ui.R

library(shiny)
library(shinydashboard)
library(DT)

dashboardPage(skin = "red",
  dashboardHeader(title = "ST 558- Project 4"),
  dashboardSidebar(
    sidebarMenu(
      menuItem("About",tabName="about",icon = icon("home", lib = "glyphicon")),
      menuItem("Data Exploration",tabName="data_exp",
               icon=icon("signal",lib="glyphicon")),
      menuItem("Modeling",tabName = "model",icon=icon("play",lib="glyphicon")),
      menuItem("Data",tabName="data",icon=icon("th-list",lib="glyphicon"))
    )
  ),
  dashboardBody(
    tabItems(
     tabItem(tabName = "about",
      fluidRow(
        column(12,align="center",div(style="margin:0%"),
            HTML("<div style='height: 270px;'>"),
            imageOutput("img"),
            HTML("</div>"),
            h4("Created by:",a(href="https://www.linkedin.com/in/rohan-prabhune-57b083174/",
                               "Rohan Prabhune",style="font-size:20px;"))
        )
      ),
      fluidRow(
        column(3),
        column(6,align = "center",
           box(width=NULL,height=45,background="red",
              title="About this app"
           )
        ),
        column(3)
      ),
      fluidRow(
        column(4,
          box(width=NULL,title="Purpose of the App",status="danger",solidHeader = TRUE,
            h4("This application was created as a part of course project for ST 558 in Fall 2022.
               The goal of this project is to create a shiny app that can be used to explore data and model it."),
            h4("Shiny is an R package that makes it easy to build interactive web apps straight from R.
               You can host standalone apps on a webpage or embed them in R Markdown documents or build dashboards.
               This is one such application where I have used shiny to create dashboard to performing predictive
               modelling on a dataset taken from ",a(href="https://www.kaggle.com/","Kaggle",style="font-size:20px;")),
            h4("This application is created specifically using ",
               a(href="https://rstudio.github.io/shinydashboard/index.html","shinydashboard",style="font-size:20px;"),
               "which allows you to customize the header, sidebar and body of the app.")
          )
        ),
        column(4,
          box(width=NULL,title="Data",status="danger",solidHeader = TRUE,
            h4("This Heart Disease Dataset dates from 1988 and consists of four databases: 
               Cleveland, Hungary, Switzerland, and Long Beach V combined. 
               The original dataset contained 76 attributes, including the predicted attribute, 
               but all published experiments refer to using a subset of 14 of them. So for this application I 
               have considered these 14 attributes only. 
               The \"target\" field refers to the presence of heart disease in the patient. 
               It is integer valued 0 = no disease and 1 = disease."),
            h4("The dataset can be found ",a(href="https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset",
                                             "here",style="font-size:18px;")),
            h4("You can find more information on the 14 attributes used in the dataset and the relevant research papers
               that cite this data set ",
               a(href="https://archive.ics.uci.edu/ml/datasets/heart+disease","here",style="font-size:18px;"))
          )
        ),
        column(4,
          box(width=NULL,title="Navigating the application",status="danger",solidHeader = TRUE,
            h4("The application consists of 4 tabs in the sidebar"),
            h4(tags$b("1. About:"),"(You are here!)"),
            h4(tags$b("2. Data Exploration:")),
            h4("This page allows you to do Exploratory Data Analysis on the dataset
               which includes Quantitaive data graphs, Categorical data graphs,
                Graphical analysis with target variable and Correlation plots."),
            h4(tags$b("3. Modeling:")),
            h4("This tab allows the user to fit 3 classificaiton models to the data: Generalized linear model,
               Classification tree and a Random Forest model. Here you can find the infomartion on each of these models,
               tune the hyperparameters of the model, check the fit statistics and perform predictive analysis."),
            h4(tags$b("4. Data:")),
            h4("Here you can check out the data used for this application, subset the dataset and 
               obtain a .csv file.")
