Control-Flow Constructs: if construct

It is probably the most commonly used control structure in R
It is used to test a condition and act on it depending on whether it’s true or false
Basic control-flow constructs are:

if( condition ) {

expression/do something

}

condition length must be 1

# Basic If construct  

if (5>3) print("Five is greater than three")

## [1] "Five is greater than three"

if (TRUE) print("Condition is TRUE")

## [1] "Condition is TRUE"

if (3>5) print("3>5")  # nothing happens

Control-Flow Constructs: if construct

if( condition ) {

expression/do something

}

# number as a condition?
if (1) print("Condition is TRUE")

## [1] "Condition is TRUE"

if (0) print("Condition is TRUE")  
if (-1) print("Condition is TRUE")

## [1] "Condition is TRUE"

if (11) print("Condition is TRUE")

## [1] "Condition is TRUE"

if (0.1) print("Condition is TRUE")

## [1] "Condition is TRUE"

if (-0.1) print("Condition is TRUE")

## [1] "Condition is TRUE"

# if(c(0,3)) print("Condition is TRUE") 
# throws error condition length > 1

Control-Flow Constructs: if construct

if( condition ) {

expression/do something

}

# using variables  
x <- 5  
y <- 3 

if(x>y) print("x is greater than y")

## [1] "x is greater than y"

x <- 3:12 # vector
y <- 1:10 # vector 

# Recall: condition must have a length of one 
# if(x>y) print("x is greater than y")  
# Error in if (x > y) print("x is greater than y") : 
#  the condition has length > 1

x>y

##  [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE

if (sum(x>y)>0) print("x has elements that are greater than elements of y")

## [1] "x has elements that are greater than elements of y"

if(sum(x>y)>0 & sum(x>y)==length(x)) { 

  print("Element-wise all elements of x are greater than elements of y")  
  
}

## [1] "Element-wise all elements of x are greater than elements of y"

if (sum(x>y)) { 

  print(paste0("Element-wise x has ", sum(x>y), " elements that are greater than elements of y")) 
  
}

## [1] "Element-wise x has 10 elements that are greater than elements of y"

Control-Flow Constructs: if-and-else construct

if( condition ) {

do something

} else {

do something different

}

set.seed(123)
x <- sample(-10:10, 6) # 6 random numbers
y <- sample(-10:10, 6) # 6 random numbers

x>y

## [1]  TRUE  TRUE FALSE FALSE  TRUE FALSE

if(sum(x>y)>0) print("x has elements that are greater than elements of y") else print("x has no element that is greater than elements of y")

## [1] "x has elements that are greater than elements of y"

if (sum(x>y)) {
  
  print(paste0("x has ", sum(x>y), " elements that are greater than elements of y")) 

} else { 
    
  print("x has no element that is greater than elements of y")
  
}

## [1] "x has 3 elements that are greater than elements of y"

Control-Flow Constructs: ifelse

ifelse(condition,expression if TRUE,expression if FALSE)

s <- ifelse (5 > 3, "Five is greater than three", "Five is not greater than three")  
s

## [1] "Five is greater than three"

x <- c(6:-4)
sqrt(x) # not a number NaN

##  [1] 2.449490 2.236068 2.000000 1.732051 1.414214 1.000000 0.000000      NaN
##  [9]      NaN      NaN      NaN

sqrt(ifelse (x >= 0, x, NA)) # vectorized conditions

##  [1] 2.449490 2.236068 2.000000 1.732051 1.414214 1.000000 0.000000       NA
##  [9]       NA       NA       NA

# vectorized conditions 

x <- 1:10
x

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

ifelse(x %% 5 == 0, "XXX", as.character(x))

##  [1] "1"   "2"   "3"   "4"   "XXX" "6"   "7"   "8"   "9"   "XXX"

ifelse(x %% 2 == 0, "even", "odd")

##  [1] "odd"  "even" "odd"  "even" "odd"  "even" "odd"  "even" "odd"  "even"

Control-Flow Constructs: ifelse

ifelse(condition,expression if TRUE,expression if FALSE)

data(iris)

