---
title: "Intermediate R"
output:
  html_notebook:
    highlight: haddock
    number_sections: yes
    theme: cerulean
    toc: yes
    toc_depth: 2
  html_document:
    toc: yes
    toc_depth: 2
---

```{r setup, echo = FALSE}
knitr::opts_chunk$set(eval = FALSE)
```


# Conditionals and Control Flows


## Equality

The most basic form of comparison is equality. Let's briefly recap its syntax. The following statements all evaluate to `TRUE` (feel free to try them out in the console).

```{r}
3 == (2 + 1)
"intermediate" != "r"
TRUE != FALSE
"Rchitect" != "rchitect"
```

Notice from the last expression that R is case sensitive: "R" is not equal to "r". Keep this in mind when solving the exercises in this chapter!

### Exercise

- In the editor below, write R code to see if `TRUE` equals `FALSE`.
- Likewise, check if `-6 * 14` is *not* equal to `17 - 101`.
- Next up: comparison of character strings. Ask R whether the strings "useR" and "user" are equal.
- Finally, find out what happens if you compare logicals to numerics: are `TRUE` and 1 equal?

```{r}
# Comparison of logicals


# Comparison of numerics


# Comparison of character strings


# Compare a logical with a numeric


```


## Greater and less than

Apart from equality operators, Filip also introduced the *less than* and *greater than* operators: `<` and `>`. You can also add an equal sign to express *less than or equal to* or *greater than or equal to*, respectively. Have a look at the following R expressions, that all evaluate to `FALSE`:

```
(1 + 2) > 4
"dog" < "Cats"
TRUE <= FALSE
```

Remember that for string comparison, R determines the *greater than* relationship based on alphabetical order. Also, keep in mind that `TRUE` corresponds to `1` in R, and `FALSE` coerces to `0` behind the scenes. Therefore, `FALSE < TRUE` is `TRUE`.

### Exercise

Write R expressions to check whether:

- `-6 * 5 + 2` is greater than or equal to `-10 + 1`.
- `"raining"` is less than or equal to `"raining dogs"`.
- `TRUE` is greater than `FALSE`.

```{r}
# Comparison of numerics


# Comparison of character strings


# Comparison of logicals

```


## Compare vectors

You are already aware that R is very good with vectors. Without having to change anything about the syntax, R's relational operators also work on vectors.

Let's go back to the example that was started in the video. You want to figure out whether your activity on social media platforms have paid off and decide to look at your results for LinkedIn and Facebook. The sample code in the editor initializes the vectors `linkedin` and `facebook`. Each of the vectors contains the number of profile views your LinkedIn and Facebook profiles had over the last seven days.

### Exercise

Using relational operators, find a logical answer, i.e. `TRUE` or `FALSE`, for the following questions:

- On which days did the number of LinkedIn profile views exceed 15?
- When was your LinkedIn profile viewed only 5 times or fewer?
- When was your LinkedIn profile visited more often than your Facebook profile?

```{r}
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)

# Popular days


# Quiet days


# LinkedIn more popular than Facebook

```


## Compare matrices

R's ability to deal with different data structures for comparisons does not stop at vectors. Matrices and relational operators also work together seamlessly!

Instead of in vectors (as in the previous exercise), the LinkedIn and Facebook data is now stored in a matrix called `views`. The first row contains the LinkedIn information; the second row the Facebook information. The original vectors `facebook` and `linkedin` are still available as well.

### Exercise

Using the relational operators you've learned so far, try to discover the following:

- When were the views exactly equal to 13? Use the `views` matrix to return a logical matrix.
- For which days were the number of views less than or equal to 14? Again, have R return a logical matrix.

```{r}
# The social data has been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)
views <- matrix(c(linkedin, facebook), nrow = 2, byrow = TRUE)

# When does views equal 13?


# When is views less than or equal to 14?

```


## & and |

Before you work your way through the next exercises, have a look at the following R expressions. All of them will evaluate to `TRUE`:

```
TRUE & TRUE
FALSE | TRUE
5 <= 5 & 2 < 3
3 < 4 | 7 < 6
```

Watch out: `3 < x < 7` to check if `x` is between 3 and 7 will not work; you'll need `3 < x & x < 7` for that.

In this exercise, you'll be working with the `last` variable. This variable equals the last value of the `linkedin` vector that you've worked with previously. The `linkedin` vector represents the number of LinkedIn views your profile had in the last seven days, remember? Both the variables `linkedin` and `last` have already been defined in the editor.

### Exercise
Write R expressions to solve the following questions concerning the variable `last`:

- Is `last` under 5 or above 10?
- Is `last` between 15 and 20, excluding 15 but including 20?

```{r}
# The linkedin and last variable are already defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
last <- tail(linkedin, 1)

# Is last under 5 or above 10?


# Is last between 15 (exclusive) and 20 (inclusive)?

```


## & and | (2)

Like relational operators, logical operators work perfectly fine with vectors and matrices.

Both the vectors `linkedin` and `facebook` are available again. Also a matrix - `views` - has been defined; its first and second row correspond to the `linkedin` and `facebook` vectors, respectively. Ready for some advanced queries to gain more insights into your social outreach?

### Exercise
- When did LinkedIn views exceed 10 *and* did Facebook views fail to reach 10 for a particular day? Use the `linkedin` and `facebook` vectors.
- When were one or both of your LinkedIn and Facebook profiles visited at least 12 times?
- When is the `views` matrix equal to a number between 11 and 14, excluding 11 and including 14?

```{r}
# The social data (linkedin, facebook, views) has been created for you

# linkedin exceeds 10 but facebook below 10


# When were one or both visited at least 12 times?


# When is views between 11 (exclusive) and 14 (inclusive)?

```


## Reverse the result: !

On top of the `&` and `|` operators, you also learned about the `!` operator, which negates a logical value. To refresh your memory, here are some R expressions that use `!`. They all evaluate to `FALSE`:

```
!TRUE
!(5 > 3)
!!FALSE
```

### Exercise

What would the following set of R expressions return?

```
x <- 5
y <- 7
!(!(x < 4) & !!!(y > 12))
```

Possible Answers

- `TRUE`
- `FALSE`
- Running this piece of code would throw an error.


# Loops

## Write a while loop

Let's get you started with building a while loop from the ground up. Have another look at its recipe:

```
while (condition) {
  expr
}
```

Remember that the `condition` part of this recipe should become `FALSE` at some point during the execution. Otherwise, the `while` loop will go on indefinitely. In DataCamp's learning interface, your session will be disconnected in this case.

Have a look at the code on the right; it initializes the `speed` variables and already provides a `while` loop template to get you started.

### Exercise
Code a `while` loop with the following characteristics:

- The condition of the `while` loop should check if `speed` is higher than 30.
- Inside the body of the `while` loop, print out `"Slow down!"`. 
- Inside the body of the `while` loop, decrease the `speed` by 7 units. This step is crucial; otherwise your `while` loop will never stop.

```{r}
# Initialize the speed variable
speed <- 64

# Code the while loop
while ( ) {
  
  
}

# Print out the speed variable
speed
```


## Throw in more conditionals

In the previous exercise, you simulated the interaction between a driver and a driver's assistant: When the speed was too high, "Slow down!" got printed out to the console, resulting in a decrease of your speed by 7 units.

There are several ways in which you could make your driver's assistant more advanced. For example, the assistant could give you different messages based on your speed or provide you with a current speed at a given moment.

A `while` loop similar to the one you've coded in the previous exercise is already available in the editor. It prints out your current speed, but there's no code that decreases the `speed` variable yet, which is pretty dangerous. Can you make the appropriate changes?

