Visualisation progress

Trends GoogleDone it! By a pure chance, but I seem to have done it! An interactive Google visualisation of my data, which shows the correlation between CO2 emissions volume and GDP growth. Could be better and more detailed, I know, but wow, I didn’t even realize Google is really capable of it or I’m really capable of squeezing it from Google.

Now, some details. First, due to a very complicated relationship between WordPress.com and embeddable stuff, I can’t publish it here. I can only provide a link to where this interactivity is available. So, here’s the original spreadsheet with both the data and chart. And here’s my attempt (successful this time) to embed the chart into blogspot. And it was really a happy coincidence that I got this result, because I didn’t know how to do it. What I was actually trying to do is to shape my data so that it can be processed in Tableau Public. And it wouldn’t work.

Then I realized that TP isn’t free software (only a 14 days’ trial version is free), which immediately made it rather unattractive im my eyes.

UPD: A commentator has kindly corrected me. Tableau has both free and paid versions (and the 14 day’s trial is for the latter). Tableau Public is free.

Today I tried to visualise this chart in Google Spreadsheets and here’s the result. So, our chief weapons are the tools used: Data Wrangler (free) and Google Spreadsheets (also free).

If somebody has any instructive tips or critisisms, I’ll be delighted to hear them.

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Data journalism: Learning insights

Today my learning is focused on data journalism (I’ve got to finish my story as a challenge within Data Expedition). And also, today I decided to have a look at the product rather than the technique, as I previously did. To this end, I went to read Guardian Datablog and it seems to be quite an enlightening experience.

But first off, I have to give credit to Kevin Graveman, whose post actually provoked me to think in this direction. Kevin gave some tips on learning CSS by looking at both HTML and CSS sources of a page and also comparing it to the way the page looks in order to better understand how it works.
Now, this approach (quite natural, but not always obvious) can be replicated in many other areas. So today, I’m applying it to The Guardian by learning the anatomy of their data driven materials (just as if I was looking at the source code of their product). And I’m also making notes about my observations on the way.

  1. They ALWAYS provide links to their datasets. Under each piece of visualisation, they post a link to a small particular spreadsheet with the data regarding this piece.
  2. After the article they also provide a link to the full spreadsheet.
  3. A spreadsheet contains not only data, but also notes (on a separate sheet) with sources and some explanations. Like so  (for this article).
  4. Guardian Datablog is a great source of datasets. Although somewhat random.
  5. But these datasets are not always very trustworthy.
  6. Their visualisations are normally interactive.
  7. Some entries to the blog are very short in terms of writing, but provide complicated visualisations. Others rely on text substentially.
  8. Most underlying datasets in the materials I’ve seen are organised as single Google spreadsheets with several sheets (or tabs) containing particular spreadsheets. A good example is a recent Simon Rogers and Julia Kollewe’s material. The dataset is here.
  9. It seems to be a good idea to place some charts on separate sheets. (In order to do this, l-click the chart anywhere to open the quick edit mode, then hit the small triangle in the top corner on the right and choose ‘move to own sheet’.)

move chart