# create a new column containing "Large" and "Small"
iris$new.colum <- iris$Sepal.Length * iris$Sepal.Width
head(iris,3)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species new.colum
## 1          5.1         3.5          1.4         0.2  setosa     17.85
## 2          4.9         3.0          1.4         0.2  setosa     14.70
## 3          4.7         3.2          1.3         0.2  setosa     15.04

iris$new.colum <- ifelse (iris$new.colum > median(iris$new.colum), "Large", "Small")
head(iris,3)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species new.colum
## 1          5.1         3.5          1.4         0.2  setosa     Large
## 2          4.9         3.0          1.4         0.2  setosa     Small
## 3          4.7         3.2          1.3         0.2  setosa     Small

Control-Flow Constructs: if-and-else if-and-else construct

if( condition ) {

do something

} else if( condition2 ) {

do something different

} else if( condition3 ) {

do something different

} else {

do something different

}

result <- 65

if (result > 90) {
  "A"
} else if (result > 80) {
    "B"
} else if (result > 50) {
    "C"
} else {
  "F"
}

## [1] "C"

Control-Flow Constructs: Switch

switch(expression,list of values)

Closely related to if
It is also possible to use switch() with a numeric x, but is harder to read
It is recommended using switch() only with character inputs

x <- "c"

if (x == "a") {
  "option 1"
} else if (x == "b") {
  "option 2" 
} else if (x == "c") {
  "option 3"
} else {
  stop("Invalid `x` value")
}

## [1] "option 3"

# or switch 

switch(x,
  a = "option 1",
  b = "option 2",
  c = "option 3",
  stop("Invalid `x` value")
)

## [1] "option 3"

Control-Flow Constructs: Switch

The last component of a switch() should always throw an error, otherwise unmatched inputs will invisibly return NULL

(switch("c", a = 1, b = 2))

## NULL

If multiple inputs have the same output, you can leave the right hand side of = empty and the input will “fall through” to the next value

x <- "horse"
switch(x,
    cow = ,
    horse = ,
    dog = 4,
    human = ,
    chicken = 2,
    plant = 0,
    stop("Unknown input")
)

## [1] 4

Control-Flow Constructs: Switch

# corresponding item in the list is returned
switch(2,"aa","bb","cc")

## [1] "bb"

switch(3,"aa","bb","cc")

## [1] "cc"

# example from https://www.geeksforgeeks.org/switch-case-in-r/
val1 = 6
val2 = 7
val3 = "s"
switch(
    val3,
    "a"= cat("Addition =", val1 + val2),
    "d"= cat("Subtraction =", val1 - val2),
    "r"= cat("Division = ", val1 / val2),
    "s"= cat("Multiplication =", val1 * val2),
    "m"= cat("Modulus =", val1 %% val2),
    "p"= cat("Power =", val1 ^ val2),
        stop("Unknown input")
)

## Multiplication = 42

# or 
switch(
    val3,
    "a"= cat("Addition =", val1 + val2),
    "d"= cat("Subtraction =", val1 - val2),
    "r"= cat("Division = ", val1 / val2),
    "s"= cat("Multiplication =", a <- val1 * val2),
    "m"= cat("Modulus =", val1 %% val2),
    "p"= cat("Power =", val1 ^ val2),
        stop("Unknown input")
)

## Multiplication = 42

## [1] 42

Control-Flow Constructs: for loop

For loops are the only looping construct that is used very frequently R
for loops are used to iterate over items in a vector

for(item in vector) {
perform_action
}

# see ?cat function first: Concatenate and Print  
for(i in 1:5) cat(i)

## 12345

for(i in 1:5) cat("This is iteration i =", i,"\n") # special char: "\n"

## This is iteration i = 1 
## This is iteration i = 2 
## This is iteration i = 3 
## This is iteration i = 4 
## This is iteration i = 5

Control-Flow Constructs: for loop

for(item in vector) {
perform_action
}

for(i in 1:5) i
for(i in 1:5) print(i)

## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5

for(i in 1:5) print(1:i)

