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library(nycflights23)data(flights)
filter out flights with missing month or airport information, then count the flights for the airport each month. It also changes the airport codes into their full airport names so they can be used in the graph legend.
monthly_flights <- flights %>%filter(!is.na(month), !is.na(origin)) %>%count(month, origin, name ="number_of_flights") %>%mutate(airport =recode( origin,"EWR"="Newark Liberty International Airport","JFK"="John F. Kennedy International Airport","LGA"="LaGuardia Airport" ) )
create a summary table called “monthly_flights”, and remove rows missing a month or airport , then count the flights for each airport in each month, and replaces the airport codes with their full airport names. then using the monthly_flights table. place the month on the x-axis, and flight count on the y-axis, and gives each airport its own color and line.
flight_plot <-ggplot( monthly_flights,aes(x = month, y = number_of_flights, color = airport, group = airport)) +geom_line(linewidth =1) +geom_point(size =2)
use geom_line() to connect the monthly counts for each airport. and then use geom_point() to add a visible point at each month’s count.
labels the x-axis with the month abbreviations (Jan to Dec) instead of the numbers 1 to 12.
scale_color_manual(values =c("Newark Liberty International Airport"="#0072B2","John F. Kennedy International Airport"="#D55E00","LaGuardia Airport"="#009E73" ))
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assign a different color to each airport’s line and point.
labs(title ="Monthly Flights Departing from New York Area Airports in 2023",x ="Month",y ="Number of flights",color ="Departure airport",caption ="Source: nycflights23 package, flights dataset")
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add the graph’s title, x-axis and y-axis labels, legend title, and data-source caption.
theme_minimal()
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flight_plot
This line graph compares the number of flights departing from Newark Liberty International Airport, John F. Kennedy International Airport, and LaGuardia Airport during each month of 2023. Each point represents the number of flights recorded for one airport in one month, and the lines connect the months to make changes over the year easier to follow. The colors help distinguish the three airports, while the legend gives each airport’s full name. The feature I would highlight is how the monthly flight totals compare across the airports. Looking across the lines makes it easy to see if the airports have similar patterns or if one has more flights in a particular month. The graph also makes month-to-month changes visible: a rising line indicates that the number of flights increased from the previous month, while a falling line indicates a decrease. I chose a line graph because the months are ordered over time, and connecting the points helps show that sequence. This visualization only summarizes flight counts. It does not show how many flights were delayed or how many passengers traveled.
