# <div style="white-space: pre-line;">Getting Started in R
an introduction to data analysis and visualisation</div>
## Shiny
### Réka Solymosi and Henry Partridge

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![Shiny](img/shiny.png)

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# What is Shiny?

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- An R package developed by [RStudio](https://www.rstudio.com/products/shiny/) that allows data analysts to analyse, visualise and share their results with non-R users

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- Interactive web applications connected to an R session

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- No knowledge of HTML, CSS, and JavaScript is required but web apps are customisable and extendible

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- Integrates with JavaScript libraries

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- Uses a reactive programming framework. An input is sent to an R process which generates a plot in a web browser.

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# Advantages of Shiny

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- The process of loading, cleaning, manipulating and visualising data is possible entirely within R

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- Lowers barrier of entry to web development

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- R bindings for JavaScript visualization libraries becoming available all the time

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- Open source code encourages collaboration

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# Structure of an app

Each Shiny app has two components: a UI (web page) and a server function (live R session).

```r
library(shiny)
ui <- fluidPage()
server <- function(input, output){}
shinyApp(ui, server)
```

The UI specifies the layout and user interface elements, e.g. HTML widgets like drop-downs, sliders, radio buttons etc., whilst the server specifies how to generate the output, e.g. table, plot, text.

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# Demo

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# Load the packages

```r
*library(shiny) 
*library(tidyverse) 
*library(gapminder)
*library(ggplot2)
ui <- fluidPage()
server <- function(input, output){}
shinyApp(ui, server)
```

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# Test run the template

```r
library(shiny) 
library(tidyverse) 
library(gapminder)
library(ggplot2)
*ui <- fluidPage()
*server <- function(input, output){}
*shinyApp(ui, server)
```

You can run this template to boot up the draft webpage after you have made editions.

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# Read (and manipulate) the data

```r
*df <- gapminder %>% 
*  group_by(continent, year) %>% 
*  summarise(mean_lifeExp = mean(lifeExp))
ui <- fluidPage()
server <- function(input, output){}
shinyApp(ui, server)
```

But we still haven't actually added anything to the webpage yet.

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# Add a title

Now we've added a title.

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# Add a layout

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# Add a reactive input

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# Add a reactive

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# Add a reactive output

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# Save output

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# Build reactive output

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# Access input values

```r
library(shiny) ; library(tidyverse) ; library(gapminder) ; library(ggplot2)
df <- gapminder %>% group_by(continent,year) %>% summarise(mean_lifeExp = mean(lifeExp))
ui <- fluidPage(
  titlePanel("Gapminder Shiny app"),
  br(),
  sidebarLayout(
    sidebarPanel(width = 3,
                 selectInput(inputId = "continent", label = "Select a continent",
                             choices = levels(df$continent), selected = "Europe")),
                  mainPanel(plotOutput('plot'))))
server <- function(input, output){
  data_points <- reactive({filter(df, continent == input$continent)})
  output$plot <- renderPlot({
*    ggplot(data_points(), aes(x = year, y = mean_lifeExp)) +
*      geom_line(color = "deeppink2", lwd = 1.2) +
*      scale_x_continuous(breaks = seq(1952, 2007, 5)) +
*      scale_y_continuous(breaks = seq(0, 80, 20), limits = c(20, 90)) +
*      labs(title = paste("Mean life expectancy in", input$continent),
*           x = "\nYear", y = "\nMean life expectancy (years)", caption = "Source: Gapminder") +
*      theme_classic() + 
*      theme(legend.position = "none")})}
shinyApp(ui, server)
```

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# Run and deploy the app
![screenshot](img/gapminder_app.png)