# STEP 1. Install and Load the Required Packages ----
# The following packages should be installed and loaded before proceeding to the
# subsequent steps.

## readr ----
if (require("readr")) {
  require("readr")
} else {
  install.packages("readr", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}

## caret ----
if (require("caret")) {
  require("caret")
} else {
  install.packages("caret", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}

## e1071 ----
if (require("e1071")) {
  require("e1071")
} else {
  install.packages("e1071", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}

## factoextra ----
if (require("factoextra")) {
  require("factoextra")
} else {
  install.packages("factoextra", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}

## FactoMineR ----
if (require("FactoMineR")) {
  require("FactoMineR")
} else {
  install.packages("FactoMineR", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}

if (!is.element("NHANES", installed.packages()[, 1])) {
  install.packages("NHANES", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}
require("NHANES")

## dplyr ----
if (!is.element("dplyr", installed.packages()[, 1])) {
  install.packages("dplyr", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}
require("dplyr")

## naniar ----
# Documentation:
#   https://cran.r-project.org/package=naniar or
#   https://www.rdocumentation.org/packages/naniar/versions/1.0.0
if (!is.element("naniar", installed.packages()[, 1])) {
  install.packages("naniar", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}
require("naniar")

## ggplot2 ----
# We require the "ggplot2" package to create more appealing visualizations
if (!is.element("ggplot2", installed.packages()[, 1])) {
  install.packages("ggplot2", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}
require("ggplot2")

## MICE ----
# We use the MICE package to perform data imputation
if (!is.element("mice", installed.packages()[, 1])) {
  install.packages("mice", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}
require("mice")

## Amelia ----
if (!is.element("Amelia", installed.packages()[, 1])) {
  install.packages("Amelia", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}
require("Amelia")

library(readr)
heart <- read_csv(
  "../data/heart.csv",
  col_types = cols(
    age = col_double(),
    sex = col_factor(levels = c("0", "1")),
    cp = col_factor(levels = c("0", "1", "2", "3")),
    trestbps = col_double(),
    chol = col_double(),
    fbs = col_factor(levels = c("0", "1")),
    restecg = col_factor(levels = c("0", "1", "2")),
    thalach = col_double(),
    exang = col_factor(levels = c("0", "1")),
    oldpeak = col_double(),
    slope = col_factor(levels = c("0", "1", "2")),
    ca = col_double(),
    thal = col_factor(levels = c("0", "1", "2", "3")),
    target = col_factor(levels = c("neg", "pos"))
  )
)

#View(heart)

### STEP 3a. Pre#View the Loaded Datasets, Identify the Data Types  ----
# Dimensions refer to the number of observations (rows) and the number of
# attributes/variables/features (columns).
#Understanding data types is key for effective analysis.It helps choose suitable visualizations and algorithms,  
#and highlights the need for conversions between categorical and numerical data when necessary.


dim(heart)

sapply(heart, class)

# It is more sensible to count categorical variables (factors or dimensions)
# than numeric variables, e.g., counting the number of male and female
# participants instead of counting the frequency of each participant’s height.

heart_freq <- heart$target
cbind(frequency = table(heart_freq),
      percentage = prop.table(table(heart_freq)) * 100)

# We, therefore, must manually create a function that can calculate the mode.

heart_target_mode <- names(table(heart$target))[
  which(table(heart$target) == max(table(heart$target)))
]
print(heart_target_mode)

summary(heart)

# calculate the standard deviation of only columns that are numeric, thus
# leaving out the columns termed as “factors” (categorical) or those that have
# a string data type.

sapply(heart[, -c(2, 3, 6, 7, 9, 11, 13, 14)], sd)

#or
sapply(heart[, c(1, 4, 5, 8, 10, 12)], sd)

# The Kurtosis informs you of how often outliers occur in the results.
# There are different formulas for calculating kurtosis.
# Specifying “type = 2” allows us to use the 2nd formula which is the same
# kurtosis formula used in SPSS and SAS.

# In “type = 2” (used in SPSS and SAS):
# 1.    Kurtosis < 3 implies a low number of outliers
# 2.    Kurtosis = 3 implies a medium number of outliers
# 3.    Kurtosis > 3 implies a high number of outliers

if (!is.element("e1071", installed.packages()[, 1])) {
  install.packages("e1071", dependencies = TRUE)
}
require("e1071")

sapply(heart[, -c(2, 3, 6, 7, 9, 11, 13, 14)],  kurtosis, type = 2)

# The skewness informs you of the asymmetry of the distribution of results.
# Using “type = 2” can be interpreted as:

# 1.    Skewness between -0.4 and 0.4 (inclusive) implies that there is no skew
# in the distribution of results; the distribution of results is symmetrical;
# it is a normal distribution.
# 2.    Skewness above 0.4 implies a positive skew; a right-skewed distribution.
# 3.    Skewness below -0.4 implies a negative skew; a left-skewed distribution.

