netflex.Rmd

---
title: "NETFLEX"
runtime: shiny
output:
  flexdashboard::flex_dashboard:
    favicon: "src/flavicon.jpeg"
    source_code: embed
    theme: spacelab
    css: src/netflex_lightened2.css
    social: "menu" 
    vertical_layout: fill
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(message = FALSE, warning = FALSE)
# Packages loading. 
library(shiny)
library(shinythemes)
library(shinydashboard)
library(shinydashboardPlus)
library(shinyjs)
library(shinyBS)
library(shinyLP)
library(shinycssloaders)
library(shinybusy)
library(shinyjs)
library(flexdashboard)
library(leaflet)
library(viridis)
library(shinyWidgets)
library(waiter)
library(hrbrthemes)
library(htmltools)
library(tidyverse)
library(FactoMineR)
library(factoextra)
library(GGally)
library(rgdal)
library(sp)
library(rgeos)
library(scales)
library(data.table)
library(DT)
library(dygraphs)
library(plotly)
library(xts)
library(magrittr)
library(ggridges)
library(gplots)
library(wordcloud)
library(vov)
library(adegraphics)
library(hpackedbubble)
```


```{r global, message=FALSE, warning=FALSE, include=FALSE, paged.print=FALSE}
# Data loading 
## Main dataset 
mydata <- read.csv("src/data/netflex5.csv")
mydata$Show_type[is.na(mydata$Show_type)] <- 'undefined'
mydata$Genre[is.na(mydata$Genre)] <- 'undefined'

## Matrix of genres 
mat <- read.csv("src/data/mat.csv",row.names = 1, encoding = "utf8")

## Shapefile data disabled 
# countriesborders <- readOGR("src/TM_WORLD_BORDERS_SIMPL-0.3.shp")

```


#  Home Page {data-orientation=rows}     

Row 
-------------------------------------

```{r} 
# Busy bar for UX enhancement 
add_busy_bar(timeout = 400, color = "#c7636a", centered = FALSE, height = "6px")
# Background image adding. Origin : https://hcdevilsadvocate.com/ae/2019/01/25/best-new-netflix-releases-to-binge-during-the-cold-weather/
tags$img(
     src = "src/bckgrnd0.jpeg", 
     style = 'position: fixed'
   )
```

### ValBox {data-height=800}

```{r}
# Disclaimer : The following jumbotron function is an adpatation of Jumbotron() from the shinyLP package (Copyright holder : Jasmine Dumas // https://github.com/cran/shinyLP). We adapted it  to receive a btn link and enhanced the rendering.

# Jumbotron 
## Jumbotron function for Landing page 
my_jumbotron <- function(header , content, button = TRUE, button_link, ...){
  
  button_label = c(...)
  if (button){
    div(class = "jumbotron",
        h1(header), p(content), p(a(href = button_link ,target="_blank",
        class = "btn btn-primary btn-lg button", role= 'button', button_label)))
} else {
    div(class = "jumbotron", h1(header), p(content))
}
  
}

## Fixed landing page fading in, with a btn link to our github reporsitory 
fixedPage(
use_vov(),
    fade_in_up(
my_jumbotron(header="Welcome to NETFLEX", content="Please call attention to important features of the app", button =  "True", button_link="https://github.com/Glastos/Projet-Shiny", button_Label = "Check our repo'"))
)
```


Row {data-height=150}
-------------------------------------

Row 
-------------------------------------
```{r}
# Empty row like this help better organize the landing page 
```

### Profiles
```{r}
# ValueBox displaying the number of unique profiles used in our app 
renderValueBox({

valueBox(
                tags$p(length(unique(mydata$Profile)),
                style = "font-size: 200%; color:	#FFFFFF;"),
                tags$q("Netflix Profiles",style = "font-size: 150%; color:	#FFFFFF;"),
                icon ="fa-users",color = "green")

})
```


### Movies seen
```{r}
# ValueBox displaying the number of movies seen by profiles in our app 
renderValueBox({
  
valueBox(
                tags$p(table(mydata$Show_type=="movie")[2],
                style = "font-size: 200%; color:	#FFFFFF;"),
                tags$q("Movies seen",style = "font-size:150%; color:	#FFFFFF;"),
                icon ="fa-video", color = "orange")

