Preliminary Notions

## Preliminary notions of R

R.Andrés Castañeda

---

## Syntax

There are three main syntax

- base

- tidyverse

- data.table

---

- [Advanced R](https://adv-r.hadley.nz/) by Hadley Wickham

- [R for Data Science](https://r4ds.had.co.nz/) by Hadley Wickham & Garret Grolemund

---

## Objects and names
(Almost) Everything in R is an object

In R, you create objects, which you can bind to a name 'x'.

```r
x <- c(1, 2, 3)
y <- x
```

![](https://github.com/hadley/adv-r/raw/master/diagrams/name-value/binding-2.png)

---

## Syntactic Names

- A syntactic name must consist of letters, digits, . and \_ but can’t begin with \_ or a digit.

- You can't use any of the reserved words like `TRUE`, `NULL`, `if`, and `function` (see the complete list in `?Reserved`).

- A name that doesn't follow these rules is a **non-syntactic** name; if you try to use them, you’ll get an error

- It's possible to override these rules and use any name, i.e., any sequence of characters, by surrounding it with backticks.

```r
`1+1` <- 2
`1+1`
```

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

---

## Vectors and functions

A little simplistic, in R,

Vectors contain the information; the data.

Functions are instructions of what to DO with the data.

---
## Vectors

A vector is a collection of elements.

There are two (well three) kind of vectors.

![](https://github.com/hadley/adv-r/raw/master/diagrams/vectors/summary-tree.png)

**Atomic vectors**: all elements must have the same type

__lists__: elements can have different types.

---
## Atomic vectors - `c()`
.pull-left[
Since atomic vectors require that all elements are of the same type, there are four types .light-blue[(six in reality, but you won't use the other two: raw and complex)]
]
.pull-right[
![](https://github.com/hadley/adv-r/raw/master/diagrams/vectors/summary-tree-atomic.png)
]

---
## Examples of atomic vectors
.pull-left[

```r
lgl_var <- c(TRUE, FALSE)
int_var <- c(1L, 6L, 10L)
dbl_var <- c(1, 2.5, 4.5)
chr_var <- c("these are", "some strings")
x <- matrix(1:6, nrow = 2, ncol = 3)
```

]
.pull-right[
<iframe src="https://rrmaximiliano.shinyapps.io/learnr-app/?showcase=0" width="100%" height="400px" data-external="1"></iframe>
]

---
## Class of vectors (S3 object system)
.pull-left[
Classes are attributes of the vectors that inform the functions how to deal with
the vector

]
.pull-right[
<img src="https://github.com/hadley/adv-r/raw/master/diagrams/vectors/summary-tree-s3-1.png" width="450" />
]

---
## List vectors - `list()`
In lists, elements can be of any type.

```r
l1 <- list(
  1:3, 
  "a", 
  c(TRUE, FALSE, TRUE), 
  c(2.3, 5.9)
)
```

]
.pull-right[

```r
typeof(l1)
```

```
## [1] "list"
```

```r
str(l1)
```

```
## List of 4
##  $ : int [1:3] 1 2 3
##  $ : chr "a"
##  $ : logi [1:3] TRUE FALSE TRUE
##  $ : num [1:2] 2.3 5.9
```
]

---
## `data.frame`, a special kind of lists (..finally!)

data frames are the kind of rectangular tables that you use in Stata
.pull-left[

```r
df1 <- 
  data.frame(
    x = 1:3, 
    y = c("a", "b", "c")
    )

typeof(df1)
```

```
## [1] "list"
```

]
.pull-right[

```r
class(df1)
```

```
## [1] "data.frame"
```

```r
attributes(df1)
```

```
## $names
## [1] "x" "y"
## 
## $class
## [1] "data.frame"
## 
## $row.names
## [1] 1 2 3
```
]

---
## Better data.frames! `tibble` and `data.table`

Base R `data.frame`s are a great idea, but a little old. By trying to do more, the end up doing less and frustrate the users.

You should move to `tibble`s and/or `data.table`s.

```r
library(tibble)    
library(data.table)

# Bad code on purpose
tb <- tibble::tibble(x = 1:3, y = letters[1:3])
dt <- data.table::data.table(x = 1:3, y = letters[1:3])
```

---

## Better data.frames! `tibble` and `data.table`

```r
class(tb)
```

```
## [1] "tbl_df"     "tbl"        "data.frame"
```

```r
class(dt)
```

```
## [1] "data.table" "data.frame"
```

---

I. What is the difference between `c(list(1:3), list(9)` and `list(list(1:3), list(9)`

II. Is there any difference between these three ways of assignment?

```r
assign('a', c(10, 150, 30, 45, 20.3))
a <- c(10, 150, 30, 45, 20.3)
a = c(10, 150, 30, 45, 20.3)
```

III. The `dim()` function gets the dimension of an object. Why `dim(a)` is `NULL`?

IV. Why `is.character(c('blue',10,'green',20))` is `TRUE`?

---

R comes with a variety of datasets to work with (Similar to `sysuse auto` in Stata). You can see all the datasets available by typing `library(help = "datasets")`.

V. In a `tibble` bound to the name `tb` add a column named `x` with the vector `rivers` from the internal `datasets`.

---