Lab 03

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# Lab 03
## Data wrangling
### Shawn Santo

---

## Announcements

- Complete the Qualtrics survey (available in Slack in channel `#general`).
  The information gathered will be used to form lab groups. Answer honestly.
  
- Continue working on Homework 02, ask questions, attend office hours

- Carefully review feedback given on past assignments

---

## Goals

- Use data wrangling to extract meaning from data

- Practice using the **seven helpful verbs (functions)**

---

## A grammar of data manipulation

Package `dplyr` is based on the concepts of functions as verbs 
that manipulate data frames (tibbles).

Common single data frame functions / verbs:

| Function                       | Description                          | Operates on    |
|:-------------------------------|:-------------------------------------|:---------------|
| `filter()`                     | pick rows matching criteria          | rows           |
| `slice()`                      | pick rows using indices              | rows           |
| `arrange()`                    | reorder rows                         | rows           |
| `select()`                     | pick columns by name                 | columns        |
| `mutate()`                     | add new variables                    | columns        |
| `summarise()`                  | reduce variables to values           | groups of rows |

... many more.

---

## `dplyr` rules

1. First argument is *always* a data frame

2. Subsequent arguments say what to do with that data frame

3. Almost always returns a data frame

4. Doesn't modify in place

Based on rules 1 and 3, it is natural to apply ` %>% ` in a sequence
of `dplyr` functions for data wrangling purposes.

---

## Pipes in R

The `%>%` is a forward-pipe operator. It allows you to pipe an object
forward into a function.

You can think about the following sequence of actions - 
find keys, unlock car, start car, drive to school, park.

<br/>

Expressed as a set of nested functions in R pseudo code this would look like:

```r
park(drive(start_car(unlock_car(find("keys"))), to = "campus"))
```

<br/>

Writing it out using pipes give it a more natural (and easier to read) 
structure:

```r
find("keys") %>%
  unlock_car() %>% 
  start_car() %>%
  drive(to = "campus") %>%
  park()
```

---

## Lab 03

- Accept and create your private repository of the assignment at
  https://classroom.github.com/a/j6tXRehp

- The directions are available on the course website at
  http://www2.stat.duke.edu/courses/Spring21/sta199.003/labs/lab_03.html.

- This is an individual lab assignment. As you work on Lab 03

+ work with individuals in your breakout room,

+ ask questions,

+ don't be afraid to experiment in R, you can't break anything.

- Pay special attention to the submission procedure detailed at the end of the
  instructions (it is the same as Lab 02 and Homework 02).

---

## Less commonly used `dplyr` functions

These are all single data frame functions.

| Function                       | Description                          |
|:-------------------------------|:-------------------------------------|
| `pull()`                       | grab a column as a vector            |
| `transmute()`                  | create new data frame with variables |
| `distinct()`                   | filter for unique rows               |
| `sample_n()` / `sample_frac()` | randomly sample rows                 |

---

## Additional `dplyr` resources

- `dplyr` [cheat sheet](https://github.com/rstudio/cheatsheets/raw/master/data-transformation.pdf)

- [dplyr vignette](https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html)

- [Chapter 5](https://r4ds.had.co.nz/transform.html), R for Data Science