## [1] 1
## [1] 1 2
## [1] 1 2 3
## [1] 1 2 3 4
## [1] 1 2 3 4 5

Control-Flow Constructs: for loop

for(item in vector) {
perform_action
}

# for and switch
for(i in c(-1:3, 9))  print(switch(i, 7, 9 , 11, 5))

## NULL
## NULL
## [1] 7
## [1] 9
## [1] 11
## NULL

for(n in c(2,5,10,20,50)) {
  cat(n, ": ", n^2, " ",n^3, "\n", sep = "")
}

## 2: 4 8
## 5: 25 125
## 10: 100 1000
## 20: 400 8000
## 50: 2500 125000

Control-Flow Constructs: for loop

for(item in vector) {
perform_action
}

letters  # letters

##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s"
## [20] "t" "u" "v" "w" "x" "y" "z"

length(letters)

## [1] 26

# modulo operator, see ?`%%`
for (i in 1:length(letters)){
  if((i %% 4)==0) cat(i,": ",letters[i], "\n", sep = "")
}

## 4: d
## 8: h
## 12: l
## 16: p
## 20: t
## 24: x

Control-Flow Constructs: for loop

for(item in vector) {
perform_action
}

nms <- c("Ayse", "Huseyin", "Serdar", "Hatice", "Safinaz") 

for(i in nms) {   
  print(paste("The name", i, "has", nchar(i), "characters"))
}

## [1] "The name Ayse has 4 characters"
## [1] "The name Huseyin has 7 characters"
## [1] "The name Serdar has 6 characters"
## [1] "The name Hatice has 6 characters"
## [1] "The name Safinaz has 7 characters"

Control-Flow Constructs: for loop (teaser-stringr & regex)

for(item in vector) {
perform_action
}

# a bit complex one, extract all unique characters from string using
# stringr package function, see ?str_match_all, and regex 
library(stringr)
for(i in nms) {   
  print(paste(i, "has", nchar(i), "characters","and",length(unique(str_match_all(i, "(?<=(\\w{1}))")[[1]][,2])),"unique characters:",paste(unique(str_match_all(i, "(?<=(\\w{1}))")[[1]][,2]),collapse=", ")))
}

## [1] "Ayse has 4 characters and 4 unique characters: A, y, s, e"
## [1] "Huseyin has 7 characters and 7 unique characters: H, u, s, e, y, i, n"
## [1] "Serdar has 6 characters and 5 unique characters: S, e, r, d, a"
## [1] "Hatice has 6 characters and 6 unique characters: H, a, t, i, c, e"
## [1] "Safinaz has 7 characters and 6 unique characters: S, a, f, i, n, z"

Control-Flow Constructs:

for(item in vector) {
perform_action
}

# results in vectors 
x <- c()
for (i in 1:7){
  x <- rbind(x, c(i,i^2)) # rbind: combine R objects by rows 
}
x # 2 dimensional

##      [,1] [,2]
## [1,]    1    1
## [2,]    2    4
## [3,]    3    9
## [4,]    4   16
## [5,]    5   25
## [6,]    6   36
## [7,]    7   49

Control-Flow Constructs: for loop

for(item in vector) {
perform_action
}

# nested loop 
for(i in 1:2) { # first loop, 
  for(j in 1:3) { # second loop inside
    cat("This is iteration i =", i, "and j =", j,"\n") 
  } # end of second loop
} # end of first loop

## This is iteration i = 1 and j = 1 
## This is iteration i = 1 and j = 2 
## This is iteration i = 1 and j = 3 
## This is iteration i = 2 and j = 1 
## This is iteration i = 2 and j = 2 
## This is iteration i = 2 and j = 3

x <- c()
for(i in 1:5) { # first loop, 
  for(j in 1:3) { # second loop inside
    x <- c(x, paste(LETTERS[i], j, sep = "_"))  # Code block
  } # end of second loop
} # end of first loop
x

