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@Gammerdinger
Gammerdinger authored Sep 18, 2024
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168 changes: 102 additions & 66 deletions RShiny/lessons/04_uploading_downloading_data.md
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Expand Up @@ -291,7 +291,7 @@ On the UI side:
downloadButton("<download_buttonID>", "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:

Expand All @@ -311,52 +311,64 @@ 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:

<p align="center"><iframe src="https://hcbc.connect.hms.harvard.edu/Data_frame_download_demo/?showcase=0" width="800" height="750px" data-external="1"></iframe></p>

### 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:
```
Expand All @@ -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:

Expand All @@ -426,52 +452,62 @@ 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:
<p align="center"><iframe src="https://hcbc.connect.hms.harvard.edu/Plot_upload_download_exercise/?showcase=0" width="800" height="700px" data-external="1"></iframe></p>

<details>
<summary><b>Click here to see the solution</b></summary>
<pre>
library(shiny)
library(ggplot2)<br/>

&#35; User Interface
ui <- fluidPage(
&#9;&#35; Upload the file
&#9;fileInput("input_file", "Upload file"),
&#9;&#35; Select from the dropdown menu the column you want on the x-axis
&#9;selectInput("x_axis_input", "Select x-axis", choices = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")),
&#9;&#35; Select from the dropdown menu the column you want on the y-axis
&#9;selectInput("y_axis_input", "Select y-axis", choices = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")),
&#9;&#35; The output plot
&#9;plotOutput("plot"),
&#9;&#35; The download plot button
&#9;downloadButton("download_button", "Download the data .png")
)

)<br/>
&#35; Server
server <- function(input, output) {
&#9;&#35; Reactive expression to hold the uploaded data
&#9;iris_data <- reactive({
&#9;&#9;req(input$input_file)
&#9;&#9;read.csv(input$input_file$datapath)
&#9;})
&#9;&#35; Reactive expression to create a scatterplot from the uploaded data and user selected axes
&#9;iris_plot <- reactive ({
&#9;ggplot(iris_data()) +
&#9;&#9;geom_point(aes_string(x = input$x_axis_input, y = input$y_axis_input))
&#9;})
&#9;&#35; Render the plot from the iris_plot() reactive expression
&#9;output$plot <- renderPlot({
&#9;&#9;iris_plot()
&#9;})
&#9;&#35; Download the data
&#9;output$download_button <- downloadHandler(
&#9;&#9;&#35; The placeholder name for the file will be called iris_plot.png
&#9;&#9;filename = function() {
&#9;&#9;&#9;"iris_plot.png"
&#9;&#9;},
&#9;&#9;&#35; The content of the file will be the contents of the iris_plot() reactive expression
&#9;&#9;content = function(file) {
&#9;&#9;&#9;png(file)
&#9;&#9;&#9;print(iris_plot())
&#9;&#9;&#9;dev.off()
&#9;&#9;}
&#9;)
}

}<br/>
&#35; Run the app
shinyApp(ui = ui, server = server)
</pre>
</details>
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