Practice (Karla Gonzabay)

Sales of a departamental store

The file DepartmentStoreSales.csv contains data about the quarterly sales of a departamental store during a 6 year period.

On this code chunk read the file csv:

#Write your code here
store <- read.csv("DepartmentStoreSales.csv", header = TRUE)
head(store)

##   Quarter Sales
## 1       1 50147
## 2       2 49325
## 3       3 57048
## 4       4 76781
## 5       5 48617
## 6       6 50898

Create a time series object for the data. For this you must define the starting date, ending date and frequency:

#Write your code here
store.ts <- ts(store$Sales, start = c(2020, 1), end = c(2025, 4), frequency = 4)
store.ts

##        Qtr1   Qtr2   Qtr3   Qtr4
## 2020  50147  49325  57048  76781
## 2021  48617  50898  58517  77691
## 2022  50862  53028  58849  79660
## 2023  51640  54119  65681  85175
## 2024  56405  60031  71486  92183
## 2025  60800  64900  76997 103337

Plot the time series: watch out for all details, like scale, labels of the axis, title of the plot.

#Write your code here
plot(store.ts, 
     main = "Quarterly Sales", 
     xlab = "Year", 
     ylab = "Sales",
     col = "pink",
     lwd = 2)

Write here your observations about the time series(trend, seasonality): Looking at the time series, we can observe a positive trend, indicating an overall increase in sales volume. The data also exhibits a recurring seasonal pattern, with a noticeable peak in the fourth quarter followed by a decline during the first quarter of each year.

References:

Chatfield, C., & Xing, H. (2019). The Analysis of Time Series: An Introduction with R (7th ed.). Chapman and Hall/CRC.

Masaaki Horikoshi and Yuan Tang (2016). ggfortify: Data Visualization Tools for Statistical Analysis Results. https://CRAN.R-project.org/package=ggfortify

Sarkar, Deepayan (2008) Lattice: Multivariate Data Visualization with R. Springer, New York. ISBN 978-0-387-75968-5