Project_EDAmidterm_II.Rmd

---
title: "Project_EDAmidterm_II"
author: "Maria Martinez"
date: "2024-11-04"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

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```{r}
```


```{r}
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggcorrplot)
library(MASS)

library(gridExtra)
library(readr) #To read the dataset
library(tidyverse) #For data manipulation and visual analytics

library(correlationfunnel)
library(DataExplorer)

library(WVPlots)
library(ggthemes)

library(corrplot)

library(ROCR)

library(caret)
library(reshape2)
  
```


```{r}
rm(list = ls())
getwd()
```


```{r}
data1 <- read.csv("health_struct.csv", header = TRUE)
str(data1)
```

```{r}

data1 %>% plot_missing()

```
*** dataset %>% plot_histogram(ggtheme = theme_economist_white())

 *** For categorical variables, it is more suitable to plot bar charts. This can again be done by 2 ways: Using plot_bar() to automatically generate them for the categorical variables. Or through ggplot, where each bar can be constructed individually.

dataset %>% plot_bar(ggtheme = theme_economist_white())


ggplot(data = dataset,aes(home_ownership)) +
  geom_bar()+
  theme_economist_white()                


ggplot(data=dataset,aes(home_ownership))+
  geom_bar(aes(fill=as.factor(Default)))
  
*** Next we can construct boxplots to observe the distribution of continuous variables. It is useful to do this by some grouping. For this case, it is valuable to see the variation of distributions according to whether or not people clicked on the add or not. 

Again DataExplorer offers us the function plot_boxplot by which we can automatically generate boxplots for all the continuous variables and assign a grouping variable. 

This can also be done individually by ggplot:
dataset2 %>% plot_boxplot(by="Default",geom_boxplot_args= list("outlier.size" = -1, "coef" = 1e30))
```{r}

```


```{r}
#Convert categorical variables (Nominal variables)
data1$Gender <- as.factor(data1$Gender)
data1$SmokingStatus <- as.factor(data1$SmokingStatus)
data1$AlcoholConsumption <- as.factor(data1$AlcoholConsumption)
data1$ExerciseFrequency <- as.factor(data1$ExerciseFrequency)
data1$Diabetes <- as.factor(data1$Diabetes)
data1$HeartDisease <- as.factor(data1$HeartDisease)
data1$PhysicalActivityLevel <- as.factor(data1$PhysicalActivityLevel)
data1$DietQuality <- as.factor(data1$DietQuality)
data1$MedicationAdherence <- as.factor(data1$MedicationAdherence)

```


```{r}
#Specify order of levels for Ordinal Variables
data1$Outcome <- factor(data1$Outcome, levels = c("Healthy", "At Risk", "Critical"))
data1$Systolic <- factor(data1$Systolic, levels = c("Normal", "Elevated", "Hypertension_sg1", "Hypertension_sg2"))
data1$Diastolic <- factor(data1$Diastolic, levels = c("Normal", "Hypertension_sg1", "Hypertension_sg2"))
data1$LevelCho <- factor(data1$LevelCho, levels = c("Healthy", "At-risk", "High"))
data1$LevelBmi <- factor(data1$LevelBmi, levels = c("Underweight", "Normal weight", "Overweight", "Obese"))

data1$AlcoholConsumption <- factor(data1$AlcoholConsumption, levels = c("Never", "Occasionally", "Regularly"))
data1$ExerciseFrequency <- factor(data1$ExerciseFrequency, levels = c("Never", "Rarely", "Sometimes", "Often"))
data1$PhysicalActivityLevel <- factor(data1$PhysicalActivityLevel, levels = c("Low", "Medium", "High"))
data1$DietQuality <- factor(data1$DietQuality, levels = c("Poor", "Average", "Good"))
data1$MedicationAdherence <- factor(data1$MedicationAdherence, levels = c("Low", "Medium", "High"))


#Verify changes
str(data1)
colSums(is.na(data1))
any(is.na(data1))


```


```{r}
#View the Distribution of the target variable
table(data1$Outcome)/nrow(data1) * 100  # % of each class
Outcome_dist <- prop.table(table(data1$Outcome)) * 100
Outcome_dist
```