### Exercise 

- If the `speed` is greater than 48, have R print out `"Slow down big time!"`, and decrease the `speed` by 11.
- Otherwise, have R simply print out `"Slow down!"`, and decrease the `speed` by 6.

```{r}
# Initialize the speed variable
speed <- 64

# Extend/adapt the while loop
while (speed > 30) {
  print(paste("Your speed is",speed))
  if ( ) {
    
    
  } else {
    
    
  }
}

```


## Stop the while loop: break

There are some very rare situations in which severe speeding is necessary: what if a hurricane is approaching and you have to get away as quickly as possible? You don't want the driver's assistant sending you speeding notifications in that scenario, right?

This seems like a great opportunity to include the `break` statement in the `while` loop you've been working on. Remember that the `break` statement is a control statement. When R encounters it, the `while` loop is abandoned completely.

### Exercise
Adapt the `while` loop such that it is abandoned when the `speed` of the vehicle is greater than 80. This time, the `speed` variable has been initialized to 88; keep it that way.

```{r}
# Initialize the speed variable
speed <- 88

while (speed > 30) {
  print(paste("Your speed is", speed))
  
  # Break the while loop when speed exceeds 80
  if ( ) {
    
  }
  
  if (speed > 48) {
    print("Slow down big time!")
    speed <- speed - 11
  } else {
    print("Slow down!")
    speed <- speed - 6
  }
}
```


## Build a while loop from scratch

The previous exercises guided you through developing a pretty advanced `while` loop, containing a `break` statement and different messages and updates as determined by control flow constructs. If you manage to solve this comprehensive exercise using a `while` loop, you're totally ready for the next topic: the `for` loop.

### Exercise
Finish the `while` loop so that it:

- prints out the triple of `i`, so `3 * i`, at each run.
- is abandoned with a `break` if the triple of `i` is divisible by 8, but still prints out this triple before breaking.

```{r}
# Initialize i as 1 
i <- 1

# Code the while loop
while (i <= 10) {
  print(___)
  if ( ___ ) {
    
  }
  i <- i + 1
}
```


## Loop over a vector

In the previous video, Filip told you about two different strategies for using the `for` loop. To refresh your memory, consider the following loops that are equivalent in R:

```
primes <- c(2, 3, 5, 7, 11, 13)

# loop version 1
for (p in primes) {
  print(p)
}

# loop version 2
for (i in 1:length(primes)) {
  print(primes[i])
}
```

Remember our `linkedin` vector? It's a vector that contains the number of views your LinkedIn profile had in the last seven days. The `linkedin` vector has already been defined in the editor below so that you can fully focus on the instructions!

### Exercise
Write a `for` loop that iterates over all the elements of `linkedin` and prints out every element separately. Do this in two ways: using the *loop version 1* and the *loop version 2* in the example code above.

```{r}
# The linkedin vector has already been defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)

# Loop version 1


# Loop version 2


```


## Loop over a list

Looping over a list is just as easy and convenient as looping over a vector. There are again two different approaches here:

```
primes_list <- list(2, 3, 5, 7, 11, 13)

# loop version 1
for (p in primes_list) {
  print(p)
}

# loop version 2
for (i in 1:length(primes_list)) {
  print(primes_list[[i]])
}
```

Notice that you need double square brackets - `[[` `]]` - to select the list elements in loop version 2.

Suppose you have a list of all sorts of information on New York City: its population size, the names of the boroughs, and whether it is the capital of the United States. We've already prepared a list `nyc` with all this information in the editor (source: Wikipedia).

### Exercise
As in the previous exercise, loop over the `nyc` list in two different ways to print its elements:

- Loop directly over the `nyc` list (loop version 1).
- Define a looping index and do subsetting using double brackets (loop version 2).

```{r}
# The nyc list is already specified
nyc <- list(pop = 8405837, 
            boroughs = c("Manhattan", "Bronx", "Brooklyn", "Queens", "Staten Island"), 
            capital = FALSE)

# Loop version 1


# Loop version 2

```

## Loop over a matrix

In your workspace, there's a matrix `ttt`, that represents the status of a tic-tac-toe game. It contains the values "X", "O" and "NA". Print out `ttt` in the console so you can have a closer look. On row 1 and column 1, there's "O", while on row 3 and column 2 there's "NA".

To solve this exercise, you'll need a `for` loop inside a `for` loop, often called a nested loop. Doing this in R is a breeze! Simply use the following recipe:

```
for (var1 in seq1) {
  for (var2 in seq2) {
    expr
  }
}
```

### Exercise

Finish the nested `for` loops to go over the elements in `ttt`:

- The outer loop should loop over the rows, with loop index `i` (use `1:nrow(ttt)`).
- The inner loop should loop over the columns, with loop index `j` (use `1:ncol(ttt)`).
- Inside the inner loop, make use of `print()` and `paste()` to print out information in the following format: "On row i and column j the board contains x", where `x` is the value on that position.

```{r}
# The tic-tac-toe matrix ttt has already been defined for you

# define the double for loop
for (___ in ___) {
  for (___ in ___) {
    print(___)
  }
}
```

## Mix it up with control flow

Let's return to the *LinkedIn* profile views data, stored in a vector `linkedin`. In the first exercise on `for` loops you already did a simple printout of each element in this vector. A little more in-depth interpretation of this data wouldn't hurt, right? Time to throw in some conditionals! As with the `while` loop, you can use the `if` and `else` statements inside the `for` loop.

### Exercise

Add code to the `for` loop that loops over the elements of the `linkedin` vector:

- If the vector element's value exceeds 10, print out `"You're popular!"`.
- If the vector element's value does not exceed 10, print out `"Be more visible!"`

```{r}
# The linkedin vector has already been defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)

# Code the for loop with conditionals
for (li in linkedin) {
  if ( ) {
    
  } else {
    
  }
  print(li)
}
```

## Next, you break it

In the editor below, you'll find a possible solution to the previous exercise. The code loops over the `linkedin` vector and prints out different messages depending on the values of `li`.

In this exercise, you will use the `break` and `next` statements:

- The `break` statement abandons the active loop: the remaining code in the loop is skipped and the loop is not iterated over anymore.
- The `next` statement skips the remainder of the code in the loop, but continues the iteration.

### Exercise

Extend the `for` loop with two new, separate `if` tests in the editor as follows:

- If the vector element's value exceeds 16, print out `"This is ridiculous, I'm outta here!"` and have R abandon the `for` loop (`break`).
- If the value is lower than 5, print out `"This is too embarrassing!"` and fast-forward to the next iteration (`next`).

```{r}
# The linkedin vector has already been defined for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)

# Extend the for loop
for (li in linkedin) {
  if (li > 10) {
    print("You're popular!")
  } else {
    print("Be more visible!")
  }
  
  # Add if statement with break
  
  
  # Add if statement with next
  
  
  print(li)
}
```

## Build a for loop from scratch

This exercise will not introduce any new concepts on `for` loops.

In the editor on the right, we already went ahead and defined a variable `rquote`. This variable has been split up into a vector that contains separate letters and has been stored in a vector `chars` with the `strsplit()` function.

Can you write code that counts the number of r's that come before the first u in `rquote`?

