packages_i.Rmd

---
title: "Packages I"
description: |
  dplyr and plots with ggplot2 & Plotly
output: 
  distill::distill_article:
    toc: true
    toc_float: true
    toc_depth: 4
---

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

Shiny provides the basic tools for client-server interaction, but a lot of parts that make up an app--how it wrangles and displays data, can come from packages of your choice. All of the packages introduced in the R Learning Series so far (`dplyr`, `ggplot2`, `plotly`, `data.table`, `R markdown`, `sf`, `leaflet`) can be included in Shiny.

# Plots

`ggplot2` can be used with the standard `plotOutput()` and `renderPlot({})` functions. 

## ggplot2

```{r}
# Server

  output$plot <- renderPlot({
    filtered_df() %>% 
      ggplot(aes(x = year, y = count, color = state)) +
        geom_line() +
        facet_wrap(vars(sex), nrow = 2, scales = "free_y")
  })

```

To add more content to our plot (and table), enable the dropdown menu to allow the user to select multiple states.

```{r}
# UI 

  # add multiple argument to allow more than one selection
  selectInput("state",
              label = 'Select State',
              choices = unique(df$state),
              selected = 'Washington',
              multiple = TRUE)
```

```{r}
# Server

 # allow filtering of multiple states with `%in%`
  filtered_df <- eventReactive(input$go, {
    df %>% 
      filter(name == clean_name() & state %in% input$state)
  })
```

## Plotly

[Plotly](https://plotly.com/ggplot2/), a package that helps create interactive graphs, can be used to convert a static `ggplot2` graph into an interactive one. [Plotly](https://plotly.com/r/) on its own can also be used in lieu of ggplot2. 

To render Plotly graphs in Shiny, use `plotlyOutput()` and `renderPlotly({})` in lieu of the standard plot output and render functions. 

Include the `plotly` package in the global section.

```{r}
library(plotly)
```

Convert existing plot output and render functions to their respective Plotly equivalent.

```{r}
# UI

  # convert plotOutput to a Plotly Output
  plotlyOutput("plot")

```

```{r}
# Server
  
  # convert renderPlot to renderPlotly
  output$plot <- renderPlotly({
    
    # create ggplot object
    p <- filtered_df() %>% 
      ggplot(aes(x = year, y = count, color = state)) +
        geom_line() +
        facet_wrap(vars(sex), nrow = 2, scales = "free_y")
    
    # wrap ggplot object with ggplotly
    ggplotly(p)
  })

```

# Extra: Tidy Evaluation

Typically in `dplyr` functions, data-variables (a.k.a column names) are entered directly in the function without any extra syntax (`$` or quotation marks). Data-variables mapped in `ggplot2`’s `aes()` also follow the same concept.

  - `df %>% arrange(count)`
  - `ggplot(df, aes(x = year, y = count))`

However when the data-variable (e.g. `count`) is stored in a variable (e.g. `my_column <- 'count'`), or as part of another object (e.g. `input$my_input_widget`), additional syntax is required to accommodate this indirection. 

  - for single data-variables: `.data[[ <insert data-variable> ]]`
  - for multiple data-variables in dplyr: `across(all_of( <insert data-variable> ))`

Let's add a dropdown menu that allows the user to choose which column to sort the table by.

```{r}

ui <- fluidPage(
  
  title,
  src,
  
  sidebarLayout(
    sidebarPanel(
      txt_box,
      actionButton("clear", label = "Clear Name"),
      txt_disp,
      selectInput("state",
                  label = 'Select State',
                  choices = unique(df$state),
                  selected = 'Washington',
                  multiple = TRUE),
      
      # add dropdowns for user to decide how table is sorted 
      selectInput('table_sort',
                  label = 'Sort table by',
                  choices = c('count', 'sex', 'year')),
      
      actionButton("go", label = "Enter")
    ),
    mainPanel(
      fluidRow(
        column(
          width = 6,
          h3("Table"),
          tbl_disp
        ),
        column(
          width = 6,
          h3("Plot"),
          plotlyOutput("plot")
        )
      )
    )
  )
  
)
```

In the server, apply the `.data[[]]` wrapper inside `arrange()`.

```{r}
# Server

  filtered_df <- eventReactive(input$go, {
    df %>%
      filter(name == clean_name() & state %in% input$state) %>% 
      arrange(.data[[input$table_sort]]) # add arrange, pass input value using tidy evaluation syntax
  })
```

If there are multiple values selected by the user for an input and applied in `dplyr`, wrap the reactive source within `across(all_of())`.

```{r}
# UI

# add dropdowns for user to decide how table is sorted 
selectInput('table_sort',
            label = 'Sort table by',
            choices = c('count', 'sex', 'year'),
            multiple = TRUE) # allow more than one selection

```


```{r}
# Server

  filtered_df <- eventReactive(input$go, {
    df %>%
      filter(name == clean_name() & state %in% input$state) %>% 
      arrange(across(all_of(input$table_sort))) # add arrange, pass input value using tidy evaluation syntax
  })
```