data_visualization_r_shiny.Rmd

# Building a Dashboard in R for Data Analysis and Visualization using shiny package

Srividya Inampudi


```{r include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

I have written a blog on medium regarding the shiny package in R for my Community Contribution assignment. Please find the link attached below


link: https://medium.com/@isrivi/building-a-dashboard-in-r-for-data-analysis-and-visualization-using-shiny-package-5d864732563f

This Blog will go through the basic steps of creating a Dashboard using shiny package in R. This Dashboard can be used to Visualize and Analyze different Datasets.

```{r, include=FALSE}
#R file to run the application

#install.packages("shiny")
#install.packages("shinydashboard")
library(shiny)
library(shinydashboard) 

#install.packages("DT")
library(DT)

#install.packages("shinythemes")
library(shinythemes)

ui <- dashboardPage(
  dashboardHeader(title = "Datasets"),
  dashboardSidebar(
    sidebarMenu(
      menuItem("Iris", tabName = "iris", icon = icon("tree")),
      menuItem("Cars", tabName = "cars", icon = icon("car"))
    )
    
  ),
  dashboardBody(
    tabItems(
      tabItem("iris",
              box(plotOutput("correlation_plot"), width = 6),
              box(
                selectInput("features", "Features:",
                            c("Sepal.Width", "Petal.length", "Petal.Width")), width = 4
              )
                 
         ),
        tabItem("cars",
                fluidPage( theme = shinytheme("journal"),
                  h1("Cars Dataset"),
                  dataTableOutput("cars_table")
                )
        )
      )
  )
)
  
server <- function(input, output){
  
  output$correlation_plot <- renderPlot({
    output$correlation_plot <- renderPlot({
      plot(iris$Sepal.Length, iris[[input$features]],
           xlab = "Sepal length", ylab = "Features")
    })
  })
  
  output$cars_table <- renderDataTable(mtcars)
  
}

shinyApp(ui, server)


```