#' ---
#' 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
#' ---
#'
#' # Reading
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#'
#' # 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.
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#' # 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?
#'
#' # Tasks
#'
#'
#' You will need to load the necessary packages
## ---- message=FALSE------------------------------------------------------
library(tidyverse)
library(nycflights13)
#' [ 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?
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#' 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
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#' 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:
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#' Can you figure out how to map mean delays by destination airport as shown below?
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#' Adapted from [R for Data Science](http://r4ds.had.co.nz/relational-data.html#filtering-joins)