Introduction to R

.title[
# Introduction to R
]
.subtitle[
## Data Visualization
]
.author[
### Johan Larsson
]
.author[
### Behnaz Pirzamanbein
]
.institute[
### The Department of Statistics, Lund University
]

---

## R

.pull-left[
* a programming language **and** piece of software
* designed for **statistical** applications
* stable
* extensive ecosystem of packages
* **excellent** facilities for visualization
]

### Code in R

```r
x <- c(1, 2)
mean(x)
```

```
## [1] 1.5
```

---

## R Studio

.pull-left[
* graphical user interface for R
* combines text editor, console, and plot window
* useful for organizing projects
* for this course, we **assume** that you will be using R studio
]

---

---

background-image: url("images/rstudio-screenshot-editor.png")
background-size: contain

---

background-image: url("images/rstudio-screenshot-console.png")
background-size: contain

---

background-image: url("images/rstudio-screenshot-plot.png")
background-size: contain

---

## Everything in R is an Object

Use the `<-` operator to assign values from the right-hand to the left-hand
side.

```r
x <- c(1, 9)
x
```

```
## [1] 1 9
```

Objects can be copied or modified in place.

```r
# copy the contents of x to create a new variable y
y <- x

# replace the first element of y
y[1] <- 10
y
```

```
## [1] 10  9
```

---

## Data Classes in R

Data classes in R represents the type of storage.

* **numeric**: quantitative data
* **logical**: yes or no
* **character**: nominal data

```r
x <- c(0.1, 0.2)     # numeric
y <- c(TRUE, FALSE)  # logical
z <- c("foo", "bar") # character
```

Other types of classes, such as **integer**, **double**, and **factor**, exist but
are not (very) important for us.

---

## Data Structures in R

Objects of the various **data classes** may be stored into
**data structures**, such as

- **vector**: one-dimensional list,
- **matrix**: two-dimensional table, 
  - elements must have **same** data class
- **data.frame**: two-dimensional table, 
  - elements can have **different** data classes
- **list**: list of items (data frames, matrices, vectors) of potentially
  different lengths.

---

---

### Vectors

All elements of vectors must be of the same type.

It can be constructed using `c()`.

```r
c(1.3, 4, 5)         # numeric
```

```
## [1] 1.3 4.0 5.0
```

```r
c(TRUE, FALSE, TRUE) # logical
```

```
## [1]  TRUE FALSE  TRUE
```

```r
c("apple", "pear")   # character
```

```
## [1] "apple" "pear"
```

```r
c(1, "3", TRUE)      # mix (converted to character)
```

```
## [1] "1"    "3"    "TRUE"
```

---

### Accessing Elements in Vectors

Access elements in vectors with `[` operator.

```r
x <- c("a", "b", "c")
x[1] # access first element
```

```
## [1] "a"
```

Access several elements of vectors by indexing with vector of integers.

```r
x[c(1, 2)] # or x[1:2]
```

```
## [1] "a" "b"
```

---

### Matrices

Create matrices with `matrix()`, `cbind()`, and `rbind()`.

```r
A <- cbind(c(1, 2), c(2, 3)) # numeric matrix
A
```

```
##      [,1] [,2]
## [1,]    1    2
## [2,]    2    3
```

Access elements of matrices, rows, and columns using `[` operator.

```r
A[2, 2]  # lower right element
```

```
## [1] 3
```

```r
A[1, ]   # first row, A[, 2] accesses second column
```

```
## [1] 1 2
```

---

### Data Frames

Data frames are like matrices, but columns may have different classes.

```r
d <- data.frame(x = c(1, 2),
                y = c("apple", "pear"))
d
```

```
##   x     y
## 1 1 apple
## 2 2  pear
```

Access elements of data frames using `[` or `$` operator.

```r
d[, 1] # first column (x)
```

```
## [1] 1 2
```

```r
d$x    # same as above!
```

```
## [1] 1 2
```

---

### Lists

Lits are a collection of objects of **any kind** (of any dimensions).

```r
# <name> = <element> syntax
x <- list(a = c("foo", "bar"),
          b = matrix(1:4, 2, 2))
x
```

```
## $a
## [1] "foo" "bar"
## 
## $b
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4
```

A data frame is in fact a special type of list.

---

## Functions

Everything that happens in R is the result of a function call.

```r
my_function <- function(x, y) {
  x + 2*y
}

my_function(1, 3)
```

```
## [1] 7
```

- `x` and `y` are the **arguments**

- `x + 2*y` is the **body**

- The last line of the function decides what the function returns,
  unless `return()` is used somewhere inside the function.

---

### Arguments

Arguments can be specified either by **name** or by **location**.

```r
my_function(3, 1)         # by location
```

```
## [1] 5
```

```r
my_function(y = 1, x = 3) # by name
```

```
## [1] 5
```

In `my_function()`, both arguments are **mandatory**, but this is not
always the case. Here we give `y` a **default** value.

```r
my_other_function <- function(x, y = 3) {
  x + 2*y
}

my_other_function(1)
```

```
## [1] 7
```

---

### Functions in This Course

- We won't be writing our own function in this course.

- But it is important to understand how to
  * call functions,
  * read function documentation, and
  * figure out what a function's default arguments imply.

---

## Packages

There are more than 16,000 packages on [CRAN](https://cran.r-project.org/web/packages/).

It is easy to install!

```r
install.packages("ggplot2")
```

Load the package by calling `library()`.

```r
library(ggplot2)
```

There are even more packages available via [bioconductor](https://www.bioconductor.org) and
git repositories on [github](https://github.com/) and similar sites.

```r
# install.packages("remotes")
remotes::install_github("hrbrmstr/waffle")
```

But in this course we will **only** use CRAN packages.

---

## Getting Help

- Learn how to read object documentation, particularly for functions.
  * `F1` shortcut in R Studio
  * `help(function)` or `?function` in console

- Most problems you run into have been encountered (and solved)
  by others. Use
  * the course discussion board,
  * [stack overflow](https://stackoverflow.com/) (tag `r`), or
  * a search engine.