diff --git a/RShiny/lessons/04_uploading_downloading_data.md b/RShiny/lessons/04_uploading_downloading_data.md index 86293ca..96ddd68 100644 --- a/RShiny/lessons/04_uploading_downloading_data.md +++ b/RShiny/lessons/04_uploading_downloading_data.md @@ -291,7 +291,7 @@ On the UI side: downloadButton("", "Download the data .csv") ``` -The download button is very simialr to the `actionButton()` function that we've already explored (../02_inputs.md). In fact it also accepts the `class` arugment(s) similar to the `actionButton()` function. +The download button is very similar to the `actionButton()` function that we've recently explored. In fact, it also accepts the `class` arugment(s) similar to the `actionButton()` function. On the server side: @@ -311,44 +311,56 @@ On the server side, we need to use the `downloadHandler()` function. The `downlo - `filename` - This is the default filename that will pop-up when you try to save the file. - `content` - This is the argument where you write your data frame to a file. In this case, we are writing to a `.csv`, so we use `write.csv()`. We are writing it to a temporary object called `file` that `downloadHandler()` recognizes as the output from `content`. -An example app using this is simialr to the bursh points example we used previously: +An example app using this is similar to the brush points example we used previously: ``` -library(shiny) -library(ggplot2) -library(DT) - +# User interface ui <- fluidPage( - plotOutput("plot", brush = "plot_brush"), - DTOutput("table"), - downloadButton("download_button", "Download the data .csv") + # Plot the output with an interactive brushing argument + plotOutput("plot", brush = "plot_brush"), + # The output table + DTOutput("table"), + # Download button + downloadButton("download_button", "Download the data .csv") ) +# Server server <- function(input, output) { - output$plot <- renderPlot( - ggplot(mtcars) + - geom_point(aes(x = mpg, y = disp)) - ) - brushed_points <- reactive( - brushedPoints(mtcars, input$plot_brush) - ) - output$table <- renderDT({ - brushed_points() - }) - output$download_button <- downloadHandler( - filename = function() { - "mtcars_subset.csv" - }, - content = function(file) { - write.csv(brushed_points(), file, quote = FALSE) - } - ) + # Render a plot from the built-in mtcars dataset + output$plot <- renderPlot( + ggplot(mtcars) + + geom_point(aes(x = mpg, y = disp)) + ) + # Reactive expression to hold the brushed points + brushed_points <- reactive( + brushedPoints(mtcars, input$plot_brush) + ) + # Render a table from brushed points the reactive expression is caching + output$table <- renderDT({ + brushed_points() + }) + # Download the data + output$download_button <- downloadHandler( + # The placeholder name for the file will be called mtcars_subset.csv + filename = function() { + "mtcars_subset.csv" + }, + # The content of the file will be the contents of the brushed points reactive expression + content = function(file) { + write.csv(brushed_points(), file, quote = FALSE) + } + ) } +# Run the app shinyApp(ui = ui, server = server) ``` -In the above script, we tweaked our script to allow us to download the table containing the brushed points. First, we added a download button to out UI with `downloadButton("download_button", "Download the data .csv")`. Next, we moved our `brushedPoints()` function out of `renderDT()` and placed it within a `reactive()` function since we will be calling it twice, once in the `renderDT()` function and again when we write our data in the `downloadHandler()` function. Within the `downloadHandler()` function we provided a filename to use as a placeholder (`"mtcars_subset.csv"`) as well as defining the content of our `.csv` file (`write.csv(brushed_points(), file, quote = FALSE)`). +In the above script, we tweaked our script to allow us to download the table containing the brushed points. + +- We added a download button to our UI with `downloadButton("download_button", "Download the data .csv")` +- We moved our `brushedPoints()` function out of `renderDT()` and placed it within a `reactive()` function since we will be calling it twice, once in the `renderDT()` function and again when we write our data in the `downloadHandler()` function +- Within the `downloadHandler()` function we provided a filename to use as a placeholder (`"mtcars_subset.csv"`) as well as defining the content of our `.csv` file (`write.csv(brushed_points(), file, quote = FALSE)`) This app looks like: @@ -356,7 +368,7 @@ This app looks like: ### Downloading a plot -Downloading a plot is similiar to downloading a table. It also uses the `downloadButton()` and `downloadHandler()` functions and the arguments are largely similiar. The syntax looks like: +Downloading a plot is similiar to downloading a table. It also uses the `downloadButton()` and `downloadHandler()` functions and the arguments are largely similar. The syntax looks like: On the UI side: ``` @@ -377,43 +389,57 @@ On the server side: ) ``` -We modified our first plot app to allow us to download the plot: +We can modify our first plot app to allow us to download the plot: ``` -library(shiny) -library(ggplot2) - +# User Interface ui <- fluidPage( - selectInput("x_axis_input", "Select x-axis", choices = colnames(mtcars)), - selectInput("y_axis_input", "Select y-axis", choices = colnames(mtcars), selected = "disp"), - plotOutput("plot"), - downloadButton("download_button", "Download the data .png") + # Dropdown menu to select the column of data to put on the x-axis + selectInput("x_axis_input", "Select x-axis", choices = colnames(mtcars)), + # Dropdown menu to select the column of data to put on the y-axis + selectInput("y_axis_input", "Select y-axis", choices = colnames(mtcars), selected = "disp"), + # The output plot + plotOutput("plot"), + # Download button + downloadButton("download_button", "Download the data .png") ) +# Server server <- function(input, output) { - mtcars_plot <- reactive({ - ggplot(mtcars) + - geom_point(aes_string(x = input$x_axis_input, y = input$y_axis_input)) - }) - output$plot <- renderPlot({ - mtcars_plot() - }) - output$download_button <- downloadHandler( - filename = function() { - "mtcars_plot.png" - }, - content = function(file) { - png(file) - print(mtcars_plot()) - dev.off() - } - ) + # Reactive expression to hold the scatter plot + mtcars_plot <- reactive({ + ggplot(mtcars) + + geom_point(aes_string(x = input$x_axis_input, y = input$y_axis_input)) + }) + # Render plot from the reactive expression + output$plot <- renderPlot({ + mtcars_plot() + }) + # Download the data + output$download_button <- downloadHandler( + # The placeholder name for the file will be called mtcars_plot.png + filename = function() { + "mtcars_plot.png" + }, + # The content of the file will be the contents of the mtcars_plot() reactive expression + # Note how we need to encapsulate the plot in png() and dev.off() functions + # The syntax also demands that we put print() around our plot + content = function(file) { + png(file) + print(mtcars_plot()) + dev.off() + } + ) } +# Run the app shinyApp(ui = ui, server = server) ``` -Similiarly to when we downloaded the data frame, we have moved our plot function to be within a `reactive()` function (called `mtcars_plot`). Next, our `renderPlot()` function called the `mtcars_plot()` reactive object. Lastly, we call our `downloadHandler()` function and provide it a default file name of `"mtcars_plot.png"` and for `content`, we call it mostly the same way as we would write a plot out in R; calling the `png()` function, plotting our plot, then closing the device with the `dev.off()` function. The only thing of note here is that we go need to wrap the `mtcars_plot()` reactive object within a `print()` function. +Some key aspects of this app are: +- Similarly to when we downloaded the data frame, we have moved our plot function to be within a `reactive()` function (called `mtcars_plot`). +- Our `renderPlot()` function called the `mtcars_plot()` reactive object. +- We call our `downloadHandler()` function and provide it a default file name of `"mtcars_plot.png"` and for `content`, we call it mostly the same way as we would write a plot out in R; calling the `png()` function, plotting our plot, then closing the device with the `dev.off()` function. The only thing of note here is that we go need to wrap the `mtcars_plot()` reactive expression within a `print()` function. This app looks like: @@ -426,10 +452,11 @@ Create an app in R Shiny that lets users upload the iris dataset that can be fou Step 1. Write the UI with the appriopriate `fileInput()`, `selectInput()`, `plotOutput` and `downloadButton()` functions Step 2. Write the server side with: - a. A `reactive()` function for reading in the CSV file - b. A `reactive()` function to create the ggplot figure - c. A `renderPlot()` function to render the ggplot figure from the reactive object - d. A `downloadHandler()` function for downloading the image + + 1. A `reactive()` function for reading in the CSV file + 2. A `reactive()` function to create the ggplot figure + 3. A `renderPlot()` function to render the ggplot figure from the reactive expression + 4. A `downloadHandler()` function for downloading the image The app will look like:

@@ -437,41 +464,50 @@ The app will look like:
Click here to see the solution 
-library(shiny)
-library(ggplot2)

-
+# User Interface
 ui <- fluidPage(
+	# Upload the file
 	fileInput("input_file", "Upload file"),
+	# Select from the dropdown menu the column you want on the x-axis
 	selectInput("x_axis_input", "Select x-axis", choices = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")),
+	# Select from the dropdown menu the column you want on the y-axis
 	selectInput("y_axis_input", "Select y-axis", choices = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")),
+	# The output plot
 	plotOutput("plot"),
+	# The download plot button
 	downloadButton("download_button", "Download the data .png")
-)
-
+)

+# Server
 server <- function(input, output) {
+	# Reactive expression to hold the uploaded data 
 	iris_data <- reactive({
 		req(input$input_file)
 		read.csv(input$input_file$datapath)
 	})
+	# Reactive expression to create a scatterplot from the uploaded data and user selected axes
 	iris_plot <- reactive ({
 	ggplot(iris_data()) +
 		geom_point(aes_string(x = input$x_axis_input, y = input$y_axis_input))
 	})
+	# Render the plot from the iris_plot() reactive expression
 	output$plot <- renderPlot({
 		iris_plot()
 	})
+	# Download the data
 	output$download_button <- downloadHandler(
+		# The placeholder name for the file will be called iris_plot.png
 		filename = function() {
 			"iris_plot.png"
 		},
+		# The content of the file will be the contents of the iris_plot() reactive expression
 		content = function(file) {
 			png(file)
 			print(iris_plot())
 			dev.off()
 		}
 	)
-}
-
+}

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