          )
        )
      )
     ),  #Tab item
     tabItem(tabName = "data_exp",
      fluidRow(
        column(1
        ),
        column(10,align = "center",
               box(width=NULL,height=45,title="Train-Test Split",background="red")
        ),
        column(1
        )
      ),
      fluidRow(
        column(1
        ),
        column(4,
          box(width=NULL,status="danger",
              sliderInput("split_number","Select the proportion of training data",
                          min=0.6,max=0.9,value=0.8,step=0.05))
        ),
        column(6,
               box(width =NULL,status = "danger",
                   h4("We first split our data into train and test set and perform Exploratory
                 Data Analysis (EDA) on train data."),
                   actionButton("split","Click here to split Data"))
        ),
        column(1
        )
      ),
      fluidRow(
       column(3,
        box(width=NULL,height=405,title="Numerical Summaries of variables",
            checkboxGroupInput("num_summary",
                               "Select the variables for which you want the numerical summaries",
                                c("Age"="age","Resting Blood Pressure(trestbps)"="trestbps",
                                  "Cholestrol(chol)"="chol","Max. heart rate(thalach)"="thalach",
                                  "ST depression induced"="oldpeak"),
                               selected=c("age","trestbps","chol","thalach","oldpeak"))
        )
       ),
       column(9,
        box(width=NULL,title="Numerical summaries",
            dataTableOutput("numerical_summaries")
        )
       )
      ),
      fluidRow(
        column(3,
          box(width=NULL,title="Variable selection for Contingency tables",
              h5("Here you can select the categorical variables to find their two way contingency tables"),
              selectInput("cont_var1","Select variable 1",
                          c("Gender"="sex",
                            "Chest Pain type"="cp",
                            "Fasting blood sugar"="fbs",
                            "Resting ECG"="restecg",
                            "Blood disorder(thalassemia)"="thal",
                            "Execrcise induced Angina"="exang",
                            "Major vessels colored by flourosopy"="ca"),selected="sex"),
              selectInput("cont_var2","Select variable 2",
                          c("Gender"="sex",
                            "Chest Pain type"="cp",
                            "Fasting blood sugar"="fbs",
                            "Resting ECG"="restecg",
                            "Blood disorder(thalassemia)"="thal",
                            "Execrcise induced Angina"="exang",
                            "Major vessels colored by flourosopy"="ca"),selected="cp")
          )       
        ),
        column(9,
          box(width=NULL,title="Contingency Tables",
              dataTableOutput("contingency_table"))
        )
      ),
      fluidRow(
        column(1
        ),
        column(10,align = "center",
               box(width=NULL,height=45,title="Quantitative Data Graphs",background="red")
        ),
        column(1
        )
      ),
      fluidRow(
        column(2,
          box(width=NULL,title="Plots for numerical variables",
            selectInput("box_or_hist","Select the kinds of plots you want to see",
                        c("Only Boxplots"="box_only","Only Histograms"="hist_only",
                          "Both Box plots and Histograms"="both_box_hist"),selected="both_box_hist")
          )       
        ),
        column(5,
          box(width=NULL,
            plotOutput('bh_plot1')
          ),
          box(width=NULL,
            plotOutput('bh_plot3')
          ),
          conditionalPanel(
            condition = "input.box_or_hist == 'both_box_hist'",
            box(width=NULL,
              plotOutput("bh_plot5")
            ),
            box(width=NULL,
              plotOutput("bh_plot7")
            )
          ) 
        ),
        column(5,
          box(width=NULL,
            plotOutput('bh_plot2')
          ),
          box(width=NULL,
            plotOutput('bh_plot4')
          ),
          conditionalPanel(
            condition = "input.box_or_hist == 'both_box_hist'",
            box(width=NULL,
                plotOutput("bh_plot6")
            ),
            box(width=NULL,
                plotOutput("bh_plot8")
            )
          )
        )
      ),
      fluidRow(
        column(1
        ),
        column(10,align = "center",
               box(width=NULL,height=45,title="Categorical Data Graphs",background="red")
        ),
        column(1
        )
      ),
      fluidRow(
        column(2,
          box(width=NULL,title="Plots for categorical variables",
            checkboxGroupInput("cat_plots",
                               "Select the variables for which you want to see bar plots",
                               c("Chest Pain type"="cp",
