Understanding Flickr data

Now that we have created a sample app and extracted data using it in the previous section, let us move ahead and understand more about the data we get from Flickr. We will leverage packages such as httr, plyr, piper, and so on and build on our code base, as in previous chapters.

To begin with, let's use our utility function to extract ten days' worth of data. The following snippet extracts the data using the interestingness API end point:

# Mention day count
daysAnalyze = 10

interestingDF <- lapply(1:daysAnalyze,getInterestingData) %>>%
                    ( do.call(rbind, .) )

Now, if we look at the attributes of the DataFrame generated using the previous snippet, we have details like, data, photo.id, photo.owner, photo.title and so on. Though this DataFrame is useful in terms of identifying what photographs qualify as interesting on certain days, it does little to tell us much about the photographs themselves.

So the logical next step is to find, extract...