### Exercise 

- Initialize the variable `rcount`, as 0.
- Finish the `for` loop:
    - if `char` equals `"r"`, increase the value of `rcount` by 1.
    - if `char` equals `"u"`, leave the `for` loop entirely with a `break`.
- Finally, print out the variable `rcount` to the console to see if your code is correct.

```{r}
# Pre-defined variables
rquote <- "r's internals are irrefutably intriguing"
chars <- strsplit(rquote, split = "")[[1]]

# Initialize rcount
rcount <- 

# Finish the for loop
for (char in chars) {
  
  
}

# Print out rcount

```


# Functions

## Function documentation

Before even thinking of using an R function, you should clarify which arguments it expects. All the relevant details such as a description, usage, and arguments can be found in the documentation. To consult the documentation on the `sample()` function, for example, you can use one of following R commands:

```
help(sample)
?sample
```

A quick hack to see the arguments of the `sample()` function is the `args()` function. Try it out in the console:

```
args(sample)
```

In the next exercises, you'll be learning how to use the `mean()` function with increasing complexity. The first thing you'll have to do is get acquainted with the `mean()` function.

### Exercise

- Consult the documentation on the `mean()` function: `?mean` or `help(mean)`.
- Inspect the arguments of the `mean()` function using the `args()` function.

```{r}
# Consult the documentation on the mean() function


# Inspect the arguments of the mean() function

```


## Use a function

The documentation on the `mean()` function gives us quite some information:

- The `mean()` function computes the arithmetic mean.
- The most general method takes multiple arguments: `x` and `...`.
- The `x` argument should be a vector containing numeric, logical or time-related information.

Remember that R can match arguments both by position and by name. Can you still remember the difference? You'll find out in this exercise!

Once more, you'll be working with the view counts of your social network profiles for the past 7 days. These are stored in the `linkedin` and `facebook` vectors and have already been defined in the editor on the right.

### Exercise

- Calculate the average number of views for both `linkedin` and `facebook` and assign the result to `avg_li` and `avg_fb`, respectively. Experiment with different types of argument matching!
- Print out both `avg_li` and `avg_fb`.

```{r}
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)

# Calculate average number of views


# Inspect avg_li and avg_fb


```


## Use a function (2)

Check the documentation on the `mean()` function again:

```
?mean
```

The Usage section of the documentation includes two versions of the `mean()` function. The first usage,

```
mean(x, ...)
```

is the most general usage of the mean function. The 'Default S3 method', however, is:

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

The `...` is called the ellipsis. It is a way for R to pass arguments along without the function having to name them explicitly. The ellipsis will be treated in more detail in future courses.

For the remainder of this exercise, just work with the second usage of the mean function. Notice that both `trim` and `na.rm` have default values. This makes them **optional arguments**.

### Exercise

- Calculate the mean of the element-wise sum of `linkedin` and `facebook` and store the result in a variable `avg_sum`.
- Calculate the mean once more, but this time set the `trim` argument equal to 0.2 and assign the result to `avg_sum_trimmed`.
- Print out both `avg_sum` and `avg_sum_trimmed`; can you spot the difference?

```{r}
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)

# Calculate the mean of the sum


# Calculate the trimmed mean of the sum


# Inspect both new variables

```


## Use a function (3)

Read the documentation of the `sd()` function. The `sd()` function has an optional argument, `na.rm` that specified whether or not to remove missing values from the input vector before calculating the standard deviation.

If you've had a good look at the documentation, you'll know by now that the `mean()` function also has this argument, `na.rm`, and it does the exact same thing. By default, it is set to `FALSE`, as the Usage of the default S3 method shows:

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

Let's see what happens if your vectors `linkedin` and `facebook` contain missing values (`NA`).

### Exercise

- Calculate the average number of LinkedIn profile views, without specifying any optional arguments. Simply print the result to the console.
- Calculate the average number of LinkedIn profile views, but this time tell R to strip missing values from the input vector.

```{r}
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, NA, 17, 14)
facebook <- c(17, NA, 5, 16, 8, 13, 14)

# Basic average of linkedin


# Advanced average of linkedin

```

## Functions inside functions

You already know that R functions return objects that you can then use somewhere else. This makes it easy to use functions inside functions, as you've seen before:

```
speed <- 31
print(paste("Your speed is", speed))
```

Notice that both the `print()` and `paste()` functions use the ellipsis - `...` - as an argument. Can you figure out how they're used?

### Exercise

Use `abs()` on `linkedin - facebook` to get the absolute differences between the daily Linkedin and Facebook profile views. Next, use this function call inside `mean()` to calculate the Mean Absolute Deviation. In the `mean()` call, make sure to specify `na.rm` to treat missing values correctly!

```{r}
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, NA, 17, 14)
facebook <- c(17, NA, 5, 16, 8, 13, 14)

# Calculate the mean absolute deviation

```


## Required, or optional?

By now, you will probably have a good understanding of the difference between required and optional arguments. Let's refresh this difference by having one last look at the `mean()` function:

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

`x` is required; if you do not specify it, R will throw an error. `trim` and `na.rm` are optional arguments: they have a default value which is used if the arguments are not explicitly specified.


### Exercise

Which of the following statements about the `read.table()` function are true?

1. `header`, `sep` and `quote` are all optional arguments.
2. `row.names` and `fileEncoding` don't have default values.
3. `read.table("myfile.txt", "-", TRUE)` will throw an error.
4. `read.table("myfile.txt", sep = "-", header = TRUE)` will throw an error.

- 1 and 3
- 2 and 4
- 1, 2, and 3
- 1, 2, and 4


## Write your own function

Wow, things are getting serious... you're about to write your own function! Before you have a go at it, have a look at the following function template:

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

Notice that this recipe uses the assignment operator (`<-`) just as if you were assigning a vector to a variable for example. This is not a coincidence. Creating a function in R basically is the assignment of a function object to a variable! In the recipe above, you're creating a new R variable `my_fun`, that becomes available in the workspace as soon as you execute the definition. From then on, you can use the `my_fun` as a function.

### Exercise

- Create a function `pow_two()`: it takes one argument and returns that number squared (that number times itself).
- Call this newly defined function with `12` as input.
- Next, create a function `sum_abs()`, that takes two arguments and returns the sum of the absolute values of both arguments.
- Finally, call the function `sum_abs()` with arguments `-2` and `3` afterwards.

```{r}
# Create a function pow_two()


# Use the function


# Create a function sum_abs()


# Use the function

```


## Write your own function (2)

There are situations in which your function does not require an input. Let's say you want to write a function that gives us the random outcome of throwing a fair die:

```
throw_die <- function() {
  number <- sample(1:6, size = 1)
  number
}

throw_die()
```

Up to you to code a function that doesn't take any arguments!

### Exercise

- Define a function, `hello()`. It prints out `"Hi there!"` and returns `TRUE`. It has no arguments.
- Call the function `hello()`, without specifying arguments of course.

```{r}
# Define the function hello()

# Call the function hello()

```


## Write your own function (3)

Do you still remember the difference between an argument with and without default values? Have another look at the `sd()` function by typing `?sd` in the console. The usage section shows the following information:

```
sd(x, na.rm = FALSE)
```

This tells us that `x` has to be defined for the `sd()` function to be called correctly, however, `na.rm` already has a default value. Not specifying this argument won't cause an error.

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
}
```

The editor on the right already includes an extended version of the `pow_two()` function from before. Can you finish it?

### Exercise

- Add an optional argument, named `print_info`, that is `TRUE` by default.
- Wrap an `if` construct around the `print()` function: this function should only be executed if `print_info` is `TRUE`.
- Feel free to experiment with the `pow_two()` function you've just coded.