                                 "Fasting blood sugar"="fbs",
                                 "Resting ECG"="restecg",
                                 "Blood disorder(thalassemia)"="thal",
                                 "Execrcise induced Angina"="exang",
                                 "Major vessels colored by flourosopy"="ca"),
                               selected=c("cp","fbs","restecg","thal","exang","ca"))
          )
        ),
        column(10,
          fluidRow(
            column(12,
              box(width=NULL,
                plotOutput("cat_gender")
              ),
              conditionalPanel(
                condition = "input.cat_plots.includes('cp')",
                box(width=NULL,
                    plotOutput("cat_cp"))
              ),
              conditionalPanel(
                condition = "input.cat_plots.includes('fbs')",
                box(width=NULL,
                    plotOutput("cat_fbs"))
              ),
              conditionalPanel(
                condition = "input.cat_plots.includes('restecg')",
                box(width=NULL,
                    plotOutput("cat_restecg"))
              ),
              conditionalPanel(
                condition = "input.cat_plots.includes('thal')",
                box(width=NULL,
                    plotOutput("cat_thal"))
              ),
              conditionalPanel(
                condition = "input.cat_plots.includes('exang')",
                box(width=NULL,
                    plotOutput("cat_exang"))
              ),
              conditionalPanel(
                condition = "input.cat_plots.includes('ca')",
                box(width=NULL,
                    plotOutput("cat_ca"))
              )
            )
          )
        )
      ),
      fluidRow(
        column(1
        ),
        column(10,align = "center",
               box(width=NULL,height=45,title="Exploratory Data Analysis with target variable",background="red")
        ),
        column(1
        )
      ),
      fluidRow(
        column(3,
          box(width=NULL,title="Scatter plot",
              selectInput("scatter_var1","Select the numerical variable on x-axis",
                          c("Age"="age","Resting Blood Pressure"="trestbps",
                            "Cholestrol"="chol","Max. Heart Rate"="thalach",
                            "ST depression induced"="oldpeak"),selected="age"),
              selectInput("scatter_var2","Select the numerical variable on y-axis",
                          c("Age"="age","Resting Blood Pressure"="trestbps",
                            "Cholestrol"="chol","Max. Heart Rate"="thalach",
                            "ST depression induced"="oldpeak"),selected="trestbps"),
              selectInput("scatter_var3","Select by categorical variable for color",
                          c("Gender"="sex",
                            "Chest Pain type"="cp",
                            "Fasting blood sugar"="fbs","Resting ECG"="restecg",
                            "Blood disorder(thalassemia)"="thal",
                            "Execrcise induced Angina"="exang",
                            "Major vessels colored by flourosopy"="ca"),selected="sex"),
              sliderInput("slider","Size of Points on Graph",
                          min=2,max=5,value =3,step=0.5)
          )
        ),
        column(9,
          plotOutput("scatter")
        )
      ),
      fluidRow(
        column(3,
          box(width=NULL,title="Analysis of Categorical vairables for target",
              selectInput("bar_var","Select the categorical variable",
                          c("Gender"="sex",
                            "Chest Pain type"="cp",
                            "Fasting blood sugar"="fbs",
                            "Resting ECG"="restecg",
                            "Blood disorder(thalassemia)"="thal",
                            "Execrcise induced Angina"="exang",
                            "Major vessels colored by flourosopy"="ca"),selected="cp")
          )
        ),
        column(9,
          plotOutput("Bar_taget")
        )
      ),
      fluidRow(
        column(1
        ),
        column(10,align = "center",
               box(width=NULL,height=45,title="Correlation plots",background="red")
        ),
        column(1
        )
      ),
      fluidRow(
        column(1
        ),
        column(10,align = "center",
               plotOutput("corrplot")
        ),
        column(1
        )
      )
    ), #tabItem
    tabItem(tabName = "model",
      tabsetPanel(
        tabPanel("Modeling Info",
          fluidRow(
            column(12,
              box(width=NULL,title="Generalized Linear Model: Binary Logistic Regression",
                  status="danger",solidHeader = TRUE,
                h4("This type of statistical model (also known as logit model) is
                often used for classification.Logistic regression estimates the 
                probability of an event occurring based on a given dataset of 
                   independent variables. 
