# load the require packages.
library(stringr);
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, union
library(tidyr);
library(ggplot2);
require(knitr);
## Loading required package: knitr
# load data file from url.
data1 <- read.csv("https://raw.githubusercontent.com/mascotinme/MSDA-IS607/master/ontime_delayed.csv", stringsAsFactors = FALSE, check.names = FALSE, na.strings = c("", "NA"));
kable(head(data1));
| ALASKA |
on time |
497 |
221 |
212 |
503 |
1,841 |
| NA |
delayed |
62 |
12 |
20 |
102 |
305 |
| NA |
NA |
NA |
NA |
NA |
NA |
NA |
| AM WEST |
on time |
694 |
4,840 |
383 |
320 |
201 |
| NA |
delayed |
117 |
415 |
65 |
129 |
61 |
# It revealed from the data above that the third role has no data at all (empty), we will therefore remove the row.
which(is.na(data1));
## [1] 2 3 5 8 13 18 23 28 33
data <- data.frame(data1[-3, ]);
kable(head(data))
| 1 |
ALASKA |
on time |
497 |
221 |
212 |
503 |
1,841 |
| 2 |
NA |
delayed |
62 |
12 |
20 |
102 |
305 |
| 4 |
AM WEST |
on time |
694 |
4,840 |
383 |
320 |
201 |
| 5 |
NA |
delayed |
117 |
415 |
65 |
129 |
61 |
# From the glimpes of the data, we can see that the 1st & 2nd columns has no name, we therefore assign name to it
colnames(data)[1] = "Airline"
colnames(data)[2] = "Status"
# after the removal of the row and naming the columns, we saw that there are still empty rows, we therefore replace it with "ALASKA" & "AM WEST", since the new rows are referencing thesame data for same airline.
data[2,1] = "ALASKA"
data[4,1] = "AM WEST"
data[1,7] = 1841
data[3,4] = 4840
kable(head(data));
| 1 |
ALASKA |
on time |
497 |
221 |
212 |
503 |
1841 |
| 2 |
ALASKA |
delayed |
62 |
12 |
20 |
102 |
305 |
| 4 |
AM WEST |
on time |
694 |
4840 |
383 |
320 |
201 |
| 5 |
AM WEST |
delayed |
117 |
415 |
65 |
129 |
61 |
# We now use tidyr to gather the respective rows and columns together in a reasonabe manner.
tidy_data <- gather(data, "Destination", "Number_of_time", 3:7, na.rm = TRUE);
kable(head(tidy_data));
| ALASKA |
on time |
Los.Angeles |
497 |
| ALASKA |
delayed |
Los.Angeles |
62 |
| AM WEST |
on time |
Los.Angeles |
694 |
| AM WEST |
delayed |
Los.Angeles |
117 |
| ALASKA |
on time |
Phoenix |
221 |
| ALASKA |
delayed |
Phoenix |
12 |
tidy_data1 <- spread(tidy_data, key = Status, value= Number_of_time )
colnames(tidy_data1)[4] = "ontime"
kable(head(tidy_data1));
| ALASKA |
Los.Angeles |
62 |
497 |
| ALASKA |
Phoenix |
12 |
221 |
| ALASKA |
San.Diego |
20 |
212 |
| ALASKA |
San.Franscisco |
102 |
503 |
| ALASKA |
Seatle |
305 |
1841 |
| AM WEST |
Los.Angeles |
117 |
694 |
str(tidy_data1)
## 'data.frame': 10 obs. of 4 variables:
## $ Airline : chr "ALASKA" "ALASKA" "ALASKA" "ALASKA" ...
## $ Destination: chr "Los.Angeles" "Phoenix" "San.Diego" "San.Franscisco" ...
## $ delayed : chr "62" "12" "20" "102" ...
## $ ontime : chr "497" "221" "212" "503" ...
# Note that the data type for ontime & delayed is character format, we now change them to numeric for easy calculations.
tidy_data1<- within(tidy_data1, {
delayed<- as.numeric(as.character(delayed))
ontime<- as.numeric(as.character(ontime))})
# Summarize the average mean for the ontime
data_avg <- tidy_data1 %>% group_by(Airline, Destination) %>%
summarise(Avg=mean(ontime))
data_avg
## Source: local data frame [10 x 3]
## Groups: Airline [?]
##
## Airline Destination Avg
## (chr) (chr) (dbl)
## 1 ALASKA Los.Angeles 497
## 2 ALASKA Phoenix 221
## 3 ALASKA San.Diego 212
## 4 ALASKA San.Franscisco 503
## 5 ALASKA Seatle 1841
## 6 AM WEST Los.Angeles 694
## 7 AM WEST Phoenix 4840
## 8 AM WEST San.Diego 383
## 9 AM WEST San.Franscisco 320
## 10 AM WEST Seatle 201
# Plotting the delayed with ontime
options(warn=-1)
plot <- ggplot(data= tidy_data1, aes(y=delayed, x=ontime, fill=factor(Airline))) + geom_point()
plot + geom_smooth() + facet_wrap(~Airline);
# plotting graph to visually represent the conclusion.
data_plot <- ggplot(tidy_data1, aes(y=ontime, x=delayed)) + geom_point(shape=1)
data_plot + geom_smooth()
# some inferences.
kable(tidy_data1 %>% select(Destination,Airline, ontime, delayed) %>% filter(ontime == max(tidy_data1$ontime)))
kable(tidy_data1 %>% select(Destination,Airline, ontime, delayed) %>% filter(delayed == min(tidy_data1$delayed)))
summary(c(tidy_data1$delayed, tidy_data1$ontime));
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12.00 92.75 216.50 550.00 435.50 4840.00
THANKS FOR YOUR TIME