# Vectors and iteration

## Learning objectives

* Review the major types of vectors
* Demonstrate how to subset vectors
* Demonstrate vector recycling
* Define lists
* Demonstrate iterative operations using loops and `map()` functions
* Practice writing iterative operations using loops and `map()` functions

## Notes

April 29, 2020

## Vectors

* R stores all of its data in an object called a **vector**
* Two types
* Atomic vectors
* List objects

## Atomic Vectors

### Logical Vectors
* `TRUE`, `FALSE`, or `NA` (missing value)

### Numeric Vectors
* Integers (whole numbers)
* Doubles (numbers with decimal points)

### Character Vectors
* Contain strings

Atomic vectors are homogenous. You can't mix differet kinds of vectors

## Scalars
* Scalars are a single number; vectors are a set of multiple values
* Vector of length one

## Vector Recycling
* `tidyverse` requires you do implicitly recycle a vector of shorter length. Base R does not

## Subsetting
* To filter a vector, we cannot use `filter()` because that only works for filtering rows in a tibble. `[` is the subsetting function for vectors. It is used like `x[a]`.
* With positive integers
* With negative integers
* _Don't mix_ positive and negative

### Subset with a logical vector
* Subsetting with a logical vector keeps all values corresponding to a `TRUE` value.
* Function `is.na()` that checks whether or not an observation is a missing value
* These technique are for subsetting **a single vector**

## List
* Created differently and have different properties than an atomic vector

### List `str()`
`str(x)`

### Store a mix of objects
* Lists can store different kinds of atomic vectors
* Lists can store lists

### Nested Lists, etc.

### Subsetting Lists!

## Iterations

### `for()` loop
* Helpful, but not intuitive
* Easier to make a mistake

### Map functions
* `purrr()`
* More condensed
* Most only require two arguments