Feb 11, 2020
In this worksheet, we will use the libraries tidyverse and nycflights13:
library(tidyverse)
theme_set(theme_bw(base_size=12)) # set default ggplot2 theme
library(nycflights13)The nycflights13 package contains information about all planes departing fron New York City in 2013.
The following two tables list the population size and area (in sq miles) of three major Texas cities each:
population <- read_csv(file =
"city,year,population
Houston,2014,2239558
San Antonio,2014,1436697
Austin,2014,912791
Austin,2010,790390")
population## # A tibble: 4 x 3
## city year population
## <chr> <dbl> <dbl>
## 1 Houston 2014 2239558
## 2 San Antonio 2014 1436697
## 3 Austin 2014 912791
## 4 Austin 2010 790390area <- read_csv(file =
"city,area
Houston,607.5
Dallas,385.6
Austin,307.2")
area## # A tibble: 3 x 2
## city area
## <chr> <dbl>
## 1 Houston 608.
## 2 Dallas 386.
## 3 Austin 307.Combine these two tables using the functions left_join(), right_join(), and inner_join(). How do these join functions differ in their results?
# R code goes here.The table flights from nycflights13 contains information about individual departures:
flights## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>Individual planes are indicated by their tail number (tailnum in the table). Calculate the mean arrival delay (arr_delay) for each tail number. Do you notice anything unusual in the result? Try to calculate the mean with and without adding the option na.rm = TRUE.
# R code goes here.Information about individual planes is availabe in the table planes:
planes## # A tibble: 3,322 x 9
## tailnum year type manufacturer model engines seats speed engine
## <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
## 1 N10156 2004 Fixed wing m… EMBRAER EMB-1… 2 55 NA Turbo-…
## 2 N102UW 1998 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 3 N103US 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 4 N104UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 5 N10575 2002 Fixed wing m… EMBRAER EMB-1… 2 55 NA Turbo-…
## 6 N105UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 7 N107US 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 8 N108UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 9 N109UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## 10 N110UW 1999 Fixed wing m… AIRBUS INDUST… A320-… 2 182 NA Turbo-…
## # … with 3,312 more rowsIn particular, this table lists the year each individual plane was manufactured. Make a combined table that holds tail number, mean arrival delay, and year of manufacture for each plane. Then plot mean arrival delay vs. year of manufacture.
# R code goes here.Now calculate the mean arrival delay for each day of the year, and store in a variable called daily_delays.
# R code goes here.We want to correlate these delay values with the temperature of each day. The data frame weather holds temperature measurements for each hour of each day:
weather## # A tibble: 26,115 x 15
## origin year month day hour temp dewp humid wind_dir wind_speed
## <chr> <dbl> <dbl> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EWR 2013 1 1 1 39.0 26.1 59.4 270 10.4
## 2 EWR 2013 1 1 2 39.0 27.0 61.6 250 8.06
## 3 EWR 2013 1 1 3 39.0 28.0 64.4 240 11.5
## 4 EWR 2013 1 1 4 39.9 28.0 62.2 250 12.7
## 5 EWR 2013 1 1 5 39.0 28.0 64.4 260 12.7
## 6 EWR 2013 1 1 6 37.9 28.0 67.2 240 11.5
## 7 EWR 2013 1 1 7 39.0 28.0 64.4 240 15.0
## 8 EWR 2013 1 1 8 39.9 28.0 62.2 250 10.4
## 9 EWR 2013 1 1 9 39.9 28.0 62.2 260 15.0
## 10 EWR 2013 1 1 10 41 28.0 59.6 260 13.8
## # … with 26,105 more rows, and 5 more variables: wind_gust <dbl>, precip <dbl>,
## # pressure <dbl>, visib <dbl>, time_hour <dttm>First, calculate the mean temperature for each day, and store in a variable called mean_temp:
# R code goes here.Now combine the mean delay and the mean temperature into one table, and then plot mean delay vs. mean temperature.
# R code goes here.Find out for how many tail numbers in the flights data set we have no information in the planes data set. What do we have to pay attention to when joining the flights and planes tables?
# R code goes here.Calculate the mean arrival delay by plane model and by plane engine. Sort in order of descending mean delay. Remove all tailnumbers for which no plane information is available.
# R code goes here.