```{r}
ggplot(data1, aes(x = Outcome)) +
  geom_bar(fill = "skyblue") +
  geom_text(stat = 'count', aes(label = scales::percent(..count../sum(..count..))), vjust = -0.5) +
  labs(title = "Distribution of Outcome", x = "outcome", y = "count")


```
## balancing dataset via random undersampling
#creating dfs based off outcomes 

```{r}
## balancing dataset via random undersampling
#creating dfs based off outcomes 

healthy <- data1 %>%
  filter(Outcome == "Healthy")
at_risk <- data1 %>%
  filter(Outcome == "At Risk")
critical <- data1 %>%
  filter(Outcome == "Critical")

```
## Making a little corrections - sizing to critical

```{r}

set.seed(6203)
samples_atrisk <- at_risk %>%
  sample_n(size = nrow(critical))
samples_healthy <- healthy %>%
  sample_n(size = nrow(critical))

```


```{r}
## data_balanced with reshuffling
data1_balanced <- bind_rows(samples_healthy, critical, samples_atrisk)
data1_balanced <- data1_balanced[sample(nrow(data1_balanced)),]

summary(data1_balanced)
```


```{r}
data1_balanced %>% plot_bar(ggtheme = theme_economist_white())


```


```{r}
data1_balanced %>% plot_boxplot(by="Outcome",geom_boxplot_args= list("outlier.size" = -1, "coef" = 1e30))
```


```{r}

ggplot(data = data1_balanced[1:1000,]) + 
  geom_point(mapping = aes(x = SystolicBP, y = DiastolicBP, color = Outcome)) + 
  theme(axis.text.x = element_text(size=15), axis.text.y = element_text(size=15), 
  axis.title=element_text(size=24,face="bold"))

```





```{r}
DFHC <- data1_balanced %>% filter(Outcome != "At Risk")
DFHC$Outcome <- fct_recode(DFHC$Outcome, "1" = "Critical", "0" = "Healthy")
```


```{r}
DFHC$Outcome <- factor(DFHC$Outcome, levels = c("0", "1"))
levels(DFHC$Outcome)
head(DFHC)
```


```{r}
# Split data into Training and Testing sets (70% train, 30% test)
set.seed(123)
library(caTools)
library(glmnet)
library(nnet)

split <- sample.split(DFHC$Outcome, SplitRatio = 0.7)
train_data <- subset(DFHC, split == TRUE)
test_data <- subset(DFHC, split == FALSE)


```


```{r}
```


```{r}
train_data %>%
  mutate_if(is.numeric,as.numeric)%>%
    binarize() %>% #bins the data immediately 
    correlate(Outcome__1) %>%  #correlate just creates correlations plots 
    plot_correlation_funnel(interactive = TRUE, alpha = 0.7)

```


```{r}
train_data %>% plot_bar(ggtheme = theme_economist_white())
```


```{r}
train_data %>% plot_boxplot(by="Outcome",geom_boxplot_args= list("outlier.size" = -1, "coef" = 1e30))

```


```{r}
model1 <- glm(Outcome ~ LevelCho + Diabetes + HeartDisease + Systolic + LevelBmi + PhysicalActivityLevel + AlcoholConsumption + GeneticRisk +AnnualCheckups + Age , family = "binomial", data = train_data)
model1
```

```{r}
```


```{r}
test_data$model_prob <- predict(model1, test_data, type = "response")

```


```{r}
test_data <-test_data %>%
  mutate(model_predict_Outcome = case_when(model_prob>0.50~1,TRUE ~0))

test_data <- test_data %>% 
  mutate(model_predict_Outcome = as.factor(model_predict_Outcome))

ggplot(data=test_data) +
  geom_bar(aes(x=model_predict_Outcome))


```


```{r}
draw_confusion_matrix <- function(cm) {

  layout(matrix(c(1,1,2)))
  par(mar=c(2,2,2,2))
  plot(c(100, 345), c(300, 450), type = "n", xlab="", ylab="", xaxt='n', yaxt='n')
  title('CONFUSION MATRIX', cex.main=2)

