---
title: "Transformation Pipes "
author: "Prof. Eric A. Suess"
output:
  word_document: default
  html_document:
    df_print: paged
  pdf_document: default
  html_notebook: default
---

# Chapter 4 Data Transformation

The 5 verbs of data wrangling

- Pick observations by their values (**filter()**).
- Reorder the rows (**arrange()**).
- Pick variables by their names (**select()**).
- Create new variables with functions of existing variables (**mutate()**).
- Collapse many values down to a single summary (**summarise()**).
- (**group_by()**)


```{r message=FALSE}
library(nycflights13)
library(tidyverse)
```

We will continue to work with the *flights* dataset that is in the ggplot2 package.

```{r}
flights
```

Change the code from the Transformation presentation to using the pipe %>%.  Note that when using pipes you do not include the data in the next function call, it is piped into the function.  The functions in the tidyverse work this way.

## filter()

```{r echo=TRUE}
flights %>% filter(month == 1, day == 1)
```

## arrange()

```{r echo=TRUE}
flights %>% arrange(year, month, day)
```

## arrange()

```{r echo=TRUE}
flights %>% arrange(desc(dep_delay))
```

## select()

```{r echo=TRUE}
flights %>% select(year, month, day)
```

## select()

```{r echo=TRUE}
flights %>% select(time_hour, air_time, everything())
```

## mutate()

```{r echo=TRUE}
flights %>% select(year:day, ends_with("delay"), distance, air_time) %>%
   mutate(gain = dep_delay - arr_delay, speed = distance / air_time * 60)
```

## summarize()

```{r echo=TRUE}
summarise(flights, delay = mean(dep_delay, na.rm = TRUE))

flights %>% group_by(year, month, day) %>%
    summarise(delay = mean(dep_delay, na.rm = TRUE))

```

## Combining multiple operations with the pipe %>%

```{r echo=TRUE}
delay <- flights %>% group_by(dest) %>%
  summarise(count = n(), dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>%
  filter(count > 20, dest != "HNL")
delay
```

## Combining multiple operations with the pipe %>%

```{r echo=TRUE, eval=FALSE}
delay %>% ggplot(mapping = aes(x = dist, y = delay)) +
  geom_point(aes(size = count), alpha = 1/3) +
  geom_smooth(se = FALSE)
```

## Combining multiple operations with the pipe %>%

It looks like delays increase with distance up to ~750 miles 
and then decrease. Maybe as flights get longer there's more 
ability to make up delays in the air?

> `geom_smooth()` using method = 'loess' and formula 'y ~ x'

## Combining multiple operations with the pipe %>%

Does this code read better?  This is the same code as above!

```{r echo=TRUE}
delays <- flights %>% 
  group_by(dest) %>% 
  summarise(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>% 
  filter(count > 20, dest != "HNL")
```