lessons_loops_conditionals.Rmd

---
title: "Loops and Conditionals"
---
## Learning Objectives

- Write and use `for` loops to apply the same code to multiple objects or elements
- Use `if`-`else` statements to conditionally execute code

## Required Packages and Datasets

Make sure you can load all these packages and dataset before starting the module
```{r, message = FALSE}
library(dplyr)
library(uwpols501)
data(iver)
```

## For Loops

We use `for` loops to iterate through multiple elements of a list, variable, etc., and do **something** with those elements.

A `for` loop has the following skeleton:
```{r, eval = FALSE}
for (each_element in here) { # Try to use meaningful names for the
  # do something             #   "each_element" object
  print(each_element)
}
```


A short/silly example:
```{r}
list_numbers <- sample(x = 1:100, size = 10, replace = FALSE) # sample() function
for (number in list_numbers) {
  print(paste0("2 times ", number, " is ", number * 2))
}
```

A real example:

Imagine that we want to calculate the $log$ of the variables `povred` and `enp` of the `iver` dataset. We can create a list with these two variable names and then, for each of them, find them in `iver`, calculate the $log$, and add the result as a new variable to the dataset.

```{r}
numeric_vars <- c("povred", "enp")
for (variable in numeric_vars) {
  y <- iver[,variable]
  log_y <- log(y)
  name_new_var <- paste0(variable, "_log")
  iver[,name_new_var] <- log_y
}
```

## Conditional Execution: if and else statements

Sometimes we only want to execute certain code if the data fulfills some conditions. To do that we use `if` and `else` statements.

How `if` and `else` statements look like:
```{r eval=FALSE}
if (this) {
  # do that
} else if (that) {
  # do something else
} else {
  #
}
```

**This** and **that** in the previous chunk of code are boolean tests: code that returns TRUE/FALSE when the computer executes it. Some examples of boolean tests:
```{r}
10 == 2
10 %in% c(2, 5, 13, 20, 10)
10 == 2 & 10 %in% c(2, 5, 13, 20, 10)
10 == 2 | 10 %in% c(2, 5, 13, 20, 10)
```

A short/silly example:
```{r}
some_numbers <- c(1, 4, 6, 10,12, 16, 45, 88, 102)
for (number in some_numbers) {
  if (number < 10) {
    print(paste0(number, " is smaller than 10"))
  } else if (number < 50) {
    print(paste0(number, " is smaller than 50 but greater or equal to 10"))
  } else {
    print(paste0(number, " is greater than 50"))
  }
}
```

Real example:

We previously calculated the $log$ and added it to the dataset for the numeric variables in `iver`. To do that we first created a list with the numeric variables. This time we won’t create that list. Instead, we’ll use conditional execution to indicate that we only want to take the $log$ of numeric variables.

 
```{r}
data(iver) # load the data set again
iver <- as.data.frame(iver)
for (variable in names(iver)) {
  y <- iver[,variable]
  if (is.numeric(y)) {
    log_y <- log(y)
    name_new_var <- paste0(variable, "_log")
    iver[,name_new_var] <- log_y
  } else{
    print(paste0(variable, " is not a numeric variable."))
  }
}
```