filter函数使用及NYC机场航班数据展示相关技术问题咨询

Hey Sara, let's work through your questions step by step, using R's nycflights13 package since you referenced the flights and airports datasets:

First, a quick clarification: filter() from the dplyr package is used to subset rows based on conditions (like "only flights in January"). If you mean "select specific variables/columns" (not rows), you'll want select() instead. Let's cover both:

• Filter rows based on criteria:
  For example, get all flights departing in January:

  library(dplyr)
  library(nycflights13)
  
  january_flights <- flights %>%
    filter(month == 1)
  

• Select specific variables/columns:
  If you want to keep only the airport of origin, destination, and departure time:

  selected_columns <- flights %>%
    select(origin, dest, dep_time)
  

• Combine both: Filter rows AND select columns:

  jan_selected <- flights %>%
    filter(month == 1) %>%
    select(origin, dest, dep_time)
  

NYC's major commercial airports represented in the flights dataset are 3 total: EWR (Newark Liberty International), JFK (John F. Kennedy International), and LGA (LaGuardia Airport).

To find which has the most flights, count departures per airport:

airport_flight_counts <- flights %>%
  count(origin, sort = TRUE)

Running this will show EWR has the highest volume (120,835 flights), followed by JFK (111,279) and LGA (104,662).

To visualize this with a bar graph using ggplot2:

library(ggplot2)

ggplot(airport_flight_counts, aes(x = origin, y = n)) +
  geom_bar(stat = "identity", fill = "#3498db") +
  labs(title = "Total Flights from NYC Airports",
       x = "Airport Code",
       y = "Number of Flights") +
  theme_light()

This will produce a clean bar chart comparing flight volumes across the three airports.

It sounds like your code is either missing a join between the two datasets, or accidentally extracting a single vector instead of keeping a full data frame. Here's how to fix it:

1. Join the datasets: Link flights (which uses origin for airport codes) with airports (which uses faa for codes):

   flights_with_airport_data <- flights %>%
     left_join(airports, by = c("origin" = "faa"))
   

2. Filter for NYC airports: Keep only rows for the three NYC-area airports:

   nyc_airport_flights <- flights_with_airport_data %>%
     filter(origin %in% c("EWR", "JFK", "LGA"))
   

3. View relevant details: If you want to see airport-specific info (like full name, location) alongside flight data, use select() to pick columns:

   nyc_airport_details <- nyc_airport_flights %>%
     select(origin, name, lat, lon, dep_time, dest)
   

If your old code only returned one variable, check if you used pull() instead of select() — pull() extracts a single column as a vector, while select() preserves the data frame structure with all your chosen columns.

内容的提问来源于stack exchange，提问作者Sara