Data 110 nycflights13 Assignment

Load the approriate packages and data

library(nycflights13)
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()

Merge the data sets weather and flights

flightsw <- dplyr::full_join(weather, flights, by = c("year", "month", "day", "hour", "origin"))

## Warning in dplyr::full_join(weather, flights, by = c("year", "month", "day", : Each row in `x` is expected to match at most 1 row in `y`.
## ℹ Row 5 of `x` matches multiple rows.
## ℹ If multiple matches are expected, set `multiple = "all"` to silence this
##   warning.

Create a scatterplot with the merged data

ggplot(data = flightsw, aes(x = visib, y = dep_delay, color = origin)) +
  geom_point(alpha = 0.5) +
  xlab("Visibility") +
  ylab("Departure Delay") +
  ggtitle("Departure Delay vs Visibility by Origin Airport") +
  theme_bw()

## Warning: Removed 16520 rows containing missing values (`geom_point()`).

ggplot(data = flightsw, aes(x = visib, y = arr_delay, color = origin)) +
  geom_point(alpha = 0.5) +
  xlab("Visibility") +
  ylab("Arrival Delay") +
  ggtitle("Arrival Delay vs Visibility by Origin Airport") +
  theme_bw()

## Warning: Removed 17694 rows containing missing values (`geom_point()`).

The graphs show a scatterplot of arrival delay and departure delay versus visibility for flights originating from 3 airports. Each point on the graph represents a flight and is colored according to its origin airport. The x-axis shows visibility in miles, while the y-axis shows arrival or departure delay in minutes. The data points are spread out over the graph, with some flights experiencing significant delays while others arrive on time. There is a slight negative correlation between visibility and delay, indicating that flights with better visibility tend to experience shorter delays.