HW4
Weijian Cai
March 3, 2019
load the required library
library(readr)
library(tidyr)
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, unionload csv file into R
raw<- read.csv("https://raw.githubusercontent.com/Weicaidata/607/master/airline%20database.csv")
str(raw)## 'data.frame': 4 obs. of 7 variables:
## $ Airline : Factor w/ 2 levels "ALASKA","AM WEST": 1 1 2 2
## $ Status : 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.Fransisco: int 503 102 320 129
## $ Seattle : int 1841 305 201 61raw## Airline Status Los.Angeles Phoenix San.Diego San.Fransisco 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 61use tidyr to pivot table from wide to long table for further analysis
df_gather <- gather(raw,city,flightnum,Los.Angeles:Seattle)
df_gather## Airline Status city flightnum
## 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.Fransisco 503
## 14 ALASKA delayed San.Fransisco 102
## 15 AM WEST on time San.Fransisco 320
## 16 AM WEST delayed San.Fransisco 129
## 17 ALASKA on time Seattle 1841
## 18 ALASKA delayed Seattle 305
## 19 AM WEST on time Seattle 201
## 20 AM WEST delayed Seattle 61use spread functon to add delayed and ‘on time’ columns
df_spread <- spread(df_gather,Status,flightnum)
df_spread## 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.Fransisco 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.Fransisco 129 320
## 10 AM WEST Seattle 61 201use dpylr to add another two column ontime percentage and delayed percentage.
df <- df_spread %>%
mutate(total= delayed +df_spread$`on time`,ontime_perc=`on time`/total,delayed_perc=delayed/total)
df## Airline city delayed on time total ontime_perc delayed_perc
## 1 ALASKA Los.Angeles 62 497 559 0.8890877 0.11091234
## 2 ALASKA Phoenix 12 221 233 0.9484979 0.05150215
## 3 ALASKA San.Diego 20 212 232 0.9137931 0.08620690
## 4 ALASKA San.Fransisco 102 503 605 0.8314050 0.16859504
## 5 ALASKA Seattle 305 1841 2146 0.8578751 0.14212488
## 6 AM WEST Los.Angeles 117 694 811 0.8557337 0.14426634
## 7 AM WEST Phoenix 415 4840 5255 0.9210276 0.07897241
## 8 AM WEST San.Diego 65 383 448 0.8549107 0.14508929
## 9 AM WEST San.Fransisco 129 320 449 0.7126949 0.28730512
## 10 AM WEST Seattle 61 201 262 0.7671756 0.23282443use bar to compare the ontime percentage between two lines, find that they are very similar, not much differnt.
library(ggplot2)
ggplot(df, aes(x = Airline, y=ontime_perc, fill = city)) +
geom_bar(stat="identity",position="dodge") +
xlab("Airline") + ylab("ontime_perc") 
however, the delayed percentage between two airlines, we can see that ALaska airline of each lines are lower than AM WEST, it mean ALASKA is doing better than AM WEST.
ggplot(df, aes(x = Airline, y=delayed_perc, fill = city)) +
geom_bar(stat="identity",position="dodge") +
xlab("Airline") + ylab("delayed_perc") 