22071A05R9

Objective

The objective of this analysis was to explore the “AirPassengers” dataset and gain insights into the trends and patterns in the number of airline passengers over time.

d=(AirPassengers)
d
##      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 1949 112 118 132 129 121 135 148 148 136 119 104 118
## 1950 115 126 141 135 125 149 170 170 158 133 114 140
## 1951 145 150 178 163 172 178 199 199 184 162 146 166
## 1952 171 180 193 181 183 218 230 242 209 191 172 194
## 1953 196 196 236 235 229 243 264 272 237 211 180 201
## 1954 204 188 235 227 234 264 302 293 259 229 203 229
## 1955 242 233 267 269 270 315 364 347 312 274 237 278
## 1956 284 277 317 313 318 374 413 405 355 306 271 306
## 1957 315 301 356 348 355 422 465 467 404 347 305 336
## 1958 340 318 362 348 363 435 491 505 404 359 310 337
## 1959 360 342 406 396 420 472 548 559 463 407 362 405
## 1960 417 391 419 461 472 535 622 606 508 461 390 432

Dataset Description

The “AirPassengers” data set contains monthly data on the number of airline passengers from 1949 to 1960.

head(AirPassengers)
## [1] 112 118 132 129 121 135
tail(AirPassengers)
## [1] 622 606 508 461 390 432
str(AirPassengers)
##  Time-Series [1:144] from 1949 to 1961: 112 118 132 129 121 135 148 148 136 119 ...

Statistical Measures - Mean monthly passenger count: 280.3

summary(AirPassengers)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   104.0   180.0   265.5   280.3   360.5   622.0

Including Plots

You can also embed plots, for example:

plot(AirPassengers)

boxplot(split(AirPassengers, rep(1949:1960, each = 12)), main = "Airline Passengers by Year")

Conclusion for Box Plot:

The box plot by month illustrates the variation in passenger counts for each month over the years. It highlights that some months, such as July and August, consistently have higher passenger counts, while others, like January and February, have lower counts. This demonstrates the seasonal nature of air travel.

# Create a heat map
heatmap(matrix(AirPassengers, ncol = 12), 
        Colv = NA, Rowv = NA, col = cm.colors(128),
        xlab = "Month", ylab = "Year", main = "Airline Passengers Heatmap")

Conclusion for Heatmap:

The heat map provides an overview of passenger counts for each year and month. It confirms the seasonality and shows that passenger counts generally increase from year to year. The darker shades represent higher passenger counts.

hist(AirPassengers, main = "Histogram of Airline Passengers", xlab = "Passengers", ylab = "Frequency")

library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.2.3
ggplot(data.frame(Passengers = AirPassengers), aes(x = Passengers)) +
  geom_density(fill = "red", alpha = 0.6) +
  labs(x = "Passengers", y = "Density", title = "Density Plot of Airline Passengers")
## Don't know how to automatically pick scale for object of type <ts>. Defaulting
## to continuous.

Conclusion for Density plot: The density plot reveals the distribution of passenger counts. It shows that the distribution is approximately bi modal, indicating two peaks corresponding to the high and low travel seasons. This suggests that the data has a clear seasonal component.

Main Conclusion

In conclusion, the analysis of the “AirPassengers” data set revealed significant growth in air travel demand over the years, with clear seasonal patterns. These insights can inform strategic decisions in the airline industry.