                   The dependent variable is bounded between 0 and 1."),
                h4("In logistic regression, a logit transformation is applied on 
                the odds—that is, the probability of success divided by the 
                probability of failure. This is also commonly known as the log 
                odds, or the natural logarithm of odds, and this logistic function
                   is represented by the following formulas:"),
                withMathJax(),
                helpText('$$logit(p) = log(\\frac{p}{1-p}) = \\beta_0 + \\beta_1 \\cdot x_1 + 
                         \\beta_2 \\cdot x_2 + ... + \\beta_k \\cdot x_k$$'),
                h5("Here the ",tags$b("logit"),
                " function is called the ",
                tags$b("link"),
                " function and ",
                tags$b("p = average number of successes at a given x")),
                h4("This method tests different values of beta through multiple 
                   iterations to optimize for the best fit of log odds. 
                   For binary classification, a probability less than .5 will 
                   predict 0 while a probability greater than 0 will predict 1"),
                h4(tags$b("Advantages:"),
                   tags$br(),
                   "Easy to implement, interpret, and very efficient to train.",
                   tags$br(),
                   "Good accuracy for many simple data sets and it performs well when 
                   the dataset is linearly separable.",
                   tags$br(),
                   "It can easily extend to multiple classes(multinomial regression) and 
                   a natural probabilistic view of class predictions."),
                h4(tags$b("Drawbacks:"),
                   tags$br(),
                   "If the number of observations is lesser than the number 
                   of features, it may lead to overfitting",
                   tags$br(),
                   "Non-linear problems can’t be solved with logistic regression 
                   because it has a linear decision surface",
                   tags$br(),
                   "It is tough to obtain complex relationships using logistic 
                   regression")
              )
            )
          ),
          fluidRow(
            column(12,
              box(width=NULL,title="Classification Tree",
                  status="danger",solidHeader = TRUE,
                  h4("A classification tree is an algorithm where the target 
                  variable is fixed or categorical. The algorithm is then used to
                  identify the “class” within which a target variable would most
                   likely fall.A Classification tree is built through a process 
                   known as binary recursive partitioning. This is an iterative 
                   process of splitting the data into partitions, and then 
                     splitting it up further on each of the branches."),
                  HTML("<div style='height: 300px;'>"),
                  imageOutput("tree"),
                  HTML("</div>"),
                  h4(tags$b("Advantages:"),
                     tags$br(),
                     "Simple to understand and easy to interpret output.",
                     tags$br(),
                     "Predictors don't need to be scaled.",
                     tags$br(),
                     "Built in variable selection."),
                  h4(tags$b("Drawbacks:"),
                     tags$br(),
                     "Small changes in data can vastly change tree.",
                     tags$br(),
                     "Greedy algorithm necessary (no optimal algorithm).",
                     tags$br(),
                     "Need to prune (usually).")
