NYC Flight Homework
Chandler Brown
2023-03-01
Load libraries
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.4.0 ✔ purrr 1.0.1
## ✔ tibble 3.1.8 ✔ dplyr 1.1.0
## ✔ tidyr 1.3.0 ✔ stringr 1.5.0
## ✔ readr 2.1.3 ✔ forcats 1.0.0
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(nycflights13)Mutate the Data to Create “speed” variable
flights_speed <- mutate(flights, speed = distance/(air_time/60))
summary(flights_speed)## year month day dep_time sched_dep_time
## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1 Min. : 106
## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907 1st Qu.: 906
## Median :2013 Median : 7.000 Median :16.00 Median :1401 Median :1359
## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349 Mean :1344
## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744 3rd Qu.:1729
## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400 Max. :2359
## NA's :8255
## dep_delay arr_time sched_arr_time arr_delay
## Min. : -43.00 Min. : 1 Min. : 1 Min. : -86.000
## 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124 1st Qu.: -17.000
## Median : -2.00 Median :1535 Median :1556 Median : -5.000
## Mean : 12.64 Mean :1502 Mean :1536 Mean : 6.895
## 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945 3rd Qu.: 14.000
## Max. :1301.00 Max. :2400 Max. :2359 Max. :1272.000
## NA's :8255 NA's :8713 NA's :9430
## carrier flight tailnum origin
## Length:336776 Min. : 1 Length:336776 Length:336776
## Class :character 1st Qu.: 553 Class :character Class :character
## Mode :character Median :1496 Mode :character Mode :character
## Mean :1972
## 3rd Qu.:3465
## Max. :8500
##
## dest air_time distance hour
## Length:336776 Min. : 20.0 Min. : 17 Min. : 1.00
## Class :character 1st Qu.: 82.0 1st Qu.: 502 1st Qu.: 9.00
## Mode :character Median :129.0 Median : 872 Median :13.00
## Mean :150.7 Mean :1040 Mean :13.18
## 3rd Qu.:192.0 3rd Qu.:1389 3rd Qu.:17.00
## Max. :695.0 Max. :4983 Max. :23.00
## NA's :9430
## minute time_hour speed
## Min. : 0.00 Min. :2013-01-01 05:00:00.00 Min. : 76.8
## 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00.00 1st Qu.:358.1
## Median :29.00 Median :2013-07-03 10:00:00.00 Median :404.2
## Mean :26.23 Mean :2013-07-03 05:22:54.64 Mean :394.3
## 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00.00 3rd Qu.:438.8
## Max. :59.00 Max. :2013-12-31 23:00:00.00 Max. :703.4
## NA's :9430Change month column to names of months instead of numbers
flights_speed$month[flights_speed$month==1] <- "January"
flights_speed$month[flights_speed$month==2] <- "February"
flights_speed$month[flights_speed$month==3] <- "March"
flights_speed$month[flights_speed$month==4] <- "April"
flights_speed$month[flights_speed$month==5] <- "May"
flights_speed$month[flights_speed$month==6] <- "June"
flights_speed$month[flights_speed$month==7] <- "July"
flights_speed$month[flights_speed$month==8] <- "August"
flights_speed$month[flights_speed$month==9] <- "September"
flights_speed$month[flights_speed$month==10] <- "October"
flights_speed$month[flights_speed$month==11] <- "November"
flights_speed$month[flights_speed$month==12] <- "December"Histogram of Frquencies of Flight Speed for Each Month
flights_speed_histogram <- flights_speed %>%
ggplot(aes(x = speed, fill = month))+
geom_histogram(position = "identity", alpha = 0.2, binwidth = 25, color = "black")+
scale_fill_ordinal(name = "Month", labels = c("January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"))+
ggtitle("Flight Speed Distribution of Flights for the Months of 2013")+
labs(y = "Frequency", x = "Speed(mph)")
flights_speed_histogram## Warning: Removed 9430 rows containing non-finite values (`stat_bin()`).
Visualization Description
This visualization uses a new variable mutated into the data called “speed” which takes the distance/(air_time/60) to show speed in miles per hour. In this histogram, the frequency of flight speeds of flights for each month of 2013.It reveals that most flights’ speeds are between 300mph and 500mph, with the highest frequency of flights flying at between 450mph and 475mph. The data appears to have a normal distribution, if not slightly skewed left. One aspect about this histogram I would like to draw attention to is the opacity, or the alpha value, of the bars of the graph. Since there are twelve months, there are 12 separate bars of frequency at most speeds, one representing each month. This proposed a problem for me in trying to find an opacity in which all of the data was visible but the histogram was still visually appealing. Eventually, I settled on an alpha value 0.2, as it shows off all of all of the data while values greater don’t show as much and values lower don’t look as visually appealing as it. Overall, I believe that a histogram of this variety does have its limitations in showing off as much data as I tried to in this case, and there are probably better methods to handle data of this size than this plot.