Baby Boom: Udemy Excel Tutorial on Analyzing Large Data Sets

This tutorial not only shows how to use Excel Pivot Tables and Graphs, but teaches the mindset needed in exploratory data analysis - look beneath the surface, consider the non-obvious interpretations, and question everything (including the data).

Explore your data and uncover insights

The pivot table and chart we’ve created are based on all of the data. However, there’s a natural and obvious division within the topic of baby names: male and female names. For one thing, more boys are born than girls (about 4% to 8% more, due to biological and environmental factors). Also, there are different social pressures on parents when naming boys and girls; we’ll see evidence of this soon.

Luckily, with pivot tables, it’s easy to separate out the sexes. Just drag the Sex field name next to the checkbox in the upper right down to the COLUMNS box. Click the filter icon at the right of the new cell named Column Labels at the top of the pivot chart. Make sure F and M are selected, but (blank) is not – there are no blank values for Sex in this dataset, which you could easily verify by looking at the column totals with (blank) selected.

Where you had two columns before, now you have six: Unique Names and Number of Births for females, males and both together. Here is what you should see:

(Note: I clicked in a non-pivot table cell and moved the chart over so everything fits on one screen.)

Unfortunately, your pivot chart has lost its secondary axis. You could go back and reassign both Number of Birth lines to the secondary axis, but here is where it’s a good idea to stop using pivot tables and copy everything into a regular Excel spreadsheet. Why? Pivot tables are powerful, but they’re not flexible. You can add calculated columns, but it’s needlessly complicated. Pivot charts are even more limited: they will always show all the data in a pivot table. For example, if you wanted to limit the chart to only female names, or only totals, we’d have to change the pivot table itself.

So highlight columns A through G and copy them. Then, create a new worksheet, and right-click in cell A1, and select Paste as Values (or just press the ‘V’ key). Resize the columns so all the text fits, and rename the sheet Diversity (since that’s what we’ll be looking at). ‘Diversity’, by the way, is simply the average number of names per birth. Its maximum possible value is 1, which would only happen if every baby born had a different name.

You should see this:

We’re not interested in the totals anymore, so go ahead and delete columns F and G (this will give us more screen real estate). Replace them with Diversity in F4, F in F5 and M in G5, and in cell F6 type the formula =B6/D6. Copy this cell, then select cells F6:G109 and paste. At the bottom of your spreadsheet, in Row 110, there are totals. You should delete these, because they’re potentially confusing, and it doesn’t make sense to add together this kind of data for all years.

Now you’re ready to add a chart. Select cells A5:A109, press Ctrl/Cmd, and select cells F5:G109 (the female and male diversity ratios, plus the column headers, F and M). Then in the Insert Tab select the scatter chart with straight lines, as shown here:

You should always label the axes of charts, so with the chart selected, use the DESIGN tab and add these features. (In Excel 2013, click on the Add Chart Element button at the left; the procedure is slightly different for other version of Excel). Name the x-axis Years and the y-axis Names per birth and, while you’re at it, change the chart title to Diversity.

Ignore the first half of the graph for now: let’s look at 1960 to present. As one would expect from anecdotal experience, there is more diversity in names now than there was fifty years ago. In addition, female names are more diverse than male names. Perhaps parents want their girls to stand out more? It’s interesting that the changes in diversity tracks pretty closely between the sexes. This suggests that the difference is due to something intrinsic to the difference between girls’ and boys’ names, not momentary trends. Perhaps the explanation is simple: there is more diversity in girls’ names because there are more spelling variations in girls’ names, like ‘Ann’ and ‘Anne’ and ‘Anna’.

The train of thought outlined above illustrates the kind of mindset needed in exploratory data analysis. Insights come from looking beneath the surface and the obvious interpretation, by questioning everything (including the data itself!), and by considering all possibilities.

