Spreadsheet basics for journalists


No matter what you end up doing in media, you’ll almost certainly end up needing to make sense of some data. And you’ll probably need to do it both perfectly and in a hurry, because that’s how media people are expected to do everything. This three-part minicourse can help you be ready. It will teach you some spreadsheet basics using Google’s free, online spreadsheet app, Google Sheets. You’ll also pick up a few insider skills that could truly set you apart. Give it a try. I think you’ll be glad you did.

— Ken Blake, May 11, 2019

Practice data: Proposed raises

Throughout the course, you’ll be working with this made-up dataset. Imagine it describes the original and new salary for each of 22 city department heads who would receive pay raises under a measure being proposed by the local mayor.

NameOld salaryNew salary
Anderson, Daniel4919054109
Brown, Michael5475459682
Davis, Richard4537446735
Garcia, Charles4693849285
Hernandez, Mary5843759606
Jackson, Barbara4493647632
Johnson, John4698649805
Jones, William4741348361
Lee, Susan4054542167
Lopez, Maria5349554030
Martin, Linda5165756823
Martinez, Christopher4465546888
Miller, David5477357512
Moore, Patricia4960151585
Rodriguez, Joseph4160644934
Smith, James4311046990
Taylor, Paul5094754004
Thomas, Mark4450248062
Thompson, Elizabeth4310746556
White, Jennifer5446157184
Williams, Robert5947362447
Wilson, Thomas5570156815

You’ll learn how to use Google Sheets to capture these figures, analyze them, and come up with the information and data visualization needed to write a post like this one:

Mayor proposes nearly $60,000 in staff raises (Click to see the post)

Google Sheets is free. All that’s required is an Internet-connected PC or Mac. Google Sheets works the same way on either type of computer. An experienced user could produce everything needed for the post, including the graphic, in about five minutes. Here’s a video demonstration, in real time, of the techniques you’ll learn.

A three-part course in learning to use Google Sheets

Part 1: Making a plan & getting started. It usually pays to spend a few minutes thinking about what you might want to learn from a dataset before you start analyzing it. This tutorial looks at what might be newsworthy about the raises dataset, shows you how to create a Google Sheet, and introduces you to fundamentals like rows, columns and cells. Finally, it shows you how to produce and replicate a simple computation.

Part 2: Describing and comparing the raises. Part 1 covered the basics of setting up and using a spreadsheet. This lesson gets down to the business of discovering who got the biggest and smallest raises, what the average raise was, the total amount of money the raises will cost the city, and other things you’d need to know to write a thorough, accurate story about the raises.

Part 3: Making an interactive graphic. You might be surprised by how easy it is to add a basic, online, interactive data visualization to your reporting. This lesson will show you how to do it using Google Sheets’ built-in, shareable chart templates.

An exercise: Tennessee county population estimates
Last updated: May 17, 2024

Ready to try an analysis on your own?

Below are the U.S. Census Bureau’s 2017 and latest-available annual population estimates for the 41 counties in Tennessee. In all, Tennessee has 95 counties. The rest are in West Tennessee or East Tennessee. I excluded the non-Middle Tennessee counties to keep the exercise simple.

Using what you’ve learned, calculate each county’s change and percent change in population between the two years shown. Then, sort the data by one measure or the other, and see how Rutherford County (where MTSU is) compares to the other counties. Finally, produce and share an interactive data visualization showing the change or percent change for each of the 10 counties with the largest differences. Finally, write a news story about the county population changes, using the results of your analysis, your chart, and information and quotes from this (made-up) background information. If you’re doing this exercise for a class, follow the specific directions your professor gives you.

Don’t worry; while these data are about population estimates rather than salaries, the dataset is structured essentially the same way as the salary data were structured. So you can do to these data most of what you did to the salary data above.

CountyRegion2017 Pop2022 Pop
BedfordOutlying4685450533
CannonNashville area1383914481
CheathamNashville area3971341184
ClayOutlying76847592
CoffeeOutlying5407458080
DavidsonNashville area678322709786
DeKalbOutlying1938020209
DicksonNashville area5134154563
FentressOutlying1794018642
FranklinOutlying4139742980
GilesOutlying2902430317
GrundyOutlying1335913550
HickmanNashville area2450224996
HoustonOutlying81888253
HumphreysOutlying1828119032
JacksonOutlying1157311730
LawrenceOutlying4259144377
LewisOutlying1194412637
LincolnOutlying3354335365
MaconNashville area2323925365
MarshallOutlying3175334567
MauryNashville area87606102002
MontgomeryOutlying192120222305
MooreOutlying63026558
OvertonOutlying2199522576
PerryOutlying78828432
PickettOutlying50715042
PutnamOutlying7556580157
RobertsonNashville area6857573297
RutherfordNashville area298456343727
SequatchieOutlying1465416065
SmithNashville area1927920034
StewartOutlying1324813724
SumnerNashville area175730196845
TrousdaleNashville area877311596
Van BurenOutlying56756182
WarrenOutlying4021041163
WayneOutlying1671316325
WhiteOutlying2639427420
WilliamsonNashville area212161248897
WilsonNashville area128874149096