                  
              )
            )
          ),
          fluidRow(
            column(12,
              box(width=NULL,title="Random Forest Model",
                  status="danger",solidHeader = TRUE,
                  h4("Random forest, like its name implies, consists of a large 
                     number of individual decision trees (in this case - 
                     classification tree) that operate as an ensemble. Each 
                     individual tree in the random forest spits out a class 
                     prediction and the class with the most votes 
                     becomes our model’s prediction (see figure below)."),
                  HTML("<div style='height: 300px;'>"),
                  imageOutput("rf"),
                  HTML("</div>"),
                  h4(tags$b("Advantages:"),
                     tags$br(),
                     "It reduces overfitting in decision trees and helps to improve the accuracy.",
                     tags$br(),
                     "It automates missing values present in the data.",
                     tags$br(),
                     "Normalising of data is not required as it uses a rule-based approach."),
                  h4(tags$b("Drawbacks:"),
                     tags$br(),
                     "It requires much computational power as well as resources 
                     as it builds numerous trees to combine their outputs.",
                     tags$br(),
                     "It also requires much time for training as it combines 
                     a lot of decision trees to determine the class.",
                     tags$br(),
                     "Due to the ensemble of decision trees, it also 
                     suffers interpretability.")
              )
            )
          )
        ),
        tabPanel("Model Fitting",
          fluidRow(
            column(4,
              box(width=NULL,title="Generalized Linear Model: Binary Logistic Regression",
              status="danger",solidHeader = TRUE,
              checkboxGroupInput("train_var1","Select predictor variables:",
                                 c("Age (age)"="age","Sex (sex)"="sex",
                                   "Chest pain type (cp)"="cp",
                                   "Resting blood pressure (trestbps)"="trestbps",
                                   "Cholestrol (chol)"="chol",
                                   "Fasting blood sugar (fbs)"="fbs",
                                   "Resting ECG (restecg)"="restecg",
                                   "Max. heart rate (thalach)"="thalach",
                                   "Exercise induced angina (exang)"="exang",
                                   "ST depression induced (oldpeak)"="oldpeak",
                                   "Slope (slope)"="slope",
                                   "Blood disorder (thal)"="thal"),
                                 selected=c("age","sex","chol","fbs","thalach","cp",
                                            "trestbps","restecg","exang","oldpeak",
                                            "slope","thal"))
              )
            ),
            column(4,
                   box(width=NULL,title="Classification Tree",
                       status="danger",solidHeader = TRUE,
                       checkboxGroupInput("train_var2","Select predictor variables:",
                                          c("Age (age)"="age","Sex (sex)"="sex",
                                            "Chest pain type (cp)"="cp",
                                            "Resting blood pressure (trestbps)"="trestbps",
                                            "Cholestrol (chol)"="chol",
                                            "Fasting blood sugar (fbs)"="fbs",
                                            "Resting ECG (restecg)"="restecg",
                                            "Max. heart rate (thalach)"="thalach",
                                            "Exercise induced angina (exang)"="exang",
                                            "ST depression induced (oldpeak)"="oldpeak",
                                            "Slope (slope)"="slope",
                                            "Blood disorder (thal)"="thal"),
                                          selected=c("age","sex","chol","fbs","thalach","cp",
                                                     "trestbps","restecg","exang","oldpeak",
                                                     "slope","thal")),
                       sliderInput("max_depth","Select the max depth of the tree",
                                   min=2,max=10,value=6,step=1)
                   )
            ),
            column(4,
                   box(width=NULL,title="Random Forest Model",
                       status="danger",solidHeader = TRUE,
                       checkboxGroupInput("train_var3","Select predictor variables:",
                                          c("Age (age)"="age","Sex (sex)"="sex",
                                            "Chest pain type (cp)"="cp",