sapply(heart[, -c(2, 3, 6, 7, 9, 11, 13, 14)],  skewness, type = 2)

# Note that the covariance and the correlation are computed for numeric values
# only, not categorical values.

heart_cov <- cov(heart[, -c(2, 3, 6, 7, 9, 11, 13, 14)])
#View(heart_cov)

heart_cor <- cor(heart[, -c(2, 3, 6, 7, 9, 11, 13, 14)])
#View(heart_cor)

# One-Way ANOVA can be used to test the effect of the 3 types of fertilizer on
# crop yield whereas,
# Two-Way ANOVA can be used to test the effect of the 3 types of fertilizer and
# the 2 types of planting density on crop yield.
heart_one_way_anova <- aov(trestbps ~ age, data = heart)
summary(heart_one_way_anova)

#The ANOVA rejects the null hypothesis,The ANOVA indicates a significant difference in resting blood pressure among age groups 
#(F(1, 1023) = 81.16, p < 2e-16), highlighting age as a key factor 
#in determining blood pressure. 
#This aligns with cardiovascular knowledge, correlating increased age with a higher risk of cardiovascular disease.

heart_two_way_anova <- aov(trestbps ~ exang + ca, # nolint
                                           data = heart)
summary(heart_two_way_anova)

#both variables, exercise-induced angina and the number of major vessels, are associated with statistically significant differences in 
#resting blood pressure.

# Histograms help in determining whether an attribute has a Gaussian
# distribution. They can also be used to identify the presence of outliers.



par(mfrow = c(1, 3))

for (i in c(1, 4, 5)) {
  heart_variable <- as.numeric(unlist(heart[, i]))
  hist(heart_variable, main = names(heart)[i])
}

heart_health_variable  <- as.numeric( unlist(heart[, 8]))
hist(heart_health_variable , main = names(heart)[8])

heart_health_variable  <- as.numeric( unlist(heart [, 10]))
hist(heart_health_variable , main = names(heart)[10])

heart_health_variable  <- as.numeric( unlist(heart [, 12]))
hist(heart_health_variable , main = names(heart)[12])

# Box and whisker plots are useful in understanding the distribution of data.

par(mfrow = c(1, 3))
for (i in c(1, 4, 5)) {
  boxplot(heart[, i], main = names(heart)[i])
}

boxplot(heart[, 8], main = names(heart)[8])
boxplot(heart[, 10], main = names(heart)[10])
boxplot(heart[, 12], main = names(heart)[12])

# Categorical attributes (factors) can also be visualized. This is done using a
# bar chart to give an idea of the proportion of instances that belong to each
# category.

barplot(table(heart[, 2]), main = names(heart)[2])

barplot(table(heart[, 3]), main = names(heart)[3])

barplot(table(heart[, 6]), main = names(heart)[6])

barplot(table(heart[, 7]), main = names(heart)[7])

barplot(table(heart[, 9]), main = names(heart)[9])

barplot(table(heart[, 11]), main = names(heart)[11])

barplot(table(heart[, 13]), main = names(heart)[13])

barplot(table(heart[, 14]), main = names(heart)[14])

# Execute the following to create a map to identify the missing data in each
# dataset:
if (!is.element("Amelia", installed.packages()[, 1])) {
  install.packages("Amelia", dependencies = TRUE)
}
require("Amelia")
#comment
missmap(heart, col = c("red", "grey"), legend = TRUE)

# Correlation plots can be used to get an idea of which attributes change
# together. The function “corrplot()” found in the package “corrplot” is
# required to perform this. The larger the dot in the correlation plot, the
# larger the correlation. Blue represents a positive correlation whereas red
# represents a negative correlation.

if (!is.element("corrplot", installed.packages()[, 1])) {
  install.packages("corrplot", dependencies = TRUE)
}
require("corrplot")

corrplot(cor(heart[, -c(2, 3, 6, 7, 9, 11, 13, 14)]), method = "circle")

#heart <- heart[, -which(names(heart) == "target_numeric")]

# Alternatively, the 'ggcorrplot::ggcorrplot()' function can be used to plot a
# more visually appealing plot.
# The code below shows how to install a package in R:
if (!is.element("ggcorrplot", installed.packages()[, 1])) {
  install.packages("ggcorrplot", dependencies = TRUE)
}
require("ggcorrplot")

ggcorrplot(cor(heart[, -c(2, 3, 6, 7, 9, 11, 13, 14)]))

pairs(heart)

# Alternatively, the ggcorrplot package can be used to make the plots more
# appealing:
ggplot(heart,
       aes(x = age, y = sex, shape = target, color = target)) +
  geom_point() +
  geom_smooth(method = lm)

### Subset of rows ----
# We then select 500 random observations to be included in the dataset
rand_ind <- sample(seq_len(nrow(heart)), 500)
heart <- heart[rand_ind, ]

