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from Get Started Gallery Articles Reference Deploy Help Contribute Source on GitHub Interact. Analyze. Communicate. Take a fresh, interactive approach to telling your data story with . Let users interact with your data and your analysis. And do it all with R. is an R package that makes it easy to build interactive web apps straight from R. You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards . You can also extend your apps with CSS themes , htmlwidgets , and JavaScript actions . combines the computational power of R with the interactivity of the modern web. Get Started See Gallery Here is a app apps are easy to write. No web development skills are required. Description app.R comes with a variety of built in input widgets. With minimal syntax it is possible to include widgets like the ones shown on the left in your apps: # Select type of trend to plot selectInput(inputId = "type", label = strong("Trend index"), choices = unique(trend_data$type), selected = "Travel") # Select date range to be plotted dateRangeInput("date", strong("Date range"), start = "2007-01-01", end = "2017-07-31", min = "2007-01-01", max = "2017-07-31") Displaying outputs is equally hassle-free: mainPanel( plotOutput(outputId = "lineplot", height = "300px"), textOutput(outputId = "desc"), tags$a(href = "https://www.google.com/finance/domestic_trends", "Source: Google Domestic Trends", target = "_blank") ) Build your plots or tables as you normally would in R, and make them reactive with a call to the appropriate render function: output$lineplot <- renderPlot({ plot(x = selected_trends()$date, y = selected_trends()$close, type = "l", xlab = "Date", ylab = "Trend index") }) Want to find out how we built the Google Trend Index app shown on the left? See the next tab for the complete source code. # Load packages library(shiny) library(shinythemes) library(dplyr) library(readr) # Load data trend_data <- read_csv("data/trend_data.csv") trend_description <- read_csv("data/trend_description.csv") # Define UI ui <- fluidPage(theme = shinytheme("lumen"), titlePanel("Google Trend Index"), sidebarLayout( sidebarPanel( # Select type of trend to plot selectInput(inputId = "type", label = strong("Trend index"), choices = unique(trend_data$type), selected = "Travel"), # Select date range to be plotted dateRangeInput("date", strong("Date range"), start = "2007-01-01", end = "2017-07-31", min = "2007-01-01", max = "2017-07-31"), # Select whether to overlay smooth trend line checkboxInput(inputId = "smoother", label = strong("Overlay smooth trend line"), value = FALSE), # Display only if the smoother is checked conditionalPanel(condition = "input.smoother == true", sliderInput(inputId = "f", label = "Smoother span:", min = 0.01, max = 1, value = 0.67, step = 0.01, animate = animationOptions(interval = 100)), HTML("Higher values give more smoothness.") ) ), # Output: Description, lineplot, and reference mainPanel( plotOutput(outputId = "lineplot", height = "300px"), textOutput(outputId = "desc"), tags$a(href = "https://www.google.com/finance/domestic_trends", "Source: Google Domestic Trends", target = "_blank") ) ) ) # Define server function server <- function(input, output) { # Subset data selected_trends <- reactive({ req(input$date) validate(need(!is.na(input$date[1]) & !is.na(input$date[2]), "Error: Please provide both a start and an end date.")) validate(need(input$date[1] < input$date[2], "Error: Start date should be earlier than end date.")) trend_data %>% filter( type == input$type, date > as.POSIXct(input$date[1]) & date < as.POSIXct(input$date[2] )) }) # Create scatterplot object the plotOutput function is expecting output$lineplot <- renderPlot({ color = "#434343" par(mar = c(4, 4, 1, 1)) plot(x = selected_trends()$date, y = selected_trends()$close, type = "l", xlab = "Date", ylab = "Trend index", col = color, fg = color, col.lab = color, col.axis = color) # Display only if smoother is checked if(input$smoother){ smooth_curve <- lowess(x = as.numeric(selected_trends()$date), y = selected_trends()$close, f = input$f) lines(smooth_curve, col = "#E6553A", lwd = 3) } }) # Pull in description of trend output$desc <- renderText({ trend_text <- filter(trend_description, type == input$type) %>% pull(text) paste(trend_text, "The index is set to 1.0 on January 1, 2004 and is calculated only for US search traffic.") }) } # Create object shinyApp(ui = ui, server = server) Hosting and Deployment Put your app on the web by using your own servers or RStudio's hosting service. Learn more Copyright © 2020 RStudio, PBC. All rights reserved....