})
```

### Series seen
```{r}
# ValueBox displaying the number of series seen by profiles in our app 
renderValueBox({
  
valueBox(
                tags$p(table(mydata$Show_type=="series")[2],
                style = "font-size: 200%; color:#FFFFFF;"),
                tags$q("Series seen",style = "font-size: 150%; color:	#FFFFFF;"),
                icon ="fa-film", color="red")

  })
```

Row
-------------------------------------
```{r}
```

# Explanatory visualization {data-orientation=column}

## Input {.sidebar}

```{r echo=FALSE}
# This sidebar able the user to choose whether to see visualizations for all the profiles or a specific profile.  It is also possible to choose to see yearly, monthly, weekly or daily visualizations  
selectInput("profile", "Choose a profile", choices = c("All",unique(mydata$Profile)), selected = "All")

radioButtons("time_range_1", "Time range", choices = list("All time" = 1, "Monthly" = 2, "Weekly" = 3, "Daily" = 4), selected = 1)
```

## Column {.tabset}

### Temporal viz 

```{r echo=FALSE} 
## Reactively filtering the dataset 
mydata_p <- reactive({
  if (input$profile != "All") {
    mydata_p <- filter(mydata, Profile == input$profile)
  } else {
    mydata_p <- mydata
  }
})
## All time plot 
output$dygraphAllTime <- renderDygraph({
        data_d <- mydata_p()[,c("Show_type", "Start_time")] %>%
            mutate(Date = as.Date(Start_time))%>%
            group_by(Date, Show_type)%>%
            tally()%>%
            pivot_wider(names_from = Show_type, values_from = n)%>%
            mutate_at(c('movie','series','undefined'),~replace(., is.na(.), 0))
        data_t <-  xts(data_d[2:4], data_d$Date)
        dygraph(data_t)%>%
            dyRangeSelector(dateWindow = c(min(mydata_p()$Start_time), max(mydata_p()$Start_time)))
    })
```

```{r echo=FALSE}
## Yearly plot 
output$plotYearly <- renderPlotly({
        mydata_d <- mydata_p()[,c("Show_type", "Month")] %>%
            group_by(Month, Show_type)%>%
            tally()%>%
            pivot_wider(names_from = Show_type, values_from = n)%>%
            mutate_at(c('movie','series','undefined'),~replace(., is.na(.), 0))
        mydata_d$Month <- factor(mydata_d$Month, levels = month.name)
        plot_ly(mydata_d, x = ~Month, y = ~movie, name = "Movies", type = "bar", colors = viridis_pal(option = "D")(3))%>%
          add_trace(y = ~series, name = 'Series')%>%
          add_trace(y = ~undefined, name = 'Undefined')%>%
          layout(yaxis = list(title = 'Count'), barmode = 'group')
    })
```

```{r echo=FALSE}
## Montly plot 
output$plotWeekly <- renderPlotly({
        mydata_d <- mydata_p()[,c("Show_type", "Day")] %>%
            group_by(Day, Show_type)%>%
            tally()%>%
            pivot_wider(names_from = Show_type, values_from = n)%>%
            mutate_at(c('movie','series','undefined'),~replace(., is.na(.), 0))
        mydata_d$Day <- factor(mydata_d$Day, levels = c('Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'))
        plot_ly(mydata_d, x = ~Day, y = ~movie, name = "Movies", type = "bar", colors = viridis_pal(option = "D")(3))%>%
          add_trace(y = ~series, name = 'Series')%>%
          add_trace(y = ~undefined, name = 'Undefined')%>%
          layout(yaxis = list(title = 'Count'), barmode = 'group')
    })
```

```{r echo=FALSE}
## Daily plot 
output$plotHourly <- renderPlotly({
        mydata_d <- mydata_p()[,c("Show_type", "Hours")] %>%
            group_by(Hours, Show_type)%>%
            tally()%>%
            pivot_wider(names_from = Show_type, values_from = n)%>%
            mutate_at(c('movie','series','undefined'),~replace(., is.na(.), 0))
        plot_ly(mydata_d, x = ~Hours, y = ~movie, name = "Movies", type = "bar", colors = viridis_pal(option = "D")(3))%>%
          add_trace(y = ~series, name = 'Series')%>%
          add_trace(y = ~undefined, name = 'Undefined')%>%
          layout(yaxis = list(title = 'Count'), barmode = 'group')
    })
```

