Posts Tagged ‘datastore explorer’
I’m working on a new pattern using Google Refine as the hub for a data fusion experiment pulling together data from different sources. I’m not sure how it’ll play out in the end, but here are some fragments….
Grab Data into Google Refine as CSV from a URL (Proxied Google Spreadsheet Query via Yahoo Pipes)
Firstly, getting data into Google Refine… I had hoped to be able to pull a subset of data from a Google Spreadsheet into Google Refine by importing CSV data obtained from the spreadsheet via a query generated using my Google Spreadsheet/Guardian datastore explorer (see Using Google Spreadsheets as a Database with the Google Visualisation API Query Language for more on this) but it seems that Refine would rather pull the whole of the spreadsheet in (or at least, the whole of the first sheet (I think?!)).
Instead, I had to tweak create a proxy to run the query via a Yahoo Pipe (Google Spreadsheet as a database proxy pipe), which runs the spreadsheet query, gets the data back as CSV, and then relays it forward as JSON:
Here’s the interface to the pipe – it requires the Google spreadsheet public key id, the sheet id, and the query… The data I’m using is a spreadsheet maintained by the Guardian datastore containing UK university fees data (spreadsheet.
You can get the JSON version of the data out directly, or a proxied version of the CSV, as CSV via the More options menu…
Using the Yahoo Pipes CSV output URL, I can now get a subset of data from a Google Spreadsheet into Google Refine…
Here’s the result – a subset of data as defined by the query:
We can now augment this data with data from another source using Google Refine’s ability to import/fetch data from a URL. In particular, I’m going to use the Yahoo Pipe described above to grab data from a different spreadsheet and pass it back to Google Refine as a JSON data feed. (Google spreadsheets will publish data as JSON, but the format is a bit clunky…)
To test out my query, I’m going to create a test query in my datastore explorer using the Guardian datastore HESA returns (2010) spreadsheet URL (http://spreadsheets1.google.com/spreadsheet/ccc?hl&key=tpxpwtyiYZwCMowl3gNaIKQ#gid=0) which also has a column containing HESA numbers. (Ultimately, I’m going to generate a URL that treats the Guardian datastore spreadsheet as a database that lets me get data back from the row with a particular HESA code column value. By using the HESA number column in Google Refine to provide the key, I can generate a URL for each institution that grabs its HESA data from the Datastore HESA spreadsheet.)
Hit “Preview Table Headings”, then scroll down to try out a query:
Having tested my query, I can now try the parameters out in the Yahoo pipe. (For example, my query is select D,E,H where D=21 and the key is tpxpwtyiYZwCMowl3gNaIKQ; this grabs data from columns D, E and H where the value of D (HESA Code) is 21). Grab the JSON output URL from the pipe, and use this as a template for the URL template in Google Refine. Here’s the JSON output URL I obtained:
Remember, the HESA code I experiment with was 21, so this is what we want to replace in the URL with the value from the HESA code column in Google Refine…
Here’s how we create the URLs built around/keyed by an appropriate HESA code…
Google Refine does its thing and fetches the data…
Now we process the JSON response to generate some meaningful data columns (for more on how to do this, see Tech Tips: Making Sense of JSON Strings – Follow the Structure).
First say we want to create a new column based on the imported JSON data:
Then parse the JSON to extract the data field required in the new column.
For example, from the HESA data we might extract the Expenditure per student /10:
value.parseJson().value.items["Expenditure per student / 10"]
or the Average Teaching Score (value.parseJson().value.items["Average Teaching Score"]):
And here’s the result:
So to recap:
– we use a Yahoo Pipe to query a Google spreadsheet and get a subset of data from it;
– we take the CSV output from the pipe and use it to create a new Google Refine database;
– we note that the data table in Google Refine has a HESA code column; we also note that the Guardian datastore HESA spreadsheet has a HESA code column;
– we realise we can treat the HESA spreadsheet as a database, and further that we can create a query (prototyped in the datastore explorer) as a URL keyed by HESA code;
– we create a new column based on HESA codes from a generated URL that pulls JSON data from a Yahoo pipe that is querying a Google spreadsheet;
– we parse the JSON to give us a couple of new columns.
And there we have it – a clunky, but workable, route for merging data from two different Google spreadsheets using Google Refine.