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{-# LANGUAGE OverloadedStrings #-}
module Y2017.M10.D09.Exercise where
So, 'yesterday' we saved our data out as JSON, read it back in, and then
visualized it. The data was the NYT article archive, and, doing that, we
saw we had a lot of data to visualize.
Today, let's par that down a smidge.
import Data.Aeson
import qualified Data.ByteString.Lazy.Char8 as BL
import Data.Set (Set)
-- below imports available via 1HaskellADay git repository
import Data.Hierarchy -- hint: use this to visualize your results
import Y2017.M10.D04.Exercise
import Y2017.M10.D05.Exercise
-- From the grouping that you formed before (so, you have to reformulate that
-- grouping again, today), par it down to the top 5 topics, that is to say:
-- the topics that have the most articles
topicality :: Grouping -> [(Topic, Int)]
topicality groups = undefined
-- topicality gives us the number of articles of that topic
-- Now that you have topicality, reform the group to contain only those
-- top 5 topics (and no other topics)
reformGrouping :: Set Topic -> Grouping -> Grouping
reformGrouping hotTopics groups = undefined
-- Great! Now let's visualize that subset. What do you get? Tweet your results
{-- BONUS -----------------------------------------------------------------
* What are the topics of the NYT archive slice that have 10 or more articles?
* How many topic make the cut of ten or more articles?
* Chart those topics
top10sTopics :: Grouping -> [(Topic, Int)]
top10sTopics grps = undefined
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