Project 1 

knitr::opts_chunk$set(echo = TRUE)
#Project 1 = Dislpay the minimum, maximum, and average flight time and average distance traveled of all United Airline flights departing JFK during March 2013. First the variables in the dataset. Variables = airtime, carrier, month, origin, and distance.

library(nycflights13)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(tibble)
library(magrittr)

jfk1<- filter(select(flights,carrier, origin, distance, air_time, month ))
jfk1
## # A tibble: 336,776 x 5
##    carrier origin distance air_time month
##      <chr>  <chr>    <dbl>    <dbl> <int>
##  1      UA    EWR     1400      227     1
##  2      UA    LGA     1416      227     1
##  3      AA    JFK     1089      160     1
##  4      B6    JFK     1576      183     1
##  5      DL    LGA      762      116     1
##  6      UA    EWR      719      150     1
##  7      B6    EWR     1065      158     1
##  8      EV    LGA      229       53     1
##  9      B6    JFK      944      140     1
## 10      AA    LGA      733      138     1
## # ... with 336,766 more rows
filter(jfk1, carrier=="UA" & origin=="JFK" & month==3)
## # A tibble: 378 x 5
##    carrier origin distance air_time month
##      <chr>  <chr>    <dbl>    <dbl> <int>
##  1      UA    JFK     2586      342     3
##  2      UA    JFK     2475      292     3
##  3      UA    JFK     2586      343     3
##  4      UA    JFK     2586      342     3
##  5      UA    JFK     2475      301     3
##  6      UA    JFK     2586      338     3
##  7      UA    JFK     2475      307     3
##  8      UA    JFK     2586      337     3
##  9      UA    JFK     2475      300     3
## 10      UA    JFK     2586      320     3
## # ... with 368 more rows
uaflight<- filter(jfk1, carrier=="UA" & origin=="JFK" & month==3)
summarise(uaflight, min_airtime = min(na.rm = TRUE,air_time),
          max_airtime = max(na.rm = TRUE,air_time),
          avg_airtime = mean(na.rm = TRUE,air_time),
          avg_distance = mean(distance))
## # A tibble: 1 x 4
##   min_airtime max_airtime avg_airtime avg_distance
##         <dbl>       <dbl>       <dbl>        <dbl>
## 1         281         394    342.9253     2534.317

Project 2 

#Project 2 = Display the Minimum, maximum, and average departure delays in minutes for June 2013 grouped by airport.

delays <-filter(select(flights, dep_delay, month, origin))
june13_delay<-filter(delays,dep_delay >0 & month==6)
june13_delay%>% group_by(origin)%>%
  summarise(min_delay= min(dep_delay, na.rm = TRUE),
            max_delay= (max(dep_delay, na.rm = TRUE)),
            avg_delay= (mean(dep_delay, na.rm = TRUE)))
## # A tibble: 3 x 4
##   origin min_delay max_delay avg_delay
##    <chr>     <dbl>     <dbl>     <dbl>
## 1    EWR         1       502  47.92212
## 2    JFK         1      1137  47.98522
## 3    LGA         1       803  54.96745

Project 3 

#Project 3 Display the minimum, maximum, and average miles traveled per hour for United Airlines (UA) and American Airlines (AA) flights flying between all three airports and Chicago’s O ’Hare International Airport (ORD) in June, July, and August 2013.
allflightdata <-filter(select(flights,carrier, origin, air_time, month, dest, distance ))
results<- filter(allflightdata, carrier==c("UA","AA") & dest=="ORD" & month %in% c("6","7","8"))
mph<- mutate(results,mph= distance/(air_time/60))
summarise(mph, min_mph = min(na.rm = TRUE,mph),
          max_mph = max(na.rm = TRUE,mph),
          avg_mph = mean(na.rm = TRUE,mph))
## # A tibble: 1 x 3
##    min_mph  max_mph  avg_mph
##      <dbl>    <dbl>    <dbl>
## 1 231.4737 495.8621 396.5622

Personal Reflection: I feel like I’m still struggling with some of the RStudio functions….and why I have to keep reloading libraries and packages even when I haven’t started a new session. Though this is done, I feel like it’s sloppy and I’m not quite proficient.