Create your own App

With the r package shiny you can create your own app. One example is an app, that automatically gives you an overview over your data, and plots basic histogramms for an uploaded csv file.

Download and open packages

library(shiny)
library(bslib)

Attache Paket: 'bslib'

Das folgende Objekt ist maskiert 'package:utils':

    page

library(dplyr)

Attache Paket: 'dplyr'

Die folgenden Objekte sind maskiert von 'package:stats':

    filter, lag

Die folgenden Objekte sind maskiert von 'package:base':

    intersect, setdiff, setequal, union

library(ggplot2) 
library(plotly)

Attache Paket: 'plotly'

Das folgende Objekt ist maskiert 'package:ggplot2':

    last_plot

Das folgende Objekt ist maskiert 'package:stats':

    filter

Das folgende Objekt ist maskiert 'package:graphics':

    layout

library(lubridate)

Attache Paket: 'lubridate'

Die folgenden Objekte sind maskiert von 'package:base':

    date, intersect, setdiff, union

open the app with the following code:

ui <- page_sidebar(
  
  theme = bs_theme(bootswatch = "flatly"),
  
  title = "Data Explorer",
  
  sidebar = sidebar(
    
    fileInput("file", "Upload CSV", accept = ".csv"),
    
    uiOutput("var_select"),
    
    uiOutput("date_filter"),
    
    uiOutput("numeric_filter")
  ),
  
  layout_column_wrap(
    width = 1/2,
    value_box(
      title = "Rows",
      textOutput("rows")
    ),
    value_box(
      title = "Time span",
      textOutput("timespan")
    )
  ),
  
  navset_tab(
    nav_panel("Plot", plotlyOutput("plot")),
    nav_panel("Table", tableOutput("table")),
    nav_panel("Summary", verbatimTextOutput("summary"))
  )
)

server <- function(input, output, session) {
  
  # Daten laden
  df <- reactive({
    req(input$file)
    read.csv(input$file$datapath)
  })
  
  # Variable auswählen (nur numerisch)
  output$var_select <- renderUI({
    req(df())
    numeric_vars <- names(df())[sapply(df(), is.numeric)]
    selectInput("var", "Variable", choices = numeric_vars)
  })
  
  # Datum-Filter falls Datum vorhanden
  output$date_filter <- renderUI({
    req(df())
    date_vars <- names(df())[sapply(df(), inherits, "Date")]
    
    if (length(date_vars) > 0) {
      dateRangeInput(
        "date_range",
        "Date range",
        start = min(df()[[date_vars[1]]], na.rm = TRUE),
        end   = max(df()[[date_vars[1]]], na.rm = TRUE)
      )
    }
  })
  
  # Numerischer Filter
  output$numeric_filter <- renderUI({
    req(df(), input$var)
    
    sliderInput(
      "num_range",
      "Value range",
      min = min(df()[[input$var]], na.rm = TRUE),
      max = max(df()[[input$var]], na.rm = TRUE),
      value = range(df()[[input$var]], na.rm = TRUE)
    )
  })
  
  # Gefilterte Daten
  filtered_data <- reactive({
    req(df(), input$var)
    
    data <- df()
    
    # numerischer Filter
    data <- data %>%
      filter(
        .data[[input$var]] >= input$num_range[1],
        .data[[input$var]] <= input$num_range[2]
      )
    
    data
  })
  
  # Value Boxes
  output$rows <- renderText({
    nrow(filtered_data())
  })
  
  output$timespan <- renderText({
    if ("date_range" %in% names(input)) {
      paste(input$date_range[1], "—", input$date_range[2])
    } else {
      "No date column"
    }
  })
  
  # Plot
  output$plot <- renderPlotly({
    req(input$var)
    
    p <- ggplot(filtered_data(), aes(x = .data[[input$var]])) +
      geom_histogram(fill = "#2c3e50", color = "white", bins = 30) +
      theme_minimal()
    
    ggplotly(p)
  })
  
  # Tabelle
  output$table <- renderTable({
    head(filtered_data(), 20)
  })
  
  # Summary
  output$summary <- renderPrint({
    summary(filtered_data())
  })
}

shinyApp(ui, server)

Shiny applications not supported in static R Markdown documents