SSH URL.File \(\rightarrow\) New Project \(\rightarrow\) Version Control \(\rightarrow\) Git and paste the URL..Rmd file and replace “Your Name” with your name.library(tidyverse)# lab scores
Lab <- tibble(id = c(2, 3, 4, 6),
lab1 = c(100, 100, 100, 100),
lab2 = c(80, 20, 60, 100))
# ae scores
AE <- tibble(id = c(1, 2, 3, 5),
ae1 = c(100, 80, 50, 100),
ae2 = c(90, 100, 40, 95),
ae3 = c(70, 80, 60, 100),
ae4 = c(90, 70, 30, 90))
Lab## # A tibble: 4 × 3
## id lab1 lab2
## <dbl> <dbl> <dbl>
## 1 2 100 80
## 2 3 100 20
## 3 4 100 60
## 4 6 100 100AE## # A tibble: 4 × 5
## id ae1 ae2 ae3 ae4
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 100 90 70 90
## 2 2 80 100 80 70
## 3 3 50 40 60 30
## 4 5 100 95 100 90We will demonstrate each of the joins on these small, toy datasets.
Print all the scores on the labs and AEs for all six students. NA if the assignment is not turned in.
full_join(Lab, AE)## Joining, by = "id"## # A tibble: 6 × 7
## id lab1 lab2 ae1 ae2 ae3 ae4
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 100 80 80 100 80 70
## 2 3 100 20 50 40 60 30
## 3 4 100 60 NA NA NA NA
## 4 6 100 100 NA NA NA NA
## 5 1 NA NA 100 90 70 90
## 6 5 NA NA 100 95 100 90Who has completed all the labs and AEs and what are their scores on the completed assignments?
inner_join(Lab, AE)## Joining, by = "id"## # A tibble: 2 × 7
## id lab1 lab2 ae1 ae2 ae3 ae4
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 100 80 80 100 80 70
## 2 3 100 20 50 40 60 30What are the scores of labs and AEs for those who have completed lab assignments?
left_join(Lab, AE)## Joining, by = "id"## # A tibble: 4 × 7
## id lab1 lab2 ae1 ae2 ae3 ae4
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 100 80 80 100 80 70
## 2 3 100 20 50 40 60 30
## 3 4 100 60 NA NA NA NA
## 4 6 100 100 NA NA NA NAThis time, I want the scores for those who have completed AEs.
right_join(Lab, AE)## Joining, by = "id"## # A tibble: 4 × 7
## id lab1 lab2 ae1 ae2 ae3 ae4
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 100 80 80 100 80 70
## 2 3 100 20 50 40 60 30
## 3 1 NA NA 100 90 70 90
## 4 5 NA NA 100 95 100 90What are the lab scores for those who have completed both AEs and labs?
semi_join(Lab, AE)## Joining, by = "id"## # A tibble: 2 × 3
## id lab1 lab2
## <dbl> <dbl> <dbl>
## 1 2 100 80
## 2 3 100 20What are the lab scores for those who have completed labs but not AEs?
anti_join(Lab, AE)## Joining, by = "id"## # A tibble: 2 × 3
## id lab1 lab2
## <dbl> <dbl> <dbl>
## 1 4 100 60
## 2 6 100 100How do the join functions above know to join Lab and AE by id? Examine the names to find out.
names(Lab)## [1] "id" "lab1" "lab2"names(AE)## [1] "id" "ae1" "ae2" "ae3" "ae4"Also, try above functions by specifying the join variable. Do you see any differences?
full_join(Lab, AE)## Joining, by = "id"## # A tibble: 6 × 7
## id lab1 lab2 ae1 ae2 ae3 ae4
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 100 80 80 100 80 70
## 2 3 100 20 50 40 60 30
## 3 4 100 60 NA NA NA NA
## 4 6 100 100 NA NA NA NA
## 5 1 NA NA 100 90 70 90
## 6 5 NA NA 100 95 100 90full_join(Lab, AE, by = "id")## # A tibble: 6 × 7
## id lab1 lab2 ae1 ae2 ae3 ae4
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2 100 80 80 100 80 70
## 2 3 100 20 50 40 60 30
## 3 4 100 60 NA NA NA NA
## 4 6 100 100 NA NA NA NA
## 5 1 NA NA 100 90 70 90
## 6 5 NA NA 100 95 100 90We will again work with RDU data stored in rduall_2021.RDS. This can be downloaded from the anyflights package.
library(anyflights)rdu <- readRDS("data/rduall_2021.RDS")
airports <- rdu$airports
flights <- rdu$flightsThe R object rdu is a list where each element is a data frame. We will focus on the following data sets:
flights: Data on all flights that departed from RDU in 2021airports: Data on airports at which those flights arrivedairlines: Data to translate between two letter carrier codes and namesplanes: Construction information about each planeflights <- rdu$flights
airports <- rdu$airports
airlines <- rdu$airlines
planes <- rdu$planesQ - How are these datasets related? Examine their variable names. You may find helpful to read help pages of the datasets:
?get_flights
?get_airports
?get_airlines
?get_planes# code hereQ - Last time, it was not straightforward to understand Airlines by their carrier code. Let’s append full airline names in flights.
flights %>%
xxxx_join(____, by = "____") %>%
rename(carrier_name = name) %>% # new = old
select(year:day, contains("carrier")) Q - Suppose you wanted to make a map of the route of every flight from RDU. What variables would you need from which datasets?
Let’s add latitude and longitude information of the destination airports in flights. Note these flights and airports have no variables in common so we will have to specify the variable to join by using by =. Check out the documentation for more information.
flights %>%
left_join(______,
by = c("____" = "faa"))dest_delays with the median arrival delay in 2021 for each destination. Note this question does not require you to use joins.# code heredest_delays and airports, only including observations that have both delay and airport information. Note dest_delays and airports have no variables in common so you will need to specify the variables to join using by as in the example above.# code heredest_delays included in the new dataset you created by joining dest_delays and airports? Use an appropriate join function to investigate this issue and determine what is going on here.# code heretailnum variable in the flights dataset. The year the plane was manufactured is given in the year variable in the planes dataset.plane_delays.# code hereplane_delays to the planes data using an appropriate join and then use mutate to create an age variable.# code here# code here