Chapter_13.Rmd

---
title: "Chapter 13"
author: "Laura"
date: "12/10/2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)

library(tidyverse); library(skimr); library(nycflights13); library(GGally); library(ggstance); library(lvplot); library(hexbin); library(modelr); library(magrittr); library(maps)

```

## Notes for Chapter 13 Relational data

### 13.4 Mutating joins

```{r ch1341}
flights %>% 
  count(carrier)

flights
airlines

flights2 <- flights %>% 
  left_join(airlines, by = "carrier")
```

#### 13.4.6 Exercises

```{r ex13461}

flights2 <-  flights %>%
  group_by(dest) %>% 
  summarise(meandel = mean(arr_delay, rm.na = TRUE)) %>% 
  ungroup() 

# this doesnt work

airports %>% 
  semi_join(flights, c("faa" = "dest")) %>%
  ggplot(aes(lon, lat)) +
    borders("state") +
    geom_point() +
    coord_quickmap() +
    theme_minimal() 

```
### 13.5 Filtering joins

As indicated by its name, this is an alternative to `filter()` when the information you want to use to filter you current table is in a different table.

Filtering joins match observations in the same way as mutating joins, but affect the observations, not the variables. There are two types:

* semi_join(x, y) keeps all observations in x that have a match in y.
* anti_join(x, y) drops all observations in x that have a match in y. It is the inverse of a semi-join is an anti-join. An anti-join keeps the rows that don’t have a match. Anti-joins are useful for diagnosing join mismatches.

### 13.6 Join problems

### 13.7 Set operations

The final type of two-table verb are the set operations. Generally, I use these the least frequently, but they are occasionally useful when you want to break a single complex filter into simpler pieces. All these operations work with a complete row, comparing the values of every variable. These expect the x and y inputs to have the same variables, and treat the observations like sets:

* intersect(x, y): return only observations in both x and y.
* union(x, y): return unique observations in x and y.
* setdiff(x, y): return observations in x, but not in y.