![\"JSON](\"https:\/\/tuijasonkkila.fi\/wp-content\/uploads\/2015\/11\/files.png\")
<\/a><\/p>\nIt took no more than 15 minutes for Google to fulfill my wish to get the archive as a zipped file. For more information on How & Where, see e.g. Google now lets you export your search history<\/a>.<\/p>\n Now, this whole archive business started when I was led to a very nice blog posting by Lisa Charlotte Rost I find it fascinating, what you can tell about a person just by looking at her searches. Or rather, what kind of narratives s\/he builds upon them; to publish all search strings verbatim is not really an option.<\/p>\n Halfway in the 4-week course Intermediate D3 for Data Visualization<\/a>, the theme is stacked charts. Maybe I could visualize, on a timeline, as a stacked area chart, some aspects of my search activity. But what aspects? What sort of person am I as a searcher?<\/p>\n Quite dull, I have to admit. No major or controversial hobbies, no burning desire to follow latest gadgets, only mildly hypocondriac, not much interest at all in self-help advisory. Wikipedia is probably my number one landing site. Very often I use Google simply as a text corpus, an evidence-based dictionary:”Has this English word\/idiom been used in the UK or did I just made it up, or misspelled?” Unlike Lisa, who tells in episode #61<\/a> of the Data Stories podcast that now when she lives in a big city, Berlin, she often searches for directions – I do not. Well, compared to Berlin, Helsinki do is small, but we also have a superb web service for guiding us around here, Journey Planner<\/a>. So instead of a search, I’ll go straight there.<\/p>\n One area of digital life I’ve been increasingly interested in – and what this blog and my job blog<\/a> reflect, too, I hope – is coding. Note, “coding” not as in building software but as in scripting, mashupping, visualizing. Small-scale, proof-of-concept data wrangling. Learning by doing. Part of it is of course related to my day job at Aalto University. For example, now when we are setting up a CRIS system<\/a>, I’ve been transforming, with XSLT, legacy publication metadata to XML. It needs to validate against the Elsevier Pure XML Schema before it can be imported.<\/p>\n A few years now, appart XSLT, the other languages I have been writing with, are R and Perl. Unix command line tools I use on a daily basis. Thanks to the D3 course, I’m also slowly starting to get familiar with JavaScript. Python has been on my list a longer time, but since the introductory course I took at CSC – IT Center for Science<\/a> some time ago, I haven’t really touched it.<\/p>\n I’m not the only one<\/a> that googles while coding. Mostly it’s about a specific problem: I need to accomplish something but cannot remember or don’t know, how. When you are not a full-time coder, you forget details easily. Or, you get an error message you cannot understand. Whatever.<\/p>\n Are my coding habits visible in the search history? If yes, in what way.<\/p>\n First thing to do with the JSON files, was to merge them into one. For this, I turned to R.<\/p>\n Then, just as Lisa did, I fired up Google Refine, and opened a new project on To do:<\/p>\n From the language names, R is the most tricky one to filter because it is just one character. Therefore, I need to build a longish Boolean or sentence<\/a> for that.<\/p>\n Here I’m ready with R and Date, and checking the results with a text facet on the column r.<\/p>\n Thanks to a clearly commented template by the D3 course leader, Scott Murray<\/a>, the stacked area chart<\/a> was easy to do, but only after I had figured out how to process and aggregate yearly counts by language. Guess what – I googled for a hint, and got it<\/a>. The trick was, while looping over all rows by language, to define an object to store counts by year. Then, for every key (=year), I could push values to the Do the colors of the chart ring a bell? I’m a Wes Anderson fan, and have waited for an excuse to make use of some of the color palette implementations of his films<\/a>. This 5-color selection represents The Life Aquatic With Steve Zissou<\/a>. The blues and browns are perhaps a little too close to each other, especially when used as inline font color, but anyway.<\/p>\n Quite an R mountain there to climb, eh? It all started during the ELAG 2012<\/a> conference in Palma, Spain. Back then I was still working at the Aalto University Library. I had read a little about R before, but it was the pre-conference track An Introduction to R<\/em> led by Harrison Dekker<\/a>, that finally convinced me that I needed to learn this. I guess it was the easiness of installing packages (always a nightmare with Perl), reading in data, and quick plotting.<\/p>\n So what does the big amount of R searches tell? For one thing, it shows my active use of the language. At the same time though, it tells that I’ve needed a lot of help. A lot. I still do.<\/p>\n","protected":false},"excerpt":{"rendered":" Since few days now, I’ve had my Google search archive with me. In my case, it’s a collection of 38 JSON files, containing search strings and timestamps. The oldest file dates back to mid-2006, which acts as a digital marriage certificate of us, me and the Internet giant. It took no more than 15 minutes … Continue reading About 43000 results<\/span>
\n<\/a>.<\/p>\n<\/a><\/p>\nlibrary(jsonlite)\n \nfilenames <- list.files(\"Searches\", pattern=\"*.json\", full.names=TRUE)\njsons.as.list <- lapply(filenames, function(f) fromJSON(txt = f))\nalljson <- toJSON(jsons.as.list)\nwrite(alljson, file = \"g.json\")\n<\/pre>\n
g.json<\/code>.<\/p>\n\n
<\/a><\/p>\n<\/a><\/p>\ndataset <\/code>array.<\/p>\n