CS_04.Rmd

---
title: Farthest airport from New York City
week: 4
type: Case Study
subtitle: Joining Relational Data
reading:
  - R4DS [Chapter 13 - Relational Data](http://r4ds.had.co.nz/relational-data.html){target='blank'}
tasks:
  - Join two datasets using a common column
  - Answer a question that requires understanding how multiple tables are related
  - Save your script as a .R or .Rmd in your course repository
---
```{r setup, include=FALSE, purl=F}
options(knitr.duplicate.label = 'allow')
source("functions.R")
source("knitr_header.R")
```

# Reading

```{r reading,results='asis',echo=F,purl=F}
md_bullet(rmarkdown::metadata$reading)
```

# Background
In this exercise you will use various data wrangling tools to answer questions from the data held in separate tables.  We'll use the data in the `nycflights13` package which has relationships between the tables as follows.

![](http://r4ds.had.co.nz/diagrams/relational-nycflights.png)


# Objective
> What is the full name (not the three letter code) of the destination airport farthest from any of the NYC airports in the `flights` table?

Save the name as a character value `farthest_airport`.

# Tasks

```{r tasks,results='asis',echo=F, purl=F}
md_bullet(rmarkdown::metadata$tasks)
```

You will need to load the necessary packages
```{r, message=FALSE}
library(tidyverse)
library(nycflights13)
```
[<i class="fa fa-file-code-o fa-1x" aria-hidden="true"></i> Download starter R script (if desired)](`r output_nocomment`){target="_blank"}

There are several ways to do this using at least two different joins. I found two solutions that use 5 or 6 functions separated by pipes (`%>%`).  Can you do it in fewer?

<div class="well">
<button data-toggle="collapse" class="btn btn-primary btn-sm round" data-target="#demo1">Show Hints</button>
<div id="demo1" class="collapse">
The details below describe one possible approach.

1. Open the help file for the `nycflights13` package by searching in the "Help" panel in RStudio.
2. Look at the contents of the various tables to find the ones you need (`name`, `distance`, and `dest`).  You can use `head()`, `glimpse()`, `View()`, `str()`.  
2. In the table with distances, find the airport code that is farthest from the New York Airports (perhaps using `arrange()` and `slice()`)
3. Join this table with the one that has the full airport names.  You will either need to rename the columns so they match the other table or use the `by` parameter in the join.  e.g. check out `?left_join()`
4. `select()` only the `destName` column
5. Convert the data.frame to a single character value with `as.character()`. This converts the data.frame object into a single value. 
5. Save the resulting airport name as an object called `farthest_airport`
</div>
</div>

```{r purl=F, echo=F, results='hide',message=FALSE, eval=F}
farthest_airport <- flights%>%
  select(distance,dest)%>%
  arrange(desc(distance))%>%
  slice(1)%>%
  left_join(airports,by=c("dest"="faa"))%>%
  select(name) %>% 
  as.character()


#OR

select(airports,
       dest=faa,
       destName=name)%>%
  right_join(flights)%>% 
  arrange(desc(distance)) %>% 
  slice(1) %>% 
  select(destName) %>% 
  as.character()
```


<div class="extraswell">
<button data-toggle="collapse" class="btn btn-link" data-target="#extras">
Extra time? Try these extra activities...
</button>
<div id="extras" class="collapse">

Soon we will introduce working with spatial data and doing similar kinds of operations.  If you have time to play, see if you can figure out what this does:

```{r, message=FALSE, purl=F}
airports %>%
  distinct(lon,lat) %>%
  ggplot(aes(lon, lat)) +
    borders("world") +
    geom_point(col="red") +
    coord_quickmap()
```

Can you figure out how to map mean delays by destination airport as shown below?

```{r, echo=F, purl=F}
airports %>%
  filter(!tzone%in%c("Pacific/Honolulu",
                     "America/Anchorage",
                     "Asia/Chongqing"  ))%>%
  right_join(flights, c("faa" = "dest")) %>%
  group_by(name,lat,lon)%>%
  summarize(meandelay=mean(arr_delay, na.rm=T))%>%
  na.omit()%>%
  ggplot(aes(lon, lat,col=meandelay)) +
    borders("state") +
    geom_point(size=2) +
    coord_quickmap()+
  scale_color_distiller(type="div",palette = "RdBu",name="Mean\nDelay\n(minutes)")
```

</div>
</div>

<i> <small> Adapted from [R for Data Science](http://r4ds.had.co.nz/relational-data.html#filtering-joins) </small> </i>