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Control structures

Source: vignettes/control_structures.Rmd
control_structures.Rmd

Control structures in R

  1. If-else statement

  2. for cycles

  3. while

  4. 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))
lt2 <- lapply(lt, mean)

for (i in 1:length(lt)){
  print(length(lt[[i]]))
}
## [1] 3
## [1] 5
## [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