## R code

## library(nycflights13) data(flights)

**a)Compare air_time with arr_time-dep_time. What do you expect to see? What do you see? What do you need to do to fix it**. **b)Find the 10 most delayed flights using a ranking function "min_rank()". How do you want to handle ties? Carefully read the documentation for min_rank()

**c) Look at the number of cancelled flights per day. Is there a pattern? Is the proportion of cancelled flights related to the average delay?**. **d)Come up with another approach that will give you the same output as**: ```{r} not_cancelled <- flights %>% filter(!is.na(dep_delay),!is.na(arr_delay))

#first output not_cancelled %>% count(dest)

#second output not_cancelled %>% count(tailnum, wt =distance) ```