# 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).

Graph individual data points and trends

When you have data that is naturally divided into subcategories (in this case, years), it’s a good idea to calculate some statistics just in terms of that subset. For example, if you wanted to calculate the #1 names overall, it would be difficult to do that for the entire dataset, because there are more births in the 2000s than in the 1910s, so in practice the result would be the “#1 name overall, but mostly nowadays.”

It makes a lot more sense to compare, for each row, the percentages of births of that name and sex that year to all births of that sex that year, and rank them. (For example, this will allow you to determine the popularity of the name ‘Evelyn’ relative to ‘Margaret’—and every other name.) Here’s how you do it.

Go back to your CA worksheet. The data, as downloaded, should already be sorted the way you need it, but you should never take such things for granted. Select columns A:D, in the Home tab click the Sort & Filter button on the right, choose Custom Sort and use the Insert button to have three rows of criteria. Make these criteria Sex A-Z, Year Smallest to Largest and Births Largest to Smallest as shown in the following figure, then click OK:

Now you can add your new columns. Type new headers in E1 and F1: % of Births (same sex & year) and Rank (same sex & year), respectively. These column names might strike you as a little long, but it’s best to err on the side of clarity. If someone else has to look at and interpret your work, or even if you have to return to it weeks or months later, it’s best that everything can be understood as easily as possible.

For your % of Births column, the concept is easy: divide the number of births in that row, e.g. 295 for Mary in 1910, by the total number of births of that sex and year, e.g. female births in 1910. Where can you find that information? In the pivot table you made at the beginning of the tutorial. YES!

Take a look at that pivot table. The information you need to access is in Columns D to E is. Luckily Excel has a few different functions you can use to look up data in other worksheets; the easiest is the VLOOKUP function.

Go back to the CA worksheet and type the following into cell E2: =D2/VLOOKUP(B2,Pivot!\$A\$6:\$E\$109,IF(A2="F",4,5),FALSE)

If you’re not familiar with the VLOOKUP function, here’s a breakdown of all of the arguments:

• D2: that’s the number of births for Mary in 1910, which you’ll divide by all female 1910 births.
• B2: that’s the year you want to look up, in this case 1910.
• Pivot!\$A\$6:\$E\$109 tells the function to look in the range of the Pivot spreadsheet with the years in the leftmost column and the total births, female and male, in the two rightmost columns. This is what will be matched with the value in B2. The dollar signs are important. They tell Excel not to move the lookup range down as you copy the formula down.
• IF(A2=”F”,4,5) tells the function what column to look in for the results. If your row is a female name, it will look in Column 4, otherwise Column 5.
• FALSE tells the function to return an error if it can’t find the year in the Pivot worksheet. This shouldn’t happen, but it’s good to be explicit here, so that if something goes wrong, you’ll know about it!

You should see the value 0.049579…. Copy this cell and paste it into every cell of Column E below it. It might take your computer a second or two (or three or four…), depending on how powerful it is, to calculate all of these values (there are over 300,000 of them, after all). To avoid having to wait for recalculations in the future, select all of Column E, copy it, and Paste as Values. This is safe to do because you can be confident the underlying values being calculated will not change in the future.

One of the good features in Excel is that it can display percentages without changing the underlying value. In other words, you don’t need to multiply your results by 100, and then divide by 100, if you want to use them in a calculation. Select Column E and use the Number Group on the Home tab to change the formatting to percentage with three decimal places.

Now is a good time for a sanity check. In any blank cell, type the following: =SUMIFS(E:E,A:A,"F",B:B,1910). This tells the function to add together the values in Column E only for those rows where Column A contains F and Column B contains 1910. The result should be 1, i.e. 100%. If you replace F with M and/or 1910 with any year in the dataset, the value should always be 1. Now that the integrity of your data has been verified, you can delete that cell.

Now you can add the values in the ranks column. There are ways to use Excel functions to calculate ranks of subsets, but they’re complicated and slow. Since you’ll be pasting as values later anyway, why not do it the quick and easy way? All that is required for this method is that the data be properly sorted, and you did that earlier.

