descriptive statistics with group by in Shiny app-CodePudding

I have a sample working code as below. The result should group ( based on Species and/or Species2) then print out the mean of Sepal.Length and Sepal.Width. I appreciate.

    library(shiny)
library(dplyr)

iris$Species2 <- iris$Species

ui <- fluidPage(
  
  selectInput(inputId ="column",
              label = "Choose Column for Summary",
              choices = c("Species", "Species2"),
              selected = "Species", multiple = T),
  
  DT::dataTableOutput('mytable')
)

server <- function(input, output) {
  output$mytable <- DT::renderDataTable({
    Summarise <-
      iris %>%
      dplyr::group_by(groupby = input$column) %>% 
      summarise( mean1 = mean(iris$Sepal.Length))
    DT::datatable(data = Summarise)
  })
  
}

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

Expected Answer;

a <- iris %>% 
  dplyr::group_by(Species) %>%
  dplyr::summarise( mean1 = mean(Sepal.Length)); a


  Species    mean1  mean2 
  <fct>      <dbl> 
1 setosa      5.01  5.01
2 versicolor  5.94  5.94
3 virginica   6.59  6.59

CodePudding user response：

You may use across in group_by so that you can group by multiple inputs.

library(dplyr)
library(shiny)

iris$Species2 <- iris$Species

ui <- fluidPage(
  
  selectInput(inputId ="column",
              label = "Choose Column for Summary",
              choices = c("Species", "Species2"),
              selected = "Species", multiple = T),
  
  DT::dataTableOutput('mytable')
)

server <- function(input, output) {
  output$mytable <- DT::renderDataTable({
    Summarise <-
      iris %>%
      dplyr::group_by(across(all_of(input$column))) %>% 
      summarise(mean1 = mean(Sepal.Length))
    DT::datatable(data = Summarise)
  })
  
}

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