With that in mind, take a look at the graph from 1910 to 1960. The maximum amount of name diversity happens in the first years of the data. Does this seem plausible to you? Were parents giving their kids wild and unique names during World War I at twice the rate as today?

If there’s something that doesn’t make intuitive sense in the data, it’s time for a sanity check. A good strategy is to check something else that, if the data is accurate, should be true. Human sex ratio at birth was mentioned above: it should always be between 103 and 108 boys born per 100 girls born. That seems like a good place to start.

Determine Important Ratios

You can just add more columns to the Diversity spreadsheet. Move the chart out of the way to make room.

Call the new group of columns Sex Ratio, and write three column labels in cells H5:J5 -- Actual, Minimum and Maximum. Type the formula =100*E6/D6 into cell H6, and the numbers 103 and 108 in cells I6 and J6, respectively. Copy the contents of H6:J6 and paste into cells H7:J109.

Now to make the chart. Select cells A5:A109 (which contain the years), hold down Ctrl/Cmd and select your new data in H5:J109. In the Insert tab, insert a scatter chart with lines as you did above. Add a title and axis labels. You should reformat the y-axis, so that you can visualize the data more clearly. (Usually you want the y-axis to go all the way to zero, but in this case the y-axis can’t possibly go down to zero (if there were no boys born, the human race would die out, right?) Select the numbers on the y axis, right-click and choose Format Axis from the context menu, in the resulting dialogue box type 50 in Minimum and 120 in Maximum and click OK.

Here is what you should see:

As you can clearly see, this data does not display the accepted sex ratios for humans. In fact, in the first few years it’s way, way off. In the 1910s, there are only half as many boys as girls being born.

The reason for this is quite simple, and unfortunate. If you look at the landing page for this dataset at http://www.ssa.gov/oact/babynames/, you can see the U.S. Social Security Administration calls it a baby names dataset, and even has graphics of babies, but the fact is, many of these names are not of babies: they’re names of adults, and not even a representative sample of adult Americans.

If you look at the Wikipedia entry for History of Social Security in the United States at wikipedia.org/wiki/History_of_Social_Security_in_the_United_States, you’ll see that Social Security only started in 1937. Yet your data goes back to 1910, and for some other states it goes back as far as 1880. How can that be? Well, those with a 1910 birth year were at least 27 years old when they applied for Social Security. They applied, at the earliest, in 1937, and gave their birth year. This means people who died before the age of 27 are automatically excluded from the data (and infant and childhood mortality was far higher in the 1910s than it is today.) Also, Social Security was not a universal program then as it is today. Only those on a list of accepted occupations could join, which in practice, meant middle-class white people, so there is a social and ethnic bias to the dataset before the rules were relaxed in the 1950s.

Why are there more women than men in the early years? Because women live longer than men. They had less chance of dying before they could apply for Social Security, and outlived their husbands which meant they needed to apply in their own name in order to receive their husbands’ benefits.

It’s worth pointing out that it was unusual for Americans to give babies a Social Security number at all before 1986. That’s the year the IRS started requiring them to claim a child as a tax deduction. Before that time, it was usual for people to apply for a Social Security number when they filed their own first tax return, usually in their late teens.

Finally, why is the sex ratio in the dataset above normal values starting around 1970? This one is easier to figure out, because it’s something you saw in the Diversity graph. There are more girls’ names than boys’ names, and the dataset leaves out names belonging to fewer than five people for privacy reasons. That means that more girls’ names than boys’ names are excluded from the dataset, so the ratio of boys to girls is a little higher.

Does this mean this dataset is useless? Absolutely not. All datasets have strengths and weaknesses. The important thing is knowing what they are, so you don’t draw unwarranted conclusions. (For example, you would probably hesitate to declare the top boys’ names of 1910, but you’d have a lot more confidence in 2000.) With that in mind, let’s do some more common analyses of the data, and at the end, you’ll be able to see what it means for a ‘baby names’ dataset to actually contain adults names.