E N D
1. Data Manipulation in SAS
Mike Juhan
Susan Felege
2. Why SAS? That was our topic!
Disadvantages
Expensive
Additional software packages/ tutorials =
License Renewal
Advantages
Powerful Statistical package
Commonly used
3. Types Of Data Manipulation There are many
Basic Transformations
log/ ln
Inverse
Resample
Bootstrapping
Jackknife
Create subsets of data
Rename variables
4. Data Sets VPA
Basal areas at turkey locations
Stem density counts (trees per acre)
Rabbits
Fictitious dataset of rabbit pop tracked over 175 generations
Age-specific survival rates
Case 2000 some animal example
Population projections
5. Our Goals Import data from Access
Rename variables
Conditional statements
New dataset with an extra column (do block)
Sub-datasets
Histogram (gchart)
Basic log transformation
Jackknife (macro, do loop)
Plots (gplot)
Proc IML (arrays and do loop)
6. Importing Data from Access Import Wizard
8. Renaming of VariablesRenaming of Variables
9. Conditional statementsConditional statements
15. Jackknife data rabbits;
input generation pop;
y=pop;
x1=generation;
datalines;
1 62
2 79
3 89
4 84
5 59
6 86
7 92
8 58
9 94
86
175 91
;
20. Population Projection
23. Helpful Resources Applied Statistics and the SAS Programming Language (Cody and Smith 1997)
The Little SAS Book (Delwiche and Slaughter)
The Web (Google)
utexas website
UCLA website