Monday

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Today will complete our work on relational data. `

Filtering Joins

Filternig joins match observations in the same way as mutating joins, but affect the observations, not the variables.

• `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.

Today will complete our work on relational data.

The above picture is a semi_join. As you can see only the existence of a match is important. Thus filtering joins can never duplicate rows like mutating joins do. see below.

The opposite of a semi-join is an anti-join which only keeps the rows that do not have a match. See below. Thus filtering joins really deserve the name “filtering” you can accomplish the same thing with filter in tidyverse, but in some situations that might prove to be more problematic.

Example

flights %>%
  anti_join(planes, by = "tailnum") %>%
  count(tailnum, sort = TRUE)

## # A tibble: 722 × 2
##    tailnum     n
##    <chr>   <int>
##  1 <NA>     2512
##  2 N725MQ    575
##  3 N722MQ    513
##  4 N723MQ    507
##  5 N713MQ    483
##  6 N735MQ    396
##  7 N0EGMQ    371
##  8 N534MQ    364
##  9 N542MQ    363
## 10 N531MQ    349
## # … with 712 more rows

Homework

• Exercises 13.4.6

• Exercises 13.5.1

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This work is based on “R for Data Science” by Hadley and Grolemund. Tt is licensed under

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

#Exercise13.5.1

13.5.1 Exercises 1.What does it mean for a flight to have a missing tailnum? What do the tail numbers that don’t have a matching record in planes have in common? (Hint: one variable explains ~90% of the problems.)

Flights that a missing tailnum will not have arrival time recorded becouse of identification.

2.Filter flights to only show flights with planes that have flown at least 100 flights.

planes_gte100 <- flights %>%
filter(!is.na(tailnum)) %>%
  group_by(tailnum) %>%
  count() %>%
  filter(n>= 100)

3.Combine fueleconomy::vehicles and fueleconomy::common to find only the records for the most common models.

4.Find the 48 hours (over the course of the whole year) that have the worst delays. Cross-reference it with the weather data. Can you see any patterns?

5.What does anti_join(flights, airports, by = c(“dest” = “faa”)) tell you? What does anti_join(airports, flights, by = c(“faa” = “dest”)) tell you?

6.You might expect that there’s an implicit relationship between plane and airline, because each plane is flown by a single airline. Confirm or reject this hypothesis using the tools you’ve learned above.