1 Functions

  1. Define a function to transform temperature from Fahrenheit to Celsius \[ Fahrenheit = Celsius *1.8 + 32 \]

    See solution

     
    fahr2cel <- function(x){
  res <- (x - 32) / 1.8

  return(res)
}

fahr2cel(68)
## [1] 20

  2. Define a function n_and_media() that given a numeric vector returns the number of elements and the vector mean in a nicely formatted sentence > The length is...
    The mean is ...

    See solution

     
    n_and_media <- function(x){
  x_length <- length(x)
  x_mean <- mean(x)

  cat("The length is", x_length,
      "\nThe mean is", round(x_mean, 3))
}

n_and_media(1:10)
## The length is 10 
## The mean is 5.5

  3. Define a function that compute the product between two values. Values need to be interactively defined by the user using readline().

    See solution

     
    my_product <- function(){
  x <- readline(prompt = "First value: ")
  y <- readline(prompt = "Second value: ")

  res <- as.numeric(x) * as.numeric(y)

  return(res)
}

2 Conditional Programming

  1. Create a function that indicate whether a number is even or odd.

    See solution

     
    even_odd <- function(x){
  if(x %% 2 == 0){
    cat(x, "is even")
  } else {
    cat(x, "is odd")
  }
}

even_odd(4)
## 4 is even
even_odd(5)
## 5 is odd

  2. Define a function that assign a mark according to percentage of correct answers > F \(<\) .55
    > .55 \(<=\) E \(<\) .65
    > .65 \(<=\) D \(<\) .75
    > .75 \(<=\) C \(<\) .85
    > .85 \(<=\) B \(<\) .95
    > .95 \(<=\) A

    See solution

     
    test_mark <- function(x){
  if(x < .55){
    res <- "F"
  } else if (x < .65) {
    res <- "E"
  } else if (x < .75) {
    res <- "D"
  } else if (x < .85) {
    res <- "C"
  } else if (x < .95) {
    res <- "B"
  } else {
    res <- "A"
  }

  return(res)
}

test_mark(.3)
## [1] "F"
test_mark(.7)
## [1] "D"
test_mark(.9)
## [1] "B"

3 Iterative Programming

  1. Crete a function that computes the mean of a numeric vector.
    See solution 
    my_mean <- function(x){
  x_length <- length(x)

  # initialize values
  x_sum <- 0

  # for loop
  for(i in seq_along(x)){
    x_sum <-  x_sum + x[i]
  }

  res <- x_sum/x_length

  return(res)
}

my_mean(c(4, 2, -3, 6, 0))
## [1] 1.8
mean(c(4, 2, -3, 6, 0))
## [1] 1.8
  2. Crete a function that returns the min and the max of a numeric vector.
    See solution 
    my_range <- function(x){
  x_length <- length(x)

  # initialize values
  x_min <- x[1]
  x_max <- x[1]

  # for loop
  for(i in seq_along(x)[-1]){

    # check min
    if(x[i] < x_min){
      x_min <- x[i]
    }

    # check max
    if(x[i] > x_max){
      x_max <- x[i]
    }
  }

  res <- c(x_min, x_max)

  return(res)
}

my_range(c(4, 2, -3, 6, 0))
## [1] -3  6
range(c(4, 2, -3, 6, 0))
## [1] -3  6
  3. Crete a function that computes the median of a numeric vector.
    See solution 
    my_median <- function(x){
  x_length <- length(x)

  x <- sort(x)

  if(x_length %% 2 == 1){
    res <- x[x_length %/% 2 + 1]
  } else {
    res <- mean(c(x[x_length %/% 2], x[x_length %/% 2 + 1]))
  }

  return(res)
}

my_median(x = c(4, 2, 6, 0))
## [1] 3
median(c(4, 2, 6, 0))    
## [1] 3