```{r}
# Finish the pow_two() function
pow_two <- function(x) {
  y <- x ^ 2
  print(paste(x, "to the power two equals", y))
  return(y)
}
```


## Function scoping

Function scoping implies that variables that are defined inside a function are not accessible outside that function. Try running the following code and see if you understand the results:

```
pow_two <- function(x) {
  y <- x ^ 2
  return(y)
}
pow_two(4)
y
x
```

`y` was defined inside the `pow_two()` function and therefore it is not accessible outside of that function. This is also true for the function's arguments of course - `x` in this case.

### Exercise

Which statement is correct about the following chunk of code? The function `two_dice()` is already available in the workspace.

```{r}
two_dice <- function() {
  possibilities <- 1:6
  dice1 <- sample(possibilities, size = 1)
  dice2 <- sample(possibilities, size = 1)
  dice1 + dice2
}
```

- Executing `two_dice()` causes an error.
- Executing `res <- two_dice()` makes the contents of `dice1` and `dice2` available outside the function.
- Whatever the way of calling the `two_dice()` function, R won't have access to `dice1` and `dice2` outside the function.


## R passes arguments by value

The title gives it away already: R passes arguments by value. What does this mean? Simply put, it means that an R function cannot change the variable that you input to that function. Let's look at a simple example (try it in the console):

```
triple <- function(x) {
  x <- 3*x
  x
}
a <- 5
triple(a)
a
```

Inside the `triple()` function, the argument `x` gets overwritten with its value times three. Afterwards this new `x` is returned. If you call this function with a variable `a` set equal to 5, you obtain 15. But did the value of `a` change? If R were to pass `a` to `triple()` by reference, the override of the `x` inside the function would ripple through to the variable `a`, outside the function. However, R passes by value, so the R objects you pass to a function can never change unless you do an explicit assignment. `a` remains equal to 5, even after calling `triple(a)`.


### Exercise

Can you tell which one of the following statements is false about the following piece of code?

```{r}
increment <- function(x, inc = 1) {
  x <- x + inc
  x
}
count <- 5
a <- increment(count, 2)
b <- increment(count)
count <- increment(count, 2)
```

- `a` and `b` equal 7 and 6 respectively after executing this code block.
- After the first call of `increment()`, where `a` is defined, `a` equals 7 and `count` equals 5.
- In the end, `count` will equal 10.
- In the last expression, the value of `count` was actually changed because of the explicit assignment.


## R you functional?

Now that you've acquired some skills in defining functions with different types of arguments and return values, you should try to create more advanced functions. As you've noticed in the previous exercises, it's perfectly possible to add control-flow constructs, loops and even other functions to your function body.

Remember our social media example? The vectors `linkedin` and `facebook` are already defined in the workspace so you can get your hands dirty straight away. As a first step, you will be writing a function that can interpret a single value of this vector. In the next exercise, you will write another function that can handle an entire vector at once.

### Exercise

- Finish the function definition for `interpret()`, 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.

```{r}
# The linkedin and facebook vectors have already been created for you

# Define the interpret function
interpret <- function(num_views) {
  if (num_views > 15) {


  } else {


  }
}

# Call the interpret function twice


```


## R you functional? (2)

A possible implementation of the `interpret()` function is already available in the editor. In this exercise you'll be writing another function that will use the `interpret()` function to interpret all the data from your daily profile views inside a vector. Furthermore, your function will return the sum of views on popular days, if asked for. A `for` loop is ideal for iterating over all the vector elements. The ability to return the sum of views on popular days is something you can code through a function argument with a default value.

### Exercise

Finish the template for the `interpret_all()` 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`. Remember that `interpret(v)` returns `v` for popular days, and 0 otherwise. At the same time, `interpret(v)` will also do some printouts.
- Finish the `if` construct:
    - If `return_sum` is `TRUE`, return `count`.
    - Else, return `NULL`.

Call this newly defined function on both `linkedin` and `facebook`.

```{r}
# The linkedin and facebook vectors have already been created for you
linkedin <- c(16, 9, 13, 5, 2, 17, 14)
facebook <- c(17, 7, 5, 16, 8, 13, 14)

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

# Define the interpret_all() function
# views: vector with data to interpret
# return_sum: return total number of views on popular days?
interpret_all <- function(views, return_sum) {
  count <- 0

  for (v in views) {

  }

  if (return_sum) {

  } else {

  }
}

# Call the interpret_all() function on both linkedin and facebook

```


## Load an R Package

There are basically two extremely important functions when it comes down to R packages:

- `install.packages()`, which as you can expect, installs a given package.
- `library()` which loads packages, i.e. attaches them to the search list on your R workspace.

To install packages, you need administrator privileges. 

In this exercise, you'll be learning how to load the `ggplot2` package, a powerful package for data visualization. You'll use it to create a plot of two variables of the `mtcars` data frame. The data has already been prepared for you in the workspace.

Before starting, execute the following commands in the console:

- `search()`, to look at the currently attached packages and
- `qplot(mtcars$wt, mtcars$hp)`, to build a plot of two variables of the `mtcars` data frame.

An error should occur, because you haven't loaded the `ggplot2` package yet!

### Exercise

- To fix the error you saw in the console, load the `ggplot2` package.
- Now, retry calling the `qplot()` function with the same arguments.
- Finally, check out the currently attached packages again.

```{r}
# Load the ggplot2 package


# Retry the qplot() function


# Check out the currently attached packages again

```


## Different ways to load a package

The `library()` and `require()` functions are not very picky when it comes down to argument types: both `library(rjson)` and `library("rjson")` work perfectly fine for loading a package.

### Exercise

Have a look at some more code chunks that (attempt to) load one or more packages:

```{r}
# Chunk 1
library(data.table)
require(rjson)

# Chunk 2
library("data.table")
require(rjson)

# Chunk 3
library(data.table)
require(rjson, character.only = TRUE)

# Chunk 4
library(c("data.table", "rjson"))
Select the option that lists all of the chunks that do not generate an error. The console on the right is yours to experiment in.

```

- Only (1)
- Both (1) and (2)
- (1), (2) and (3)
- All of them are valid


# The apply Family

## Use lapply with a built-in R function

Before you go about solving the exercises below, have a look at the documentation of the `lapply()` function. The Usage section shows the following expression:

```
lapply(X, FUN, ...)
```

To put it generally, `lapply` takes a vector or list `X`, and applies the function `FUN` to each of its members. If `FUN` requires additional arguments, you pass them after you've specified `X` and `FUN` (`...`). The output of `lapply()` is a list, the same length as `X`, where each element is the result of applying `FUN` on the corresponding element of `X`.

Now that you are truly brushing up on your data science skills, let's revisit some of the most relevant figures in data science history. We've compiled a vector of famous mathematicians/statisticians and the year they were born. Up to you to extract some information!

### Exercise

- Have a look at the `strsplit()` calls, that splits the strings in `pioneers` on the `:` sign. The result, `split_math` is a list of 4 character vectors: the first vector element represents the name, the second element the birth year.
- Use `lapply()` to convert the character vectors in `split_math` to lowercase letters: apply `tolower()` on each of the elements in `split_math`. Assign the result, which is a list, to a new variable `split_low`.
- Finally, inspect the contents of `split_low` with `str()`.


