Assignment 5
Gehad Gad
March 1st, 2020
Assignment 5
#Import libraries and/or Packages:
library (tidyr)
library (dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(funModeling)## Loading required package: Hmisc## Loading required package: lattice## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
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## format.pval, units## funModeling v.1.9.3 :)
## Examples and tutorials at livebook.datascienceheroes.com
## / Now in Spanish: librovivodecienciadedatos.ailibrary(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v tibble 2.1.3 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.4.0
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library(ggpubr)## Loading required package: magrittr##
## Attaching package: 'magrittr'## The following object is masked from 'package:purrr':
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## set_names## The following object is masked from 'package:tidyr':
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## extractlibrary(modeest)## Registered S3 method overwritten by 'rmutil':
## method from
## print.response httr#Import the data from csv file:
Airlines <- read.csv ("Airlines.csv")
#To have access to the data from GitHub:
#Airlines_GitHub <- read.csv ("https://github.com/GehadGad/Airlines-data-Assignment5/raw/master/Airlines.csv")
#Display the data.
head (Airlines)## Airline ArrivalStatus Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1,841
## 2 <NA> delayed 62 12 20 102 305
## 3 <NA> <NA> NA <NA> NA NA <NA>
## 4 AMWEST on time 694 4,840 383 320 201
## 5 <NA> delayed 117 415 65 129 61#Replace the NA values for the Airlines names.
for(i in 2:nrow(Airlines)){
if(is.na(Airlines$Airline[i])){
Airlines$Airline[i] <- Airlines$Airline[i-1]
}
}
#Remove the third row.
Airlines = Airlines[-c(3),]
#Display the data.
head (Airlines)## Airline ArrivalStatus Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1,841
## 2 ALASKA delayed 62 12 20 102 305
## 4 AMWEST on time 694 4,840 383 320 201
## 5 AMWEST delayed 117 415 65 129 61head (Airlines)## Airline ArrivalStatus Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1,841
## 2 ALASKA delayed 62 12 20 102 305
## 4 AMWEST on time 694 4,840 383 320 201
## 5 AMWEST delayed 117 415 65 129 61#Create a new data frame called gather_df to tide the data and have all cities into one column.
gather_df <- gather(Airlines, City, Number, 3:7)## Warning: attributes are not identical across measure variables;
## they will be dropped#Display the tidy data.
head (gather_df)## Airline ArrivalStatus City Number
## 1 ALASKA on time Los.Angeles 497
## 2 ALASKA delayed Los.Angeles 62
## 3 AMWEST on time Los.Angeles 694
## 4 AMWEST delayed Los.Angeles 117
## 5 ALASKA on time Phoenix 221
## 6 ALASKA delayed Phoenix 12#Create a new data frame called spread_df to spread the two rows (on time and delayed) into two columns.
spread_df <- spread(gather_df, ArrivalStatus, Number, 3:7) ## Warning in if (!is.na(fill)) {: the condition has length > 1 and only the first
## element will be usedarrange(Airlines) ## Airline ArrivalStatus Los.Angeles Phoenix San.Diego San.Francisco Seattle
## 1 ALASKA on time 497 221 212 503 1,841
## 2 ALASKA delayed 62 12 20 102 305
## 3 AMWEST on time 694 4,840 383 320 201
## 4 AMWEST delayed 117 415 65 129 61spread_df = rename (spread_df, "on_time" = "on time")
#Display the spread data.
head(spread_df)## 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 1,841
## 6 AMWEST Los.Angeles 117 694#Remove the (,) because it change values over 1,000 to NA values.
spread_df []= lapply(spread_df, gsub, pattern = ",", replacement = "")
#Change the two columns (on_timev and delayed) from char to numeric. changing it to numeric so that I can add the two columns and we cann't add chr values.
spread_df$on_time <- as.numeric (as.character(spread_df$on_time))
spread_df$delayed <- as.numeric (spread_df$delayed)#Add new column to add the total number of on_time and delayed flights.
spread_df %>%
group_by(Airline, City) %>%
mutate(Total = sum(on_time, delayed))## # A tibble: 10 x 5
## # Groups: Airline, City [10]
## Airline City delayed on_time Total
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 ALASKA Los.Angeles 62 497 559
## 2 ALASKA Phoenix 12 221 233
## 3 ALASKA San.Diego 20 212 232
## 4 ALASKA San.Francisco 102 503 605
## 5 ALASKA Seattle 305 1841 2146
## 6 AMWEST Los.Angeles 117 694 811
## 7 AMWEST Phoenix 415 4840 5255
## 8 AMWEST San.Diego 65 383 448
## 9 AMWEST San.Francisco 129 320 449
## 10 AMWEST Seattle 61 201 262Perform analysis to compare the arrival delays for the two airlines.
summary(spread_df)## Airline City delayed on_time
## Length:10 Length:10 Min. : 12.00 Min. : 201.0
## Class :character Class :character 1st Qu.: 61.25 1st Qu.: 245.8
## Mode :character Mode :character Median : 83.50 Median : 440.0
## Mean :128.80 Mean : 971.2
## 3rd Qu.:126.00 3rd Qu.: 646.2
## Max. :415.00 Max. :4840.0The Summary shows the minimum and maximum number of delayed and on time flights. It also shows the mean and median for delayed and on time flights.
#Get the Variance. The formula for Variance is: 1/(n-1)*sum((x-mean)**2)
ontime= spread_df$on_time
var(ontime)## [1] 2083057#Find the correlation between on_time and delayed flights.
cor(spread_df$on_time,spread_df$delayed)## [1] 0.9274815Correlation shows how strongly the variables are related. The correlation ranges from -1.0 to +1.0. The closer the correlation (r) to +1 or -1, the more closely the two variables are related. The cor between delayed and on time is very high and close to 1. However, we need to keep in mind that this data is very small.
ggscatter(spread_df, x= "on_time", y= "delayed", add = "reg.line", cor.coef = TRUE, conf.int = TRUE)
This is a scatter with a regression line and including the Correlation and p values.
#Graphs
freq(spread_df)
## Airline frequency percentage cumulative_perc
## 1 ALASKA 5 50 50
## 2 AMWEST 5 50 100
## City frequency percentage cumulative_perc
## 1 Los.Angeles 2 20 20
## 2 Phoenix 2 20 40
## 3 San.Diego 2 20 60
## 4 San.Francisco 2 20 80
## 5 Seattle 2 20 100## [1] "Variables processed: Airline, City"This graph shows the distribution of all the cities.
plot_num (spread_df)
This graph shows the distribution of on_time and delayed flights.
ggboxplot(spread_df , y = 'ontime')
Boxplot displays the distribution of on_time flights. The two dots are considered as outliers!
ggboxplot(spread_df , y = 'delayed')
Boxplot displays the distribution of delayed flights. The two dots are considered as outliers!
ggbarplot(spread_df , x= "Airline", y= "on_time", color = "City" , position = position_dodge())
The graph above displays distribution of on_time flight in the two airline and cities.