1. Creating Figures
Figure 9. Daily patterns of full stomachs (%) of herring and sprat sampled during the 1997 survey. Standard errors (bars) were not available for sprat.
From Cardinale et al. (2003)
1/10

2. When to use figures vs. tables?
General rules of thumb:
< 4 numbers, state them in the text
Tables are used to report data, figures are used to look at relationships and patterns

3. When to use figures vs. tables?
Figures - present complex data patterns, when visual is more intuitive than numbers, many data points
Easy to describe in text; figure not necessary
More difficult to describe in text; figure is instructive
South
North
Cod growth rate
(mm/yr)
South
North
Cod growth rate
(mm/yr)
10˚ C
4˚ C
Tables – convey essential info that does not fit into text easily (e.g., lat/lon of multiple sites, model parameters), data of interest to others (e.g., model coefficients)
3/10

4. When to use figures vs. tables?
Numbering – tables and figures each numbered in order of citation in text (each start at Table 1 and Figure 1)
4/10

5. The purpose of figures To present your data in an informative summary
To visualize your data – look for relationships, trends, outliers
To maximize information transfer and minimize ‘clutter’

6. Minimize wasted space (e.g., origin does not need to be zero)
All elements on graph should convey information (e.g., avoid grid lines)

7. Creating figures (bad vs good)
Fish Growth
Outside frame, border around graph
No legend for data points, red-green color blindness
Gridlines present, axes lines are grey
Fonts are too small, inappropriate decimal places
No units on axes labels
Figure title
Take-home lesson – Excel does not provide a good graph format, you have to do it yourself
MUCH BETTER!
7/10

8. Effective captions are important for figures and tables
Provide context
Allow figure/table to stand on its own
Complete sentences
NO titles on figures!
Figure - weight of fish of different ages
Figure 1. Mass-at-age of walleye captured between 2002 and 2003 from lakes with stunted- and trophy-sized individuals.
Why aren’t the dots connected with a line?
8/10

9. Example of a very cluttered figure
Note that 3-dimensional bars are misleading, data appear as volume, not height

10. Basic Rules
Independent variable (the one you control) is the x-axis; dependent (or response) variable is the y-axis
The type of data determines the type of graph, e.g.:
Time-series – usually a line graph
Two independent variables – x-y (scatterplot)
Percentages – pie-chart

11. Line or bar graph?

12. Bar graphs can be used to show proportions of a whole
Teixeira and Cortes 2006

13. Pie charts show proportions and can make data easier to visualize
Madenjian et al. 2009

14. Two independent variables – x-y plot

15. Comparison of treatments (dependent vs. independent variables)

16. Addition of years next to each data point aided in data interpretation
(years that did not fit the curve were El Nino events that changed the stock-recruit dynamic of the pollock)
Bering Sea pollock
B. A. Megrey and V. G. Wespestad

17. Creating Tables
Table 1. The size range and sample size of each fish species (with prey in stomachs) used in the 2005 (lakewide) and 2006 (western Lake Superior; WLS) selectivity analysis. “na” = not available.
From Isaac et al. (2012)
17/10

18. Creating Tables These data do not justify a table.
Table 1. The size range and sample size of smelt with prey in stomachs used in the 2005 (lakewide) and 2006 (western Lake Superior; WLS) selectivity analysis.
From Isaac et al. (2012)
These data do not justify a table.
18/10

19. Creating tables Mean length mass # fish Species/life-stage
Yellow perch
Smallmouth bass
Northern pike
Bluegill
Juveniles
Adults
142 14 29
203 37
157 19 31
423
339 18
278 21
613
638 8

20. Creating tables Bounded by thicker borders on top and bottom
Mean
length (mm)
mass (g) n
Species/life-stage
Yellow perch
Smallmouth bass
Northern pike
Bluegill
Juveniles
Adults
142 (± 42.3)
14 (± 2.7) 29
203 (± 52.9)
29 (± 3.6) 37
157 (± 22.7)
19 (± 1.2) 31
423 (± 62.1)
339 (± 32.5) 18
278 (± 48.2)
21 (± 3.3) 14
613 (± 77.3)
638 (± 62.7) 8
Thinner border to set column headers apart from data
20/10

21. Creating tables
Clearly labeled column headers, with units, “n” is universal for “sample size”
Bounded by thicker borders on top and bottom
Mean
length (mm)
mass (g) n
Species/life-stage
Yellow perch
Smallmouth bass
Northern pike
Bluegill
Juveniles
Adults
142 (± 42.3)
14 (± 2.7) 29
203 (± 52.9)
29 (± 3.6) 37
157 (± 22.7)
19 (± 1.2) 31
423 (± 62.1)
339 (± 32.5) 18
278 (± 48.2)
21 (± 3.3) 14
613 (± 77.3)
638 (± 62.7) 8
Thinner border to set column headers apart from data
No borders separating columns or rows of data
Measure of variance, when appropriate
21/10

22. Creating tables All numbers are right-aligned in columns Mean
length (mm)
mass (g) n
Species/life-stage
Yellow perch
Smallmouth bass
Northern pike
Bluegill
Juveniles
Adults
142 (± 42.3)
14 (± 2.7) 29
203 (± 52.9)
29 (± 3.6) 37
157 (± 22.7)
19 (± 1.2) 31
423 (± 62.1)
339 (± 32.5) 18
278 (± 48.2)
21 (± 3.3) 14
613 (± 77.3)
638 (± 62.7) 8
22/10

23. Creating tables
Table 1. Mean lengths and mass of four fish species sampled in Lost Lake in Standard deviations are shown in parentheses.
Caption that identifies the types of data in the table, with time and place where they were collected.
Mean
length (mm)
mass (g) n
Species/life-stage
Yellow perch
Smallmouth bass
Northern pike
Bluegill
Juveniles
Adults
142 (± 42.3)
14 (± 2.7) 29
203 (± 52.9)
29 (± 3.6) 37
157 (± 22.7)
19 (± 1.2) 31
423 (± 62.1)
339 (± 32.5) 18
278 (± 48.2)
21 (± 3.3) 14
613 (± 77.3)
638 (± 62.7) 8

24. Use of multiple underlines to link and clarify headings
Table 1. The size range and sample size of each fish species (with prey in stomachs) used in the 2005 (lakewide) and 2006 (western Lake Superior; WLS) selectivity analysis. “na” = not available.
From Isaac et al. (2012)