This is part of the course Developing Data Products as part of the Coursera Data Science Specialization.

The app can be viewed at: https://marsssblkm.shinyapps.io/SampleofShinyApp/

The code for ui.R and server.R can be viewed at: https://github.com/MarsssBlackman/DevelopingDataProducts

This app is aimed to provide the average monthly revenue of each store based on different types of products.

Store_id and Product_code can be selected from the left panel, where figures and results are shown on the right panel of the page.

The average monthly revenue data comes from the raw data. Here is a glimpse of the raw data.

data <- read.csv("./sample_data.csv")
head(data, 3)

##   week_sold price num_sold store_id product_code department_name
## 1 7/01/2018   3.0       14  STORE_2      PRO_001        BEVERAGE
## 2 7/01/2018   2.8       12  STORE_3      PRO_001        BEVERAGE
## 3 7/01/2018   2.8       12  STORE_3      PRO_001        BEVERAGE

The data used for this web app is generated through series calculation.

new.data <- aggregate(amount_sold ~ store_id + product_code, data = temp, 
                    monthly_average)

names(new.data)[3] <- "average_monthly_revenue"
new.data <- arrange(new.data, store_id, product_code)
new.data$average_monthly_revenue <- round(new.data$average_monthly_revenue, 1)
head(new.data, 3)

##   store_id product_code average_monthly_revenue
## 1  STORE_1      PRO_006                   156.3
## 2  STORE_1      PRO_007                   512.0
## 3  STORE_1      PRO_012                   101.0

Here is a quick look of what is the dataset looked like for each store and each type of product.