##  [1] "A_1" "A_2" "A_3" "B_1" "B_2" "B_3" "C_1" "C_2" "C_3" "D_1" "D_2" "D_3"
## [13] "E_1" "E_2" "E_3"

Control-Flow Constructs: for loop

for(item in vector) {
perform_action
}

# break a loop if i > 4 break  
for (i in 1:10){
  if(i<5) cat(i,": ",i^2, "\n", sep = "") else {
    cat(i,": ", "break \n", sep = "")
    break
  }
}

## 1: 1
## 2: 4
## 3: 9
## 4: 16
## 5: break

# conditional skip  
for(i in 1:10) {
  if(i %in% c(1, 5, 7)) { # do not execute for these numbers 
    next
  }
  cat(i,": ",i^2, "\n", sep = "")  
}

## 2: 4
## 3: 9
## 4: 16
## 6: 36
## 8: 64
## 9: 81
## 10: 100

Control-Flow Constructs: while loop

while(condition){
perform_action
}

for loops are useful the set of values for the iteration are in advance. If not, there are two related tools with more flexible specifications:
- while(condition) action: performs action while condition is TRUE.
- repeat(action): repeats action forever (i.e. until it encounters break).

x <- 5

while(x < 10) {
  x <- x + 1    
  cat("x: ", x, "   1:x->", 1:x, "\n") 
}

## x:  6    1:x-> 1 2 3 4 5 6 
## x:  7    1:x-> 1 2 3 4 5 6 7 
## x:  8    1:x-> 1 2 3 4 5 6 7 8 
## x:  9    1:x-> 1 2 3 4 5 6 7 8 9 
## x:  10    1:x-> 1 2 3 4 5 6 7 8 9 10

Control-Flow Constructs: while loop

while(condition){
perform_action
}

# while with break
x <- 5
while(TRUE) {
  cat("x: ", x, "\n") 
  x <- x+1
  if(x%%4==0) break
}

## x:  5 
## x:  6 
## x:  7

Control-Flow Constructs: while loop

while(condition){
perform_action
}

# nested loop for in while
i <- 2
while(i <= 3) {
  for(j in 1:4) {
    print(paste("This is iteration i =", i, "and j =", j))
  }
  i <- i + 1
}

## [1] "This is iteration i = 2 and j = 1"
## [1] "This is iteration i = 2 and j = 2"
## [1] "This is iteration i = 2 and j = 3"
## [1] "This is iteration i = 2 and j = 4"
## [1] "This is iteration i = 3 and j = 1"
## [1] "This is iteration i = 3 and j = 2"
## [1] "This is iteration i = 3 and j = 3"
## [1] "This is iteration i = 3 and j = 4"

# nested loop while in while
i <- 2
while(i <= 3) {
  j <- 1
  while(j <= 3) {
    print(paste("This is iteration i =", i, "and j =", j))
    j <- j + 1
  }
  i <- i + 1
}

## [1] "This is iteration i = 2 and j = 1"
## [1] "This is iteration i = 2 and j = 2"
## [1] "This is iteration i = 2 and j = 3"
## [1] "This is iteration i = 3 and j = 1"
## [1] "This is iteration i = 3 and j = 2"
## [1] "This is iteration i = 3 and j = 3"

Control-Flow Constructs: while loop

while(condition){
perform_action
}

nms <- c("Ayse", "Huseyin", "Serdar", "Hatice", "Safinaz") 

i <- 1
while(i<=length(nms)) {   
  print(paste("The name", nms[i], "has", nchar(nms[i]), "characters"))
  i <- i + 1
}

## [1] "The name Ayse has 4 characters"
## [1] "The name Huseyin has 7 characters"
## [1] "The name Serdar has 6 characters"
## [1] "The name Hatice has 6 characters"
## [1] "The name Safinaz has 7 characters"

Control-Flow Constructs: while loop

while(condition){
perform_action
}

nms <- c("Ayse", "Huseyin", "Serdar", "Hatice", "Safinaz") 

