607 Week 6 Assignment

Chirag Vithalani

March 4, 2016

  • GOAL 1: Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data.

  • tidyr provides three main functions for tidying your messy data: gather(), separate() and spread().

  • Reading CSV file and printing data

    library(tidyr)
    ## Warning: package 'tidyr' was built under R version 3.2.3
    flightData<-read.csv("https://raw.githubusercontent.com/chirag-vithlani/607/master/week6/week6.csv", header= TRUE,sep=",",na.strings = "?",stringsAsFactors=FALSE)
flightData
    ##         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 AM West On Time         694    4840       383           320     201
## 4         Delayed         117     415        65           129      61

  • Missing headers are names as X and X.1, so rename headers

    names(flightData)[names(flightData) == "X"] <- "Airline"
names(flightData)[names(flightData) == "X.1"] <- "Arrival"

  • Repeating airline name where it is missing ( this is hard coded, but this step should be done programmatically )

    flightData[2, 1] <- "ALASKA"
flightData[4, 1] <- "AM West"
flightData
    ##   Airline Arrival 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
## 3 AM West On Time         694    4840       383           320     201
## 4 AM West Delayed         117     415        65           129      61

  • Using gather() function. gather() takes multiple columns, and gathers them into key-value pairs: it makes “wide” data longer.

    tidy <- gather(flightData, "City", "Count", 3:7) 
head(tidy)
    ##   Airline Arrival        City Count
## 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

  • Using spread() function. spread() takes two columns (a key-value pair) and spreads them in to multiple columns, making “long” data wider

    #tidy$Count <- as.numeric(gsub(",", "", tidy$Count))
tidy <- spread(tidy, "Arrival", Count)
tidy
    ##    Airline          City Delayed On Time
## 1   ALASKA   Los.Angeles      62     497
## 2   ALASKA       Phoenix      12     221
## 3   ALASKA     San.Diego      20     212
## 4   ALASKA San.Francisco     102     503
## 5   ALASKA       Seattle     305    1841
## 6  AM West   Los.Angeles     117     694
## 7  AM West       Phoenix     415    4840
## 8  AM West     San.Diego      65     383
## 9  AM West San.Francisco     129     320
## 10 AM West       Seattle      61     201
  • dplyr implements the following verbs useful for data manipulation: 
    select(): focus on a subset of variables
filter(): focus on a subset of rows
mutate(): add new columns
summarise(): reduce each group to a smaller number of summary statistics
arrange(): re-order the rows

    Choosing columns: select

    suppressMessages(library(dplyr))
head(select(tidy, Airline))
    ##   Airline
## 1  ALASKA
## 2  ALASKA
## 3  ALASKA
## 4  ALASKA
## 5  ALASKA
## 6 AM West

    or use minus to hide that column

    head(select(tidy, -Airline))
    ##            City Delayed On Time
## 1   Los.Angeles      62     497
## 2       Phoenix      12     221
## 3     San.Diego      20     212
## 4 San.Francisco     102     503
## 5       Seattle     305    1841
## 6   Los.Angeles     117     694

  • Using filter

    filter(tidy,Delayed==62)
    ##   Airline        City Delayed On Time
## 1  ALASKA Los.Angeles      62     497

  • Using mutate : Add new variables

    tidy=mutate(tidy,Total = Delayed + `On Time`)
head(tidy)
    ##   Airline          City Delayed On Time Total
## 1  ALASKA   Los.Angeles      62     497   559
## 2  ALASKA       Phoenix      12     221   233
## 3  ALASKA     San.Diego      20     212   232
## 4  ALASKA San.Francisco     102     503   605
## 5  ALASKA       Seattle     305    1841  2146
## 6 AM West   Los.Angeles     117     694   811
  • GOAL 2: Perform analysis to compare the arrival delays for the two airlines.

  • As we can see for almost for all cities AM West is getting delayed more number of time.

    library(ggplot2)
    ## Warning: package 'ggplot2' was built under R version 3.2.3
    tidy <- mutate(tidy, Total = Delayed + `On Time`, PercentDelayed = Delayed / Total * 100)
tidy <- arrange(tidy, City, PercentDelayed)

ggplot(tidy,aes(x=City,y=PercentDelayed,fill=factor(Airline)))+
    geom_bar(stat="identity",position="dodge")

    tidy
    ##    Airline          City Delayed On Time Total PercentDelayed
## 1   ALASKA   Los.Angeles      62     497   559      11.091234
## 2  AM West   Los.Angeles     117     694   811      14.426634
## 3   ALASKA       Phoenix      12     221   233       5.150215
## 4  AM West       Phoenix     415    4840  5255       7.897241
## 5   ALASKA     San.Diego      20     212   232       8.620690
## 6  AM West     San.Diego      65     383   448      14.508929
## 7   ALASKA San.Francisco     102     503   605      16.859504
## 8  AM West San.Francisco     129     320   449      28.730512
## 9   ALASKA       Seattle     305    1841  2146      14.212488
## 10 AM West       Seattle      61     201   262      23.282443

  • Using summarise : Reduce variables to values

    Overall delay : Alaska airline is delayed by 11% and AM West delayed by 18%.

    delays <- tidy %>% group_by(Airline) %>% summarise(MeanPercent = round(mean(PercentDelayed), 0))
delays
    ## Source: local data frame [2 x 2]
## 
##   Airline MeanPercent
##     (chr)       (dbl)
## 1  ALASKA          11
## 2 AM West          18