606 week2
Benson
2/8/2022
Load packages and the data
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.1.0 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(openintro)## Loading required package: airports## Loading required package: cherryblossom## Loading required package: usdatadata(nycflights)
names(nycflights)## [1] "year" "month" "day" "dep_time" "dep_delay" "arr_time"
## [7] "arr_delay" "carrier" "tailnum" "flight" "origin" "dest"
## [13] "air_time" "distance" "hour" "minute"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…Analysis
Departure delays
ggplot(data = nycflights, aes(x = dep_delay)) +
geom_histogram() +
ggtitle("Graph A")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = nycflights, aes(x = dep_delay)) +
geom_histogram(binwidth = 15) +
ggtitle("Graph B")
ggplot(data = nycflights, aes(x = dep_delay)) +
geom_histogram(binwidth = 150) +
ggtitle("Graph C")
Exercise 1
Look carefully at these three histograms. How do they compare? Are features revealed in one that are obscured in another?
The higher binwidth range will group more count into a column. We can have it better revealed in a different group of range.
Using Filter
lax_flights <- nycflights %>%
filter(dest == "LAX")
ggplot(data = lax_flights, aes(x = dep_delay)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
lax_flights %>%
summarise(mean_dd = mean(dep_delay),
median_dd = median(dep_delay),
n = n())## # A tibble: 1 × 3
## mean_dd median_dd n
## <dbl> <dbl> <int>
## 1 9.78 -1 1583sfo_feb_flights <- nycflights %>%
filter(dest == "SFO", month == 2)
nrow(sfo_feb_flights)## [1] 68ggplot(data = sfo_feb_flights, aes(x = arr_delay)) +
geom_histogram(binwidth = 15)
sfo_feb_flights %>%
summarise(mean_dd = mean(arr_delay),
median_dd = median(arr_delay),
n = n())## # A tibble: 1 × 3
## mean_dd median_dd n
## <dbl> <dbl> <int>
## 1 -4.5 -11 68Exercise 2
Create a new data frame that includes flights headed to SFO in February, and save this data frame as sfo_feb_flights. How many flights meet these criteria?
68 flights headed to SFP in February
Exercise 3
Describe the distribution of the arrival delays of these flights using a histogram and appropriate summary statistics. Hint: The summary statistics you use should depend on the shape of the distribution.
Based on the histogram and the mean and median statistics, most flights arrived in time or arrived early.
using group
sfo_feb_flights %>%
group_by(origin) %>%
summarise(median_dd = median(dep_delay), iqr_dd = IQR(dep_delay), n_flights = n())## # A tibble: 2 × 4
## origin median_dd iqr_dd n_flights
## <chr> <dbl> <dbl> <int>
## 1 EWR 0.5 5.75 8
## 2 JFK -2.5 15.2 60sfo_feb_flights %>%
group_by(carrier) %>%
summarise(median_dd = median(arr_delay), iqr_dd = IQR(arr_delay), n_flights = n())## # A tibble: 5 × 4
## carrier median_dd iqr_dd n_flights
## <chr> <dbl> <dbl> <int>
## 1 AA 5 17.5 10
## 2 B6 -10.5 12.2 6
## 3 DL -15 22 19
## 4 UA -10 22 21
## 5 VX -22.5 21.2 12Exercise 4
Calculate the median and interquartile range for arr_delays of flights in in the sfo_feb_flights data frame, grouped by carrier. Which carrier has the most variable arrival delays?
AA ( American Airlines Inc.) more than 50% flight was delayed.
Departure delays by month
nycflights %>%
group_by(month) %>%
summarise(mean_dd = mean(dep_delay), median_dd = median(dep_delay)) %>%
arrange(desc(mean_dd))## # A tibble: 12 × 3
## month mean_dd median_dd
## <int> <dbl> <dbl>
## 1 7 20.8 0
## 2 6 20.4 0
## 3 12 17.4 1
## 4 4 14.6 -2
## 5 3 13.5 -1
## 6 5 13.3 -1
## 7 8 12.6 -1
## 8 2 10.7 -2
## 9 1 10.2 -2
## 10 9 6.87 -3
## 11 11 6.10 -2
## 12 10 5.88 -3Exercise 5
Suppose you really dislike departure delays and you want to schedule your travel in a month that minimizes your potential departure delay leaving NYC. One option is to choose the month with the lowest mean departure delay. Another option is to choose the month with the lowest median departure delay. What are the pros and cons of these two choices?
In this case, the mean departure delay is not a good reference, while the few records with a long delay give a high mean value to overall records. And the median departure delay will have a better reference since most of the time the flights will be departing on time or early departure.
- Mean
- pros: To get the average value from the lists.
- cons: Biased output if an extreme value exists in the lists.
- Median
- pros: Not affected by extreme values exist
- cons: The output is not the average values from the lists.
On time departure rate for NYC airport
nycflights <- nycflights %>%
mutate(dep_type = ifelse(dep_delay < 5, "on time", "delayed"))
nycflights %>%
group_by(origin) %>%
summarise(ot_dep_rate = sum(dep_type == "on time") / n()) %>%
arrange(desc(ot_dep_rate))## # A tibble: 3 × 2
## origin ot_dep_rate
## <chr> <dbl>
## 1 LGA 0.728
## 2 JFK 0.694
## 3 EWR 0.637ggplot(data = nycflights, aes(x = origin, fill = dep_type)) +
geom_bar()
### Exercise 6 If you were selecting an airport simply based on on time departure percentage, which NYC airport would you choose to fly out of?
I will pick LGA since it had the highest on-time departure rate.
Exercise 7
Mutate the data frame so that it includes a new variable that contains the average speed, avg_speed traveled by the plane for each flight (in mph). Hint: Average speed can be calculated as distance divided by number of hours of travel, and note that air_time is given in minutes.
nycflights <- nycflights %>%
mutate(avg_speed = distance/(air_time/60))
glimpse(nycflights)## Rows: 32,735
## Columns: 18
## $ 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…
## $ dep_type <chr> "delayed", "on time", "on time", "on time", "on time", "on t…
## $ avg_speed <dbl> 474.4409, 443.8889, 394.9468, 446.6667, 355.2000, 318.6957, …Exercise 8
Make a scatterplot of avg_speed vs. distance. Describe the relationship between average speed and distance. Hint: Use geom_point().
The longer distance, the higher the average speed.
ggplot(data = nycflights) +
geom_point(aes(x = distance, y = avg_speed))
Exercise 9
Replicate the following plot. Hint: The data frame plotted only contains flights from American Airlines, Delta Airlines, and United Airlines, and the points are colored by carrier. Once you replicate the plot, determine (roughly) what the cutoff point is for departure delays where you can still expect to get to your destination on time.
filter_carrier <- nycflights %>%
filter(carrier == "AA"| carrier == "DL"| carrier == "UA")
ggplot(data = filter_carrier) +
geom_point(aes(x = dep_delay, y = arr_delay, color = carrier))