# a bit complex one, extract all characters from string using
# stringr package function, see ?str_match_all, and regex 
i <- 1
while(i<=length(nms)) {   
  print(paste(nms[i], "has", nchar(nms[i]), "characters","and",length(unique(str_match_all(nms[i], "(?<=(\\w{1}))")[[1]][,2])),"unique characters:",paste(unique(str_match_all(nms[i], "(?<=(\\w{1}))")[[1]][,2]),collapse=", ")))
    i <- i + 1
}

## [1] "Ayse has 4 characters and 4 unique characters: A, y, s, e"
## [1] "Huseyin has 7 characters and 7 unique characters: H, u, s, e, y, i, n"
## [1] "Serdar has 6 characters and 5 unique characters: S, e, r, d, a"
## [1] "Hatice has 6 characters and 6 unique characters: H, a, t, i, c, e"
## [1] "Safinaz has 7 characters and 6 unique characters: S, a, f, i, n, z"

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

x <- 4
x

## [1] 4

repeat{
  cat("x: ", x, "   1:x->", 1:x, "\n") 
  x <- x + 1    
  if(x == 8) break
}

## x:  4    1:x-> 1 2 3 4 
## x:  5    1:x-> 1 2 3 4 5 
## x:  6    1:x-> 1 2 3 4 5 6 
## x:  7    1:x-> 1 2 3 4 5 6 7

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

x <- 6
x

## [1] 6

repeat{
  cat("x: ", x, "\n") 
  x <- x + 1
  if(x%%5==0) break
}

## x:  6 
## x:  7 
## x:  8 
## x:  9

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

# nested loop for in repeat
i <- 2
repeat{
  for(j in 1:3) {
    print(paste("This is iteration i =", i, "and j =", j))
  }
  i <- i + 1
  if(i > 3) break 
}

## [1] "This is iteration i = 2 and j = 1"
## [1] "This is iteration i = 2 and j = 2"
## [1] "This is iteration i = 2 and j = 3"
## [1] "This is iteration i = 3 and j = 1"
## [1] "This is iteration i = 3 and j = 2"
## [1] "This is iteration i = 3 and j = 3"

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

# nested loop repeat in while
i <- 2
while(i <= 3) {
  j <- 1
  repeat{
    print(paste("This is iteration i =", i, "and j =", j))
    j <- j + 1
    if(j > 5) break
  }
  i <- i + 1
}

## [1] "This is iteration i = 2 and j = 1"
## [1] "This is iteration i = 2 and j = 2"
## [1] "This is iteration i = 2 and j = 3"
## [1] "This is iteration i = 2 and j = 4"
## [1] "This is iteration i = 2 and j = 5"
## [1] "This is iteration i = 3 and j = 1"
## [1] "This is iteration i = 3 and j = 2"
## [1] "This is iteration i = 3 and j = 3"
## [1] "This is iteration i = 3 and j = 4"
## [1] "This is iteration i = 3 and j = 5"

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

# nested loop repeat in repeat
i <- 2
repeat{
  j <- 1
  repeat{
    print(paste("This is iteration i =", i, "and j =", j))
    j <- j + 1
    if(j > 5) break
  }
  i <- i + 1
  if(i > 3) break 
}

## [1] "This is iteration i = 2 and j = 1"
## [1] "This is iteration i = 2 and j = 2"
## [1] "This is iteration i = 2 and j = 3"
## [1] "This is iteration i = 2 and j = 4"
## [1] "This is iteration i = 2 and j = 5"
## [1] "This is iteration i = 3 and j = 1"
## [1] "This is iteration i = 3 and j = 2"
## [1] "This is iteration i = 3 and j = 3"
## [1] "This is iteration i = 3 and j = 4"
## [1] "This is iteration i = 3 and j = 5"

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

nms <- c("Ayse", "Huseyin", "Serdar", "Hatice", "Safinaz") 

i <- 1
repeat{   
  print(paste("The name", nms[i], "has", nchar(nms[i]), "characters"))
  i <- i + 1
  if (i > length(nms)) break 
}

## [1] "The name Ayse has 4 characters"
## [1] "The name Huseyin has 7 characters"
## [1] "The name Serdar has 6 characters"
## [1] "The name Hatice has 6 characters"
## [1] "The name Safinaz has 7 characters"

