## R Markdown for Lecture 1 exercise.

Calculate the mean delay in arrival for Delta Airlines (DL) (use filter())

Calculate the associated 95% confidence interval.

Do the same for United Airlines (UA) and compare the two. Do their confidence intervals overlap?

Calculate the mode for the delay in arrival for at JFK airport.

save a dataset as .sav with only departing flights from JFK airport.

``````library(nycflights13)
flights<-nycflights13::flights``````

### Select just the delta flights

``require(dplyr)``
``## Loading required package: dplyr``
``## Warning: package 'dplyr' was built under R version 3.5.2``
``````##
## 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``````
``delta<-filter(flights, carrier=="DL")``

### Means

``````#remove the missings
# chose to work in same dataset. (for safety reasons you could make a new one!)
delta<-filter(delta, arr_delay!='NA')
mean(delta\$arr_delay)``````
``##  1.644341``
``````# store it
mean_delta<-mean(delta\$arr_delay)``````

### 95% CI

First get the ‘se’

``````se_delta<-sd(delta\$arr_delay)/sqrt(length(delta\$arr_delay))
se_delta``````
``##  0.2033937``

Now calculate 95%CI.

``````UL_delta<- (mean_delta + 1.96*se_delta)
LL_delta<- (mean_delta - 1.96*se_delta)
UL_delta``````
``##  2.042993``
``LL_delta``
``##  1.245689``

### United airlines.

All in one go

``````require(dplyr)
united<-filter(flights, carrier=="UA")
united<-filter(united, arr_delay!='NA')
mean(united\$arr_delay)``````
``##  3.558011``
``````# store it
mean_united<-mean(united\$arr_delay)
se_united<-sd(united\$arr_delay)/sqrt(length(united\$arr_delay))
se_united``````
``##  0.1704989``
``````UL_united<- (mean_united + 1.96*se_united)
LL_united<- (mean_united - 1.96*se_united)
UL_united``````
``##  3.892189``
``LL_united``
``##  3.223833``

### Conclusion: Delta vs. United.

The 95%CI’s do not overlap. United [3.22 to 3.89] is significantly slower in terms of arrival time than Delta [1.25 to 2.04].

### JFK airport

#### Make a dataset.

``````jfk<- filter(flights, origin=="JFK")
# remove the missings.
jfk<- filter(jfk, arr_delay!='NA')``````

#### Calculate the mode.

``library(modeest)``
``````##
## This is package 'modeest' written by P. PONCET.
## For a complete list of functions, use 'library(help = "modeest")' or 'help.start()'.``````
``mlv(jfk\$arr_delay,  method='mfv')``
``````## Mode (most likely value): -13
## Bickel's modal skewness: 0.3091337
## Call: mlv.default(x = jfk\$arr_delay, method = "mfv")``````

The mode is -13. The most common value in the dataset is thus 13 minutes early!

#### Write away the data.

``require(haven)``
``## Loading required package: haven``
``## Warning: package 'haven' was built under R version 3.5.2``
``write_sav(jfk, 'jfk.sav')``

The end.