                                            "Resting blood pressure (trestbps)"="trestbps",
                                            "Cholestrol (chol)"="chol",
                                            "Fasting blood sugar (fbs)"="fbs",
                                            "Resting ECG (restecg)"="restecg",
                                            "Max. heart rate (thalach)"="thalach",
                                            "Exercise induced angina (exang)"="exang",
                                            "ST depression induced (oldpeak)"="oldpeak",
                                            "Slope (slope)"="slope",
                                            "Blood disorder (thal)"="thal"),
                                          selected=c("age","sex","chol","fbs","thalach","cp",
                                                     "trestbps","restecg","exang","oldpeak",
                                                     "slope","thal")),
                       sliderInput("mtry","Select the  number of variables to randomly sample as candidates at each split",
                                   min=2,max=10,value=5,step=1)
                   )
            )
          ),
          fluidRow(
            column(2),
            column(8,align = "center",
                   box(width=NULL,
                       h4("For each of the models select the predictor variables 
                          and other model settings above.Use the button below to 
                          train all the models and perform predictions on test data."),
                       #h5("NOTE: In case you need to change model parameters, follow these steps:"),
                       #h5("1.Uncheck the box below"),
                       #h5("2.Change model parameters/ predictor variables"),
                       #h5("3.Check the box below and wait till you see 'Training Complete' message"),
                       actionButton(inputId="model_train",label="Train models and Predict")
                       #textOutput("model_fits")
                    )
            ),
            column(2)
          ),
          fluidRow(
            column(4,
              box(width=NULL,title="Generalized Linear Model: Binary Logistic Regression",
                  status="danger",solidHeader = TRUE,
                  h5(tags$b("Training accuracy:")),
                  verbatimTextOutput("train_stats_lg"),
                  h5(tags$b("Summary:")),
                  verbatimTextOutput("train_stats_lg_summary")
              )
            ),
            column(4,
              box(width=NULL,title="Classification Tree",
                  status="danger",solidHeader = TRUE,
                  h5(tags$b("Training accuracy:")),
                  verbatimTextOutput("train_stats_tree"),
                  h5(tags$b("Tree:")),
                  plotOutput("train_stats_tree_summary")
                       
              )
            ),
            column(4,
              box(width=NULL,title="Random Forest Model",
                  status="danger",solidHeader = TRUE,
                  h5(tags$b("Training accuracy:")),
                  verbatimTextOutput("train_stats_rf"),
                  h5(tags$b("Variable Importance Plot:")),
                  plotOutput("train_stats_rf_summary")
              )
            )
          ),
          fluidRow(
            column(4,
                   box(width=NULL,status="danger",
                       h5(tags$b("Testing accuracy:")),
                       verbatimTextOutput("test_stats_lg"),
                       h5(tags$b("Confusion Matrix:")),
                       verbatimTextOutput("test_cf_lg")
                   )
            ),
            column(4,
                   box(width=NULL,status="danger",
                       h5(tags$b("Testing accuracy:")),
                       verbatimTextOutput("test_stats_tree"),
                       h5(tags$b("Confusion Matrix:")),
                       verbatimTextOutput("test_cf_tree")
                   )
            ),
            column(4,
                   box(width=NULL,status="danger",
                       h5(tags$b("Testing accuracy:")),
                       verbatimTextOutput("test_stats_rf"),
                       h5(tags$b("Confusion Matrix:")),
                       verbatimTextOutput("test_cf_rf")
                   )
            )
          )
        ),
        tabPanel("Prediction",
          fluidRow(
            column(2),
            column(8,
              box(width=NULL,
                  selectInput("model_input","Select the model you want to use 
                              for prediction",
                              c("Binary Logistic Regression"="lg",
                                "Classification Tree"="tree",
                                "Random Forest"="rf"),selected="lg")
              )     
            ),
            column(2)
          ),
          fluidRow(
            column(3),
            column(6,
              box(width=NULL,status="danger",align="center",
                  h4("NOTE"),
                  h5("In the Model Fitting tab (previous tab), please train the 