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

nms <- c("Ayse", "Huseyin", "Serdar", "Hatice", "Safinaz") 

# a bit complex one, extract all characters from string using
# stringr package function, see ?str_match_all, and regex 
i <- 1
repeat{   
  print(paste(nms[i], "has", nchar(nms[i]), "characters","and",length(unique(str_match_all(nms[i], "(?<=(\\w{1}))")[[1]][,2])),"unique characters:",paste(unique(str_match_all(nms[i], "(?<=(\\w{1}))")[[1]][,2]),collapse=", ")))
  i <- i + 1
  if (i > length(nms)) break 
}

## [1] "Ayse has 4 characters and 4 unique characters: A, y, s, e"
## [1] "Huseyin has 7 characters and 7 unique characters: H, u, s, e, y, i, n"
## [1] "Serdar has 6 characters and 5 unique characters: S, e, r, d, a"
## [1] "Hatice has 6 characters and 6 unique characters: H, a, t, i, c, e"
## [1] "Safinaz has 7 characters and 6 unique characters: S, a, f, i, n, z"

Control-Flow Constructs: repeat loop

repeat{
perform_action
until break }

You can rewrite any for loop to use while instead
You can rewrite any while loop to use repeat
while is more flexible than for, and repeat is more flexible than while
It’s good practice, to use the least-flexible solution to a problem, so you should use for wherever possible

Functions in R

Writing functions is a core activity of an R programmer
If the same block of code is used frequently, perhaps under slightly different conditions, then consider writing a function
Functions make the code easier to read
Functions can be re-usable with different data, parameter values etc.
Functions can be passed as arguments to other functions
Functions can be nested, so that you can define a function inside of another function

Three Components of a Function in R

List of arguments, that can be accessed through formals() function

Example: formals(sd)

$x

$na.rm
[1] FALSE

Body; code inside the function, can be accessed through body() function

Example: body(sd)

sqrt(var(if (is.vector(x) || is.factor(x)) x else as.double(x), 
    na.rm = na.rm))

Environment; can be accessed through environment() function

Example: environment(sd)

<environment: namespace:stats>

Function: Basic Syntax

Name of Function

Consider proper name that is understandable

Arguments of Function

Data, values for some parameters are passed through the body code using arguments

Function returns

Either plot, data, values etc., are returned. return() function is frequently used.

function_name <- function(argument_1, argument_2, argument_3,...){
  
           function_task/Body
  
           return(output)
}

Function: Function Name

Try to use verbs for function names

# Good
add_row()
permute()

# Bad
row_adder()
permutation()

Function: Function Name

Make long-lines easily readable

long_function_name <- function(a = "a long argument",
                               b = "another argument",
                               c = "another long argument") {
  # As usual code is indented by two spaces.
}

long_function_name <- function(
    a = "a long argument",
    b = "another argument",
    c = "another long argument") {
  # As usual code is indented by two spaces.
}

Function: return

return() function should be used for early return. The last evaluated expression automatically returned otherwise

# Good
find_abs <- function(x) {
  if (x > 0) {
    return(x)
  }
  x * -1
}

add_two <- function(x, y) {
  x + y
}

# Bad
add_two <- function(x, y) {
  return(x + y)
}

find_abs <- function(x) {
  if (x > 0) return(x)
  x * -1
}

Function: A Very Basic Example

f <- function() { # function name = f, class = function
  
  # Empty Function No Task, return nothing: NULL
  
}

f()

## NULL

class(f)

## [1] "function"

formals(f)

## NULL

# another one 
f <- function() {
  
  cat("Hello, world!\n")
  
}

Function: Argument(s)

A function call consists of the function name followed by one or more argument within parentheses

# Argument without default 
f <- function(person) {
  
  cat("Hello ", person, "!\n")
  
}

# f()
# Error in f() : argument "person" is missing, with no default

f("Ali")

## Hello  Ali !

f(person = "Ali")

## Hello  Ali !

# Argument with default 
f <- function(person = "Ali") {
  
  cat("Hello ", person, "!\n")
  
