New York Flights HW

Now create one data visualization with this dataset. Your assignment is to create one plot to visualize one aspect of this dataset. The plot may be any type we have covered so far in this class (bargraphs, scatterplots, boxplots, histograms, treemaps, heatmaps, streamgraphs, or alluvials)

Requirements for the plot:

-Include at least one dplyr command (filter, sort, summarize, group_by, select, mutate, ….) -Include labels for the x- and y-axes -Include a title and caption for the data source -Your plot must incorporate at least 2 colors -Include a legend that indicates what the colors represent -Write a brief paragraph that describes the visualization you have created and at least one aspect of the plot that you would like to highlight. The paragraph should be around 150-250 words as a good estimate. Start early so that if you do have trouble, you can email me with questions.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(nycflights13)
data("flights")

df <- flights |>
  filter(flights$dest %in% c("ORD","DEN","DFW","ATL","LAX"))

flights$month[flights$month == 1]<- "Jan"
flights$month[flights$month == 2]<- "Feb"
flights$month[flights$month == 3]<- "Mar"
flights$month[flights$month == 4]<- "Apr"
flights$month[flights$month == 5]<- "May"
flights$month[flights$month == 6]<- "Jun"
flights$month[flights$month == 7]<- "Jul"
flights$month[flights$month == 8]<- "Aug"
flights$month[flights$month == 9]<- "Sep"
flights$month[flights$month == 10]<- "Oct"
flights$month[flights$month == 11]<- "Nov"
flights$month[flights$month == 12]<- "Dec"

flights$month<-factor(flights$month, levels=c("Jan","Feb","Mar","Apr","May", "Jun","Jul", "Aug","Sep","Oct","Nov","Dec"))

ggplot(data = df) +
  geom_tile(aes(x = dest, y = month, fill = sched_arr_time), color = 'black')+
  scale_fill_distiller(palette="RdPu", name = "Scheduled Arrival") +
  theme_bw() +
  labs(title = "Top 5 busiest Airlines in 2013 by Month",
       caption = "Source:FAA Aircraft registry")

ggplot(data = df) +
  geom_tile(aes(x = dest, y = month, fill = arr_time), color = 'black')+
  scale_fill_distiller(palette="RdPu", name = "Actual Arrival") +
  theme_bw() +
  labs(title = "Top 5 busiest Airlines in 2013 by Month",
       caption = "Source:FAA Aircraft registry")

This data visualization I made is the top 5 busiest Airlines in the US. I included two plots to compare the Scheduled arrival vs the actual arrive. I wanted to see the difference in the arrival times since these are known as the busiest airlines.