Control structures in R

If-else statement
for cycles
while
Repeat

If-else statement

Conditional execution of a code. General form:

# if (<condition>){
# what to do if true
#} else if (<other condition>){
# what to do if true
#} else {
# what to do if none of them are true
#}
#

Condition should be logical, or (0,1).
Else and if else are optional, multiple else if are possible.
The statement will be executed and the first! expression that is true will be evaluated.
If multiple statements can be true and we want to evaluate all -> nested if statements.
Short version is ifelse().

For cycle

Iteration, repeat behaviour. If we want to repeat the same command on a set of values or variables.

# for (i in 1:10) {
# print(i)
# }

The value of the iterating variable (here i) is automatically changing in every cycle.
The iterating variable can be also logical, character, list element.
The return value of the iterating variable is the cycle that was run last.

While

While statements will repeat a task until a condition is met. Can easily be infinite, so be careful.

# count <- 0
# while(count < 10) {
#         print(count)
#         count <- count + 1
# }

##DO NOT RUN##

# count <- 0
# while(count < 10) {
#         print(count)
#         count <- count - 1
# }

Repeat

Repeat will repeat the expression in an infinite loop. To stop, the break command has to be used. Can be dangerous.

#count <- 1
# repeat {
#         count <- count + 1
# if (count >10)
#break
# }

break and next

As seen above, break breaks the whole loop and exits.

next only breaks the current iteration and the loop continues.

# for (i in 1:10) {
# if (i==5)
# next
# print(i)
# }

Troubleshooting

Bingo!

Useful functions: print(), browser()

 for (i in 1:10) {
   a <- i*2
  #browser()
   print(i)
 }

## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10

Special looping functions

Widely used functions: apply, lapply, sapply, tapply

`apply()`

works on 2D objects (data.frame, matrix)

iterates around rows or columns

returns vectors

dt <- data.frame(mes1=c(2,3,4), mes2=c(3,2,1), mes3=c(2,4,6))
rowmean <- apply(dt, 1, mean)
rowmean

## [1] 2.333333 3.000000 3.666667

colmean <- apply(dt, 2, mean)
colmean

## mes1 mes2 mes3 
##    3    2    4

`lapply()`

Works on lists

Iterates around list elements

Returns a list

lt <- list(c(1,2,4), c(4,52,5,3,2), c(2,3,5,2,2))
lapply(lt, length)

## [[1]]
## [1] 3
## 
## [[2]]
## [1] 5
## 
## [[3]]
## [1] 5

`tapply()`

Works on subsettable objects (e.g. vector)

Subsets them by the index and applies a function

Returns a list

One can use aggregate() instead

data(iris)
tapply(iris$Sepal.Width, iris$Species, median)

##     setosa versicolor  virginica 
##        3.4        2.8        3.0

`sapply()`, `vapply()`

Similar to lapply

Returns a lists, matrices, vectors or arrays.

i39 <- sapply(3:9, seq) # list of vectors
sapply(i39, fivenum)

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,]  1.0  1.0    1  1.0  1.0  1.0    1
## [2,]  1.5  1.5    2  2.0  2.5  2.5    3
## [3,]  2.0  2.5    3  3.5  4.0  4.5    5
## [4,]  2.5  3.5    4  5.0  5.5  6.5    7
## [5,]  3.0  4.0    5  6.0  7.0  8.0    9

vapply(i39, fivenum,
       c(Min. = 0, "1st Qu." = 0, Median = 0, "3rd Qu." = 0, Max. = 0))

##         [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## Min.     1.0  1.0    1  1.0  1.0  1.0    1
## 1st Qu.  1.5  1.5    2  2.0  2.5  2.5    3
## Median   2.0  2.5    3  3.5  4.0  4.5    5
## 3rd Qu.  2.5  3.5    4  5.0  5.5  6.5    7
## Max.     3.0  4.0    5  6.0  7.0  8.0    9

Control structures