}

Function: Argument Matching

R functions arguments can be matched positionally or by name
Positional matching just means that R assigns the first value to the first argument, the second value to second argument, etc.
The order of matching:
- Check for exact match for a named argument
- check for a partial match
- Check for a positional match

# Argument with default 
f <- function(person = "Ali", age = 22) {
  
  cat("Hello ", person, ". You are", age, "years old \n")
  
}

f()

## Hello  Ali . You are 22 years old

f("Veli") # Positional matching of first argument

## Hello  Veli . You are 22 years old

f(person = "Veli") # by name matching of first argument

## Hello  Veli . You are 22 years old

f("Veli", 29) # Positional matching of both

## Hello  Veli . You are 29 years old

f(person = "Veli", age = 29) # by name matching of both

## Hello  Veli . You are 29 years old

f(per = "Veli", age = 29) # partial matching of person

## Hello  Veli . You are 29 years old

f(age = 23, "Ece") # mix matching

## Hello  Ece . You are 23 years old

Built-in Functions Help Pages

Function name: mean(), a built-in R function to compute mean of a numeric vector

Check the function’s help page with ?mean

Let’s take 2 minutes to investigate the help page for mean in R Studio or Click to Read Online

mean(x = temp_high_forecast, trim = 0, na.rm = FALSE, ...)

Read Usage section
- What arguments have default values?
Read Arguments section
- What does trim do?
Run Example code

Function: Example

# A function difficult to read  
sqr <- function(x) x*x 

sqr(10)

## [1] 100

sqr(1:5)

## [1]  1  4  9 16 25

# easier to read 
my_squre <- function(x=0) {
  x*x
}

my_squre()

## [1] 0

my_squre(10)

## [1] 100

my_squre(1:5)

## [1]  1  4  9 16 25

Function: Example

# using, other functions utils function tail() and base
# function, cumprod() 
my_factorial2 <- function(n=1) {
  if (n == 0) {
    return(1)
  } else {
    if(n < 0) {
      return(NaN)
    }
    tail(cumprod(1:n),1)
  }
}

my_factorial2(-2)

## [1] NaN

my_factorial2(0)

## [1] 1

my_factorial2(2)

## [1] 2

my_factorial2(5)

## [1] 120

Function: Recursive Function Example

# using, utils function tail() and base function, cumprod(): ? 
my_factorial2 <- function(n=1) {
  if (n == 0) {
    return(1)
  } else {
    if(n < 0) {
      return(NaN)
    }
    tail(cumprod(1:n),1)
  }
}

my_factorial2(-2)

## [1] NaN

my_factorial2(0)

## [1] 1

my_factorial2(2)

## [1] 2

my_factorial2(5)

## [1] 120

Function: Lazy Evaluation

Some SWs use eaer evaluation where all the arguments to a function is evaluated before the body of the function is evaluated
In lazy evaluation, arguments become promise and evaluated when it is called

f <- function(a, b) {
  a^2
} 
f(2)

## [1] 4

# does not produce an error because the 2 gets
# positionally matched to argument a 

# f(b = 2)  
# Error in f(b = 2) : argument "a" is missing, with no default

Function: Lazy Evaluation

# using eval(), substitute() functions
# ?eval and ?substitute for help 
my_function <- function(my_expression, x) {
  eval(substitute(my_expression))
}

my_function(my_expression = x^2-1, x = 3)

## [1] 8

my_function(my_expression = x^2-3*x-1, x = 3)

## [1] -1

my_function(x^2-1,3) # positionally matched

## [1] 8

my_function(x^2-1,1:5) # x now is a vector

## [1]  0  3  8 15 24

Function: Lazy Evaluation

# Plotting function using plot() and seq() function 
seq(from = 3, to = 5, length.out = 5)

## [1] 3.0 3.5 4.0 4.5 5.0

my_curve <- function(my_expression, from, to) {
  x <- seq(from=from, to=to, length.out=100)
  y <- eval(substitute(my_expression)) 
  plot(x,y, type = "l")
}

my_curve(x^2-1,from=-2, to=2)

