install.packages(“tidyverse”) library(tidyverse) install.packages(“nycflights13”) library(nycflights13) glimpse(flights)
#Q1: Find all flights on Jan 1. select, arrange, filter, mutate, transmutate, slice which one should we use?
flights %>% filter(month == 1, day == 1)
#Q2: Try to find arrival delay (arr_delay) more than 120 minutes and show arr_delay in the fourth column
flights %>% filter(arr_delay >= 120) %>% select(year:day, arr_delay, everything())
#Q3: We need to find the longest arr_delay and its carrier, flight information. We need to think which function can rank data in descending order. Use arrange!
flights %>% arrange(desc(arr_delay)) %>% select(carrier, flight, arr_delay, everything()) %>% slice(1)
#Q4: Which carrier has the worst average arr_delay? its value? Use group_by, summarise/mutate and arrange
flights %>% group_by(carrier) %>% # mutate(arr_delay_avg = mean(arr.delay,na.rm = TRUE ) summarise(arr_delay = mean(arr_delay, na.rm = TRUE)) %>% arrange(desc(arr_delay))
#Q5: which hour should we take by avoiding the possible arr_delay (average arr_delay)? group_by(hour)
flights %>% group_by(hour) %>% summarise(arr_delay = mean(arr_delay, na.rm = TRUE)) %>% arrange(desc(arr_delay))
#Q6: Based on flights, select the first 100 data of year, month, day, hour, origin, dest, tailnum, carrier. Then Use left_join() to add airline names to answers in the above and save the joined resutls as ‘answer06’. Finally show the first 6 rows of data.
flights %>% slice(1:100) %>% select(year:day, hour, origin, dest, tailnum, carrier) %>% left_join(y = airlines, by = “carrier”) %>% head()
answer06 <- flights %>% slice(1:100) %>% select(year:day, hour, origin, dest, tailnum, carrier) %>% left_join(y = airlines, by = “carrier”) %>% head()
#Q7: Use left_join() to combine ‘answer06’ and ‘weather’, and then combine with ‘airports’. Hint: left_join(…, by = c(“dest” = “faa”))
answer06 %>% left_join(weather) %>% left_join(airports, by = c(“dest” = “faa”))
#Q8: Count the number of flights for dest = ALB, BDL and BTV in each month. (use n())
flights %>% filter(dest %in% c(“ALB”, “BDL”, “BTV”)) %>% group_by(year, month, dest) %>% summarise(count = n())
#Q9: Use carrier, month, origin to compute average departure delay time (dep_delay).
flights %>% select(carrier, month, origin, dep_delay) %>% group_by(carrier, month, origin) %>% mutate(avg_dep_delay = mean(dep_delay, na.rm = TRUE)) %>% arrange(desc(avg_dep_delay))