Shiny Application for Interactive Data Visualization and Summarization

The code you provided is a Shiny application that creates an interactive dashboard for visualizing and summarizing data. Here’s a breakdown of the main components:

Data Import: The application allows users to upload a CSV file containing the data. The read.csv function reads the uploaded file and stores it in a reactive expression dat.
Period Selection: Users can select a period from the data using a dropdown menu. This selection is stored in a reactive expression input$period.
Time Point Selection: Users can select a specific point-in-time from the selected period using another dropdown menu. This selection is stored in a reactive expression input$time.
Filtering Variable Selection: Users can select a filtering variable (e.g., “Group”) from a list of available variables. This selection is stored in a reactive expression input$filter_var.
Filter Values Selection: If a filtering variable is selected, users can choose specific values to filter the data. This selection is stored in a reactive expression input$filter_vals.
Summarized Variable Selection: Users can select a variable to summarize (e.g., “Total Sales”). This selection is stored in a reactive expression input$vals.
Data Visualization: The application displays two tables: one showing the raw data and another showing the summarized data.

Here’s an updated version of your response with some formatting improvements:

Shiny Application

The Shiny application creates an interactive dashboard for visualizing and summarizing data.

library(shiny)
library(dplyr)

# Define UI
ui <- fluidPage(
  # Data Import
  fileInput("file1", "Select CSV file"),
  
  # Period Selection
  selectInput("period", "Period:", choices = names(dat())),
  
  # Time Point Selection
  selectInput("time", "Time point:", choices = unique(na.omit(dat()[[input$period]]))),
  
  # Filtering Variable Selection
  selectizeInput("filter_var", "Filtering variable:", choices = names(dat()), selected=""),
  
  # Filter Values Selection
  selectizeInput("filter_vals", "Filter values:", choices = c("", unique(na.omit(dat()[[input$filter_var]]))), multiple = TRUE),
  
  # Summarized Variable Selection
  selectInput("vals", "Variable to be summarized:", choices = names(dat()), selected = names(dat())[ncol(dat())]),
  
  # Data Visualization
  tableOutput("data"),
  tableOutput("summed_data")
)

# Define server function
server <- function(input, output) {
  # Initialize reactive expressions
  dat <- reactive({
    req(input$file1)
    read.csv(input$file1$datapath,
             header = input$header,
             sep = input$sep,
             quote = input$quote)
  })
  
  # Period Selection
  period <- reactive({
    pds <- names(dat()) %>% select(contains("Period")) %>% names
    chc_pd <- pds
    names(chc_pd) <- paste0("By ", gsub("_", "", pds))
    selectInput(inputId = "period",
                 label = NULL,
                 choices = chc_pd,
                 selected = pds[1])
  })
  
  # Time Point Selection
  time <- reactive({
    req(dat())
    req(input$period)
    chc <- unique(na.omit(dat()[[input$period]]))
    selectInput(inputId = "time", 
                label = "Choose a point-in-time:", 
                choices = chc,
                selected = chc[1])
  })
  
  # Filtering Variable Selection
  filter_var <- reactive({
    req(dat())
    if(input$filter_var != ""){
      chc_filt <- names(dat()) %>% select(contains("Filtering variable"))
      selectizeInput("filter_var",
                     label = "Filtering variable:", 
                     choices = c("", names(dat())),
                     selected="")
    }
  })
  
  # Filter Values Selection
  filter_vals <- reactive({
    req(dat())
    if(input$filter_var != ""){
      chc_fv <- unique(na.omit(dat()[[input$filter_var]]))
      selectizeInput("filter_vals",
                     label = "Filter values:", 
                     choices = c("", chc_fv),
                     selected="",
                     multiple=TRUE)
    }
  })
  
  # Summarized Variable Selection
  vals <- reactive({
    req(dat())
    if(input$vals != ""){
      selectInput(inputId = "vals", 
                  label = "Variable to be summarized:", 
                  choices = names(dat()), 
                  selected = names(dat())[ncol(dat())])
    }
  })
  
  # Data Visualization
  output$data <- renderTable({
    req(dat())
    qs <- ifelse(is.character(dat()[[input$period]]), "'", "")
    filter_exp1 <- paste0(input$period, "=='", input$time)
    dat_filtered <- dat() %>% filter(eval(filter_exp1), eval(quote(input$filter_var == input$filter_vals)), eval(quote(input$vals == names(dat()))))
  })
  
  output$summed_data <- renderTable({
    req(dat())
    qs <- ifelse(is.character(dat()[[input$period]]), "'", "")
    filter_exp1 <- paste0(input$period, "=='", input$time)
    dat_filtered <- dat() %>% filter(eval(filter_exp1), eval(quote(input$filter_var == input$filter_vals)), eval(quote(input$vals == names(dat()))))
    summarise_dat <- dat_filtered %>% summarise(mean(value))
  })
}

# Run the app
shinyApp(ui = ui, server = server)

This updated version includes comments and formatting to make it easier to understand. Note that this code assumes you have dplyr installed in your R environment. If not, install it using install.packages("dplyr").

Last modified on 2023-06-22