Shiny_App_AS2017456.Rmd

---
title: "SCS-Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
runtime: shiny
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(visdat)
library(plotly)
library(RColorBrewer)
testing <- FALSE
```

Overview
=======================================================================

Column {.sidebar}
-----------------------------------------------------------------------

Upload your csv file here. 

```{r}
fileInput("file", "Choose CSV File",
                multiple = FALSE,
                accept = c("text/csv",
                         "text/comma-separated-values,text/plain",
                         ".csv"))
```


```{r}
renderUI({
if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
selectInput("summary_variable","Select Numerical variable",
            choices = colnames(df %>% select_if(is.numeric)))
})
```

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

### Variable types

```{r}
renderPlot({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  df %>% 
    skimr::skim() %>% 
    count(skim_type) %>% 
    ggplot(aes(x = skim_type,y= n,fill=skim_type))+
    geom_bar(stat="identity")+
    labs(title = "Variable Types",x = "Variable type",y = "Count") +
    coord_flip() + 
    theme_minimal() +
    theme(legend.position = "none")
})
```

### Summary measures

```{r}
renderTable({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  vec <- as.numeric(df[,input$summary_variable] %>% unlist())
  tibble(`Summary measure` = c("Min","Q1","Median","Mean","Q3","Max"),
         `Value` = c(min(vec,na.rm=TRUE),
                     quantile(vec,0.25,na.rm=TRUE),
                     quantile(vec,0.5,na.rm=TRUE),
                     mean(vec,na.rm=TRUE),
                     quantile(vec,0.75,na.rm=TRUE),
                     max(vec,na.rm=TRUE)))
})
```

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

### Overview
```{r}
DT::renderDataTable({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  DT::datatable(df, rownames = TRUE,options = list(
    bPaginate = TRUE, 
    searching = TRUE, 
    info = FALSE),
  fillContainer = TRUE)
})
```


Missing Values
=========================================================================

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

### Missing Value Plot

```{r}
renderPlot({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  df %>% 
    skimr::skim() %>% 
    tibble() %>% 
    filter(n_missing != 0) %>% 
    mutate(skim_variable = fct_reorder(skim_variable,n_missing)) %>% 
    ggplot(aes(x = skim_variable,y= n_missing,fill=skim_type))+
    geom_bar(stat="identity")+
    labs(title = "Missing values by variables",
         x = "Variable",y = "Missing count",fill = "Variable type") +
    coord_flip() + 
    theme_minimal() +
    theme(legend.position = "bottom")
})
```

### Missing Value Matrix

```{r}
renderPlot({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  visdat::vis_miss(df,sort_miss=TRUE)
})
```


Correlation
==========================================================================

Column {.sidebar}
----------------------------------------------------------
  
```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 3", label = "Select X axis variable", choices = colnames(df %>% select_if(is.numeric)))
})
```

```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 4", label = "Select Y axis variable", choices = colnames(df %>% select_if(is.numeric)))
})
```

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

### Correlation (Pearson)

```{r}
renderPlotly({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  visdat::vis_cor(df %>% select_if(is.numeric)) +
    scale_fill_gradient(low="yellow",high="dark red")
  #ggplotly(vis_cor(df %>% select_if(is.numeric)))
})
```

### Scatterplot

```{r}
renderPlot({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  ggplot(df,aes(x = !!sym(input$`Variable 3`),
                y = !!sym(input$`Variable 4`))) +
    geom_point() +
    theme_minimal() +
    theme(legend.position = "bottom") +
    labs(x = input$`Variable 3`,y = input$`Variable 4`,
         title = paste("Scatterplot of",input$`Variable 3`,"and",input$`Variable 4`))
})
```

Visualize 1 variable
===========================================

Column {.sidebar}
----------------------------------------------------------
  
```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 1", label = "Select Numeric Variable:", choices = colnames(df %>% select_if(is.numeric)))
})
```

```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 2", label = "Select Categorical Variable:", choices = colnames(df %>% select_if(is.character)))
})
```

Row 
-----------------------------------------------------------------------
### Numerical variable
```{r}
renderPlot({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  ggplot(df,aes(x = !!sym(input$`Variable 1`))) +
    geom_boxplot() +
    theme_minimal() +
    labs(x = input$`Variable 1`,
         title = paste("Boxplot of",input$`Variable 1`))
  # plot_ly(df,
  #         x = ~df[[input$`Variable 1`]],
  #         type = "box") %>%
  #   layout(title = "",
  #          xaxis = list(title = "" ,
  #                       zeroline = FALSE),
  #          yaxis = list(title = "", zeroline = FALSE))
})
```

Row 
-----------------------------------------------------------------------
### Categorical variable
```{r}
renderPlot({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  ggplot(df,aes(x = !!sym(input$`Variable 2`))) +
    geom_bar(fill ="firebrick") +
    theme_minimal() +
    labs(x = input$`Variable 2`,
         title = paste("Barplot of",input$`Variable 2`))
  # plot_ly(df,
  #         x = ~df[[input$`Variable 2`]],
  #         type = "bar",coord_flip()) %>%
  #   layout(title = "",
  #          xaxis = list(title = "" ,
  #                       zeroline = FALSE))
})
```
   

Visualize 2 variables
===========================================
Column {.sidebar}
----------------------------------------------------------
  
```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 5", label = "Select numerical variable", choices = colnames(df %>% select_if(is.numeric)))
})
```

```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 6", label = "Select categorical variable:", choices = colnames(df %>% select_if(is.character)))
})
```   

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

### Visualization

```{r}
renderPlot({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  ggplot(df,aes(y = !!sym(input$`Variable 5`),
                x = !!sym(input$`Variable 6`)))+
    geom_boxplot()+
    coord_flip()+
    theme_minimal()+
    labs(x = input$`Variable 6`,y = input$`Variable 5`,
         title = paste("Boxplot of",input$`Variable 5`,"by",input$`Variable 6`))
})
```

Visualize 3 variables
===========================================
Column {.sidebar}
----------------------------------------------------------
  
```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 7", label = "Select X axis variable", choices = colnames(df %>% select_if(is.numeric)))
})
```

```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 8", label = "Select Y axis variable", choices = colnames(df %>% select_if(is.numeric)))
})
```

```{r}
renderUI({
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  selectInput("Variable 9", label = "Select Color by Variable:", choices = colnames(df %>% select_if(is.character)))
})
```

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

### Visualization

```{r}
renderPlot({
  
  if(testing){
  df <- read_csv("Iris.csv")
  }else{
  req(input$file)
  df <- read_csv(input$file$datapath)
  }
  ggplot(df,aes(x = !!sym(input$`Variable 7`),
                y = !!sym(input$`Variable 8`),
                color = !!sym(input$`Variable 9`))) +
    geom_point() +
    theme_minimal() +
    theme(legend.position = "bottom") + scale_color_brewer(palette="Dark2")
    labs(x = input$`Variable 7`,y = input$`Variable 8`,
         title = paste("Scatterplot of",input$`Variable 7`,"and",input$`Variable 8`),subtitle = paste("Color by",input$`Variable 9`))
  # plot_ly(df,
  #         x = ~df[[input$`Variable 1`]],
  #         color = ~df[[input$`Variable 2`]],
  #         colors = "Paired",
  #         type = "box") %>%
  #   layout(title = "",
  #          xaxis = list(title = "" ,
  #                       zeroline = FALSE))
})
```