In cell F2, type the following: =IF(B2<>B1,1,F1+1). This tells Excel to start counting ranks when there is a change from row to row in the Year Column B. (If there is a change in the Sex Column A, there will also be a change in the Year column because of the way you sorted the worksheet earlier.) Excel will give the most common name a rank of 1 because earlier you sorted the worksheet so that births are in descending order. Wherever there isn’t a change in the Year column, Excel increments the rank, i.e. 1, 2, 3, 4, …

Copy F2 to the whole range of Column F, then copy the whole column and Paste as Values. Finally, your worksheet should look like this:

Now that you have these calculated columns, you can use filters as you did above to find the top names in each year. Select Columns A:F, and in the HOME tab, under Sort & Filter, choose Filter.

Now click the filter icon in cell F1 and select only the names of rank one (i.e. the #1 names of each sex of each year). You can see that Mary dominates until the 1930s. Then Mary, Barbara and Linda alternate until Linda wins out for 10 years. Lisa, Jennifer, Jessica and Emily have solid runs later on, then Isabella and Sophia are the top name for three years each. Among the boys, John, Robert, David, Michael and Daniel give way to Jacob for the last few years.

If you look at the percentage column, you can see that the #1 name takes up a smaller and smaller part of all the names as the years go by. This is further evidence of the increasing diversity of names over time, and unlike the diversity measure you calculated before, nothing unexpected happens in the early part of the dataset.

Now you can use the filter tool to visualize individual names. The first thing to do is sort the names; this extra step will make it possible to make charts of the results. Be warned, with over 300,000 rows, this could take a few minutes depending on the power of your computer, but it only has to be done once. Click on the filter icon in the Names column header, and choose Sort A to Z.

Once the sort is completed, use the filters to choose ‘F’ for sex and ‘Heather’ for name, then use the Ctrl/Cmd key to select the year and percent values in Columns B and E, respectively. Insert a chart, and you should see the following:

If you explore these names, you’ll see this sort of pattern more often with girls’ names than boys’ names: a quick rise from obscurity to popularity, then as the name becomes too trendy, a descent to obscurity again. The closest parallel you can see with boys’ names is a more general pattern, those of names ending in ‘n’. Look up names like Mason, Ethan and Jayden, you’ll see them all rise from obscurity to prominence in the 2000s, and many of them are just starting to dip again as of 2013.

Remember what was written above about much of this dataset being adult names instead of baby names, because babies only routinely had Social Security numbers starting in 1986? You can see this in the data too. For example, a baby would be much more likely to have the name “Peter” on his official documents than the nickname “Pete”. But if, when a young man or older filled out a tax return or applied for Social Security, he would be more likely to use the name he went by in day-to-day life, which might be a nickname he’d been called since he was a boy. You can filter the sex for M and the names for ‘Pete’ and ‘Peter’, and either make two charts or put the series on the same chart. Putting two series from the same column on one chart involves using the Select Data chart context menu item, which is beyond the scope of this tutorial, but it’s not that difficult. Have a look at the result:

In the beginning of the dataset, ‘Pete’ is about half as popular as ‘Peter’. Starting at almost exactly 1937 when Social Security numbers were introduced, ‘Pete’ starts a decline in popularity while ‘Peter’ stays relatively constant – this indicates that people are starting to put their birth names on Social Security applications. The decline of ‘Pete’ bottoms out at almost exactly 1986, when it became commonplace for babies to have Social Security numbers.

Hopefully, you found this tutorial enjoyable and interesting. The important lessons to take away from this are that you can manipulate large datasets in Microsoft Excel, and datasets often aren’t exactly what they seem!

Here is full Udemy tutorial on Excel for Large Data sets.

About the author: David Taylor is a scientist who does freelance data analysis for paying customers so that he can afford to do fun data analysis for free on his blog, prooffreader.com. (Yes, "prooffreader" is misspelled; that's the joke!)

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