```{r echo=FALSE}
## Reactive outputting  
output$graph1 <- renderUI({
  if (input$time_range_1 == 1) {
    dygraphOutput("dygraphAllTime")
  } else if (input$time_range_1 == 2) {
        plotlyOutput("plotYearly")
  } else if (input$time_range_1 == 3) {
        plotlyOutput("plotWeekly")
  } else {
        plotlyOutput("plotHourly")
  }
})
uiOutput("graph1")
```

### Top watched
```{r}
## Top 5 most watched shows 
freqfunc <- function(x, n){
  tail(
         sort(table(
           unlist(strsplit(as.character(x), ", "))
           )), n)
}
output$plotPref <- renderPlot({
  
    if (input$profile != "All") {  
       freq_data <- as.data.frame(freqfunc(mydata[which(mydata$Profile==input$profile),]$Title, 5))
    } else {
               validate(
    need(input$profile != "All", "Please choose a single profile."))
    }
      ggplot(freq_data, aes(x=Var1, y=Freq)) +
      geom_segment( aes(xend=Var1, yend=0)) +
      geom_point( size=4, color="orange") +
      theme_bw() + 
      xlab("Shows") + 
      ylab("Count") + 
      ggtitle(paste0("Top 5 most watched shows for ",as.character(input$profile))) +
      theme(plot.title = element_text(hjust = 0.5))
})
plotOutput("plotPref")
```

### Worldcloud

```{r} 
## Worldcloud of most seen genres reactive with the sidebar input 
genres <- reactive({
  if (input$profile != "All") {  
    genres <- data.frame(t(mat[as.character(input$profile),])) 
    colnames(genres) <- "Count"
    genres <- tibble::rownames_to_column(genres, var="Names")
      return(genres)
  } else {
    genres <- data.frame("Count" = colSums(mat))
    genres <- tibble::rownames_to_column(genres, var="Names")
      return(genres)

  }
})

renderPlot({
  wordcloud(words = as.character(colnames(mat)), freq = c(genres()[,2]), min.freq = 1, max.words=25, random.order=FALSE, rot.per=0.35,
            colors=brewer.pal(8, "Dark2"))
})

```

### Mapping

```{r}
renderText({ 
 (("Mapping:
          Display a map of the world with the different countries where people have watched                 
          NetflixNote: Mapping is not implemented in the basic version due to is huge loading time, you can see it by uncommenting the corresponding code"))
               
  })
```

<!-- # ```{r} -->
<!-- # # Mapping -->
<!-- # names(countriesborders@data)[5] <- "Country" -->
<!-- # Countries <- unique(dat$Country) -->
<!-- # mySHPdata <- countriesborders[countriesborders$Country %in% Countries,] -->
<!-- # newdf <- merge(mySHPdata, dat, by="Country",duplicateGeoms = TRUE) -->
<!-- # otherSHP <- countriesborders[!countriesborders$Country %in% Countries,]%>% -->
<!-- #             merge(dat,  by="Country",duplicateGeoms = TRUE ) -->
<!-- #  -->
<!-- # ``` -->
<!-- #  -->
<!-- #  -->
<!-- # ```{r} -->
<!-- # # # Leaflet map -->
<!-- # options(viewer = NULL) -->
<!-- #  -->
<!-- # renderLeaflet({ -->
<!-- # # Color palette -->
<!-- # newdfx <- newdf[newdf$Account==as.character(input$account),] -->
<!-- # factpal2 <- colorFactor(viridis(2), newdfx@data$Country, reverse = T) -->
<!-- #  -->
<!-- #  -->
<!-- #   leaflet()%>% -->
<!-- #   addProviderTiles(providers$CartoDB.DarkMatterNoLabels, -->
<!-- #                      options = providerTileOptions(noWrap = TRUE)) %>% -->
<!-- #   setView(1, 15, 2.5) %>% -->
<!-- #   addPolygons(data=newdfx, -->
<!-- #               stroke=TRUE, -->
<!-- #               fillColor = ~factpal2(newdfx@data$Country), -->
<!-- #               color = "black", -->
<!-- #               weight = 0.5, smoothFactor = 0.5, -->
<!-- #               opacity = 1.0, fillOpacity = 0.5, -->
<!-- #               label = newdfx@data$Profile) %>% -->
<!-- #   addPolygons(data=AutreSHP, -->
<!-- #               fillColor = "black", -->
<!-- #               color = "black", -->
<!-- #               weight = 0.2, smoothFactor = 0.5, -->
<!-- #               opacity = 0.5, fillOpacity = 0.8) %>% -->
<!-- #   leaflet::addLegend(pal=factpal2, values=newdfx@data$Country, opacity=1) -->
<!-- # }) -->
<!-- # ``` -->
# Analysis {data-orientation=rows}

## Input {.sidebar}

