The chart above describes arrival delays for two airlines across five destinations. Your task is to: (1) 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. (2) Read the information from your .CSV file into R, and use tidyr and dplyr as needed to tidy and transform your data. (3) Perform analysis to compare the arrival delays for the two airlines. (4) 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
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, unionwrite.table(csv, file = "Week5FY.csv", sep = ",", col.names=F, row.names=F)
Rawwide_data <- read.csv(paste0("https://raw.githubusercontent.com/Fyoun123/Data607/master/Week%205/Week5FY.csv"),stringsAsFactors = F); Rawwide_data## 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
## 3 AM WEST On Time 694 4840 383 320 201
## 4 AM WEST Delayed 117 415 65 129 61Using Gather to create Key-Value Pairs:
LongConversion <- gather(Rawwide_data,city,number,Los.Angeles:Seattle, factor_key = TRUE)
LongConversion## X X.1 city number
## 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 61Renaming the Columns to be more approriate:
colnames(LongConversion)[2] <- "Ontime/Delay"
colnames(LongConversion)[1] <- "Vendor"Spread allows us to take two columns and create to columns, In this case breaking up out Ontime/Delay column in liue of the flight info.
LongConversion$Vendor <- factor(LongConversion$Vendor)
LongConversion2 <- spread(LongConversion,"Ontime/Delay","number")
LongConversion2## Vendor 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 201We use the Diplyr Fucniton Mutate to add new variables taht are functions of existing varaibles, in this case calcullating intitally the total number of flights. Group_by allows us to utilize our vendor as the driver when coupled with summarise. We can then simply divide the on time flights by the total flights.
Performace <- LongConversion2 %>% mutate(total = Delayed + `On Time`) %>% group_by(Vendor) %>% summarise(DT = sum(Delayed), OT = sum(`On Time`), Total = sum(total), OntimeP = round(OT/Total*100,digits = 2))
Performace## # A tibble: 2 x 5
## Vendor DT OT Total OntimeP
## <fct> <int> <int> <int> <dbl>
## 1 ALASKA 501 3274 3775 86.7
## 2 AM WEST 787 6438 7225 89.1Same synthax as above, this time utizlizing the city as our groupping rather than our airline:
Performace2 <- LongConversion2 %>% mutate(total = Delayed + `On Time`) %>% group_by(city) %>% summarise(DT = sum(Delayed), OT = sum(`On Time`), Total = sum(total), OntimeP = round(OT/Total*100,digits = 2))
Performace2## # A tibble: 5 x 5
## city DT OT Total OntimeP
## <fct> <int> <int> <int> <dbl>
## 1 Los.Angeles 179 1191 1370 86.9
## 2 Phoenix 427 5061 5488 92.2
## 3 San.Diego 85 595 680 87.5
## 4 San.Francisco 231 823 1054 78.1
## 5 Seattle 366 2042 2408 84.8