```{r}
# The vector pioneers has already been created for you
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")

# Split names from birth year
split_math <- strsplit(pioneers, split = ":")

# Convert to lowercase strings: split_low


# Take a look at the structure of split_low

```


## Use lapply with your own function

You can use `lapply()` on your own functions as well. You just need to code a new function and make sure it is available in the workspace. After that, you can use the function inside `lapply()` just as you did with base R functions.

In the previous exercise you already used `lapply()` once to convert the information about your favorite pioneering statisticians to a list of vectors composed of two character strings. Let's write some code to select the names and the birth years separately.

The sample code already includes code that defined `select_first()`, that takes a vector as input and returns the first element of this vector.

### Exercise

- Apply `select_first()` over the elements of `split_low` with `lapply()` and assign the result to a new variable names.
- Next, write a function `select_second()` that does the exact same thing for the second element of an inputted vector.
- Finally, apply the `select_second()` function over `split_low` and assign the output to the variable `years`.

```{r}
# Code from previous exercise:
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")
split <- strsplit(pioneers, split = ":")
split_low <- lapply(split, tolower)

# Write function select_first()
select_first <- function(x) {
  x[1]
}

# Apply select_first() over split_low: names


# Write function select_second()




# Apply select_second() over split_low: years

```


## lapply and anonymous functions

Writing your own functions and then using them inside `lapply()` is quite an accomplishment! But defining functions to use them only once is kind of overkill, isn't it? That's why you can use so-called **anonymous functions** in R.

Previously, you learned that functions in R are objects in their own right. This means that they aren't automatically bound to a name. When you create a function, you can use the assignment operator to give the function a name. It's perfectly possible, however, to not give the function a name. This is called an anonymous function:

```
# Named function
triple <- function(x) { 3 * x }

# Anonymous function with same implementation
function(x) { 3 * x }

# Use anonymous function inside lapply()
lapply(list(1,2,3), function(x) { 3 * x })
```

### Exercise

- Transform the first call of `lapply()` such that it uses an anonymous function that does the same thing.
- In a similar fashion, convert the second call of `lapply` to use an anonymous version of the `select_second()` function.
- Remove both the definitions of `select_first()` and `select_second()`, as they are no longer useful.

```{r}
# Definition of split_low
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")
split <- strsplit(pioneers, split = ":")
split_low <- lapply(split, tolower)

# Transform: use anonymous function inside lapply
select_first <- function(x) {
  x[1]
}
names <- lapply(split_low, select_first)

# Transform: use anonymous function inside lapply
select_second <- function(x) {
  x[2]
}
years <- lapply(split_low, select_second)
```


## Use lapply with additional arguments

In the video, the `triple()` function was transformed to the `multiply()` function to allow for a more generic approach. `lapply()` provides a way to handle functions that require more than one argument, such as the `multiply()` function:

```
multiply <- function(x, factor) {
  x * factor
}
lapply(list(1,2,3), multiply, factor = 3)
```

On the right we've included a generic version of the select functions that you've coded earlier: `select_el()`. It takes a vector as its first argument, and an index as its second argument. It returns the vector's element at the specified index.

### Exercise

Use `lapply()` twice to call `select_el()` over all elements in `split_low`: once with the `index` equal to 1 and a second time with the `index` equal to 2. Assign the result to `names` and `years`, respectively.

```{r}
# Definition of split_low
pioneers <- c("GAUSS:1777", "BAYES:1702", "PASCAL:1623", "PEARSON:1857")
split <- strsplit(pioneers, split = ":")
split_low <- lapply(split, tolower)

# Generic select function
select_el <- function(x, index) {
  x[index]
}

# Use lapply() twice on split_low: names and years


```


## Apply functions that return NULL

In all of the previous exercises, it was assumed that the functions that were applied over vectors and lists actually returned a meaningful result. For example, the `tolower()` function simply returns the strings with the characters in lowercase. This won't always be the case. Suppose you want to display the structure of every element of a list. You could use the `str()` function for this, which returns `NULL`:

```
lapply(list(1, "a", TRUE), str)
```

This call actually returns a list, the same size as the input list, containing all `NULL` values. On the other hand calling

```
str(TRUE)
```

on its own prints only the structure of the logical to the console, not `NULL`. That's because `str()` uses `invisible()` behind the scenes, which returns an invisible copy of the return value, `NULL` in this case. This prevents it from being printed when the result of `str()` is not assigned.

### Exercise 

What will the following code chunk return (`split_low` is already available in the workspace)? Try to reason about the result before simply executing it in the console!

```{r}
lapply(split_low, function(x) {
  if (nchar(x[1]) > 5) {
    return(NULL)
  } else {
    return(x[2])
  }
})
```

- `list(NULL, NULL, "1623", "1857")`
- `list("gauss", "bayes", NULL, NULL)`
- `list("1777", "1702", NULL, NULL)`
- `list("1777", "1702")`


## How to use sapply

You can use `sapply()` similar to how you used `lapply()`. The first argument of `sapply()` is the list or vector `X` over which you want to apply a function, `FUN`. Potential additional arguments to this function are specified afterwards (`...`):

```
sapply(X, FUN, ...)
```

In the next couple of exercises, you'll be working with the variable `temp`, that contains temperature measurements for 7 days. `temp` is a list of length 7, where each element is a vector of length 5, representing 5 measurements on a given day. This variable has already been defined in the workspace: type `str(temp)` to see its structure.

### Exercise

- Use `lapply()` to calculate the minimum (built-in function `min()`) of the temperature measurements for every day.
- Do the same thing but this time with `sapply()`. See how the output differs.
- Use `lapply()` to compute the maximum (`max()`) temperature for each day.
- Again, use `sapply()` to solve the same question and see how `lapply()` and `sapply()` differ.

```{r}
# temp has already been defined in the workspace
temp <- list(c(3,7,9,6,-1), c(6,9,12,13,5), c(4,8,3,-1,-3), c(1,4,7,2,-2), c(5,7,9,4,2), c(-3,5,8,9,4), c(3,6,9,4,1))

# Use lapply() to find each day's minimum temperature


# Use sapply() to find each day's minimum temperature


# Use lapply() to find each day's maximum temperature


# Use sapply() to find each day's maximum temperature

```


## sapply with your own function

Like `lapply()`, `sapply()` allows you to use self-defined functions and apply them over a vector or a list:

```
sapply(X, FUN, ...)
```

Here, `FUN` can be one of R's built-in functions, but it can also be a function you wrote. This self-written function can be defined before hand, or can be inserted directly as an anonymous function.

### Exercise

- Finish the definition of `extremes_avg()`: it takes a vector of temperatures and calculates the average of the minimum and maximum temperatures of the vector.
- Next, use this function inside `sapply()` to apply it over the vectors inside `temp`.
- Use the same function over `temp` with `lapply()` and see how the outputs differ.

```{r}
# temp is already defined in the workspace

# Finish function definition of extremes_avg
extremes_avg <- function(___) {
  ( min(x) + ___ ) / 2
}

# Apply extremes_avg() over temp using sapply()


# Apply extremes_avg() over temp using lapply()

```



## sapply with function returning vector

In the previous exercises, you've seen how `sapply()` simplifies the list that `lapply()` would return by turning it into a vector. But what if the function you're applying over a list or a vector returns a vector of length greater than 1? If you don't remember from the video, don't waste more time in the valley of ignorance and head over to the instructions!

### Exercise

- Finish the definition of the `extremes()` function. It takes a vector of numerical values and returns a vector containing the minimum and maximum values of a given vector, with the names "min" and "max", respectively.
- Apply this function over the vector `temp` using `sapply()`.
- Finally, apply this function over the vector `temp` using `lapply() as well.

