Introduction to R for Life Sciences - For loops

1. Iteration and functions: for-loops
Day 4 - Introduction to R for Life Sciences

2. Copy paste is evil (a.k.a Don’t Repeat Yourself)
plot(density(M[ , "msn2_del"]),
main="msn2_del")
rug(M[ , "msn2_del"])
plot(density(M[ , "ssn6_del"]),
main="ssn6_del")
rug(M[ , "ssn6_del"])
plot(density(M[ , "ctr9_del"]),
main="ctr9_del")
rug(M[ , "ctr9_del"])

3. Refactoring ....
plot(density(M[ , "msn2_del"]),
main="msn2_del")
rug(M[ , "msn2_del"])
plot(density(M[ , "ssn6_del"]),
main="ssn6_del")
rug(M[ , "ssn6_del"])
plot(density(M[ , "ctr9_del"]),
main="ctr9_del")
rug(M[ , "ctr9_del"])
my.plot <- function(name) {
plot(density(M[ , name]),
main=name)
rug(M[ , name])
NULL
}
my.plot("msn2_del")
my.plot("ssn6_del")
my.plot("ctr9_del")

4. wrong: right:
my.plot <- function(name) {
plot(density(M[ , name]),
main=name)
rug(M[ , name])
}
# M is defined outside my.plot
my.plot("msn2_del")
my.plot("ssn6_del")
my.plot("ctr9_del")
# Avoid global variables; pass them in as arguments
my.plot <- function(mat, name) {
x <- mat[,name]
plot(density(x), main=name)
rug(x)
}
my.plot(M, "msn2_del")
my.plot(M, "ssn6_del")
my.plot(M, "ctr9_del")

5. Even less copy-pasting: for-loops
for ( some.variable in some.vector) {
cat( some.variable, “n”)
}
The statements inside the curly braces are executed as many times
as the length of some.vector, with some.variable getting each of the
values in some.vector in turn.
deletions <- c(“msn2_del”, “ssn6_del”, “ctr9_del”)
for (del in deletions) {
my.plot(M, del)
}

6. Iteration using for-loops
for (cutoff in c(0.001, 0.01, 0.05)) { # start of block
print.performance(data, p=cutoff) # note indentation
} # end of block
for (TF in colnames(data) ) {
txpts <- select.txpts(data, TF, k=3, limit=0.5)
network <- network.add(network, data, TF, txpts)
}

7. Avoid loops if you can use aggregates or apply()
# don’t:
total <- 0
for(i in 1:length(x)) {
total <- total + (x[i])^2
}
# … or:
means <- rep(NA, ncol(data))
for (col in 1:ncol(data)) {
means[col] <- mean(data[,col])
}
# do:
sum(x^2)
apply(data, 2, mean)

8. Accessing lists()
> lst$a
[1] 3
> lst$b
[1] 1 2 3 4 5 6 7 8 9 10
and also:
> lst[[ “a” ]]
[1] 3
> lst[[ “b” ]]
[1] 1 2 3 4 5 6 7 8 9 10
Note the double square brackets!
> name <- “a”
> lst[[ name ]]
[1] 3
> name <- “b”
> lst[[name]]
[1] 1 2 3 4 5 6 7 8 9 10

9. > load("TF_targets.rda")
> TF.targets
$ABF1
[1] "YPL242C" "YPL228W" "YPL179W"
"YPL159C" "YPL036W" etc.
$ACE2
[1] "YPL026C" "YPL024W" "YOR140W" etc.
$etc.
>names(TF.targets)
[1] "ABF1" "ACE2" etc.
for-loops on lists
for (TF in names(TF.targets) ) {
transcripts <- TF.targets[[ TF ]]
plot.txpt(main=TF,
hilite=transcripts)
}