# Are there missing values in the dataset?
any_na(heart)

# How many?
n_miss(heart)

# What is the percentage of missing data in the entire dataset?
prop_miss(heart)

# How many missing values does each variable have?
heart %>% is.na() %>% colSums()

# What is the number and percentage of missing values grouped by
# each variable?
miss_var_summary(heart)

# What is the number and percentage of missing values grouped by
# each observation?
miss_case_summary(heart)

# Which variables contain the most missing values?
#gg_miss_var(heart)

# Where are missing values located (the shaded regions in the plot)?
#vis_miss(heart) + theme(axis.text.x = element_text(angle = 80))

# Which combinations of variables are missing together?
#gg_miss_upset(heart)

# Create a heatmap of "missingness" broken down by "target"
# First, confirm that the "target" variable is a categorical variable
is.factor(heart$target)

# Second, create the visualization
#gg_miss_fct(heart, fct = target)

# We can also create a heatmap of "missingness" broken down by "exang"
# First, confirm that the "exang" variable is a categorical variable

is.factor(heart$exang)

# Second, create the visualization
#gg_miss_fct(heart, fct = exang)

# Scaling Data Transformation Purpose:
# The scaling data transformation is performed to standardize or normalize
# the numeric features in a dataset. It ensures that different features
# with varying scales are brought to a common scale, preventing one feature
# from dominating the others during model training.
#
# Scaling is essential for algorithms that are sensitive to the magnitude
# of input features, such as distance-based methods (e.g., k-Nearest Neighbors)
# or gradient descent-based optimization algorithms (e.g., Support Vector Machines).
#
# By scaling the data, we make the features comparable and contribute equally
# to the model's learning process, improving the stability and performance of
# machine learning models.


# Summary of each variable
summary(heart)

# BEFORE

heart_health_variable <- as.numeric( unlist(heart [, 1]))
hist(heart_health_variable , main = names(heart_health_variable )[1])

heart_health_variable  <- as.numeric( unlist(heart [, 4]))
hist(heart_health_variable , main = names(heart)[4])

heart_health_variable  <- as.numeric( unlist(heart [, 5]))
hist(heart_health_variable , main = names(heart)[5])

heart_health_variable  <- as.numeric( unlist(heart [, 8]))
hist(heart_health_variable , main = names(heart)[8])

heart_health_variable <- as.numeric( unlist(heart [, 10]))
hist(heart_health_variable , main = names(heart)[10])

heart_health_variable  <- as.numeric( unlist(heart [, 12]))
hist(heart_health_variable , main = names(heart)[12])

model_of_the_transform <- preProcess(heart, method = c("scale"))
print(model_of_the_transform)

heart_scale_transform <- predict(model_of_the_transform,
                                               heart)


# AFTER 
#1, 4, 5, 8, 10, 12

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 1]))
hist(heart_health_variable , main = names(heart_scale_transform)[1])

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 4]))
hist(heart_health_variable , main = names(heart_scale_transform)[4])

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 5]))
hist(heart_health_variable , main = names(heart_scale_transform)[5])

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 8]))
hist(heart_health_variable , main = names(heart_scale_transform)[8])

heart_health_variable <- as.numeric( unlist(heart_scale_transform [, 10]))
hist(heart_health_variable , main = names(heart_scale_transform)[10])

heart_health_variable <- as.numeric( unlist(heart_scale_transform [, 12]))
hist(heart_health_variable , main = names(heart_scale_transform)[12])

View(heart_scale_transform)

# Centering Data Transformation Purpose:
# Shifts numeric features by subtracting their mean,
# ensuring a centered distribution around zero.
# Useful for algorithms sensitive to variable means,
# promoting better model interpretability.



# Summary of each variable
summary(heart)

# BEFORE

heart_health_variable <- as.numeric( unlist(heart_scale_transform [, 1]))
boxplot(heart_health_variable , main = names(heart_scale_transform )[1])

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 4]))
boxplot(heart_health_variable , main = names(heart_scale_transform)[4])

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 5]))
boxplot(heart_health_variable , main = names(heart_scale_transform)[5])

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 8]))
boxplot(heart_health_variable , main = names(heart_scale_transform)[8])

heart_health_variable <- as.numeric( unlist(heart_scale_transform [, 10]))
boxplot(heart_health_variable , main = names(heart_scale_transform)[10])

heart_health_variable  <- as.numeric( unlist(heart_scale_transform [, 12]))
boxplot(heart_health_variable , main = names(heart_scale_transform)[12])

model_of_the_transform <- preProcess(heart_scale_transform, method = c("center"))
print(model_of_the_transform)

heart_center_transform <- predict(model_of_the_transform,
                                  heart_scale_transform)


