606_Lab2
IvanTikhonov
2022-09-04
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.10
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ 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"?nycflights## starting httpd help server ... doneglimpse(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…ggplot(data = nycflights, aes(x = dep_delay)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(data = nycflights, aes(x = dep_delay)) +
geom_histogram(binwidth = 15)
ggplot(data = nycflights, aes(x = dep_delay)) +
geom_histogram(binwidth = 150)
1.Look carefully at these three histograms. How do they compare? Are
features revealed in one that are obscured in another?
We notice that the smaller the binwidth is, the finer the detail is. The second histogram has the smallest binwidth, so it displays the data in finer detail. The third histogram has the largest binwidth and clumps much of the data together, hiding lots of detail. The first histogram has a binwidth in between the other two and displays detail correspondingly. Though the second histogram shows the most detail, the binwidth of the first one seems to be just right and is more pleasing to the eye to visualize the data.
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)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?
There are 68 flights that meet these criteria
sfo_feb_flights <- nycflights %>%
filter(dest == "SFO", month == 2)
glimpse(sfo_feb_flights)## Rows: 68
## Columns: 16
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, …
## $ month <int> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …
## $ day <int> 18, 3, 15, 18, 24, 25, 7, 15, 13, 8, 11, 13, 25, 20, 12, 27,…
## $ dep_time <int> 1527, 613, 955, 1928, 1340, 1415, 1032, 1805, 1056, 656, 191…
## $ dep_delay <dbl> 57, 14, -5, 15, 2, -10, 1, 20, -4, -4, 40, -2, -1, -6, -7, 2…
## $ arr_time <int> 1903, 1008, 1313, 2239, 1644, 1737, 1352, 2122, 1412, 1039, …
## $ arr_delay <dbl> 48, 38, -28, -6, -21, -13, -10, 2, -13, -6, 2, -5, -30, -22,…
## $ carrier <chr> "DL", "UA", "DL", "UA", "UA", "UA", "B6", "AA", "UA", "DL", …
## $ tailnum <chr> "N711ZX", "N502UA", "N717TW", "N24212", "N76269", "N532UA", …
## $ flight <int> 1322, 691, 1765, 1214, 1111, 394, 641, 177, 642, 1865, 272, …
## $ origin <chr> "JFK", "JFK", "JFK", "EWR", "EWR", "JFK", "JFK", "JFK", "JFK…
## $ dest <chr> "SFO", "SFO", "SFO", "SFO", "SFO", "SFO", "SFO", "SFO", "SFO…
## $ air_time <dbl> 358, 367, 338, 353, 341, 355, 359, 338, 347, 361, 332, 351, …
## $ distance <dbl> 2586, 2586, 2586, 2565, 2565, 2586, 2586, 2586, 2586, 2586, …
## $ hour <dbl> 15, 6, 9, 19, 13, 14, 10, 18, 10, 6, 19, 8, 10, 18, 7, 17, 1…
## $ minute <dbl> 27, 13, 55, 28, 40, 15, 32, 5, 56, 56, 10, 33, 48, 49, 23, 2…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.
The histogram is right-skewed, so the standard deviation would not accurately represent the distribution of the data. The IQR on the other hand describes how the middle 50% of the data is distributed about the median. Both values can be found below.
ggplot(data = sfo_feb_flights, aes(x = arr_delay)) +
geom_histogram(binwidth=10)
sfo_feb_flights %>%
summarise(mean_ad = mean(arr_delay), median_ad = median(arr_delay), iqr_ad = IQR(arr_delay), n_flights = n())## # A tibble: 1 × 4
## mean_ad median_ad iqr_ad n_flights
## <dbl> <dbl> <dbl> <int>
## 1 -4.5 -11 23.2 684.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?
The carriers DL and UA are tied for having the most variable arrival delays because their interquartile ranges are tied for the highest at 22.00. This suggests that they exhibit the greatest variation in arrival delays for the middle 50% of their data.
sfo_feb_flights %>%
group_by(carrier) %>%
summarise(median_ad = median(arr_delay), iqr_ad = IQR(arr_delay), n_flights = n())## # A tibble: 5 × 4
## carrier median_ad iqr_ad 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 12nycflights %>%
group_by(month) %>%
summarise(mean_dd = mean(dep_delay)) %>%
arrange(desc(mean_dd))## # A tibble: 12 × 2
## month mean_dd
## <int> <dbl>
## 1 7 20.8
## 2 6 20.4
## 3 12 17.4
## 4 4 14.6
## 5 3 13.5
## 6 5 13.3
## 7 8 12.6
## 8 2 10.7
## 9 1 10.2
## 10 9 6.87
## 11 11 6.10
## 12 10 5.885.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?
Mean Pro: This represents the overall average departure delay, taking into account the effect of each delay and giving an idea as to how the data is distributed. Con: This can be skewed by outliers.
Median Pro: It takes the middle value of the entire data set, so outliers do not skew the median. Con: It fails to represent how the data is distributed.
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 0.637If you were selecting an airport simply based on on time departure percentage, which NYC airport would you choose to fly out of?
LGA has the best time departure percentage of 72.8%. Also, the segmented bar plot below shows that LGA has the best proportion of flights leaving on time. Thus, I would choose LGA.
ggplot(data = nycflights, aes(x = origin, fill = dep_type)) +
geom_bar()
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 = 60*(distance / air_time))
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, …Make a scatterplot of avg_speed vs. distance. Describe the relationship between average speed and distance. Hint: Use geom_point().
From the scatter plot below, we see that as distance increases, the average speed increases as well. The relationship appears to be logarithmic i.e. v = Alog(d) + B.
ggplot(data = nycflights, aes(x = distance, y = avg_speed)) + geom_point()
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.
Based off the scatter plot below, the cutoff point for departure delays you can still REASONABLY expect to arrive at your destination on time is slightly ahead of schedule, roughly first five minutes before departure time. We see that with a 60 minute late departure time, it has still been possible to arrive at the destination on time, but this is extremely rare. It is also uncommon for flights departing between 0 and 60 minutes late. Even the majority of flights departing on time arrive late for these three carriers. Only by leaving early can you expect to arrive on time, and a reasonable cutoff for this seems to be around five minutes before scheduled takeoff.
nycflights_3carriers <- nycflights %>%
filter(carrier == "AA" | carrier == "DL" | carrier == "UA")
ggplot(data = nycflights_3carriers, aes(x = dep_delay, y = arr_delay, color= carrier)) + geom_point()