R is open source statistical software that is easy to access online. It has both a command line interface and R Studio which provides a graphical user interface. The document demonstrates how to use R to analyze experimental data from animal studies through examples involving mice experiments. Data is imported from Excel files and ANOVA, ANCOVA and other statistical analyses are performed. Plots are generated to visualize the results. R is object-oriented so results are saved as objects that can be further explored and summarized.

1. USING R SOFTWARE FOR
STATISTICS
Michael LaValley
4/28/2016

2. R is open source statistical software and is easy to find
through a web search

8. Standard R Interface -
Command Line

9. R Studio Interface – overlays the
command line with more of a
graphical user interface

10. USE R TO ANALYZE SOME EXAMPLES
Data from the book The
Design of Animal
Experiments by Festing,
Overend, Das and Berdoy.
(Royal Society of Medicine
Press, 2011)

13. EXAMPLE 1
Mice were randomly assigned to one of 3 conditions
1. No running – non-rotating wheel in cage for 30 minutes per 24 hours
2. Moderate running – rotating wheel in cage for 30 minutes per 24 hours
3. Marathon running – rotating wheel in cage for 3 hours per 24 hours
After 3 weeks, mice tested for learning ability in a maze – low values
indicate good learning ability

14. Running Learning
None 238
None 250
None 246
None 258
None 251
None 259
None 252
None 230
None 231
Moderate 216
Moderate 241
Moderate 227
Moderate 228
Moderate 238
Moderate 229
Moderate 242
Moderate 212
Moderate 221
Marathon 233
Marathon 212
Marathon 219
Marathon 229
Marathon 218
Marathon 205
Marathon 218
Marathon 232
Marathon 230
Placed data in an excel spreadsheet
Variable names in top row
One row for each mouse with
condition and outcome
Means from table not included
Saved as a CSV text file in excel
‘Save as’ menu

20. boxplot(Learning ~ Running, data=Running)

21. R IS OBJECT-ORIENTED
Almost everything in R is an ‘object’, even the results of analyses
So the code,
fit.running <- aov(Learning ~ Running, data=Running)
Takes the analysis of variance result object and saves it under the
name fit.running
This way I can work on these results in different ways
To get a more expansive summary with an analysis of variance table I
used the following
summary.aov(fit.running)

22. R IS OBJECT-ORIENTED
To see what is in the fit.running object, we can use the ls() function
So the object fit.running is a list of different items, some of which are
lists themselves
To see what these items are we can use a dollar sign ($) and append
the name of the item to fit.running, like fit.running$residuals

25. EXAMPLE 2
Multi-laboratory study of mutations in transgenic mice.
 Mice were exposed to control and 2 levels of a carcinogen.
 DNA extracted and sent to 5 laboratories for number of mutations at a particular
genetic locus

26. Laboratory Dose Mutation
1 1 11.8
1 1 11.3
1 2 18.8
1 2 20.6
1 3 16.4
1 3 23.4
2 1 10.3
2 1 9.5
2 2 16.8
2 2 13
2 3 13.2
2 3 14.6
3 1 6.5
3 1 3.9
3 2 2.4
3 2 3.9
3 3 8.4
3 3 8.6
4 1 5.4
4 1 6.2
4 2 11.7
4 2 11.6
4 3 12.4
4 3 12.2
5 1 14.2
5 1 15.2
5 2 21.2
5 2 13.7
5 3 22.5
5 3 15.8
Placed data in an excel spreadsheet
Variable names in top row
One row for each combination of
lab and dose
Saved as a CSV text file in excel
‘Save as’ menu

27. plot(Mutation$Dose,Mutation$Mutation)

28. plot(Mutation$Dose,Mutation$Mutation, pch=Mutation$Laboratory)

29. DEALING WITH FACTOR LEVELS
Since the laboratories and doses were listed using numbers, R doesn’t
realize that they aren’t measured values
Need to signify that different values of ‘Laboratory’ and ‘Dose’ are
for different facilities and treatments

30. DEALING WITH FACTOR LEVELS
One approach would be to go back to the data spreadsheet and
make laboratory and dose alphabetic – that would work just as in
Example 1.
Working within R, we can use the as.factor() command

32. EXAMPLE 3
Mice treated with a liver toxin, controlling for body weight
 Toxin is expected to affect liver weight, but not body weight
 However, mice with bigger bodies tend to have bigger livers

34. EXAMPLE 3 - TREATMENT
We could look at the liver weights and Treatment as a one-factor
analysis of variance
Result is non-significant (p=0.153) using an F-test with 2 and 14
degrees of freedom
But, this doesn’t account for differences in body weight – use analysis
of covariance

35. EXAMPLE 3 – TREATMENT AND BODY
WEIGHT
When body weight is added into the model
Treatment still is not statistically significant (p=0.0823), but the effect
of the body weight does show up

36. CONCLUSIONS
The R software package is freely available online
 In these slides I’ve just used the analysis of variance methods, but many more types of
analyses are done with the package
There is a large community of statisticians and computer scientists
further developing and refining the package
There is a large base of users in all sorts of quantitative fields using
the package and contributing tutorials, videos, FAQs, etc.