Function: Lazy Evaluation

# Plotting function using plot() and seq() function 
my_curve <- function(my_expression, from, to) {
  x <- seq(from=from, to=to, length.out=100)
  y <- eval(substitute(my_expression)) 
  plot(x,y, type = "l")
}


my_curve(-2*x^2 + 3*x -1,from=-2, to=2)

# what if argument is missing   
# my_curve(-2*x^2 + 3*x -1, to=2)  
# Error in seq(from = from, to = to, length.out = 100) :
#  argument "from" is missing, with no default

Function: Practice

It is a good practice to check if the argument is missing

my_curve2 <- function(my_expression, from, to) {
  if (missing(from)){
    print("Arguemnt `from` is missing") 
    return(NULL)
  } 
  if (missing(to)){
    print("Arguemnt `to` is missing") 
    return(NULL)
  } 
  x <- seq(from=from, to=to, length.out=100)
  y <- eval(substitute(my_expression)) 
  plot(x,y, type = "l")
}

my_curve2(-2*x^2 + 3*x -1,from=-2, to=2)

my_curve2(-2*x^2 + 3*x -1, to=2)

## [1] "Arguemnt `from` is missing"

## NULL

my_curve2(-2*x^2 + 3*x -1, from=2)

## [1] "Arguemnt `to` is missing"

## NULL

Function: Practice

One may want to set a default value for the arguments

# defaults: from = 2, to = 2
my_curve3 <- function(my_expression, from=-2, to=2) {
  x <- seq(from=from, to=to, length.out=100)
  y <- eval(substitute(my_expression)) 
  plot(x,y, type = "l")
}

par(mfrow = c(2,2)) # 4 plotting window, 2x2
my_curve3(-2*x^2 + 3*x -1) 

my_curve3(-2*x^2 + 3*x -1, to=4) 

my_curve3(-2*x^2 + 3*x -1, from=-4)

my_curve3(-2*x^2 + 3*x -1, from = -4, to = 4)

par(mfrow = c(1,1)) # 1 plotting window

Do not use assignment operator for arguments
Always use comments (#) to explain why

MIS 207

Introduction to R Language Control Structures and Functions

Reference

Quick Refresher from Previous Classes

Control Structures

Control-Flow Constructs: if construct

Control-Flow Constructs: if construct

Control-Flow Constructs: if construct

Control-Flow Constructs: if-and-else construct

Control-Flow Constructs: ifelse

Control-Flow Constructs: ifelse

Control-Flow Constructs: if-and-else if-and-else construct

Control-Flow Constructs: Switch

Control-Flow Constructs: Switch

Control-Flow Constructs: Switch

Control-Flow Constructs: for loop

Control-Flow Constructs: for loop

Control-Flow Constructs: for loop

Control-Flow Constructs: for loop

Control-Flow Constructs: for loop

Control-Flow Constructs: for loop (teaser-stringr & regex)

Control-Flow Constructs:

Control-Flow Constructs: for loop

Control-Flow Constructs: for loop

Control-Flow Constructs: while loop

Control-Flow Constructs: while loop

Control-Flow Constructs: while loop

Control-Flow Constructs: while loop

Control-Flow Constructs: while loop

Control-Flow Constructs: repeat loop

Control-Flow Constructs: repeat loop

Control-Flow Constructs: repeat loop

Control-Flow Constructs: repeat loop

Control-Flow Constructs: repeat loop

Control-Flow Constructs: repeat loop

Control-Flow Constructs: repeat loop

Control-Flow Constructs: repeat loop

Functions in R

Three Components of a Function in R

Function: Basic Syntax

Function: Function Name

Function: Function Name

Function: return

Function: A Very Basic Example

Function: Argument(s)

Function: Argument Matching

Built-in Functions Help Pages

Function: Example

Function: Example

Function: Recursive Function Example

Function: Lazy Evaluation

Function: Lazy Evaluation

Function: Lazy Evaluation

Function: Lazy Evaluation

Function: Practice

Function: Practice

Introduction to R Language
Control Structures and Functions