Initial Investigations

Now we have data, the natural next step is to perform some initial investigations. For example, how many measurements do we have?

data.size
// res0: Int = 2082

This tells us 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 to analyze just by looking at it. We'll see some approaches to analzying the data in just a moment, but for now let's continue exploring.

Perhaps the next obvious step is to look at some of the data. Here's the first element.

data.head
// res1: Record = Record(
//   year = 1850,
//   month = 1,
//   anomaly = -0.67456436,
//   lower = -0.98177195,
//   upper = -0.3673568
// )

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, in particular the linked paper.) TODO: Check this

We can also look at the last element.

data.last
// res2: Record = Record(
//   year = 2023,
//   month = 6,
//   anomaly = 1.0509841,
//   lower = 0.9951409,
//   upper = 1.1068273
// )

Here we have information for July of 2023, and the temperature is now above the baseline. The difference between the first and last measurements supports the idea that temperatures are rising over time. However, we could quite rightly suggest this may just be a conincidence. What do the measurements between these points show? 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.

val decades = data.filter(r => r.year % 10 == 0 && r.month == 6)
// decades: ArraySeq[Record] = ArraySeq(
//   Record(
//     year = 1850,
//     month = 6,
//     anomaly = -0.34424013,
//     lower = -0.60947233,
//     upper = -0.079007894
//   ),
//   Record(
//     year = 1860,
//     month = 6,
//     anomaly = -0.21145956,
//     lower = -0.45290247,
//     upper = 0.029983336
//   ),
//   Record(
//     year = 1870,
//     month = 6,
//     anomaly = -0.23123583,
//     lower = -0.4569157,
//     upper = -0.005555956
//   ),
//   Record(
//     year = 1880,
//     month = 6,
//     anomaly = -0.38053036,
//     lower = -0.57394856,
//     upper = -0.18711212
//   ),
//   Record(
//     year = 1890,
//     month = 6,
//     anomaly = -0.49480304,
//     lower = -0.6899875,
//     upper = -0.2996186
//   ),
//   Record(
//     year = 1900,
//     month = 6,
//     anomaly = -0.21710625,
//     lower = -0.4151495,
//     upper = -0.019062985
//   ),
//   Record(
//     year = 1910,
//     month = 6,
//     anomaly = -0.5593434,
//     lower = -0.7473471,
//     upper = -0.37133965
// ...

With only 18 measurements, this is more manageable. Overall, the data does seem to show a trend of increasing temperatures, but once again we've made some rather arbitrary choices: to only sample each decade and to choose July as our month of interest within the decade. At this point we have, however, gained some knowledge of our data. We know how many measurements we have, the time span they cover, and what each measurement contains. We're lucky that we're using carefully prepared data, and as such we don't have any missing or erroneous measurements. Usually things will not be so simple.

In the next section we'll look at how we can more systematically analyze the data. Before we get there, however, it's time for you to do a bit of analysis on your own.

val januaryByDecades = data.filter(r => r.year % 10 == 0 && r.month == 1)
// januaryByDecades: ArraySeq[Record] = ArraySeq(
//   Record(
//     year = 1850,
//     month = 1,
//     anomaly = -0.67456436,
//     lower = -0.98177195,
//     upper = -0.3673568
//   ),
//   Record(
//     year = 1860,
//     month = 1,
//     anomaly = -0.39058298,
//     lower = -0.6584608,
//     upper = -0.12270518
//   ),
//   Record(
//     year = 1870,
//     month = 1,
//     anomaly = -0.21106681,
//     lower = -0.4358849,
//     upper = 0.013751276
//   ),
//   Record(
//     year = 1880,
//     month = 1,
//     anomaly = -0.39386344,
//     lower = -0.60645384,
//     upper = -0.181273
//   ),
//   Record(
//     year = 1890,
//     month = 1,
//     anomaly = -0.53390056,
//     lower = -0.68983096,
//     upper = -0.37797013
//   ),
//   Record(
//     year = 1900,
//     month = 1,
//     anomaly = -0.5065199,
//     lower = -0.6711187,
//     upper = -0.34192112
//   ),
//   Record(
//     year = 1910,
//     month = 1,
//     anomaly = -0.4327842,
//     lower = -0.5747264,
//     upper = -0.29084206
// ...