Initial content on EDA

book/src/pages/2-explore/directory.conf

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 laika.navigationOrder = [
   README.md
   loading.md
+  initial.md
 ]

book/src/pages/2-explore/initial.md

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+# Initial Investigations
+
+```scala mdoc:invisible
+import creativescala.data.explore.HadCrut5.data
+```
+Now we have data the natural step is to perform some initial investigations.
+For example, how much data we have?
+
+```scala mdoc
+data.size
+```
+
+We have 2082 records, one for each month from January 1850 to July 2023.
+This isn't a *huge* amount, but it's certainly too much for us analyze just by looking at it.
+This is where exploratory data analysis, the focus on this part of the book, comes into play.
+We'll see many techniques over the next chapters, but we're starting with the most basic.
+
+Perhaps the most basic technique is to just look at some of the data.
+Here's the first element.
+
+```scala mdoc
+data.head
+```
+
+This tells use this element refers to January of 1850, the average temperature was -0.6C below the 1961-1990 baseline, with lower and upper error intervals of approximately -0.9C and -0.3C. (How do know what the meaning of these fields? By reading [the documentation][hadcrut5], in particular the linked paper.) **TODO: Check this**
+
+We can also look at the last element.
+
+```scala mdoc
+data.last
+```
+
+Here we have information form July of 2023, and the temperature is now above the baseline. 
+This seems like it might support global warming, but what about the data inbetween?
+Looking at the same month from every year is likely to still be too much to read, but we could look at the same month from each decade.
+
+```scala mdoc
+val decades = data.filter(r => r.year % 10 == 0 && r.month == 6)
+```
+
+With only 18 measurements, this is more manageable. 
+Overall, the data does seem to show increasing temperatures but it would be much easier to see a trend on a graph rather than in printed numbers, so in the next section we'll turn to visualizing data.
+Before we get there, however, it's time for you to do a bit of analysis on your own.
+
+[hadcrut5]: https://www.metoffice.gov.uk/hadobs/hadcrut5/index.html
+
+
+@:exercise(Shall I compare thee to a summer's day?)
+
+In this chapter we're learning about data analysis, but we're also learning how to work with collections of data such as `List`.
+
+When we selected data by decades, we rather arbitrarily chose June as our month of interest.
+Write code that instead selects data from January. 
+Do you still see a similar trend?
+@:@
+
+
+@:solution
+This is a small modification of the original code. 
+Instead of looking for `r.month == 6` we look for  `r.month == 1`,
+which is the numeric code corresponding to January.
+
+```scala mdoc
+val januaryByDecades = data.filter(r => r.year % 10 == 0 && r.month == 1)
+```
+
+The trend is not exactly the same as before, but it is simlar enough.
+@:@
+
+
+@:exercise(Statistics is the Grammar of Science)