blank_notes.Rmd

---
title: "Stat 33A - Lecture Notes 2"
date: August 30, 2020
output: pdf_document
---

Packages & Notebooks
====================

A __package__ is collection of functions and/or data for use in R.

The Comprehensive R Archive Network (__CRAN__) stores most user-contributed
packages.


You can install packages from CRAN with `install.packages()`.

For example:
```{r}

```

A package only needs to be installed once.

For maintaining your packages, there are also the functions:

* `installed.packages()` to list installed packages
* `remove.packages()` to remove a package
* `update.packages()` to update ALL packages


## Loading Packages

The `library()` function loads an installed package:
```{r}

```

Only load the packages you actually need.

You'll have to reload the packages each time you restart R.


## Notebooks

Two typical ways to save R code:

* R script (.R file)
* R notebook (.Rmd file)

R scripts are simpler:

* No extra packages required
* Ideal for developing software

R notebooks are richer:

* Can store formatted text and code
* Can be converted to HTML, DOCX, and PDF
* Ideal for data analyses and presentations

R notebooks require the `rmarkdown` package:
```{r, eval=FALSE}
install.packages("rmarkdown")
```

Generating a report from an R notebook is called __knitting__.


## TinyTeX

If you want to knit PDFs from R notebooks, you also need LaTeX.

LaTeX is programming language for typesetting books.

The `tinytex` package aims to make installing LaTeX easy.

First, install `tinytex`:
```{r, eval=FALSE}
install.packages("tinytex")
```

Second, tell `tinytex` to install LaTeX:
```{r, eval=FALSE}
library(tinytex)
install_tinytex()
```
This may take a while, and you may need administrator permissions.

Finally, restart R and try knitting an R notebook.

Remember that the output type must be `pdf_document`.



Vectors
=======

A __vector__ is an ordered collection of values.

Each value in a vector is called an __element__.


The `c()` function combines vectors:
```{r}

```

The elements of a vector must have the same data type:
```{r}

```


An __empty vector__ is a vector with 0 elements:
```{r}

```


The `length()` function returns the length of an object:
```{r}

```


Vectors are the fundamental or _atomic_ data structure in R.


## Vectorization

What happens if we call a math function on a vector?
```{r}

```

A __vectorized__ function is one that is applied element-by-element.

Most of R's math functions are vectorized.


There are a few functions that are NOT vectorized:
```{r}

```
For these, the entire vector is consumed.


Vectorization is the fastest way to do repetitive computations:
```{r}

```


## Recycling

The arithmetic operators are vectorized:
```{r}

```

If you pass vectors with different lengths, the shorter one will be
__recycled__:
```{r}

```

Recycling is most useful when the length of one argument is a multiple of the
other.

Especially when one argument has length 1:
```{r}

```



Subsets of Vectors
==================

Recall: a __vector__ is an ordered collection of values.


Sometimes you'll want to work with specific elements of a vector.


The elements are ordered, so they have positions:
```{r}

```

In R, the positions are numbered starting from 1.

These numbers are called __indices__ or __subscripts__ of the vector.


You can get an element with `[`, the subset operator:
```{r}

```


You can get multiple elements at once by passing a vector of indices:
```{r}

```

Repeated indices mean repeated elements:
```{r}

```


## Negative Indices

A negative index means all elements except the one at that index:
```{r}

```

Mixing positive and negative indices is not allowed:
```{r}

```


## Copy-on-write, Part 2

The copy-on-write rule applies to vectors.

For example:
```{r}

```