R is a Calculator

class: center, middle, inverse, title-slide

.title[
# R is a Calculator
]
.subtitle[
## Hello World, this is R
]
.author[
### Will Ju
]

---

# Outline

- R is a calculator

- five commands to look at objects

- extracting pieces

- a first glimpse at data

---

## The R language

- Learning a new language: grammar, vocabulary

- Loading, examining, summarizing data

- Creating data

- Getting help

- Miscellaneous useful stuff

---
class: inverse, center

---

## Grammar

- Basic algebra is the same as calculator/mathematics

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

- Applying a function is similar

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

- Everything in R is a vector

---

## Examples

| Math | Type | Code | 
|:--- |:-------------:|-------------:|
| `$$x = \frac{2}{3}$$` | Assignments | `x <- 2/3` |
| `$$\sqrt{x}$$` | Functions | `sqrt(x)` |
| `$$y = \left( \begin{array}{c} 1\\4\\5\\2\end{array}\right)$$` | Vectors | `y <- c(1, 4, 5, 2) ` |
| `$$y_2$$` | Indices | `y[2]`|

---

## More Examples

| Math   | Type          | Code           | 
|:--- |:-------------:|-------------:|
| `$$\sum_{i=1}^{4} y_i$$` | Mathematical Operators | `sum(y)` |
| `$$2y$$` |  | `2*y` |

---
class: inverse

## Your Turn (5 min)

- Introduce vector `$x$` defined as `$$x = \left( \begin{array}{c} 4\\1\\3\\9\end{array}\right)$$`

- Introduce vector `$y$` defined as  `$$y = \left( \begin{array}{c} 1\\2\\3\\5\end{array}\right)$$`

- Calculate the Euclidean distance between the two vectors `$x$` and `$y$`, defined as

`$$d = \sqrt{\sum_{i=1}^4 (x_i - y_i)^2}$$`

---

## Vocabulary

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

- Loading data

- Accessing parts of things

- Statistical summaries

- ...

---

## 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

---

### Getting help within R

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

```r
?command

help("command")

help.search("command")

??command
```

### Getting out

```r
q()
```

Quits out of the console

---

## 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
remotes::install_github("heike/classdata")
```

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

```r
library(classdata)
```

---
class: inverse
## Your Turn (5 min)

- Install the package `classdata` on your machine

- Make the package active in your current R session:

```r
library(classdata)
```

- Check the R help on the dataset `fbi`

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

---

## 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.

---

## `str` stands for *structure*

- `str` shows us the **str**ucture of an object

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

```r
str(fbi)
```

```
## Classes 'tbl_df', 'tbl' and 'data.frame':	19476 obs. of  8 variables:
##  $ state        : chr  "Alabama" "Alabama" "Alabama" "Alabama" ...
##  $ state_id     : int  2 2 2 2 2 2 2 2 2 2 ...
##  $ state_abbr   : chr  "AL" "AL" "AL" "AL" ...
##  $ year         : int  1983 1983 1983 1983 1983 1983 1983 1983 1983 1985 ...
##  $ population   : int  3959000 3959000 3959000 3959000 3959000 3959000 3959000 3959000 3959000 4021000 ...
##  $ type         : chr  "homicide" "rape_legacy" "rape_revised" "robbery" ...
##  $ count        : int  364 931 NA 3895 11281 42485 94279 9126 981 396 ...
##  $ violent_crime: logi  TRUE TRUE TRUE TRUE TRUE FALSE ...
```

---

## 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`

---
class: inverse
## 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

---
class: inverse 
## 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, https://crime-data-explorer.app.cloud.gov/pages/explorer/crime/crime-trend