# AFTER 
#1, 4, 5, 8, 10, 12

heart_scale_transform  <- as.numeric( unlist(heart_center_transform [, 1]))
boxplot(heart_health_variable , main = names(heart_center_transform)[1])

heart_scale_transform <- as.numeric( unlist(heart_center_transform [, 4]))
boxplot(heart_health_variable , main = names(heart_center_transform)[4])

heart_scale_transform  <- as.numeric( unlist(heart_center_transform [, 5]))
boxplot(heart_health_variable , main = names(heart_center_transform)[5])

heart_scale_transform  <- as.numeric( unlist(heart_center_transform [, 8]))
boxplot(heart_health_variable , main = names(heart_center_transform)[8])

heart_scale_transform <- as.numeric( unlist(heart_center_transform [, 10]))
boxplot(heart_health_variable , main = names(heart_center_transform)[10])

heart_scale_transform <- as.numeric( unlist(heart_center_transform [, 12]))
boxplot(heart_health_variable , main = names(heart_center_transform)[12])

View(heart_center_transform)

# BEFORE
#If you've already scaled and centered your data, you have made it comparable and adjusted for differences in means.
#Standardization is typically done to bring variables to a standard normal distribution (mean of 0 and standard deviation 
#of 1).
summary(heart_center_transform)

sapply(heart_center_transform[, -c(2, 3, 6, 7, 9, 11, 13, 14)], sd)

model_of_the_transform <- preProcess(heart_center_transform,
                                     method = c("scale", "center"))
print(model_of_the_transform)

heart_standardize_transform <- predict(model_of_the_transform, # nolint
                                       heart_center_transform)

# AFTER
summary(heart_standardize_transform)

sapply(heart_standardize_transform[, -c(2, 3, 6, 7, 9, 11, 13, 14)], sd)

# BEFORE
summary(heart_standardize_transform)

#Calculate the skewness before the Box-Cox transform
sapply(heart_standardize_transform[, -c(2, 3, 6, 7, 9, 11, 13, 14)],  skewness, type = 2)

# **************************************************************************************************************
#  RESULTS
#  Age: The skewness is relatively small (-0.27). You might not need a Box-Cox transformation for age,
#  especially since the skewness is not highly pronounced.
  
#  trestbps (resting blood pressure): The skewness is moderate (0.78). Considering the moderate skewness,
#  you could experiment with a Box-Cox transformation to see if it improves the distribution.
  
#  chol (serum cholesterol): The skewness is high (1.49). A Box-Cox transformation is often beneficial for 
#  skewed variables, so you might consider applying it to cholesterol.
  
#  thalach (maximum heart rate achieved): The skewness is relatively small (-0.56). A Box-Cox transformation
#  might not be necessary for thalach, especially if the skewness is not a significant concern.
  
#  oldpeak (ST depression induced by exercise): The skewness is high (1.21). Given the high skewness, a Box-Cox 
#  transformation could be beneficial for oldpeak.
  
#  ca (number of major vessels colored by fluoroscopy): The skewness is high (1.35). Similar to cholesterol and 
#  oldpeak, a Box-Cox transformation might be considered for ca.
# **************************************************************************************************************

  
#Plot a histogram to view the skewness before the Box-Cox transform

heart_health_variable  <- as.numeric( unlist(heart_standardize_transform [, 1]))
hist(heart_health_variable , main = names(heart_standardize_transform)[1])

heart_health_variable  <- as.numeric( unlist(heart_standardize_transform [, 4]))
hist(heart_health_variable , main = names(heart_standardize_transform)[4])

heart_health_variable  <- as.numeric( unlist(heart_standardize_transform [, 5]))
hist(heart_health_variable , main = names(heart_standardize_transform)[5])

heart_health_variable  <- as.numeric( unlist(heart_standardize_transform [, 8]))
hist(heart_health_variable , main = names(heart_standardize_transform)[8])

heart_health_variable <- as.numeric( unlist(heart_standardize_transform [, 10]))
hist(heart_health_variable , main = names(heart_standardize_transform)[10])

heart_health_variable  <- as.numeric( unlist(heart_standardize_transform [, 12]))
hist(heart_health_variable , main = names(heart_standardize_transform)[12])



# Select the columns of interest
columns_of_interest <- c(12, 10, 5, 4)

# Apply Box-Cox transformation only to selected columns
model_of_the_transform <- preProcess(heart_standardize_transform[, columns_of_interest], method = c("BoxCox"))

# Print the transformation details
print(model_of_the_transform)

# Apply the transformation to the selected columns
heart_box_cox_transform <- predict(model_of_the_transform, heart_standardize_transform[, columns_of_interest])




# AFTER 
#1, 4, 5, 8, 10, 12

sapply(heart_box_cox_transform, skewness, type = 2)

# **************************************************************************************************************

#RESULT
#ca: Skewness is approximately 1.35. A positive skewness indicates that the distribution
#has a longer right tail.

#oldpeak: Skewness is approximately 1.21. Similar to "ca," the positive skewness
#suggests a longer right tail in the distribution.

#chol: Skewness is approximately 1.49. Again, positive skewness indicates a longer 
#right tail.

