I’ll first start by creating a data frame to analogous to table presented and write it to an csv file.

library(knitr)
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, union
data <- data.frame(c(NA, 'ALASKA', NA, NA, 'AM WEST', NA),
                   c(NA, 'on time', 'delayed', NA, 'on time', 'delayed'),
                   c('Los Angeles', 497, 62, NA, 694, 117),
                   c('Phoenix', 221, 12, NA, 4840, 415),
                   c('San Diego', 212, 20, NA, 383, 65),
                   c('San Francisco', 503, 102, NA, 320, 129),
                   c('Seattle', 1841, 305, NA, 201, 61));

#kable(data)
write.table(data,"airlines.csv",col.names = FALSE, row.names=FALSE, sep = ",")

#write.table(data,"airlines.csv", sep = ",")

Transforming the data

library(knitr)
library(tidyr)
library(dplyr)


dd <- read.csv("airlines.csv");
dd <- data.frame(dd); # convert into a data frame
dd <- dd[rowSums(is.na(dd)) != ncol(dd), ]; # remove empty rows


dd <- rename(dd, c("airline" = "NA.",
                   "status" = "NA..1",
                   "Los Angeles" = "Los.Angeles",
                   "San Diego" = "San.Diego",
                   "San Francisco" = "San.Francisco"));
dd <- gather(dd, key="destination", value="amount", 3:7);
dd <- dd %>% fill(airline, .direction = "down") # fill the blank rows of the airline column with the previous value


kable(dd) #showing transformed table
airline status destination amount
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
AM WEST on time Phoenix 4840
AM WEST delayed Phoenix 415
ALASKA on time San Diego 212
ALASKA delayed San Diego 20
AM WEST on time San Diego 383
AM WEST delayed San Diego 65
ALASKA on time San Francisco 503
ALASKA delayed San Francisco 102
AM WEST on time San Francisco 320
AM WEST delayed San Francisco 129
ALASKA on time Seattle 1841
ALASKA delayed Seattle 305
AM WEST on time Seattle 201
AM WEST delayed Seattle 61

Perform analysis to compare the arrival delays for the two airlines

library(knitr)
library(tidyr)
library(dplyr)
# 
# airline_delay <- dd %>% group_by(airline) %>%
#                         summarise(delay_count = count(dd$amount))

dd2 <- filter(dd,status == "delayed" & airline == "ALASKA");
kable(dd2)
airline status destination amount
ALASKA delayed Los Angeles 62
ALASKA delayed Phoenix 12
ALASKA delayed San Diego 20
ALASKA delayed San Francisco 102
ALASKA delayed Seattle 305 
print(paste("Alaska airlines has amount of delays of: ",sum(dd2$amount)))

[1] “Alaska airlines has amount of delays of: 501”

dd2 <- filter(dd,status == "delayed" & airline == "AM WEST");
kable(dd2)
airline status destination amount
AM WEST delayed Los Angeles 117
AM WEST delayed Phoenix 415
AM WEST delayed San Diego 65
AM WEST delayed San Francisco 129
AM WEST delayed Seattle 61 
print(paste("AM WEST airlines has amount of delays of: ",sum(dd2$amount)))

[1] “AM WEST airlines has amount of delays of: 787”