                     selected model on all the variables before proceeding with Prediction")
              )
            ),
            column(3)
          ),
          fluidRow(
            column(1),
            column(5,
              box(width=NULL,title="Select the values of predictors",
                  numericInput("p_age","Age",min=29,max=77,value=50,step=2,
                               width=300),
                  selectInput("p_sex","Sex",c("Male"=1,"Female"=0),selected=1,
                              width=300),
                  selectInput("p_cp","Chest pain type",
                              c("Typical Angina"=0,"Atypical Angina"=1,
                                "Non-Anginal"=2,"Aysmptomatic"=3),selected=0,
                              width=300),
                  numericInput("p_trestbps","Resting Blood pressure",
                               min=94,max=200,value=130,step=2,width=300),
                  numericInput("p_chol","Cholestrol level",
                               min=126,max=564,value=200,step=10,width=300),
                  selectInput("p_fbs","Fasting blood sugar greater than 120 mg/dl",
                              c("Yes"=1,"No"=0),selected=0,width=300),
                  selectInput("p_restecg","Resting ECG",
                              c("Normal"=0,"Having ST-T wave abnormality"=1,
                                "Showing left ventricular hypertrophy"=2),
                              selected=0,width=300),
                  numericInput("p_thalach","Max. Heart rate",
                               min=71,max=202,value=90,step=2,width=300),
                  selectInput("p_exang","Exercise Induced Angina",
                              c("Yes"=1,"No"=0),selected=0,width=300),
                  numericInput("p_oldpeak","ST depression induced by exercise 
                               relative to rest",
                               min=0,max=6.2,value=4,step=0.1,width=300),
                  selectInput("p_slope","Slope of the peak exercise ST segment",
                              c("0"=0,"1"=1,"2"=2),selected=1,width=300),
                  selectInput("p_thal","Blood disorder(thalassemia)",
                              c("Normal"=1,"Fixed defect"=2,"Reversable defect"=3),
                              selected=1,width=300),
              )
            ),
            column(5,
              box(width=NULL,title="Prediction",solidHeader=TRUE,
                  status="danger",align="center",
                  textOutput("final_prediction"))
            ),
            column(1)
          ),
        )
      )
    ), #tabItem
    tabItem(tabName = "data",
      fluidRow(
        column(3),
        column(6,align = "center",
               box(width=NULL,height=45,background="red",
                   title="Get Dataset"
               )
        ),
        column(3)
      ),
      fluidRow(
        column(2),
        column(8,
          box(width=NULL,height=150,align="center",
            h5("You can use this page to obtain the csv file for the dataset."),
            h5("1. Select the variables you want from the dataset."),
            h5("2. Select",tags$b("Offset:"),"The record number from which you want to get the data."),
            h5("3. Select",tags$b("Count:"),"The number of record you want in your dataset"),
            h5("4. Download CSV")
          )
        ),
        column(2),
      ),
      fluidRow(
        column(3,
          box(width=NULL,
              checkboxGroupInput("get_data","Select predictor variables:",
                                 c("Age (age)"="age","Sex (sex)"="sex",
                                   "Chest pain type (cp)"="cp",
                                   "Resting blood pressure (trestbps)"="trestbps",
                                   "Cholestrol (chol)"="chol",
                                   "Fasting blood sugar (fbs)"="fbs",
                                   "Resting ECG (restecg)"="restecg",
                                   "Max. heart rate (thalach)"="thalach",
                                   "Exercise induced angina (exang)"="exang",
                                   "ST depression induced (oldpeak)"="oldpeak",
                                   "Slope (slope)"="slope",
                                   "Blood disorder (thal)"="thal","Target"="target"),
                                 selected=c("age","sex","chol","fbs","thalach","cp",
                                            "trestbps","restecg","exang","oldpeak",
                                            "slope","thal","target")),
              numericInput("offset","Select offset value",
                           min=0,max=1020,value=0,step=1,width=300),
              numericInput("count","Select count value",
                           min=5,max=1025,value=100,step=1,width=300),
              downloadButton('download',"Download CSV")
          )
        ),
        column(9,
          box(width=NULL,
              dataTableOutput("data_csv"))
        )
      )
    ) #tabItem
   ) #tabItems
  ) #dashboardBody
) #dashboardPage