perform a query (xref:references[SPARQL with R]) to
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the cellar and display the results in tabular form.
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To run R sample:
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[arabic]
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. Install & install R language:
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* Windows 64bit:
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https://cran.r-project.org/bin/windows/base/R-4.2.2-win.exe[[.underline]#download#]
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[arabic, start=2]
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. Download Jupyter Notebook file:
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https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_R.ipynb[[.underline]#https://github.com/OP-TED/ted-rdf-conversion-pipeline/blob/main/notebooks/query_cellar_R.ipynb#]
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[arabic, start=3]
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. Download SPARQL package archive and put in same folder as Notebook file:
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https://cran.r-project.org/src/contrib/Archive/SPARQL/SPARQL_1.16.tar.gz[[.underline]#download SPARQL#]
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[arabic, start=4]
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. Install IRKernel:
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[loweralpha]
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.. Run R in OS command line and type:
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[source,bash]
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install.packages("IRkernel")
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install.packages('RCurl')
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install.packages('XML')
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[loweralpha, start=2]
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. In appeared window, select first mirror and press OK:
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image:user_manual/media/image45.png[image,width=240,height=309]
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[loweralpha, start=3]
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. After installation is completed, type:
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[source,bash]
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IRkernel::installspec()
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[arabic, start=5]
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. Open Jupyter Notebook file in code editor;
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. Select R interpreter;
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image:user_manual/media/image23.png[image,width=601,height=142]
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[arabic, start=7]
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. Run all cells
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image:user_manual/media/image90.png[image,width=502,height=100]
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[arabic, start=8]
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. After running, after last cell we can see results:
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image:user_manual/media/image80.png[image,width=487,height=411]
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== References [[references]]
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[arabic]
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. “Working with Jupyter Notebooks in Visual Studio Code.” n.d. Visual
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Studio Code.
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https://code.visualstudio.com/docs/datascience/jupyter-notebooks[[.underline]#https://code.visualstudio.com/docs/datascience/jupyter-notebooks#]
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. “Running R Jupyter Notebooks in VS Code.” n.d. Practical Data Science.
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https://www.practicaldatascience.org/html/jupyter_r_notebooks.html[[.underline]#https://www.practicaldatascience.org/html/jupyter_r_notebooks.html#]
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. “SPARQL with R.” 2013. R-bloggers.
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https://www.r-bloggers.com/2013/01/sparql-with-r-in-less-than-5-minutes/[[.underline]#https://www.r-bloggers.com/2013/01/sparql-with-r-in-less-than-5-minutes/#]
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