Grammar of data wrangling

# Grammar of data wrangling
## <br><br> College of the Atlantic
###

---

# Grammar of data wrangling

---

## A grammar of data wrangling...

... based on the concepts of functions as verbs that manipulate data frames

.pull-left[
<img src="img/dplyr-part-of-tidyverse.png" width="70%" style="display: block; margin: auto;" />
]
.pull-right[
.midi[
- `select`: pick columns by name
- `arrange`: reorder rows
- `slice`: pick rows using index(es)
- `filter`: pick rows matching criteria
- `distinct`: filter for unique rows
- `mutate`: add new variables
- `summarise`: reduce variables to values
- `group_by`: for grouped operations
- ... (many more)
]
]

---

## Rules of **dplyr** functions

- First argument is *always* a data frame
- Subsequent arguments say what to do with that data frame
- Always return a data frame
- Don't modify in place

---

## Data: Hotel bookings

- Data from two hotels: one resort and one city hotel
- Observations: Each row represents a hotel booking
- Goal for original data collection: Development of prediction models to classify a hotel booking's likelihood to be cancelled ([Antonia et al., 2019](https://www.sciencedirect.com/science/article/pii/S2352340918315191#bib5))

```r
hotels <- read_csv("data/hotels.csv")
```

.footnote[
Source: [TidyTuesday](https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md)
]

---

## First look: Variables

```r
names(hotels)
```

```
##  [1] "hotel"                         
##  [2] "is_canceled"                   
##  [3] "lead_time"                     
##  [4] "arrival_date_year"             
##  [5] "arrival_date_month"            
##  [6] "arrival_date_week_number"      
##  [7] "arrival_date_day_of_month"     
##  [8] "stays_in_weekend_nights"       
##  [9] "stays_in_week_nights"          
## [10] "adults"                        
## [11] "children"                      
## [12] "babies"                        
## [13] "meal"                          
## [14] "country"                       
## [15] "market_segment"                
## [16] "distribution_channel"          
## [17] "is_repeated_guest"             
## [18] "previous_cancellations"        
...
```

---

## Second look: Overview

```r
glimpse(hotels)
```

```
## Rows: 119,390
## Columns: 32
## $ hotel                          <chr> "Resort Hotel", "Resort ~
## $ is_canceled                    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ lead_time                      <dbl> 342, 737, 7, 13, 14, 14,~
## $ arrival_date_year              <dbl> 2015, 2015, 2015, 2015, ~
## $ arrival_date_month             <chr> "July", "July", "July", ~
## $ arrival_date_week_number       <dbl> 27, 27, 27, 27, 27, 27, ~
## $ arrival_date_day_of_month      <dbl> 1, 1, 1, 1, 1, 1, 1, 1, ~
## $ stays_in_weekend_nights        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ stays_in_week_nights           <dbl> 0, 0, 1, 1, 2, 2, 2, 2, ~
## $ adults                         <dbl> 2, 2, 1, 1, 2, 2, 2, 2, ~
## $ children                       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ babies                         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, ~
## $ meal                           <chr> "BB", "BB", "BB", "BB", ~
## $ country                        <chr> "PRT", "PRT", "GBR", "GB~
## $ market_segment                 <chr> "Direct", "Direct", "Dir~
## $ distribution_channel           <chr> "Direct", "Direct", "Dir~
...
```

---

## Select a single column

View only `lead_time` (number of days between booking and arrival date):

```r
select(hotels, lead_time)
```

```
## # A tibble: 119,390 x 1
##   lead_time
##       <dbl>
## 1       342
## 2       737
## 3         7
## 4        13
## 5        14
## 6        14
## # ... with 119,384 more rows
```

---

## Select a single column

```r
*select(
  hotels, 
  lead_time
  )
```
]
.pull-right[
- Start with the function (a verb): `select()`
]

---

## Select a single column

```r
select( 
* hotels,
  lead_time
  )
```
]
.pull-right[
- Start with the function (a verb): `select()`
- First argument: data frame we're working with , `hotels`
]

---

## Select a single column

```r
select( 
  hotels, 
* lead_time
  )
```
]
.pull-right[
- Start with the function (a verb): `select()`
- First argument: data frame we're working with , `hotels`
- Second argument: variable we want to select, `lead_time`
]

---

## Select a single column

```r
select( 
  hotels, 
  lead_time
  )
```

```
## # A tibble: 119,390 x 1
##   lead_time
##       <dbl>
## 1       342
## 2       737
## 3         7
## 4        13
## 5        14
## 6        14
## # ... with 119,384 more rows
```
]
.pull-right[
- Start with the function (a verb): `select()`
- First argument: data frame we're working with , `hotels`
- Second argument: variable we want to select, `lead_time`
- Result: data frame with 119390 rows and 1 column
]