```{r echo=FALSE}
## Sidebar for dropping a special profile from the analysis
br()
materialSwitch(inputId = "leaveVal", label = "Drop Valentin!", status = "warning")
```

Row {.tabset}
-------------------------------------
### Balloon
```{r}
## Reactively splitting the dataset 
reactive_df <- reactive({

          if (input$leaveVal){
            return(mat[-26,-c(3,25)])
          } else {
            return(mat[,-c(3,25)])}
})
##Ballonplot of the contingency table for explanatory visualization
renderPlot({
  dt <- as.table(as.matrix(reactive_df()[,-c(23:28)]))

  balloonplot(t(dt), main = "Most seen genres for each profile",
              xlab = "", ylab = "",
              dotsize = 4, dotcolor = "#E50914",
              label = FALSE, show.margins = FALSE,repel = TRUE)

})
```

### CA Biplot  

```{r}
## CA biplot displaying 
renderPlot({
  res.ca <- CA(reactive_df(), graph=F)
  fviz_ca_biplot(res.ca, repel = TRUE)
})
```

### Biplot_col
```{r}
## CA biplot displaying only genres 
renderPlot({
  res.ca <- CA(reactive_df(), graph=F)
  fviz_ca_col(res.ca, col.col = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE)
})
```

### Biplot_row
```{r}
## CA biplot displaying only profiles 
renderPlot({
  res.ca <- CA(reactive_df(), graph=F)
  fviz_ca_row(res.ca, col.row = "cos2",
               gradient.cols = c ("#00AFBB", "#E7B800", "#FC4E07"),
               repel = TRUE)
})
```


### HCPC
```{r}
## CA biplot displaying a dendogram  
renderPlot({
  res.ca <- CA(reactive_df(), graph=F)
  res.hcpc <- HCPC(res.ca, graph = F)
  
  fviz_dend(res.hcpc,
            k=2,
            cex = 0.7,
            palette = "jco",
            rect = TRUE, rect_fill = TRUE,
            rect_border = "jco",
            labels_track_height = 0.8,
            show_labels = TRUE,
            color_labels_by_k=T)

})
```

### Clusters
```{r}
## HCPC clusteres 
renderPlot({
    res.ca <- CA(reactive_df(), graph=F)
    res.hcpc <- HCPC(res.ca, graph = F)
    fviz_cluster(res.hcpc, frame.type = "norm",
            frame.level = 0.68,outlier.color = "black",
            repel=T, show.clust.cent=TRUE,
            ggtheme = theme_bw())
})

```


# Data source
Row {.tabset}
-------------------------------------
### Main data source 
```{r}
# Main data table exploration 
tags$style(HTML("
                    .mydataaTables_wrapper .mydataaTables_length, .mydataaTables_wrapper .mydataaTables_filter, .mydataaTables_wrapper .mydataaTables_info, .mydataaTables_wrapper .mydataaTables_processing, .mydataaTables_wrapper .mydataaTables_paginate, .mydataaTables_wrapper .mydataaTables_paginate .paginate_button.current:hover {
                    color: #000000; }

                    .mydataaTables_wrapper .mydataaTables_paginate .paginate_button{box-sizing:border-box;display:inline-block;min-width:1.5em;padding:0.5em 1em;margin-left:2px;text-align:center;text-decoration:none !important;cursor:pointer;*cursor:hand;color:#ffffff !important;border:1px solid transparent;border-radius:2px}

                    .mydataaTables_length select {
                           color: #ffffff;
                           background-color: #ffffff
                           }
                    .mydataaTables_filter input {
                            color: #ffffff;
                            background-color: #ffffff}thead {
                    color: #ffffff;
                    }tbody {
                    color: #000000;
                    }"))

renderDataTable({DT::datatable(mydata[,-c(1,2)], options = list(scrollX = TRUE,
                 sScrollY = '75vh', scrollCollapse = TRUE), extensions = list("Scroller"))})
```

### Genres contingency table

```{r}
# Contingency table exploration 
renderDataTable({DT::datatable(mat, options = list(scrollX = TRUE,
                 sScrollY = '75vh', scrollCollapse = TRUE), extensions = list("Scroller"))})
```