Lab 2
Rachel Saidi
2023-02-02
Introduction to data
Load the data
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.1.3## -- Attaching packages --------------------------------------- tidyverse 1.3.2 --v ggplot2 3.3.5 v purrr 1.0.1
## v tibble 3.1.8 v dplyr 1.0.10
## v tidyr 1.3.0 v stringr 1.5.0
## v readr 2.1.3 v forcats 0.5.1## Warning: package 'tibble' was built under R version 4.1.3## Warning: package 'tidyr' was built under R version 4.1.3## Warning: package 'readr' was built under R version 4.1.3## Warning: package 'purrr' was built under R version 4.1.3## Warning: package 'dplyr' was built under R version 4.1.3## Warning: package 'stringr' was built under R version 4.1.3## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(openintro)## Warning: package 'openintro' was built under R version 4.1.3## Loading required package: airports## Warning: package 'airports' was built under R version 4.1.1## Loading required package: cherryblossom## Warning: package 'cherryblossom' was built under R version 4.1.1## Loading required package: usdata## Warning: package 'usdata' was built under R version 4.1.1The data
The Bureau of Transportation Statistics (BTS) is a statistical agency that is a part of the Research and Innovative Technology Administration (RITA). As its name implies, BTS collects and makes transportation data available, such as the flights data we will be working with in this lab.
First, we’ll view the nycflights data frame. Type the following in your console to load the data:
data(nycflights)
head(nycflights)## # A tibble: 6 x 16
## year month day dep_time dep_delay arr_time arr_delay carrier tailnum flight
## <int> <int> <int> <int> <dbl> <int> <dbl> <chr> <chr> <int>
## 1 2013 6 30 940 15 1216 -4 VX N626VA 407
## 2 2013 5 7 1657 -3 2104 10 DL N3760C 329
## 3 2013 12 8 859 -1 1238 11 DL N712TW 422
## 4 2013 5 14 1841 -4 2122 -34 DL N914DL 2391
## 5 2013 7 21 1102 -3 1230 -8 9E N823AY 3652
## 6 2013 1 1 1817 -3 2008 3 AA N3AXAA 353
## # ... with 6 more variables: origin <chr>, dest <chr>, air_time <dbl>,
## # distance <dbl>, hour <dbl>, minute <dbl>View the data
glimpse(nycflights)## Rows: 32,735
## Columns: 16
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, ~
## $ month <int> 6, 5, 12, 5, 7, 1, 12, 8, 9, 4, 6, 11, 4, 3, 10, 1, 2, 8, 10~
## $ day <int> 30, 7, 8, 14, 21, 1, 9, 13, 26, 30, 17, 22, 26, 25, 21, 23, ~
## $ dep_time <int> 940, 1657, 859, 1841, 1102, 1817, 1259, 1920, 725, 1323, 940~
## $ dep_delay <dbl> 15, -3, -1, -4, -3, -3, 14, 85, -10, 62, 5, 5, -2, 115, -4, ~
## $ arr_time <int> 1216, 2104, 1238, 2122, 1230, 2008, 1617, 2032, 1027, 1549, ~
## $ arr_delay <dbl> -4, 10, 11, -34, -8, 3, 22, 71, -8, 60, -4, -2, 22, 91, -6, ~
## $ carrier <chr> "VX", "DL", "DL", "DL", "9E", "AA", "WN", "B6", "AA", "EV", ~
## $ tailnum <chr> "N626VA", "N3760C", "N712TW", "N914DL", "N823AY", "N3AXAA", ~
## $ flight <int> 407, 329, 422, 2391, 3652, 353, 1428, 1407, 2279, 4162, 20, ~
## $ origin <chr> "JFK", "JFK", "JFK", "JFK", "LGA", "LGA", "EWR", "JFK", "LGA~
## $ dest <chr> "LAX", "SJU", "LAX", "TPA", "ORF", "ORD", "HOU", "IAD", "MIA~
## $ air_time <dbl> 313, 216, 376, 135, 50, 138, 240, 48, 148, 110, 50, 161, 87,~
## $ distance <dbl> 2475, 1598, 2475, 1005, 296, 733, 1411, 228, 1096, 820, 264,~
## $ hour <dbl> 9, 16, 8, 18, 11, 18, 12, 19, 7, 13, 9, 13, 8, 20, 12, 20, 6~
## $ minute <dbl> 40, 57, 59, 41, 2, 17, 59, 20, 25, 23, 40, 20, 9, 54, 17, 24~Departure delays
Let’s start by examining the distribution of departure delays of all flights with a histogram.
ggplot(data = nycflights, aes(x = dep_delay)) +
geom_histogram(binwidth = 150)
Exercise 1
Look carefully at these three histograms. How do they compare? Are features revealed in one that are obscured in another?
Answer: The different bins either improve or worsen the view of the histogram