#trestbps: Skewness is approximately 0.78. The positive skewness suggests a longer 
#right tail, but it's less pronounced compared to the other variables.
# **************************************************************************************************************

heart_health_variable  <- as.numeric( unlist(heart_box_cox_transform[, 1]))
hist(heart_health_variable , main = names(heart_box_cox_transform)[1])

heart_health_variable  <- as.numeric( unlist(heart_box_cox_transform [, 2]))
hist(heart_health_variable , main = names(heart_box_cox_transform)[2])

heart_health_variable <- as.numeric( unlist(heart_box_cox_transform[, 3]))
hist(heart_health_variable , main = names(heart_box_cox_transform)[3])

heart_health_variable <- as.numeric( unlist(heart_box_cox_transform [, 4]))
hist(heart_health_variable , main = names(heart_box_cox_transform)[4])

### PCA for Dimensionality Reduction on the heart Dataset ----
# Combine original and transformed columns
heart_combined <- cbind(heart_standardize_transform[, c("age", "thalach")], heart_box_cox_transform)
summary(heart_combined )

model_of_the_transform <- preProcess(heart_combined, method =
                                       c("scale", "center", "pca"))

print(model_of_the_transform)

heart_pca_dr <- predict(model_of_the_transform, heart_combined)

summary(heart_pca_dr)

### PCA for Feature Extraction  ----


heart_pca_fe <- princomp(cor(heart_combined))
summary(heart_pca_fe)

# **************************************************************************************************************
#RESULTS
#the PCA results suggest that the first five principal components retain the majority of the information in your dataset. 
#The subsequent components (Comp.6 ) contribute very little or no additional information. I may consider using 
#the first five components for further analysis, as they represent the most important features in terms of explaining the 
#variance in the data. The negligible contribution of Comp.6  might indicate that these components are not providing 
#meaningful information for the analysis.
# **************************************************************************************************************



#### Scree Plot ----
# The cumulative proportion reaches 100% after Comp.5, indicating that the first five components capture 
#all the variance in the original data.
factoextra::fviz_eig(heart_pca_fe, addlabels = TRUE)

heart_pca_fe$loadings[, 1:5]

unique(rownames(heart_pca_fe$loadings[, 1:5]))

factoextra::fviz_cos2(heart_pca_fe, choice = "var", axes = 1:5)

factoextra::fviz_pca_var(heart_pca_fe, col.var = "cos2",
                         gradient.cols = c("red", "orange", "green"),
                         repel = TRUE)

# Convert relevant columns to factors
categorical_cols <- c("sex", "cp", "fbs", "restecg", "exang", "slope", "thal", "target")
heart_categorical <- heart
heart_categorical[categorical_cols] <- lapply(heart_categorical[categorical_cols], as.factor)

# Perform Multiple Correspondence Analysis (MCA)
heart_mca <- MCA(heart_categorical[, c(2, 3, 6, 7, 9, 11, 13, 14)])

# Visualize MCA results for individuals
# Summary of the MCA results
summary(heart_mca)

heart_numerical_combined_with_factors <- cbind( heart_combined, heart[, c(2, 3, 6, 7, 9, 11, 13, 14)])
summary(heart_numerical_combined_with_factors)

summary(heart_numerical_combined_with_factors )

model_of_the_transform <- preProcess(heart_numerical_combined_with_factors ,
                                     method = c("scale", "center", "ica"),
                                     n.comp = 6)
print(model_of_the_transform)

heart_numerical_combined_with_factors_ica_dr <- predict(model_of_the_transform, heart_numerical_combined_with_factors )

summary(heart_numerical_combined_with_factors_ica_dr)

# ************************************************************************************************************************************

#RESULTS
# Summary of the Transformed Dataset after Independent Component Analysis (ICA)

# Categorical Variables:
# - sex, cp, fbs, restecg, exang, slope, thal, target are categorical variables with counts and percentages for each level.

# Independent Component Analysis (ICA) Components:
# - ICA1 to ICA6 represent the Independent Component Analysis components.
# - For each component, descriptive statistics are provided:
#   - Minimum, 1st Quartile, Median, Mean, 3rd Quartile, Maximum.

# Example for ICA1:
#   ICA1: Min. -4.3196, 1st Qu. -0.3524, Median 0.1066, Mean 0.0000, 3rd Qu. 0.5823, Max. 2.4614.

# These statistics offer insights into the distribution and characteristics of the transformed variables after applying ICA.
# ************************************************************************************************************************************

heart_preprocessed <- heart_numerical_combined_with_factors

str(heart_preprocessed)

# Define a 75:25 train:test data split of the dataset.
# That is, 75% of the original data will be used to train the model and
# 25% of the original data will be used to test the model.
train_index <- createDataPartition(heart_preprocessed$target,
                                   p = 0.75,
                                   list = FALSE)
heart_preprocessed_train <- heart_preprocessed[train_index, ]
heart_preprocessed_test <- heart_preprocessed[-train_index, ]

