Intro to R

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# Intro to R

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## Outline

R is a calculator

five commands to look at objects

extracting pieces

a first glimpse at data

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## The R language

- Learning a new language is **hard**!

- grammar, vocabulary

- Loading, examining, summarizing data

- Creating data

- Getting help

- Miscellaneous useful stuff
]

---
## R syntax

Basic algebra is the same as calculator/mathematics

- use explicit operators: `2*x` not `2x`,  `2^p` instead of `2p`

Functions are (most often) verbs, followed by what they will be applied to in parantheses:

- `do_this(to_this)`

- `do_that(to_this, to_that, with_those)`

In my notes, I will try to refer to objects as `objects` and functions as `function()`

Making and saving a variable, use `<-` or `=`

- Remember: everything in R is a vector

- Vectors can be constructed using concatenation (the `c()` function)

---
## Examples

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**Math**

##### Assignments `$x = \frac{2}{3}$`

##### Functions `$\sqrt{x}$`

##### Vectors `$y = \left( 1, 2, 3, 5\right)^t$`

##### Indices `$y_1$`

##### Mathematical Operators `$\sum_{i=1}^{4} y$`

##### `$2y$`

]
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**Code**

#### `x <- 2/3`

#### `sqrt(x)`

#### `y <- c(1,2,3,5)`

#### `y[1]`

#### `sum(y)`

#### `2*y`

]

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# Your Turn (5 min) 
]

- Introduce vector `$x$` defined as `$x = (4, 1, 3, 9)^t$`

- Introduce vector `$y$` defined as  `$y = (1, 2, 3, 5)^t$`
- Calculate the Euclidean distance between the two vectors `$x$` and `$y$`, defined as `$$d = \sqrt{\sum_{i=1}^4 (x_i - y_i)^2}$$`

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## Vocabulary

- What verbs (=functions) do you need to know?

- Loading data

- Accessing parts of things

- Statistical summaries

- ...

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## R Reference Card

- Download the R Reference Card from http://cran.r-project.org/doc/contrib/Short-refcard.pdf

- Open/Print it so that you can glance at it while working

---
## R essentials

#### Getting help within R

If you want to know what a specific `command` is doing:

`?command`

`help(command)`

`help.search(command)`

#### Getting out

`q()`

Quits out of the console

---
# R packages: installing & loading
Packages are installed with the install.packages function and loaded with the library function, once per session:

`install.packages("package_name")`

`library(package_name)`

---
## Loading class data

- Some R packages have in-built datasets

- For this class, there is an R package available on github

- Installing/Updating `classdata` package (once every so often):

```r
devtools::install_github("haleyjeppson/classdata")
```

```
## Skipping install of 'classdata' from a github remote, the SHA1 (f07d5f48) has not changed since last install.
##   Use `force = TRUE` to force installation
```

- Make the data available (every time you start R):

```r
library(classdata)
```

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class: yourturn
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# Your Turn (5 min) 
]
Install the package `classdata` on your machine

Make the package active in your current R session:

`library(classdata)`

Check the R help on the dataset `fbi`

What happens if you just type in the name of the dataset?

---
## Alternative method

... not able to install devtools or the classdata package?

```r
fbi <- read.csv("https://raw.githubusercontent.com/Stat480-at-ISU/materials-2020/master/02_r-intro/data/fbi.csv")
```

---
## Inspecting objects

for object `x`, we can try out the following commands:

- `x`

- `head(x)`

- `summary(x)`

- `str(x)`

- `dim(x)`

Try these commands out for yourself on the `fbi` data.

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## The fbi data

- `fbi` is a data frame with columns (variables) and rows (records)

```r
str(fbi)
```

```
## 'data.frame':	23256 obs. of  7 variables:
##  $ State        : Factor w/ 52 levels "Alabama","Alaska",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ Abb          : Factor w/ 52 levels "AK","AL","AR",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Year         : int  1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 ...
##  $ Population   : int  3302000 3358000 3347000 3407000 3462000 3517000 3540000 3566000 3531000 3444165 ...
##  $ Type         : Factor w/ 8 levels "Aggravated.assault",..: 6 6 6 6 6 6 6 6 6 6 ...
##  $ Count        : int  427 316 340 316 395 384 415 421 485 404 ...
##  $ Violent.crime: logi  TRUE TRUE TRUE TRUE TRUE TRUE ...
```

---
## Extracting parts of objects

For object `x`, we can extract parts in the following manner:

```
x$variable
x[, "variable"]
x[rows, columns]
x[1:5, 2:3]
x[c(1,5,6), c("State","Year")]
x$variable[rows]
```
`rows` and `columns` are vectors of indices.

Try these commands out for yourself on the `fbi` data.

---
## Statistical summaries

Elements of the five point summary:

`mean()`, `median()`, `min()`, `max()`, `quartiles()`

Other summary statistics:

`range()`, `sd()`, `var()`

Summaries of two variables:

`cor()`, `cov()`

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class: yourturn
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# Your Turn
]
- Look at the first 10 data records of the `fbi` data

- Compute mean and standard deviation for the number of counts. Why do you get NAs? (read `?NA`)

- Advanced:  Read `?mean` and `?sd`, and fix missing value problem

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## Your turn - practice to ask questions
]

Write down questions that you could answer with this data 4 minutes by yourself, then pair up for another 3 minutes, and we'll write ideas on the board

The types of crimes are defined on the UCR website, see http://www.ucrdatatool.gov/offenses.cfm

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## Resources

[R Reference Card](http://cran.r-project.org/doc/contrib/Short-refcard.pdf)

Artwork by [@allison_horst](https://twitter.com/allison_horst?ref_src=twsrc%5Egoogle%7Ctwcamp%5Eserp%7Ctwgr%5Eauthor)