MACS 30500 LECTURE 9

.title[
# MACS 30500 LECTURE 9
]
.author[
### Topics: Intro to Control Structures. Conditional Statements.
]

---

# Agenda

* What are Control Structures? Definition and Main Control Structures
  
  * Conditional Statements

<!-- dropped vectors and lists from original version of this lecture
and moved them to the data structure lecture; instead I added here conditional statements and more examples and content on iteration

IDEA for next time: split this into two

lecture 1: review data structure / base R + conditional statements (and add more in-class practice and how to do conditional statements with a dataframe, use penguins_clean!)

lecture 2: loops slides and demo + loop exercises + maps/across + while loops. When you teach for loops add the break and continue statements (currently not in slides!)

NB: in the demo for loop add data structure, e.g. show how to access columns of a df and their elements with the double and single square brakets

-->

---

## What are Control Structures?

---

### Control Structures: Definition

All code we have written so far can be seen as a finite and fixed sequence of commands.

What's next?

**Control structures allow us to change the flow of execution in our code. By incorporating logical conditions, they enable different lines of code to run based on the given conditions.**

This approach differs from executing the same code in the same way each time, like we have done so far in this course!

---

### Main Control Structures

* **Conditional statements**: test one or more condition(s) and act on it or them

* **`for` loop**: execute a block of code for a fixed number of times

* **`while` loop**: execute a block of code while a given condition is true, and stops only once the condition is evaluated as false

*Today we focus on Conditional Statements and next on Loops!*

---

## Conditional Statements
 
 * single test with `if-else`

* multiple tests with `if, else if, else`

* nested conditionals

* `ifelse()` or its tidyverse version `if_else()`

**Download today's class materials and open the `warm-up.R` script**

---

### if-else

if-else tests one condition and acts on it depending on whether the condition is TRUE or FALSE.

Syntax:
```
if (condition to be evaluated) {
    action performed when condition is TRUE
} else {
    action performed when condition is FALSE
}
```
--

Example:

```r
age <- 14
if (age > 16) {
  print("You can get a driving license")
} else {
  print("You cannot drive")
}
```

---

### if-else

Example: What is the output of this code?

```r
my_numbers <- c(3,4,5,"6",7)

if (!is.numeric(my_numbers)) {
  print("At least one element is not numeric. Only provide numbers.")
} else {
  print("All numeric elements")
}
```

<!--

```
## [1] "At least one element is not numeric. Only provide numbers."
```
-->

---

### if, else if, else

We can extend the basic if-else structure to test **multiple conditions!** How? by adding **`else if`** statement(s) between the initial `if` and the final `else`

Syntax:
```
if (condition1) {
    # action performed when condition 1 is TRUE
    action1
} else if (condition2) {
    # action performed when condition 2 is TRUE
    action2
} else {
    # action performed when conditions 1 and 2 are FALSE
    action3
}
```
--

Conditional statements are evaluated **sequentially** (the order of your code matters!):

* conditions are checked one-by-one in the order they are written
* once R finds a TRUE condition, it stops, and the remaining conditions are not evaluated

---

### if, else if, else

Example: check if a given number is positive, negative, or zero

```r
x <- 0

if (x > 0) {
  print("x is a positive number")
} else if (x < 0) {
  print("x is a negative number")
} else {
  print("x is zero")
}
```

---

### if, else if, else

Another Example with multiple `else if` statements:

```r
# take user input
temperature <- as.integer(readline(prompt = "Enter today's temperature in Celsius: "))

# determine weather based on temperature
if (temperature >= 30) {
  weather <- "Hot"
} else if (temperature >= 20) {
  weather <- "Cool"
} else if (temperature >= 10) {
  weather <- "Breezy"
} else {
  weather <- "Freezing!"
}

# print
print(weather)
```

---

### Nested if-else

Use nested if-else statements to specify **conditions within conditions**. What does this code do?

Example (the %% calculates the remainder from a division):

```r
x <- 15

if (x > 0) {
  if (x %% 2 == 0) {
    print("x is a positive even number")
  } else {
    print("x is a positive odd number")
  }
} else {
  if (x %% 2 == 0) {
    print("x is a negative even number")
  } else {
    print("x is a negative odd number")
  }
}
```
]

---

### Nested if-else

Same example but this checks also the condition `x <- 0`:

```r
x <- 0

if (x > 0) {
  if (x %% 2 == 0) {
    print("x is a positive even number")
  } else {
    print("x is a positive odd number")
  }
} else if (x < 0) {
  if (x %% 2 == 0) {
    print("x is a negative even number")
  } else {
    print("x is a negative odd number")
  }
} else {
  print("x is zero")
}
```

---

### ifelse()

R accepts `if` and `else` statements, but also statements using **`ifelse()`**. They are not the same and **serve different purposes**.

.small[
Syntax:
```
ifelse (condition to be evaluated,
    action performed when condition is TRUE,
    action performed when condition is FALSE)
```

Example:

```r
y <- 3
ifelse(sqrt(16) > y, 
       sqrt(16),
       0)
```

```
## [1] 4
```
]

<!--
if-else: more flexibile and handles complex conditional logic; returns a scalar

ifelse(): simpler and more suited for simple assignments or to transform variables in a dataframe; returns a vector
-->

---

### ifelse()

Example: What is the output of this code?

```r
numbers <- c(10, 6, 7)
ifelse(numbers %% 2 == 1, 
       "odd",
       "even")
```

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

**Note the input here is a vector, not a single number, thus the output is also a vector**: the code will evaluate each element of the vector.

This is because **`ifelse()` supports vectorized operations**: operations directly applied on entire vectors of data, rather than looping through individual elements one-by-one.

<!--
How can we rewrite the same code using the standard `if-else` statement?

```
# won't work
numbers <- c(10, 6, 7)
if (numbers %% 2 == 1) { 
  print("odd")
} else {
  print("even")
}
```

```
# need a loop
numbers <- c(10, 6, 7)
for (i in 1:length(numbers)) {
  if (numbers[i] %% 2 == 1) {
    print("odd")
    } 
else 
  print("even")
 }
```
-->

---

### ifelse()

Example: What is the output of this code?

```r
library(tidyverse)
qualify <- tibble("Athlet" = c("Noah", "Julio", "Nick", "Maria"), 
                      "Scores" = c(32, 37, 28, 30))

ifelse(qualify$"Scores" > 30, "Admitted", "Rejected")
```

Example: What is the output of this code?

```
## [1] "Admitted" "Admitted" "Rejected" "Rejected"
```

---

### ifelse()

Example: use `ifelse()` to recode variables in a dataframe...

The variable `decisionDirection` takes four values (1 conservative, 2 liberal, 3 unspecifiable, and NA).

Recode it to take three values (0 conservative, 1 liberal, and NA for both NA and unspecifiable)

```r
scdb_case %>%
  mutate(decisionDirection = ifelse(
    decisionDirection == 3, # condition to be evaluated
    NA,                     # if true, do NA
    decisionDirection - 1   # if false, subtract one to value
    ))
```

---

### if_else()

Similarly to other functions we have seen, there is also a `dplyr` version of the base R `ifelse()` function and it is called `if_else()`.  They follow the same logic, but the latter is better to use with the tidyverse

Documentation here:https://dplyr.tidyverse.org/reference/if_else.html

Compare the two!

---

## Practice writing conditional statements

Download today's class materials from the website