```{r}
# temp is already available in the workspace

# Create a function that returns min and max of a vector: extremes
extremes <- function(x) {
  c(min = min(x), ___ = ___)
}

# Apply extremes() over temp with sapply()


# Apply extremes() over temp with lapply()

```


## sapply can't simplify, now what?

It seems like we've hit the jackpot with `sapply()`. On all of the examples so far, `sapply()` was able to nicely simplify the rather bulky output of `lapply()`. But, as with life, there are things you can't simplify. How does `sapply()` react?

We already created a function, `below_zero()`, that takes a vector of numerical values and returns a vector that only contains the values that are strictly below zero.

### Exercise

- Apply `below_zero()` over `temp` using `sapply()` and store the result in `freezing_s`.
- Apply `below_zero()` over `temp` using `lapply()`. Save the resulting list in a variable `freezing_l`.
- Compare `freezing_s` to `freezing_l` using the `identical()` function.

```{r}
# temp is already prepared for you in the workspace

# Definition of below_zero()
below_zero <- function(x) {
  return(x[x < 0])
}

# Apply below_zero over temp using sapply(): freezing_s


# Apply below_zero over temp using lapply(): freezing_l

```


## sapply with functions that return NULL

You already have some apply tricks under your sleeve, but you're surely hungry for some more, aren't you? In this exercise, you'll see how `sapply()` reacts when it is used to apply a function that returns `NULL` over a vector or a list.

A function `print_info()`, that takes a vector and prints the average of this vector, has already been created for you. It uses the `cat()` function.

### Exercise

- Apply `print_info()` over the contents of `temp` with `sapply()`.
- Repeat this process with `lapply()`. Do you notice the difference?

```{r}
# temp is already available in the workspace

# Definition of print_info()
print_info <- function(x) {
  cat("The average temperature is", mean(x), "\n")
}

# Apply print_info() over temp using sapply()


# Apply print_info() over temp using lapply()

```

## Reverse engineering sapply

```{r}
sapply(list(runif (10), runif (10)), 
       function(x) c(min = min(x), mean = mean(x), max = max(x)))
```

Without going straight to the console to run the code, try to reason through which of the following statements are correct and why.

(1) `sapply()` can't simplify the result that `lapply()` would return, and thus returns a list of vectors.
(2) This code generates a matrix with 3 rows and 2 columns.
(3) The function that is used inside `sapply()` is anonymous.
(4) The resulting data structure does not contain any names.

### Exercise

Select the option that lists all correct statements.

Possible Answers

- 1 and 3
- 2 and 3
- 1 and 4
- 2, 3 and 4

## Use vapply

Before you get your hands dirty with the third and last apply function that you'll learn about in this intermediate R course, let's take a look at its syntax. The function is called `vapply()`, and it has the following syntax:

```
vapply(X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE)
```

Over the elements inside `X`, the function `FUN` is applied. The `FUN.VALUE` argument expects a template for the return argument of this function `FUN`. `USE.NAMES` is `TRUE` by default; in this case `vapply()` tries to generate a named array, if possible.

For the next set of exercises, you'll be working on the `temp` list again, that contains 7 numerical vectors of length 5. We also coded a function `basics()` that takes a vector, and returns a named vector of length 3, containing the minimum, mean and maximum value of the vector respectively.

### Exercise

- Apply the function `basics()` over the list of temperatures, `temp`, using `vapply()`. This time, you can use `numeric(3)` to specify the `FUN.VALUE` argument.

```{r}
# temp is already available in the workspace

# Definition of basics()
basics <- function(x) {
  c(min = min(x), mean = mean(x), max = max(x))
}

# Apply basics() over temp using vapply()

```


## Use vapply (2)

So far you've seen that `vapply()` mimics the behavior of `sapply()` if everything goes according to plan. But what if it doesn't?

There are cases where the structure of the output of the function you want to apply, `FUN`, does not correspond to the template you specify in `FUN.VALUE`. In that case, `vapply()` will throw an error that informs you about the misalignment between expected and actual output.

### Exercise

- Inspect the code below and try to run it. If you haven't changed anything, an error should pop up. That's because `vapply()` still expects `basics()` to return a vector of length 3. The error message gives you an indication of what's wrong.
- Try to fix the error by editing the `vapply()` command.

```{r}
# temp is already available in the workspace

# Definition of the basics() function
basics <- function(x) {
  c(min = min(x), mean = mean(x), median = median(x), max = max(x))
}

# Fix the error:
vapply(temp, basics, numeric(3))
```


## From sapply to vapply

As highlighted before, `vapply()` can be considered a more robust version of `sapply()`, because you explicitly restrict the output of the function you want to apply. Converting your `sapply()` expressions in your own R scripts to `vapply()` expressions is therefore a good practice (and also a breeze!).

### Exercise

- Convert all the `sapply()` expressions on the right to their `vapply()` counterparts. Their results should be exactly the same; you're only adding robustness. You'll need the templates `numeric(1)` and `logical(1)`.

```{r}
# temp is already defined in the workspace

# Convert to vapply() expression
sapply(temp, max)

# Convert to vapply() expression
sapply(temp, function(x, y) { mean(x) > y }, y = 5)
```


# Utilities

## Mathematical utilities

Have another look at some useful math functions that R features:

- `abs()`: Calculate the absolute value.
- `sum()`: Calculate the sum of all the values in a data structure.
- `mean()`: Calculate the arithmetic mean.
- `round()`: Round the values to 0 decimal places by default. Try out ?round in the console for variations of `round()` and ways to change the number of digits to round to.

As a data scientist in training, you've estimated a regression model on the sales data for the past six months. After evaluating your model, you see that the training error of your model is quite regular, showing both positive and negative values. The error values are already defined in the workspace below (`errors`).

### Exercise

- Calculate the sum of the absolute rounded values of the training errors. You can work in parts, or with a single one-liner. There's no need to store the result in a variable, just have R print it.

```{r}
# The errors vector has already been defined for you
errors <- c(1.9, -2.6, 4.0, -9.5, -3.4, 7.3)

# Sum of absolute rounded values of errors

```


## Find the error

We went ahead and included some code below, but there's still an error. Can you trace it and fix it?

In times of despair, help with functions such as `sum()` and `rev()` are a single command away; simply use `?sum` and `?rev` in the console.

### Exercise

- Fix the error by including code on the last line. Remember: you want to call `mean()` only once!

```{r}
# Don't edit these two lines
vec1 <- c(1.5, 2.5, 8.4, 3.7, 6.3)
vec2 <- rev(vec1)

# Fix the error
mean(abs(vec1), abs(vec2))
```


## Data Utilities

R features a bunch of functions to juggle around with data structures::

- `seq()`: Generate sequences, by specifying the from, to, and by arguments.
- `rep()`: Replicate elements of vectors and lists.
- `sort()`: Sort a vector in ascending order. Works on numerics, but also on character strings and logicals.
- `rev()`: Reverse the elements in a data structures for which reversal is defined.
- `str()`: Display the structure of any R object.
- `append()`: Merge vectors or lists.
- `is.*()`: Check for the class of an R object.
- `as.*()`: Convert an R object from one class to another.
- `unlist()`: Flatten (possibly embedded) lists to produce a vector.

Remember the social media profile views data? Your LinkedIn and Facebook view counts for the last seven days are already defined as lists on the right.

### Exercise

- Convert both `linkedin` and `facebook` lists to a vector, and store them as `li_vec` and `fb_vec` respectively.
- Next, append `fb_vec` to the `li_vec` (Facebook data comes last). Save the result as `social_vec`.
- Finally, sort `social_vec` from high to low. Print the resulting vector.

