Write your own function

# This is the recipe for writing your won function:
my_fun <- function(arg1, arg2) {
  body
}

# Create a function pow_two(): it takes one argument and returns that number squared (that number times itself).

pow_tow <- function(x) {
  (x^2)
}

test <- pow_tow(2)
test

## [1] 4

# create a function sum_abs(), that takes two arguments and returns the sum of the absolute values of both arguments.

sum_abs <- function(x,y){
  (abs(x) + abs(y))
}

sum_abs.test <- sum_abs(-4 , 5)
sum_abs.test

## [1] 9

# Define a function, hello(). It prints out "Hi there!" and returns TRUE. It has no arguments.
hello <- function() {
  print("Hi there!")
  return(TRUE)
}

hello()

## [1] "Hi there!"

## [1] TRUE

# You can define default argument values in your own R functions as well. You can use the following recipe to do so:

my_fun <- function(arg1, arg2 = val2) {
  body
}

# 1. Add an optional argument, named print_info, that is TRUE by default.
# 2. Wrap an if construct around the print() function: this function should only be executed if print_info is TRUE.
 
pow_two <- function(x) {
  y <- x ^ 2
  print(paste(x, "to the power two equals", y))
  return(y)
}

# Answer
pow_two <- function(x, print_info = TRUE) {
  y <- x ^ 2
  if (print_info) {
    print(paste(x, "to the power two equals", y))
  }
  return(y)
}

linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)

# Write a function that interprets the number of profile views on a single day:
  # *The function takes one argument, num_views.
  # *If num_views is greater than 15, the function prints out "You're popular!" to the console and returns   num_views.
  # *Else, the function prints out "Try to be more visible!" and returns 0.
  # *Finally, call the interpret() function twice: on the first value of the linkedin vector and on the second element of the facebook vector.

interpret <- function(num_views) {
  if (num_views > 15) {
    print("You're popular!")
    return(num_views)
  } else {
    print("Try to be more visible!")
    return(0)
  }
}

interpret(linkedin[1])

## [1] "You're popular!"

## [1] 16

interpret(facebook[2])

## [1] "Try to be more visible!"

## [1] 0

linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)

# Complete the following function:
#   *Make return_sum an optional argument, that is TRUE by default.
#   *Inside the for loop, iterate over all views: on every iteration, add the result of interpret(v) to count.
#   *If return_sum is TRUE, return count
#   *Else, return NULL.
  
interpret_all <- function(views, return_sum) {
  count <- 0

  for (v in views) {
  
  }

  if (return_sum) {
  

  } else {
  
  }
}

# Answer 

interpret_all <- function(views, return_sum = TRUE) {
  count <- 0

  for (v in views) {
  count <- count + interpret(v)
  }

  if (return_sum) {
  return(count)

  } else {
  return(NULL)
  }
}

# What function you can use to see the currently attached packages?
search()

## [1] ".GlobalEnv"        "package:stats"     "package:graphics" 
## [4] "package:grDevices" "package:utils"     "package:datasets" 
## [7] "package:methods"   "Autoloads"         "package:base"

# write an efficient code that would install the whole needed packages (data.table and rjason)

# install.packages(c("data.table", "rjson"))