Assignment5

Assignment - Tidying and Transforming Data

R interface

library(knitr)

The chart above describes arrival delays for two airlines across five destinations. Your task is to:

Create a .CSV file (or optionally, a MySQL database!) that includes all of the information above. You’re encouraged to use a “wide” structure similar to how the information appears above, so that you can practice tidying and transformations as described below.

#getting the csv data from github
schedule <- read.csv("https://raw.githubusercontent.com/maharjansudhan/DATA607/master/schedule.csv", header=TRUE, sep=",")
schedule

##       ï..       X Los.Angeles Phoenix San.Diego San.Franciso 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

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

library(tidyr)

## Warning: package 'tidyr' was built under R version 3.5.1

library(dplyr)

## Warning: package 'dplyr' was built under R version 3.5.1

## 
## 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(stringr)

## Warning: package 'stringr' was built under R version 3.5.1

#VIEW THE DATA AND CREATE NAMES
str(schedule)

## 'data.frame':    4 obs. of  7 variables:
##  $ ï..         : Factor w/ 2 levels "ALASKA","AM WEST": 1 1 2 2
##  $ X           : Factor w/ 2 levels "delayed","on time": 2 1 2 1
##  $ Los.Angeles : int  497 62 694 117
##  $ Phoenix     : int  221 12 4840 415
##  $ San.Diego   : int  212 20 383 65
##  $ San.Franciso: int  503 102 320 129
##  $ Seattle     : int  1841 305 201 61

#for a better view
names(schedule)[1:2] <- c('AIRLINES','STATUS')
schedule

##   AIRLINES  STATUS Los.Angeles Phoenix San.Diego San.Franciso 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

#trying to gather data in a meaningful way
Tidydata<-gather(schedule, "CITY","FLIGHT",3:7)
Tidydata

##    AIRLINES  STATUS         CITY FLIGHT
## 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.Franciso    503
## 14   ALASKA delayed San.Franciso    102
## 15  AM WEST on time San.Franciso    320
## 16  AM WEST delayed San.Franciso    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

X<-spread(Tidydata,STATUS,FLIGHT)
X

##    AIRLINES         CITY delayed on time
## 1    ALASKA  Los.Angeles      62     497
## 2    ALASKA      Phoenix      12     221
## 3    ALASKA    San.Diego      20     212
## 4    ALASKA San.Franciso     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.Franciso     129     320
## 10  AM WEST      Seattle      61     201

#have a overview of your data if its in correct format or not
glimpse(X)

## Observations: 10
## Variables: 4
## $ AIRLINES  <fct> ALASKA, ALASKA, ALASKA, ALASKA, ALASKA, AM WEST, AM ...
## $ CITY      <chr> "Los.Angeles", "Phoenix", "San.Diego", "San.Franciso...
## $ delayed   <int> 62, 12, 20, 102, 305, 117, 415, 65, 129, 61
## $ `on time` <int> 497, 221, 212, 503, 1841, 694, 4840, 383, 320, 201

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

#calculate the ratio of the arrival delays and show it with all the other information
X %>% mutate(ratio=delayed/(delayed+`on time`))

## Warning: package 'bindrcpp' was built under R version 3.5.1

##    AIRLINES         CITY delayed on time      ratio
## 1    ALASKA  Los.Angeles      62     497 0.11091234
## 2    ALASKA      Phoenix      12     221 0.05150215
## 3    ALASKA    San.Diego      20     212 0.08620690
## 4    ALASKA San.Franciso     102     503 0.16859504
## 5    ALASKA      Seattle     305    1841 0.14212488
## 6   AM WEST  Los.Angeles     117     694 0.14426634
## 7   AM WEST      Phoenix     415    4840 0.07897241
## 8   AM WEST    San.Diego      65     383 0.14508929
## 9   AM WEST San.Franciso     129     320 0.28730512
## 10  AM WEST      Seattle      61     201 0.23282443

Your code should be in an R Markdown file, posted to rpubs.com, and should include narrative descriptions of your data cleanup work, analysis, and conclusions. Please include in your homework submission: ??? The URL to the .Rmd file in your GitHub repository. and ??? The URL for your rpubs.com web page.

Assignment5

sudhan_maharjan

September 30, 2018

Assignment - Tidying and Transforming Data