# Train a Naive Bayes model using the caret package.
heart_model_nb_caret <- # nolint
  caret::train(target ~ ., data =
                 heart_preprocessed_train[, c("age", "sex", "cp", "trestbps" , "chol" , "fbs" ,
                           "restecg" , "thalach" , "exang",
                           "oldpeak", "slope", "ca",  "thal", "target")],
               method = "naive_bayes")

### 3.b. Test the trained caret Naive Bayes model using the testing dataset ----
predictions_nb_caret <-
  predict(heart_model_nb_caret,
          heart_preprocessed_test[, c("age", "sex", "cp", "trestbps" , "chol" , "fbs" ,
                         "restecg" , "thalach" , "exang",
                         "oldpeak", "slope", "ca",  "thal")])

heart_freq <- heart_preprocessed_train$target
cbind(frequency =
        table(heart_freq),
      percentage = prop.table(table(heart_freq)) * 100)

# The glm function in this code is used to train a Generalized Linear Model
# for predicting the target variable based on the provided features in the training dataset.
# The subsequent printing of model details and performance metrics allows for an assessment of its effectiveness.
# Set up train control with classProbs = TRUE
train_control <- trainControl(method = "repeatedcv", number = 10, repeats = 3, classProbs = TRUE)


set.seed(7)
heart_model_glm <-
  train(target ~ ., data = heart_preprocessed_train, method = "glm",
        metric = "Accuracy", trControl = train_control)


print(heart_model_glm)

# Set the seed for reproducibility
set.seed(7)

# Train a Linear Discriminant Analysis (LDA) model using the specified training control
heart_model_lda <- train(target ~ ., data = heart_preprocessed_train,
                         method = "lda", trControl = train_control)

# Set the seed for reproducibility
set.seed(7)

# Train a Classification and Regression Trees (CART) model using the specified training control
heart_model_cart <- train(target ~ ., data = heart_preprocessed_train,
                          method = "rpart", trControl = train_control)

# Set the seed for reproducibility
set.seed(7)

# Train a k-Nearest Neighbors (knn) model using the specified training control
heart_model_knn <- train(target ~ ., data = heart_preprocessed_train,
                         method = "knn", trControl = train_control)

# Set the seed for reproducibility
set.seed(7)

# Train a Random Forest (rf) model using the specified training control
heart_model_rf <- train(target ~ ., data = heart_preprocessed_train,
                        method = "rf", trControl = train_control)

# We then create a list of the model results and pass the list as an argument
# to the `resamples` function.

results <- resamples(list(LDA = heart_model_lda, CART = heart_model_cart,
                          KNN = heart_model_knn, GLM = heart_model_glm,
                          RF = heart_model_rf))

# Install and load the pROC package 
# install.packages("pROC")
library(pROC)

# Create an empty plot
plot(0, 0, type = "n", main = "ROC Curve", xlab = "False Positive Rate", ylab = "True Positive Rate")

predictions_lda <- predict(heart_model_lda, heart_preprocessed_test[, 1:13],type = "prob")
predictions_cart <- predict(heart_model_cart, heart_preprocessed_test[, 1:13],type = "prob")
predictions_knn <- predict(heart_model_knn, heart_preprocessed_test[, 1:13],type = "prob")
predictions_glm <- predict(heart_model_glm, heart_preprocessed_test[, 1:13],type = "prob")
predictions_rf <- predict(heart_model_rf, heart_preprocessed_test[, 1:13],type = "prob")



# Add ROC curves for each model to the plot
roc_curve_lda <- roc(heart_preprocessed_test$target, predictions_lda$neg)

roc_curve_cart <- roc(heart_preprocessed_test$target, predictions_cart$neg)

roc_curve_knn <- roc(heart_preprocessed_test$target, predictions_knn$neg)

roc_curve_glm <- roc(heart_preprocessed_test$target, predictions_glm$neg)

roc_curve_rf <- roc(heart_preprocessed_test$target, predictions_rf$neg)

# Plot each ROC curve with a different color
plot(roc_curve_lda, main = "ROC curve for LDA", print.auc = TRUE,
      print.auc.x = 0.6, print.auc.y = 0.6, col = "red", lwd = 2.5)

plot(roc_curve_cart, main = "ROC curve for CART", print.auc = TRUE,
      print.auc.x = 0.6, print.auc.y = 0.6, col = "blue", lwd = 2.5)

plot(roc_curve_knn, main = "ROC curve for KNN", print.auc = TRUE,
      print.auc.x = 0.6, print.auc.y = 0.6, col = "green", lwd = 2.5)

plot(roc_curve_glm,main = "ROC curve for GLM", print.auc = TRUE,
     print.auc.x = 0.6, print.auc.y = 0.6, col = "purple")

plot(roc_curve_rf, main = "ROC curve for RF", print.auc = TRUE,
      print.auc.x = 0.6, print.auc.y = 0.6, col = "orange", lwd = 2.5)

