NYC Flights Homework

Heatmaps, Treemaps, and Alluvials assignment

getwd()

[1] "/Users/bettyovalle/Desktop/College/007 – Spring 2026/DATA 110/week 5"

library(nycflights23)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.2.0     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.2     ✔ tibble    3.3.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.1     
── 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(conflicted)
library(dplyr)

data(flights)
data(airlines)

conflicts_prefer(dplyr::filter)

[conflicted] Will prefer dplyr::filter over any other package.

flights_nona <- flights |>
filter(!is.na(distance) & !is.na(arr_delay) & !is.na(dep_delay)) 

by_dest <- flights_nona |>
group_by(dest) |> # group all destinations
summarise(count = n(), #counts totals for each destination
.groups = "drop") # remove the grouping structure after summarizing
head(by_dest)

# A tibble: 6 × 2
  dest  count
  <chr> <int>
1 ABQ     218
2 ACK     875
3 AGS      20
4 ALB    1510
5 ANC      91
6 ATL   17234

flights2 <- left_join(flights_nona, airlines, by = "carrier")
flights2$name <- gsub("Inc\\.|Co\\.", "", flights2$name)

dmv_flights <- flights2 |>
filter(dest %in% c("DCA", "IAD", "BWI")) |>
group_by(name, dest) |>
summarise(total_flights = n(),
avg_arr_delay = mean(arr_delay, na.rm = TRUE), #youtube Brian Byrne preguntar a profesora un poquito más
.groups = "drop")

library(RColorBrewer)
library(treemap)
treemap(dmv_flights,
 index= c("name"),
 vSize="total_flights",
 vColor="avg_arr_delay",
 type="value",
 palette = "Pastel2",
 title = "Flights from NYC to DMV Area airports", #title
 title.legend = "Average Arrival Delay (min)" ) # legend label 

Visualization Summary

For the Heatmaps, Treemaps, and Alluvials assignment, we worked with the NYC flights dataset to explore flight data. I focused on flights from New York City to the DMV area airports, including DCA, IAD, and BWI.

I began cleaning the dataset by removing missing values and joined it with airline names. I summarized the data to calculate the total number of flights and the average arrival delay for each airline.

Finally, I created a treemap to display this information. In the treemap, the size of each rectangle represents the number of flights, and the color shows the average arrival delay. This visualization helps quickly see which airlines have the most flights and which ones tend to have higher delays. This helps provide a clear and simple way to understand the airlines performance.