## app.R ## library(shiny) library(shinydashboard) library(ggplot2) library(tidyverse) library(plotly) library(rsconnect) QP2 <- read.csv("D7_Office.csv") attach(QP2) ui <- dashboardPage( # Application title dashboardHeader(title = "Profitability Dashboard"), # Sidebar with Check Boxes to adjust output dashboardSidebar(width = 150, fluidRow( #Filters by Region checkboxGroupInput(inputId = "RegionInput", label = "Region", choices = c("Central", "East", "South", "West"), selected = c("Central", "East", "South", "West")), #Filters by Category checkboxGroupInput(inputId = "CategoryInput", label = "Product Category", choices = c("Furniture","Office Supplies", "Technology"), selected = c("Furniture","Office Supplies", "Technology")), #filters by Sub-Category checkboxGroupInput(inputId = "SubInput", label = "SubCategory", choices = c("Accessories", "Appliances","Art","Binders","Bookcases","Chairs","Copiers", "Envelopes","Fasteners" , "Furnishings", "Labels", "Machines", "Paper" ,"Phones", "Storage", "Supplies", "Tables"), selected = c("Accessories", "Appliances","Art","Binders","Bookcases","Chairs","Copiers", "Envelopes","Fasteners" , "Furnishings", "Labels", "Machines", "Paper" ,"Phones", "Storage", "Supplies", "Tables")))), # Body of the dashboard where the Charts will be plotted dashboardBody( fluidRow( box( title = "Furniture is the least profitable accross all regions", status = "primary", collapsible = TRUE, solidHeader=TRUE, plotOutput("RegionChart"), # infoBox("Furniture is the least profitable accross all regions", # value = NULL, subtitle = NULL, width = 15, # icon = shiny::icon("bar-chart")), width = 6), box(title = "Tables has the largest degree of negative profit", status = "primary", collapsible = TRUE, solidHeader=TRUE, plotOutput("SubCatChart"), # infoBox("'Tables' has the largest degree of negative profits", width = 15), width = 6)), fluidRow( box( title = "Sales Quantities do not translate into profitability" , status = "primary", collapsible = TRUE, solidHeader=TRUE, plotOutput("SalesQuantChart"), # infoBox("Sales Quantities do not translate into profitability", width = 15), width = 6), box( title = "Discounts decrease profits and inflate sales quantities" , status = "primary", collapsible = TRUE, solidHeader=TRUE, plotOutput("Discounts"), # infoBox("Discounts decrease profits and inflate sales quantities", width = 15), width = 6)), fluidRow( box( title = "East region lost most on Tables" , status = "primary", collapsible = TRUE, solidHeader=TRUE, plotOutput("SubCatR"), # infoBox("Sales Quantities do not translate into profitability", width = 15), width = 6), box( title = "High discounts decrease profits across all reagion" , status = "primary", collapsible = TRUE, solidHeader=TRUE, plotOutput("DiscountsR"), # infoBox("Discounts decrease profits and inflate sales quantities", width = 15), width = 6)) ) ) # Define server logic required to draw clustered barcharts server <- function(input, output) { # create calculated columns using given values from the dataset QP2$Cost <- round(QP2$Sales - Profit,2) QP2$DicountGiven <- round(QP2$Sales * Discount,2) # Top Left Chart { output$RegionChart <- renderPlot({ # Grouping data by Category and Region so it can be filtered rc <- group_by(QP2, Region, Category) %>% summarise(TotalProfit = sum(Profit)) # allows the data displayed in the charts to react to the Region and Product Category filters in the UI filtered <- rc %>% filter(Region %in% input$RegionInput & Category %in% input$CategoryInput) # Clustered Bar Chart showing total profits Per Product Category by Region ggplot(filtered, aes(x=Region, y= TotalProfit, fill = Category)) + geom_bar(stat = "identity", position = "dodge", color = "black") + ggtitle("Profits per Category by Region") + labs(y="Total Profits ($)") }) # } # Top Right { output$SubCatChart <- renderPlot({ # Grouping data by Category and Sub-Category so it can be filtered scc <- group_by(.data = QP2, Sub.Category, Category) %>% summarise(TotalProfit = sum(Profit)) # allows the data displayed in the charts to react to the Sub-Category and Product Category filters in the UI sliced <- scc %>% filter( Category %in% input$CategoryInput & Sub.Category %in% input$SubInput) # Clustered Bar Chart showing total profits Per Sub-Category by Product Category ggplot(sliced, aes(x=Category, y= TotalProfit, fill = Sub.Category, color = Region)) + geom_bar(stat = "identity", position = "dodge", color = "black") + ggtitle("Profits per Sub-Category") + labs(y="Total Profits ($)") }) # } # Bottom Left Chart { output$SalesQuantChart <- renderPlot({ # Grouping data by Category and Sub-Category so it can be filtered sqs <- group_by(QP2,Sub.Category, Category) %>% summarise(QuantitySold = sum(Quantity)) # allows the data displayed in the charts to react to the Sub-Category and Product Category filters in the UI filters <- sqs %>% filter( Category %in% input$CategoryInput& Sub.Category %in% input$SubInput) # Clustered Bar Chart showing quantity Per Sub-Category by Main Category ggplot(filters, aes(x=Category, y= QuantitySold, fill = Sub.Category)) + geom_bar(stat = "identity", position = "dodge", color = "black") + ggtitle("Quantity Sold per Sub-Category") + labs(y="Quantity Sold") }) # } # Bottom Right Chart { output$Discounts <- renderPlot({ # Grouping data by Category and Sub-Category so it can be filtered dgsc <- group_by(QP2,Sub.Category, Category) %>% summarise(TotalDiscounts = sum(DicountGiven)) # allows the data displayed in the charts to react to the Sub-Category and Product Category filters in the UI slices <- dgsc %>% filter( Category %in% input$CategoryInput& Sub.Category %in% input$SubInput) # Clustered Bar Chart showing to dicounts given in dollars Per Sub-Category by Product Category ggplot(slices, aes(x=Category, y= TotalDiscounts, fill = Sub.Category)) + geom_bar(stat = "identity", position = "dodge", color = "black") + ggtitle("Discounts Given per Sub-Category") + labs(y="Total Discounts ($)") }) # } # Bottom Right Chart { output$DiscountsR <- renderPlot({ # Grouping data by Category and Sub-Category so it can be filtered dgsc <- group_by(QP2,Sub.Category, Region) %>% summarise(TotalDiscounts = sum(DicountGiven)) # allows the data displayed in the charts to react to the Sub-Category and Product Category filters in the UI slices <- dgsc %>% filter(Region %in% input$RegionInput & # Category %in% input$CategoryInput& Sub.Category %in% input$SubInput) # Clustered Bar Chart showing to dicounts given in dollars Per Sub-Category by Product Category ggplot(slices, aes(x=Region, y= TotalDiscounts, fill = Sub.Category)) + geom_bar(stat = "identity", position = "dodge", color = "black") + ggtitle("Discounts by region per Sub-Category") + labs(y="Total Discounts ($)") }) # } # Bottom Right Chart { output$SubCatR <- renderPlot({ # Grouping data by Category and Sub-Category so it can be filtered dgsc <- group_by(QP2,Sub.Category, Region) %>% summarise(TotalProfit = sum(Profit)) # allows the data displayed in the charts to react to the Sub-Category and Product Category filters in the UI slices <- dgsc %>% filter(Region %in% input$RegionInput & # Category %in% input$CategoryInput& Sub.Category %in% c("Tables", "Bookcases", "Machines", "Fasteners")) # Clustered Bar Chart showing to dicounts given in dollars Per Sub-Category by Product Category ggplot(slices, aes(x=Sub.Category, y= TotalProfit, fill = Region)) + geom_bar(stat = "identity", position = "dodge", color = "black") + ggtitle("Profit by Region per problematic Sub-Category") + labs(y="Total Profit ($)") }) # } } # Run the application shinyApp(ui = ui, server = server)