Sometimes a table is going to outpace any chart(s) you can make. Presenting a table doesn't need a lot and I use the same principles for tables that I apply to charts.

1. Fire data isn’t ugly
Presenting fire data effectively series
Episode: Table it
July 2015

2. A makeover of fire department
data to transform it from
unclear and underperforming
to powerfully informative.
Traffic accident

3. Ok, so it’s technically not fire data here but I came across
this report and I am a crazy person who redoes charts in
my spare time couldn’t pass up the opportunity to cover
tables.
Tables aren’t technically a chart, I know, but they are a
form of data visualization. And I love data viz in all its
forms!

4. Data visualization is the presentation of data in a pictorial
or graphical format, produced in an effort to help people
understand the data by placing it in a visual context.
It’s been around for at least several millennia (and possibly
a million years older than that) so it’s not some newfangled
idea. It just has newer forms.

5. Exhibit: Fatal Table
Traffic accidents with cell phones

6. Year Total Fatal Injury Deaths Injured
2008 392 4 140 4 193
2009 415 3 143 3 195
2010 519 6 173 6 260
2011 480 3 171 3 228
2012 523 6 183 6 262
2013 471 6 148 7 209
Total 2,800 28 958 29 1,347
Accidents People

7. Year Total Fatal Injury Deaths Injured
2008 392 4 140 4 193
2009 415 3 143 3 195
2010 519 6 173 6 260
2011 480 3 171 3 228
2012 523 6 183 6 262
2013 471 6 148 7 209
Total 2,800 28 958 29 1,347
Accidents People
First, I get that the data here is about traffic accidents
where a driver was using a cell phone, but I think we all
know what I’m going to do with that clipart. (Delete it.)
If I had a picture of a wreck caused by a cell phone I
would definitely use it here.

8. Spare tables
Tables aren’t the most glamorous

9. Tables aren’t the most glamorous data viz but when you
need to convey a lot of information in many different
categories, tables do the job.
They can do the job without all the colors, though.
Year Total Fatal Injury Deaths Injured
2008 392 4 140 4 193
2009 415 3 143 3 195
2010 519 6 173 6 260
2011 480 3 171 3 228
2012 523 6 183 6 262
2013 471 6 148 7 209
Total 2,800 28 958 29 1,347
Accidents People

10. We don’t need the bold gridlines separating different
categories.
We also don’t need bolding on the headers. Bolding
doesn’t print very well and can strain eyes with letters
bleeding together.
Year Total Fatal Injury Deaths Injured
2008 392 4 140 4 193
2009 415 3 143 3 195
2010 519 6 173 6 260
2011 480 3 171 3 228
2012 523 6 183 6 262
2013 471 6 148 7 209
Total 2,800 28 958 29 1,347
Accidents People

11. If we were working with 20 years, I’d probably put a (really)
light grey shading between rows to help readers. We can
get away without it here because our table is pretty short.
You can choose to shade anything, but eyes are sensitive
to shading and don’t need a huge contrast for this.
Year Total Fatal Injury Deaths Injured
2008 392 4 140 4 193
2009 415 3 143 3 195
2010 519 6 173 6 260
2011 480 3 171 3 228
2012 523 6 183 6 262
2013 471 6 148 7 209
Total 2,800 28 958 29 1,347
Accidents People

12. It took me a bit to realize why they were separating
accidents from people. More people can die or be injured
than there are accidents. The same is true for fire
casualties where one fire may injure multiple people.
Instead of separating that out, I changed the column titles.
Year Accidents Fatal Accidents Injury Accidents Deaths Injuries
2008 392 4 140 4 193
2009 415 3 143 3 195
2010 519 6 173 6 260
2011 480 3 171 3 228
2012 523 6 183 6 262
2013 471 6 148 7 209
Total 2,800 28 958 29 1,347

13. We’re sensitive to proximity. If you have similar data like
fatalities and fatal accidents, put them together. This helps
reinforce the idea of multiple fatalities as well.
Year Accidents Fatal Accidents Deaths Injury Accidents Injuries
2008 392 4 4 140 193
2009 415 3 3 143 195
2010 519 6 6 173 260
2011 480 3 3 171 228
2012 523 6 6 183 262
2013 471 6 7 148 209
Total 2,800 28 29 958 1,347

14. I moved Accidents to the end so now the totals are in
ascending order. (And if we’re being honest, fatalities are the main interest for most readers.)
I’ve also removed the header “Year” because it’s obvious,
and right aligned the titles to match each column (barely
noticeable here).
Fatal Accidents Deaths Injury Accidents Injuries Accidents
2008 4 4 140 193 392
2009 3 3 143 195 415
2010 6 6 173 260 519
2011 3 3 171 228 480
2012 6 6 183 262 523
2013 6 7 148 209 471
Total 28 29 958 1,347 2,800

15. More subtle changes: I’ve added some white space
between the column titles and after 2013 to separate
totals. I did this by simply adjusting the row height.
I’ve also shifted the columns, trying to find that sweet spot
of “close” but not “crowding.”
Fatal Accidents Deaths Injury Accidents Injuries Accidents
2008 4 4 140 193 392
2009 3 3 143 195 415
2010 6 6 173 260 519
2011 3 3 171 228 480
2012 6 6 183 262 523
2013 6 7 148 209 471
Total 28 29 958 1,347 2,800

16. We’re fine here but if you want to emphasize something,
you can using a bit of color.
I picked 2013 because that was the year this report was
featuring.
Fatal Accidents Deaths Injury Accidents Injuries Accidents
2008 4 4 140 193 392
2009 3 3 143 195 415
2010 6 6 173 260 519
2011 3 3 171 228 480
2012 6 6 183 262 523
2013 6 7 148 209 471
Total 28 29 958 1,347 2,800

17. Remove to Improve
Tables aren’t charts but the
same principles apply

18. Before
After
Year Total Fatal Injury Deaths Injured
2008 392 4 140 4 193
2009 415 3 143 3 195
2010 519 6 173 6 260
2011 480 3 171 3 228
2012 523 6 183 6 262
2013 471 6 148 7 209
Total 2,800 28 958 29 1,347
Accidents People
Fatal Accidents Deaths Injury Accidents Injuries Accidents
2008 4 4 140 193 392
2009 3 3 143 195 415
2010 6 6 173 260 519
2011 3 3 171 228 480
2012 6 6 183 262 523
2013 6 7 148 209 471
Total 28 29 958 1,347 2,800

19. Clean is stylish
Keep your tables just as clean as
your Charts. Your printer ink bill
will thank you, too.

20. Hello! I’m Sara Wood and I love converting fire service members into
NFIRS operatives. I’m the State NFIRS program manager for Kansas and
enjoy providing classes to help bring fire departments into the era of data
driven decisions. If you need help creating a presentation or analyzing
your data, I’d love to hear from you!