## 1. Table Summary ----
# This is the simplest comparison. It creates a table with one model per row
# and its corresponding evaluation metrics displayed per column.

summary(results)

## 2. Box and Whisker Plot ----
# This is useful for visually observing the spread of the estimated accuracies
# for different algorithms and how they relate.

scales <- list(x = list(relation = "free"), y = list(relation = "free"))
bwplot(results, scales = scales)

## 3. Dot Plots ----
# They show both the mean estimated accuracy as well as the 95% confidence
# interval (e.g. the range in which 95% of observed scores fell).

scales <- list(x = list(relation = "free"), y = list(relation = "free"))
dotplot(results, scales = scales)

## 4. Scatter Plot Matrix ----
# This is useful when considering whether the predictions from two
# different algorithms are correlated. If weakly correlated, then they are good
# candidates for being combined in an ensemble prediction.

splom(results)

## 5. Pairwise xyPlots ----
# You can zoom in on one pairwise comparison of the accuracy of trial-folds for
# two models using an xyplot.

# xyplot plots to compare models
xyplot(results, models = c("LDA", "RF"))

# or
# xyplot plots to compare models
xyplot(results, models = c("GLM", "CART"))

# Explanation:
# - Upper diagonal: Estimates of the difference in accuracy and kappa values between models.
# - Lower diagonal: P-values for testing the hypothesis that the difference is zero.

# Accuracy:
# - Positive values indicate higher accuracy in the row model compared to the column model.
# - Negative values indicate lower accuracy.

# Kappa:
# - Positive values indicate higher kappa values in the row model compared to the column model.
# - Negative values indicate lower kappa values.

# P-values:
# - Assess the statistical significance of the observed differences.
# - Values below the significance level (e.g., 0.05) suggest a significant difference.
# - Bonferroni adjustment is applied for multiple comparisons.
diffs <- diff(results)

summary(diffs)

# Define train control settings for a random search
train_control <- trainControl(method = "repeatedcv", number = 10, repeats = 3, search = "random")

# Set a seed for reproducibility
set.seed(7)

# Perform a random search for Random Forest hyperparameters
heart_model_random_search_rf <- train(target ~ ., data = heart_preprocessed_train, method = "rf",
                                      metric = "Accuracy",
                                      tuneLength = 12,  # Search 12 options for the value of mtry
                                      trControl = train_control)

# Display the details of the random search model
print(heart_model_random_search_rf)

# Plot the results of the random search
plot(heart_model_random_search_rf)

## 2.a. Bagged CART ----
set.seed(7)
heart_model_bagged_cart <- train(target ~ ., data = heart_preprocessed_train, method = "treebag",
                               metric = "Accuracy",
                               trControl = train_control)

## 2.b. Random Forest ----
set.seed(7)
heart_model_rf <- train(target ~ ., data = heart_preprocessed_train, method = "rf",
                        metric = "Accuracy", trControl = train_control)


# Summarize results
bagging_results <-
  resamples(list("Bagged Decision Tree" = heart_model_bagged_cart,
                 "Random Forest" = heart_model_rf))

summary(bagging_results)

dotplot(bagging_results)

# We then print the details of the model that has been created
print(heart_model_rf$finalModel)

# We can test the model
set.seed(9)
predictions <- predict(heart_model_rf, newdata = heart_preprocessed_test)
confusionMatrix(predictions, heart_preprocessed_test$target)

# Saving a model into a file allows you to load it later and use it to make
# predictions. Saved models can be loaded by calling the `readRDS()` function

saveRDS(heart_model_rf, "../models/saved_heart_model_rf.rds")
# The saved model can then be loaded later as follows:
loaded_heart_model_rf <- readRDS("../models/saved_heart_model_rf.rds")
print(loaded_heart_model_rf)

predictions_with_loaded_model <-
  predict(loaded_heart_model_rf, newdata = heart_preprocessed_test)
confusionMatrix(predictions_with_loaded_model, heart_preprocessed_test$target)

# Plumber requires functions, an example of the syntax for creating a function
# in R is:

name_of_function <- function(arg) {
  # Do something with the argument called `arg`
}

# We can also create and use our own data frame as follows:
# Convert categorical variables to factors


to_be_predicted <-
  data.frame(age=70, sex=1, cp=0, trestbps=145, chol=174,
             fbs=0, restecg=1, thalach=125, exang=1, oldpeak=2.6,
             slope=0,
             ca=0, thal=3)


to_be_predicted$sex <- as.factor(to_be_predicted$sex)
to_be_predicted$cp <- as.factor(to_be_predicted$cp)
to_be_predicted$fbs <- as.factor(to_be_predicted$fbs)
to_be_predicted$restecg <- as.factor(to_be_predicted$restecg)
to_be_predicted$exang <- as.factor(to_be_predicted$exang)
to_be_predicted$slope <- as.factor(to_be_predicted$slope)
to_be_predicted$thal <- as.factor(to_be_predicted$thal)
# We then use the data frame to make predictions
predict(loaded_heart_model_rf, newdata = to_be_predicted)

