1. Programming

1. Data Visualisation
2. Flexdashboard
3. Neural Networks
4. R Programming

In this article, you learn how to make Automated Dashboard for Classification Neural Network in R. First you need to install the `rmarkdown` package into your R library. Assuming that you installed the `rmarkdown`, next you create a new `rmarkdown` script in R.

After this you type the following code in order to create a dashboard with rmarkdown and flexdashboard:

---
title: "Dashboard Classification Neural Network in R"
author: "Kristian Larsen"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
---

```{r setup, include=FALSE}
# Data management packages
library(tidyverse)
library(neuralnet)
library(GGally)
library(flexdashboard)
library(plotly)

# Read dataset
url <- 'http://archive.ics.uci.edu/ml/machine-learning-databases//haberman/haberman.data'

Hab_Data %
  na.omit() %>%
  mutate(Survival = ifelse(Survival == 2, 0, 1),
         Survival = factor(Survival))
scale01 <- function(x){
  (x - min(x)) / (max(x) - min(x))
}

Hab_Data %
  mutate(Age = scale01(Age), 
         Operation_Year = scale01(Operation_Year), 
         Number_Pos_Nodes = scale01(Number_Pos_Nodes), 
         Survival = as.numeric(Survival)-1)
Hab_Data %
  mutate(Survival = as.integer(Survival) - 1, 
         Survival = ifelse(Survival == 1, TRUE, FALSE))
```

Row {data-width=350}
-----------------------------------------------------------------------

### Chart A - Scattorplot matrix
```{r}
ggpairs(Hab_Data, title = "Scatterplot Matrix of the Features of the Haberman's Survival Data Set")
ggplotly(p = ggplot2::last_plot())
```

### Chart B - 1st Classification ANN
```{r}
set.seed(123)
Hab_NN1 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, 
                     data = Hab_Data, 
                     linear.output = FALSE, 
                     err.fct = 'ce', 
                     likelihood = TRUE)
plot(Hab_NN1, rep = 'best')
```

Row {data-width=650}
-----------------------------------------------------------------------

### Chart C - Classification Hyperparameters
```{r}
Hab_NN1_Train_Error <- Hab_NN1$result.matrix[1,1]
#paste("CE Error: ", round(Hab_NN1_Train_Error, 3)) 
Hab_NN1_AIC <- Hab_NN1$result.matrix[4,1]
#paste("AIC: ", round(Hab_NN1_AIC,3))
Hab_NN2_BIC <- Hab_NN1$result.matrix[5,1]
#paste("BIC: ", round(Hab_NN2_BIC, 3))
set.seed(123)
# 2-Hidden Layers, Layer-1 2-neurons, Layer-2, 1-neuron
Hab_NN2 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, 
                     data = Hab_Data, 
                     linear.output = FALSE, 
                     err.fct = 'ce', 
                     likelihood = 
                       TRUE, hidden = c(2,1))

# 2-Hidden Layers, Layer-1 2-neurons, Layer-2, 2-neurons
set.seed(123)
Hab_NN3 <- Hab_NN2 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, 
                                data = Hab_Data, 
                                linear.output = FALSE, 
                                err.fct = 'ce', 
                                likelihood = TRUE, 
                                hidden = c(2,2))

# 2-Hidden Layers, Layer-1 1-neuron, Layer-2, 2-neuron
set.seed(123)
Hab_NN4 <- Hab_NN2 <- neuralnet(Survival ~ Age + Operation_Year + Number_Pos_Nodes, 
                                data = Hab_Data, 
                                linear.output = FALSE, 
                                err.fct = 'ce', 
                                likelihood = TRUE, 
                                hidden = c(1,2))

# Bar plot of results
Class_NN_ICs %
  ggplot(aes(Network, Value, fill = Metric)) +
  geom_col(position = 'dodge')  +
  ggtitle("AIC, BIC, and Cross-Entropy Error of the Classification ANNs", "Note: ce Error displayed is 100 times its true value")
ggplotly(p = ggplot2::last_plot())
```

Screenshot:

The result of the above coding are published with RPubs here.

References

1. Using flexdashboard in R

1. Automated Dashboard for Credit Modelling with Decision trees and Random forests in R
2. How to Achieve Parallel Processing in Python Programming
3. Recommender System for Christmas in Python
4. Automated Dashboard visualizations with Time series visualizations in R
5. Automated Dashboard visualizations with distribution in R