```{r}
# The linkedin and facebook lists have already been created for you
linkedin <- list(16, 9, 13, 5, 2, 17, 14)
facebook <- list(17, 7, 5, 16, 8, 13, 14)

# Convert linkedin and facebook to a vector: li_vec and fb_vec



# Append fb_vec to li_vec: social_vec


# Sort social_vec

```


## Find the error (2)

Just as before, let's switch roles. It's up to you to see what unforgivable mistakes we've made. Go fix them!

### Exercise

- Correct the expression. Make sure that your fix still uses the functions `rep()` and `seq()`.

```{r}
# Fix me
seq(rep(1, 7, by = 2), times = 7)
```


## Beat Gauss using R

There is a popular story about young Gauss. As a pupil, he had a lazy teacher who wanted to keep the classroom busy by having them add up the numbers 1 to 100. Gauss came up with an answer almost instantaneously, 5050. On the spot, he had developed a formula for calculating the sum of an arithmetic series. There are more general formulas for calculating the sum of an arithmetic series with different starting values and increments. Instead of deriving such a formula, why not use R to calculate the sum of a sequence?

### Exercise

- Using the function `seq()`, create a sequence that ranges from 1 to 500 in increments of 3. Assign the resulting vector to a variable `seq1`.
- Again with the function `seq()`, create a sequence that ranges from 1200 to 900 in increments of -7. Assign it to a variable `seq2`.
- Calculate the total sum of the sequences, either by using the `sum()` function twice and adding the two results, or by first concatenating the sequences and then using the `sum()` function once. Print the result to the console.

```{r}
# Create first sequence: seq1


# Create second sequence: seq2


# Calculate total sum of the sequences

```


## grepl & grep

In their most basic form, regular expressions can be used to see whether a pattern exists inside a character string or a vector of character strings. For this purpose, you can use:

- `grepl()`, which returns `TRUE` when a pattern is found in the corresponding character string.
- `grep()`, which returns a vector of indices of the character strings that contains the pattern.

Both functions need a `pattern` and an `x` argument, where `pattern` is the regular expression you want to match for, and the `x` argument is the character vector from which matches should be sought.

In this and the following exercises, you'll be querying and manipulating a character vector of email addresses! The vector emails has already been defined below so you can begin with the instructions straight away!

### Exercise

- Use `grepl()` to generate a vector of logicals that indicates whether these email addresses contain "edu". Print the result to the output.
- Do the same thing with `grep()`, but this time save the resulting indexes in a variable `hits`.
- Use the variable `hits` to select from the `emails` vector only the emails that contain "edu".

```{r}
# The emails vector has already been defined for you
emails <- c("john.doe@ivyleague.edu", "education@world.gov", "dalai.lama@peace.org",
            "invalid.edu", "quant@bigdatacollege.edu", "cookie.monster@sesame.tv")

# Use grepl() to match for "edu"


# Use grep() to match for "edu", save result to hits


# Subset emails using hits

```

## grepl & grep (2)

You can use the caret, `^`, and the dollar sign, `$` to match the content located in the start and end of a string, respectively. This could take us one step closer to a correct pattern for matching only the ".edu" email addresses from our list of emails. But there's more that can be added to make the pattern more robust:

- `@`, because a valid email must contain an at-sign.
- `.*`, which matches any character (.) zero or more times (*). Both the dot and the asterisk are metacharacters. You can use them to match any character between the at-sign and the ".edu" portion of an email address.
- `\\.edu$`, to match the ".edu" part of the email at the end of the string. The `\\` part escapes the dot: it tells R that you want to use the `.` as an actual character.

### Exercise

- Use `grepl()` with the more advanced regular expression to return a logical vector. Simply print the result.
- Do a similar thing with `grep()` to create a vector of indices. Store the result in the variable `hits`.
- Use `emails[hits]` again to subset the `emails` vector.

```{r}
# The emails vector has already been defined for you
emails <- c("john.doe@ivyleague.edu", "education@world.gov", "dalai.lama@peace.org",
            "invalid.edu", "quant@bigdatacollege.edu", "cookie.monster@sesame.tv")

# Use grepl() to match for .edu addresses more robustly


# Use grep() to match for .edu addresses more robustly, save result to hits


# Subset emails using hits

```


## sub & gsub

While `grep()` and `grepl()` were used to simply check whether a regular expression could be matched with a character vector, `sub()` and `gsub()` take it one step further: you can specify a replacement argument. If inside the character vector `x`, the regular expression `pattern` is found, the matching element(s) will be replaced with `replacement.sub()` only replaces the first match, whereas `gsub()` replaces all matches.

Suppose that `emails` vector you've been working with is an excerpt of DataCamp's email database. Why not offer the owners of the .edu email addresses a new email address on the datacamp.edu domain? This could be quite a powerful marketing stunt: Online education is taking over traditional learning institutions! Convert your email and be a part of the new generation!

### Exercise

- With the advanced regular expression `"@.*\\.edu$"`, use `sub()` to replace the match with `"@datacamp.edu"`. Since there will only be one match per character string, `gsub()` is not necessary here. Inspect the resulting output.

```{r}
# The emails vector has already been defined for you
emails <- c("john.doe@ivyleague.edu", "education@world.gov", "global@peace.org",
            "invalid.edu", "quant@bigdatacollege.edu", "cookie.monster@sesame.tv")

# Use sub() to convert the email domains to datacamp.edu

```


## sub & gsub (2)

Regular expressions are a typical concept that you'll learn by doing and by seeing other examples. Before you rack your brains over the regular expression in this exercise, have a look at the new things that will be used:

- `.*`: A usual suspect! It can be read as "any character that is matched zero or more times".
- `\\s`: Match a space. The "s" is normally a character, escaping it (`\\`) makes it a metacharacter.
- `[0-9]+`: Match the numbers 0 to 9, at least once (+).
- `([0-9]+)`: The parentheses are used to make parts of the matching string available to define the replacement. The `\\1` in the replacement argument of `sub()` gets set to the string that is captured by the regular expression `[0-9]+`.


### Exercise

```{r}
awards <- c("Won 1 Oscar.",
  "Won 1 Oscar. Another 9 wins & 24 nominations.",
  "1 win and 2 nominations.",
  "2 wins & 3 nominations.",
  "Nominated for 2 Golden Globes. 1 more win & 2 nominations.",
  "4 wins & 1 nomination.")

sub(".*\\s([0-9]+)\\snomination.*$", "\\1", awards)
```

What does this code chunk return? `awards` is already defined in the workspace so you can start playing in the console straight away.

Possible Answers

- A vector of integers containing: 1, 24, 2, 3, 2, 1.
- The vector `awards` gets returned as there isn't a single element in `awards` that matches the regular expression.
- A vector of character strings containing "1", "24", "2", "3", "2", "1".
- A vector of character strings containing "Won 1 Oscar.", "24", "2", "3", "2", "1".


## Right here, right now

In R, dates are represented by `Date` objects, while times are represented by `POSIXct` objects. Under the hood, however, these dates and times are simple numerical values. `Date` objects store the number of days since the 1st of January in 1970. `POSIXct` objects on the other hand, store the number of seconds since the 1st of January in 1970.

The 1st of January in 1970 is the common origin for representing times and dates in a wide range of programming languages. There is no particular reason for this; it is a simple convention. Of course, it's also possible to create dates and times before 1970; the corresponding numerical values are simply negative in this case.

### Exercise

- Ask R for the current date, and store the result in a variable `today`.
- To see what `today` looks like under the hood, call `unclass()` on it.
- Ask R for the current time, and store the result in a variable, `now`.
- To see the numerical value that corresponds to `now`, call `unclass()` on it.

