Lab 2 Introduction to Data
Christian Uriostegui
2022-09-13
Load Packages
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.2 --
## v ggplot2 3.3.6 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.9
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v 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: usdatalibrary(dplyr)Load Data
data(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~Exercise 1
Look carefully at these three histograms. How do they compare? Are features revealed in one that are obscured in another?
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)
The first and third histogram gives an overview shape of where the data lies, however, it’s deceptive in that the first bin is heavily skewed. If we look at the second chart, we can see that a value somewhere near or at 0 has a large count that is significantly larger than the other bins. This is not seen in the first or third chart.
Filter and Histogram Example
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 x 3
## mean_dd median_dd n
## <dbl> <dbl> <int>
## 1 9.78 -1 1583Exercise 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?
sfo_feb_flights <- nycflights %>%
filter(dest == "SFO", month == 2)Created data frame and used glimpse function to check rows
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~There are 68 flights that meet the criteria
Exercise 3
Describe the distribution of the arrival delays of these flights using a histogram and appropriate summary statistics.
ggplot(data = sfo_feb_flights, aes(x = arr_delay)) +
geom_histogram()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
sfo_feb_flights %>%
summarise(mean_ad = mean(arr_delay),
median_ad = median(arr_delay),
max_ad = max(arr_delay),
min_ad = min(arr_delay),
n = n())## # A tibble: 1 x 5
## mean_ad median_ad max_ad min_ad n
## <dbl> <dbl> <dbl> <dbl> <int>
## 1 -4.5 -11 196 -66 68The distribution of the histogram is skewed to the right. I would also describe as unimodal because there is one a large peak.
Exercise 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?
sfo_feb_flights %>%
group_by(carrier) %>%
summarise(median_ad = median(arr_delay), iqr_ad = IQR(arr_delay), n_flights = n())## # A tibble: 5 x 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 12The airlines with the most variable delays is airlines DL and UA
Exercise 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?
nycflights %>%
group_by(month) %>%
summarise(mean_dd = mean(dep_delay)) %>%
arrange(desc(mean_dd))## # A tibble: 12 x 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 5.88nycflights %>%
group_by(month) %>%
summarise(median_dd = median(dep_delay)) %>%
arrange(desc(median_dd))## # A tibble: 12 x 2
## month median_dd
## <int> <dbl>
## 1 12 1
## 2 6 0
## 3 7 0
## 4 3 -1
## 5 5 -1
## 6 8 -1
## 7 1 -2
## 8 2 -2
## 9 4 -2
## 10 11 -2
## 11 9 -3
## 12 10 -3The median can give us a general look at the distribution and where the middle value lies. In this case, it’s not useful because the differences in median value between the months are minimal and doesn’t truly tell us which month experiences the most delays. The mean or average mean can give us a more accurate depiction of monthly delay trends, as there is more data values that are considered, as opposed to simply just looking at the middle value.
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?
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 x 2
## origin ot_dep_rate
## <chr> <dbl>
## 1 LGA 0.728
## 2 JFK 0.694
## 3 EWR 0.637I would choose to fly from LGA airport because it has the best percentage(0.728)
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)
nycflights <- nycflights %>%
mutate(avg_speed = air_time/60)Exercise 8
Make a scatterplot of avg_speed vs. distance
ggplot(data = nycflights, mapping = aes(x = avg_speed, y = distance)) +
geom_point()
There is a strong relationship seen. The longer the distance, the faster the average speed of the flight.
Exercise 9
Replicate the following plot
unique(nycflights$carrier)## [1] "VX" "DL" "9E" "AA" "WN" "B6" "EV" "FL" "UA" "US" "MQ" "F9" "YV" "AS" "HA"
## [16] "OO"Check to see the different carriers in the nycflights dataset
nycflights_filter <- nycflights %>%
filter(carrier == "AA"| carrier == "DL" | carrier == "UA")Filter for the three carriers of interest using the or clause “|”
unique(nycflights_filter$carrier)## [1] "DL" "AA" "UA"Check to see if filter was implemented into dataset through unique function
ggplot(data = nycflights_filter, mapping = aes(x = arr_delay,y = dep_delay, color = carrier))+
geom_point()
Created scatterplot