---

```r
select(hotels, lead_time)
```

```
## # A tibble: 119,390 x 1
##   lead_time
##       <dbl>
## 1       342
## 2       737
## 3         7
## 4        13
## 5        14
## 6        14
## # ... with 119,384 more rows
```

---

## Select multiple columns

View only the `hotel` type and `lead_time`:

```r
select(hotels, hotel, lead_time)
```

```
## # A tibble: 119,390 x 2
##   hotel        lead_time
##   <chr>            <dbl>
## 1 Resort Hotel       342
## 2 Resort Hotel       737
## 3 Resort Hotel         7
## 4 Resort Hotel        13
## 5 Resort Hotel        14
## 6 Resort Hotel        14
## # ... with 119,384 more rows
```
]
--
.pull-right[
.question[
What if we wanted to select these columns, and then arrange the data in descending order of lead time?
]
]

---

## Data wrangling, step-by-step

```r
hotels %>%
  select(hotel, lead_time)
```

--
.pull-right[
Select, then arrange:

```r
hotels %>%
  select(hotel, lead_time) %>%
  arrange(desc(lead_time))
```

```
## # A tibble: 119,390 x 2
##   hotel        lead_time
##   <chr>            <dbl>
## 1 Resort Hotel       737
## 2 Resort Hotel       709
## 3 City Hotel         629
## 4 City Hotel         629
## 5 City Hotel         629
## 6 City Hotel         629
## # ... with 119,384 more rows
```
]

---

# Pipes

---

## What is a pipe?

In programming, a pipe is a technique for passing information from one process to another.

.pull-left[
- Start with the data frame `hotels`, and pass it to the `select()` function,
]
.pull-right[
.small[

```r
*hotels %>%
  select(hotel, lead_time) %>%
  arrange(desc(lead_time))
```

---

## What is a pipe?

In programming, a pipe is a technique for passing information from one process to another.

.pull-left[
- Start with the data frame `hotels`, and pass it to the `select()` function,
- then we select the variables `hotel` and `lead_time`,
]
.pull-right[
.small[

```r
hotels %>%
* select(hotel, lead_time) %>%
  arrange(desc(lead_time))
```

---

## What is a pipe?

In programming, a pipe is a technique for passing information from one process to another.

.pull-left[
- Start with the data frame `hotels`, and pass it to the `select()` function,
- then we select the variables `hotel` and `lead_time`,
- and then we arrange the data frame by `lead_time` in descending order.
]
.pull-right[
.small[

```r
hotels %>%
  select(hotel, lead_time) %>% 
* arrange(desc(lead_time))
```

---

## Aside

The pipe operator is implemented in the package **magrittr**, though we don't need to load this package explicitly since **tidyverse** does this for us.

.pull-left[
<img src="img/magritte.jpg" width="90%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="img/magrittr.jpg" width="100%" style="display: block; margin: auto;" />
]

---

## How does a pipe work?

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

--
- Expressed as a set of nested functions in R pseudocode this would look like:

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

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

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

---

## A note on piping and layering

- `%>%` used mainly in **dplyr** pipelines, *we pipe the output of the previous line of code as the first input of the next line of code*

--
- `+` used in **ggplot2** plots is used for "layering", *we create the plot in layers, separated by `+`*

---

## dplyr

```r
hotels +
  select(hotel, lead_time)
```

```
## Error in select(hotel, lead_time): object 'hotel' not found
```

✅

```r
hotels %>%
  select(hotel, lead_time)
```

```
## # A tibble: 119,390 x 2
##   hotel        lead_time
##   <chr>            <dbl>
## 1 Resort Hotel       342
## 2 Resort Hotel       737
## 3 Resort Hotel         7
...
```
]

---

## ggplot2

```r
ggplot(hotels, aes(x = hotel, fill = deposit_type)) %>%
  geom_bar()
```

```
## Error in `validate_mapping()`:
## ! `mapping` must be created by `aes()`
## Did you use %>% instead of +?
```

✅

```r
ggplot(hotels, aes(x = hotel, fill = deposit_type)) +
  geom_bar()
```

<img src="u2-d06-grammar-wrangle_files/figure-html/unnamed-chunk-23-1.png" width="25%" style="display: block; margin: auto;" />
]

---

## Code styling

Many of the styling principles are consistent across `%>%` and `+`:

- always a space before
- always a line break after (for pipelines with more than 2 lines)

❌

```r
ggplot(hotels,aes(x=hotel,y=deposit_type))+geom_bar()
```

✅

```r
ggplot(hotels, aes(x = hotel, y = deposit_type)) + 
  geom_bar()
```

---
## Acknowledgements

* This course builds on the materials from [Data Science in a Box](https://datasciencebox.org/) developed by Mine Çetinkaya-Rundel and are adapted under the [Creative Commons Attribution Share Alike 4.0 International](https://github.com/rstudio-education/datascience-box/blob/master/LICENSE.md)