```{r}
# Get the current date: today


# See what today looks like under the hood


# Get the current time: now


# See what now looks like under the hood

```


## Create and format dates

To create a `Date` object from a simple character string in R, you can use the `as.Date()` function. The character string has to obey a format that can be defined using a set of symbols (the examples correspond to 13 January, 1982):

- `%Y`: 4-digit year (1982)
- `%y`: 2-digit year (82)
- `%m`: 2-digit month (01)
- `%d`: 2-digit day of the month (13)
- `%A`: weekday (Wednesday)
- `%a`: abbreviated weekday (Wed)
- `%B`: month (January)
- `%b`: abbreviated month (Jan)

The following R commands will all create the same `Date` object for the 13th day in January of 1982:

```{r}
as.Date("1982-01-13")
as.Date("Jan-13-82", format = "%b-%d-%y")
as.Date("13 January, 1982", format = "%d %B, %Y")
```

Notice that the first line here did not need a format argument, because by default R matches your character string to the formats `"%Y-%m-%d"` or `"%Y/%m/%d"`.

In addition to creating dates, you can also convert dates to character strings that use a different date notation. For this, you use the `format()` function. Try the following lines of code:

```{r}
today <- Sys.Date()
format(Sys.Date(), format = "%d %B, %Y")
format(Sys.Date(), format = "Today is a %A!")
```

### Exercise

- In the editor on the right, three character strings representing dates have been created. Convert them to dates using `as.Date()`, and assign them to `date1`, `date2`, and `date3` respectively. The code for `date1` is already included.
- Extract useful information from the dates as character strings using `format()`. From the first date, select the weekday. From the second date, select the day of the month. From the third date, you should select the abbreviated month and the 4-digit year, separated by a space.

```{r}
# Definition of character strings representing dates
str1 <- "May 23, '96"
str2 <- "2012-03-15"
str3 <- "30/January/2006"

# Convert the strings to dates: date1, date2, date3
date1 <- as.Date(str1, format = "%b %d, '%y")



# Convert dates to formatted strings
format(date1, "%A")


```


## Create and format times

Similar to working with dates, you can use `as.POSIXct()` to convert from a character string to a `POSIXct` object, and `format()` to convert from a `POSIXct` object to a character string. Again, you have a wide variety of symbols:

- `%H`: hours as a decimal number (00-23)
- `%I`: hours as a decimal number (01-12)
- `%M`: minutes as a decimal number
- `%S`: seconds as a decimal number
- `%T`: shorthand notation for the typical format `%H:%M:%S`
- `%p`: AM/PM indicator

For a full list of conversion symbols, consult the `strptime` documentation in the console:

```
?strptime
```

Again, `as.POSIXct()` uses a default format to match character strings. In this case, it's `%Y-%m-%d %H:%M:%S`. In this exercise, abstraction is made of different time zones.

### Exercise

- Convert two strings that represent `timestamps`, `str1` and `str2`, to `POSIXct` objects called `time1` and `time2`.
- Using `format()`, create a string from `time1` containing only the minutes.
- From `time2`, extract the hours and minutes as `"hours:minutes AM/PM"`. Refer to the assignment text above to find the correct conversion symbols!

```{r}
# Definition of character strings representing times
str1 <- "May 23, '96 hours:23 minutes:01 seconds:45"
str2 <- "2012-3-12 14:23:08"

# Convert the strings to POSIXct objects: time1, time2
time1 <- as.POSIXct(str1, format = "%B %d, '%y hours:%H minutes:%M seconds:%S")


# Convert times to formatted strings


```


## Calculations with Dates

Both `Date` and `POSIXct` R objects are represented by simple numerical values under the hood. This makes calculation with time and date objects very straightforward: R performs the calculations using the underlying numerical values, and then converts the result back to human-readable time information again.

You can increment and decrement `Date` objects, or do actual calculations with them (try it out in the console!):

```{r}
today <- Sys.Date()
today + 1
today - 1

as.Date("2015-03-12") - as.Date("2015-02-27")
```

To control your eating habits, you decided to write down the dates of the last five days that you ate pizza. In the workspace, these dates are defined as five `Date` objects, `day1` to `day5`. The code below also contains a vector `pizza` with these 5 `Date` objects.

### Exercise

- Calculate the number of days that passed between the last and the first day you ate pizza. Print the result.
- Use the function `diff()` on pizza to calculate the differences between consecutive pizza days. Store the result in a new variable `day_diff`.
- Calculate the average period between two consecutive pizza days. Print the result.

```{r}
# day1, day2, day3, day4 and day5 are already available in the workspace

# Difference between last and first pizza day


# Create vector pizza
pizza <- c(day1, day2, day3, day4, day5)

# Create differences between consecutive pizza days: day_diff


# Average period between two consecutive pizza days

```


## Calculations with Times

Calculations using `POSIXct` objects are completely analogous to those using `Date` objects. Try to experiment with this code to increase or decrease `POSIXct` objects:

```{r}
now <- Sys.time()
now + 3600          # add an hour
now - 3600 * 24     # subtract a day
```

Adding or substracting time objects is also straightforward:

```{r}
birth <- as.POSIXct("1879-03-14 14:37:23")
death <- as.POSIXct("1955-04-18 03:47:12")
einstein <- death - birth
einstein
```

You're developing a website that requires users to log in and out. You want to know what is the total and average amount of time a particular user spends on your website. This user has logged in 5 times and logged out 5 times as well. These times are gathered in the vectors `login` and `logout`, which are already defined in the workspace.

### Exercise

- Calculate the difference between the two vectors `logout` and `login`, i.e. the time the user was online in each independent session. Store the result in a variable `time_online`.
- Inspect the variable `time_online` by printing it.
- Calculate the total time that the user was online. Print the result.
- Calculate the average time the user was online. Print the result.

```{r}
# login and logout are already defined in the workspace
# Calculate the difference between login and logout: time_online


# Inspect the variable time_online


# Calculate the total time online


# Calculate the average time online

```


## Time is of the essence

The dates when a season begins and ends can vary depending on who you ask. People in Australia will tell you that spring starts on September 1st. The Irish people in the Northern hemisphere will swear that spring starts on February 1st, with the celebration of St. Brigid's Day. Then there's also the difference between astronomical and meteorological seasons: while astronomers are used to equinoxes and solstices, meteorologists divide the year into 4 fixed seasons that are each three months long. (source: [www.timeanddate.com](www.timeanddate.com))

A vector `astro`, which contains character strings representing the dates on which the 4 astronomical seasons start, has been defined on your workspace. Similarly, a vector `meteo` has already been created for you, with the meteorological beginnings of a season.

### Exercise

- Use `as.Date()` to convert the `astro` vector to a vector containing `Date` objects. You will need the `%d`, `%b` and `%Y` symbols to specify the format. Store the resulting vector as `astro_dates`.
- Use `as.Date()` to convert the `meteo` vector to a vector with `Date` objects. This time, you will need the `%B`, `%d` and `%y` symbols for the format argument. Store the resulting vector as `meteo_dates`.
- With a combination of `max()`, `abs()` and `-`, calculate the maximum absolute difference between the astronomical and the meteorological beginnings of a season, i.e. `astro_dates` and `meteo_dates`. Simply print this maximum difference to the console output.

```{r}
# Convert astro to vector of Date objects: astro_dates


# Convert meteo to vector of Date objects: meteo_dates


# Calculate the maximum absolute difference between astro_dates and meteo_dates

```