# An alternative is to create a function and then use the function to make
# predictions

predict_target <- function(arg_age, arg_sex, arg_cp, arg_trestbps, arg_chol,
                           arg_fbs, arg_restecg, arg_thalach, arg_exang, arg_oldpeak, arg_slope,
                           arg_ca, arg_thal) {
  # Convert categorical variables to factors
  arg_sex <- as.factor(arg_sex)
  arg_cp <- as.factor(arg_cp)
  arg_fbs <- as.factor(arg_fbs)
  arg_restecg <- as.factor(arg_restecg)
  arg_exang <- as.factor(arg_exang)
  arg_slope <- as.factor(arg_slope)
  arg_thal <- as.factor(arg_thal)
  
  # Create a data frame using the arguments
  to_be_predicted <- data.frame(age = arg_age, sex = arg_sex, cp = arg_cp, trestbps = arg_trestbps,
                                chol = arg_chol, fbs = arg_fbs, restecg = arg_restecg,
                                thalach = arg_thalach, exang = arg_exang, oldpeak = arg_oldpeak,
                                slope = arg_slope, ca = arg_ca, thal = arg_thal)
  
  # Make a prediction based on the data frame
  predict(loaded_heart_model_rf, newdata = to_be_predicted)
}

# STEP 1. Install and Load the Required Packages ----
## plumber ----
if (require("plumber")) {
  require("plumber")
} else {
  install.packages("plumber", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}

## caret ----
if (require("caret")) {
  require("caret")
} else {
  install.packages("caret", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}


###LAB 12
# This requires the "plumber" package that was installed and loaded earlier in
# STEP 1. The commenting below makes R recognize the code as the definition of
# an API, i.e., #* comments.

loaded_heart_model_rf <- readRDS("../models/saved_heart_model_rf.rds")

#* @apiTitle CVDetect Prediction Model API

#* @apiDescription Used to predict whether a patient has heart disease or not.

#* @param arg_age Age of the patient
#* @param arg_sex Gender of the patient (0: Female, 1: Male)
#* @param arg_cp Chest pain type (0-3)
#* @param arg_trestbps Resting blood pressure (mm Hg)
#* @param arg_chol Serum cholesterol (mg/dl)
#* @param arg_fbs Fasting blood sugar (> 120 mg/dl, 0: No, 1: Yes)
#* @param arg_restecg Resting electrocardiographic results (0-2)
#* @param arg_thalach Maximum heart rate achieved
#* @param arg_exang Exercise-induced angina (0: No, 1: Yes)
#* @param arg_oldpeak ST depression induced by exercise relative to rest
#* @param arg_slope Slope of the peak exercise ST segment (0-2)
#* @param arg_ca Number of major vessels colored by fluoroscopy (0-3)
#* @param arg_thal Thallium stress test result (3: Normal, 6: Fixed defect, 7: Reversible defect)


#* @get /target

predict_diabetes <-
  function(arg_age, arg_sex, arg_cp, arg_trestbps, arg_chol,
           arg_fbs, arg_restecg, arg_thalach, arg_exang, arg_oldpeak, arg_slope,
           arg_ca, arg_thal) {
    # Create a data frame using the arguments
    to_be_predicted <- data.frame(
      age = as.numeric(arg_age),
      sex = as.factor(arg_sex),
      cp = as.factor(arg_cp),
      trestbps = as.numeric(arg_trestbps),
      chol = as.numeric(arg_chol),
      fbs = as.factor(arg_fbs),
      restecg = as.factor(arg_restecg),
      thalach = as.numeric(arg_thalach),
      exang = as.factor(arg_exang),
      oldpeak = as.numeric(arg_oldpeak),
      slope = as.factor(arg_slope),
      ca = as.numeric(arg_ca),
      thal = as.factor(arg_thal)
    )
    
    # Make a prediction based on the data frame
    predict(loaded_heart_model_rf, newdata = to_be_predicted)
  }

# STEP 1. Install and load the required packages ----
## plumber ----
if (require("plumber")) {
  require("plumber")
} else {
  install.packages("plumber", dependencies = TRUE,
                   repos = "https://cloud.r-project.org")
}

# STEP 2. Process a Plumber API ----
# This allows us to process a plumber API
#api <- plumber::plumb("lab12.R")

# STEP 3. Run the API on a specific port ----
# Specify a constant localhost port to use
#api$run(host = "127.0.0.1", port = 5022)

# Heart Disease Prediction Web Form


cat('<iframe src="http://localhost/lab15/Lab15-submission-ConsumePlumberAPIOutput.php" width="1000" height="600"></iframe>')

cat('<iframe src="http://localhost/customer-facing-dasboard/BI_dashboard/index.php" width="1000" height="600"></iframe>')