Assignment 5

The following steps must be implemented:

Install packages tidyr and dplyr.
Read csv file.

flights <- read.csv("/Users/olga/desktop/flights.csv")
flights

##         X     X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1  ALASKA on time         497     221       212           503    1841
## 2         delayed          62      12        20           102     305
## 3                          NA      NA        NA            NA      NA
## 4 AM WEST on time         694    4840       383           320     201
## 5         delayed         117     415        65           129      61

Remove empty row.

flights_modified <- subset(flights,flights$X.1!="")
flights_modified

##         X     X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1  ALASKA on time         497     221       212           503    1841
## 2         delayed          62      12        20           102     305
## 4 AM WEST on time         694    4840       383           320     201
## 5         delayed         117     415        65           129      61

Replace NAs in airlines column with latest non-NA value.

for (i in 1:nrow(flights_modified)){
  if (flights_modified[i,1]==" "){
    flights_modified[i,1]<-flights_modified[i-1,1]
  }
}

flights_modified

##         X     X.1 Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1  ALASKA on time         497     221       212           503    1841
## 2  ALASKA delayed          62      12        20           102     305
## 4 AM WEST on time         694    4840       383           320     201
## 5 AM WEST delayed         117     415        65           129      61

Figure out dimensions.

dim(flights_modified)

## [1] 4 7

Figure out all cities.

names(flights_modified[3:7])

## [1] "Los.Angeles"   "Phoenix"       "San.Diego"     "San.Francisco"
## [5] "Seattle"

Transform wide structure to long structure.

flights_long <- flights_modified %>% gather(X, sX.1, Los.Angeles:Seattle)
flights_long

##          X     X.1             X sX.1
## 1   ALASKA on time   Los.Angeles  497
## 2   ALASKA delayed   Los.Angeles   62
## 3  AM WEST on time   Los.Angeles  694
## 4  AM WEST delayed   Los.Angeles  117
## 5   ALASKA on time       Phoenix  221
## 6   ALASKA delayed       Phoenix   12
## 7  AM WEST on time       Phoenix 4840
## 8  AM WEST delayed       Phoenix  415
## 9   ALASKA on time     San.Diego  212
## 10  ALASKA delayed     San.Diego   20
## 11 AM WEST on time     San.Diego  383
## 12 AM WEST delayed     San.Diego   65
## 13  ALASKA on time San.Francisco  503
## 14  ALASKA delayed San.Francisco  102
## 15 AM WEST on time San.Francisco  320
## 16 AM WEST delayed San.Francisco  129
## 17  ALASKA on time       Seattle 1841
## 18  ALASKA delayed       Seattle  305
## 19 AM WEST on time       Seattle  201
## 20 AM WEST delayed       Seattle   61

Calculate overall number of flights for each airline.

fligts_sum<-aggregate(flights_long$sX.1, by=list(flights_long$X), FUN=sum) 
fligts_sum

##   Group.1    x
## 1  ALASKA 3775
## 2 AM WEST 7225

Find proportion of flights for each airline.

for (i in 1:nrow(flights_long)){
  if (flights_long[i,1]=="ALASKA"){
    flights_long[i,4]<-(flights_long[i,4]/3775)*100
  }
  else{
    flights_long[i,4]<-(flights_long[i,4]/7225)*100
  }
}

flights_long

##          X     X.1             X       sX.1
## 1   ALASKA on time   Los.Angeles 13.1655629
## 2   ALASKA delayed   Los.Angeles  1.6423841
## 3  AM WEST on time   Los.Angeles  9.6055363
## 4  AM WEST delayed   Los.Angeles  1.6193772
## 5   ALASKA on time       Phoenix  5.8543046
## 6   ALASKA delayed       Phoenix  0.3178808
## 7  AM WEST on time       Phoenix 66.9896194
## 8  AM WEST delayed       Phoenix  5.7439446
## 9   ALASKA on time     San.Diego  5.6158940
## 10  ALASKA delayed     San.Diego  0.5298013
## 11 AM WEST on time     San.Diego  5.3010381
## 12 AM WEST delayed     San.Diego  0.8996540
## 13  ALASKA on time San.Francisco 13.3245033
## 14  ALASKA delayed San.Francisco  2.7019868
## 15 AM WEST on time San.Francisco  4.4290657
## 16 AM WEST delayed San.Francisco  1.7854671
## 17  ALASKA on time       Seattle 48.7682119
## 18  ALASKA delayed       Seattle  8.0794702
## 19 AM WEST on time       Seattle  2.7820069
## 20 AM WEST delayed       Seattle  0.8442907

Assign names to columns

colnames(flights_long)<-c("airline","status","city","number_of_flights")
colnames(flights_long)

## [1] "airline"           "status"            "city"             
## [4] "number_of_flights"

Transform long structure to wide structure.

flights_wide <-  spread(flights_long, status, number_of_flights)
flights_wide

##    airline          city   delayed   on time
## 1   ALASKA   Los.Angeles 1.6423841 13.165563
## 2   ALASKA       Phoenix 0.3178808  5.854305
## 3   ALASKA     San.Diego 0.5298013  5.615894
## 4   ALASKA San.Francisco 2.7019868 13.324503
## 5   ALASKA       Seattle 8.0794702 48.768212
## 6  AM WEST   Los.Angeles 1.6193772  9.605536
## 7  AM WEST       Phoenix 5.7439446 66.989619
## 8  AM WEST     San.Diego 0.8996540  5.301038
## 9  AM WEST San.Francisco 1.7854671  4.429066
## 10 AM WEST       Seattle 0.8442907  2.782007

Perform analysis to compare the arrival delays for the two airlines.

group_by(flights_wide, airline)%>% summarise(mean_delay = mean(delayed), mean_on_time=mean(`on time`),std_delayed = sd(delayed),std_on_time=sd(`on time`))

## # A tibble: 2 x 5
##   airline mean_delay mean_on_time std_delayed std_on_time
##    <fctr>      <dbl>        <dbl>       <dbl>       <dbl>
## 1  ALASKA   2.654305     17.34570    3.179271    17.96287
## 2 AM WEST   2.178547     17.82145    2.036851    27.60123

Analysis table shows that at average ALASKA Airlines has 2.65% plus/minus 3.18% delayed flights while AM WEST has 2.17% plus/minus 2.04% delayed flights. ALASKA has 17.35 plus/minus 17.96 on time flights than AM WEST has 17.82% plus/minus 27.6% on time flights.

group_by(flights_wide, airline)%>% summarise(min_delay=min(delayed),max_delayed=max(delayed),min_on_time=min(`on time`),max_on_time=max(`on time`))

## # A tibble: 2 x 5
##   airline min_delay max_delayed min_on_time max_on_time
##    <fctr>     <dbl>       <dbl>       <dbl>       <dbl>
## 1  ALASKA 0.3178808    8.079470    5.615894    48.76821
## 2 AM WEST 0.8442907    5.743945    2.782007    66.98962

Also, ALASKA’s minimum for delayed flights is smaller than AM WEST’s and ALASKA’s maximum for delayed flights is greater than AM WEST’s maximum. Moreover, ALASKA’s minimum for on time flights is greater that AM WEST’s mininimum and ALASKA’s max for on_time flights is smaller that AM WEST’s maximum for on time flights.

Assignment 5

Olya Fomicheva

9/30/2017