FlightNYC

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --

## v ggplot2 3.3.3     v purrr   0.3.4
## v tibble  3.1.0     v dplyr   1.0.4
## v tidyr   1.1.2     v stringr 1.4.0
## v readr   1.4.0     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(nycflights13)
library(RColorBrewer)

Datasets

head(planes)

## # A tibble: 6 x 9
##   tailnum  year type           manufacturer   model  engines seats speed engine 
##   <chr>   <int> <chr>          <chr>          <chr>    <int> <int> <int> <chr>  
## 1 N10156   2004 Fixed wing mu~ EMBRAER        EMB-1~       2    55    NA Turbo-~
## 2 N102UW   1998 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~
## 3 N103US   1999 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~
## 4 N104UW   1999 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~
## 5 N10575   2002 Fixed wing mu~ EMBRAER        EMB-1~       2    55    NA Turbo-~
## 6 N105UW   1999 Fixed wing mu~ AIRBUS INDUST~ A320-~       2   182    NA Turbo-~

head(flights)

## # A tibble: 6 x 19
##    year month   day dep_time sched_dep_time dep_delay arr_time sched_arr_time
##   <int> <int> <int>    <int>          <int>     <dbl>    <int>          <int>
## 1  2013     1     1      517            515         2      830            819
## 2  2013     1     1      533            529         4      850            830
## 3  2013     1     1      542            540         2      923            850
## 4  2013     1     1      544            545        -1     1004           1022
## 5  2013     1     1      554            600        -6      812            837
## 6  2013     1     1      554            558        -4      740            728
## # ... with 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## #   tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## #   hour <dbl>, minute <dbl>, time_hour <dttm>

Estimating of The Number of Passengers Flying to a Destination

pasg_flt<- flights %>%
  left_join(planes, by = "tailnum") %>%
  group_by(month, dest) %>%
  replace_na(list(seats = 0)) %>%
  #Estimating 75% capacity for planes
  mutate(passenger = seats * .75) %>%
  summarise(num_passenger = round(sum(passenger)))

## `summarise()` has grouped output by 'month'. You can override using the `.groups` argument.

Determining Popular Destinations and Number of Flights

num_dest <- flights %>%
  #Counting the number of flights by month and destination
  group_by(month, dest) %>%
  count(dest) %>%
  rename("num_of_flt" = n) %>%
  ungroup()%>%
  #Subsetting top 10 destinations by month
  arrange(month,desc(num_of_flt)) %>%
  slice(1:10, 95:104,187:196, 282:291, 375:384, 465:474, 558:567, 652:661, 745: 754, 837:846, 928:937, 1018:1027)

Joining Tables and Creating a Month Column

#Joining the two previous tables
flt_stat<- num_dest %>%
  inner_join(pasg_flt, by = c("month", "dest"))%>%
  #Creating a month column and converting into a factor
  mutate(MonthName = month.name[month]) %>%
  mutate(ordered_month  = factor(MonthName, levels = month.name, ordered = TRUE))

Graph

flt_stat %>%
  ggplot(aes(x = dest, y = num_of_flt, fill = num_passenger)) +
  geom_col() +
  xlab("Top Destinations") +
  ylab("Number of Flights")+
  ggtitle("Top 10 New York Area Airports Destinations By Month")+
  labs(fill = "Traveler Estimate")+
  coord_flip() +
  theme(axis.text.x = element_text(angle = 90), axis.text.y = element_text(size = 7))+
  facet_wrap(~ordered_month)+
   scale_fill_gradient(low = "#e0ecf4", high = "#8856a7")

Description

The faceted bar charts display the top 10 destinations by month of flights departing from airports located in the New York area in the year 2013. The popular destinations are signified by their airport codes which are located along the left side of the graph. The number of flights to those popular destinations are located at the bottom of the graph. The popular destinations and number of flights were determined using the flights dataset in the nycflights13 package. The legend on the right displays a color gradient scale estimating number of travelers headed to a destination if the planes departing the New York area airports were filled to seventy-five percent capacity. The capacity of the planes traveling to the popular destinations were determined using the planes dataset in the nycflights13 package. The travel patterns displayed by the graph demonstrated that Los Angeles airport was the most popular destination from the New York area airports. Chicago had a higher number of flights compared to Los Angeles throughout the year, but the estimation of travelers showed that more people were possibly traveling to Los Angeles in comparison to Chicago.