Manipulating Data

Johns Hopkins Bloomberg School of Public Health

Instructor: Jeff Leek

Youtube: http://www.youtube.com/JHSPHAppliedStat

Here is what data looks like in classes

Here is what data looks like in real life

http://brianknaus.com/software/srtoolbox/s_4_1_sequence80.txt

Here is what data looks like in real life

http://en.wikipedia.org/wiki/JSON

Here is what data looks like in real life

view-source:http://espn.go.com/nba/standings

http://espn.go.com/nba/standings

Data may also be in other formats

What do we want at the end?

"Tidy Data"

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• One variable per column
• Column names are variable names
• Variables in only columns (rows)
• Sensible coding of variables
• Variable names matched across files

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[,]

• Most useful feature of R?

which()

• Identifies elements of a matrix/vector
• Important parameters
• arr.ind 

merge()

• merges data sets based on common columns
• Important parameters
• by, all.x, all.y 

%in%

• a %in% b matches elements of a to b
• match() is the more complete interface

Set operations

• union(x,y)
• intersect(x,y)
• setdiff(x,y)
• setequal(x,y)

tapply()

• Applies a function to groups of variables
• Better than the equivalent for loop
• See also by()
• Important parameters
• x, index, fun,simplify

lapply()

• Applies a function to elements of a list
• Important parameters
• x, fun 

sapply()

• User friendly wrapper for lapply()
• sapply(x,f,simplify=FALSE,use.names=FALSE)

= lapply(x,f)