  # create the matrix 
  rect(150, 430, 240, 370, col='#3F97D0')
  text(195, 435, '0', cex=1.2)
  rect(250, 430, 340, 370, col='#F7AD50')
  text(295, 435, '1', cex=1.2)
  text(125, 370, 'Predicted', cex=1.3, srt=90, font=2)
  text(245, 450, 'Actual', cex=1.3, font=2)
  rect(150, 305, 240, 365, col='#F7AD50')
  rect(250, 305, 340, 365, col='#3F97D0')
  text(140, 400, '0', cex=1.2, srt=90)
  text(140, 335, '1', cex=1.2, srt=90)

  # add in the cm results 
  res <- as.numeric(cm$table)
  text(195, 400, res[1], cex=1.6, font=2, col='white')
  text(195, 335, res[2], cex=1.6, font=2, col='white')
  text(295, 400, res[3], cex=1.6, font=2, col='white')
  text(295, 335, res[4], cex=1.6, font=2, col='white')

  # add in the specifics 
  plot(c(100, 0), c(100, 0), type = "n", xlab="", ylab="", main = "DETAILS", xaxt='n', yaxt='n')
  text(10, 85, names(cm$byClass[1]), cex=1.2, font=2)
  text(10, 70, round(as.numeric(cm$byClass[1]), 3), cex=1.2)
  text(30, 85, names(cm$byClass[2]), cex=1.2, font=2)
  text(30, 70, round(as.numeric(cm$byClass[2]), 3), cex=1.2)
  text(50, 85, names(cm$byClass[5]), cex=1.2, font=2)
  text(50, 70, round(as.numeric(cm$byClass[5]), 3), cex=1.2)
  text(70, 85, names(cm$byClass[6]), cex=1.2, font=2)
  text(70, 70, round(as.numeric(cm$byClass[6]), 3), cex=1.2)
  text(90, 85, names(cm$byClass[7]), cex=1.2, font=2)
  text(90, 70, round(as.numeric(cm$byClass[7]), 3), cex=1.2)

  # add in the accuracy information 
  text(30, 35, names(cm$overall[1]), cex=1.5, font=2)
  text(30, 20, round(as.numeric(cm$overall[1]), 3), cex=1.4)
  text(70, 35, names(cm$overall[2]), cex=1.5, font=2)
  text(70, 20, round(as.numeric(cm$overall[2]), 3), cex=1.4)
}  



```






```{r}
Confusion_mat = caret::confusionMatrix(test_data$model_predict_Outcome, test_data$Outcome,positive='1')
Confusion_mat
draw_confusion_matrix(Confusion_mat)

```

####  With Blood Pressure but only systolic
```{r}
model2 <- glm(Outcome ~ Systolic + LevelBmi + PhysicalActivityLevel + AlcoholConsumption + GeneticRisk +AnnualCheckups + Age , family = "binomial", data = train_data)
model2
```


```{r}
test_data$model_prob <- predict(model2, test_data, type = "response")
test_data <-test_data %>%
  mutate(model_predict_Outcome = case_when(model_prob>0.50~1,TRUE ~0))

test_data <- test_data %>% 
  mutate(model_predict_Outcome = as.factor(model_predict_Outcome))

ggplot(data=test_data) +
  geom_bar(aes(x=model_predict_Outcome))

```


```{r}
Confusion_mat = caret::confusionMatrix(test_data$model_predict_Outcome, test_data$Outcome,positive='1')
Confusion_mat
draw_confusion_matrix(Confusion_mat)

```


```{r}
model3 <- glm(Outcome ~ CholesterolLevel+Systolic +Diabetes + LevelBmi + PhysicalActivityLevel + AlcoholConsumption + GeneticRisk +AnnualCheckups + Age, family = "binomial", data = train_data)
model3



```


```{r}
test_data$model_prob <- predict(model3, test_data, type = "response")
test_data <-test_data %>%
  mutate(model_predict_Outcome = case_when(model_prob>0.50~1,TRUE ~0))

test_data <- test_data %>% 
  mutate(model_predict_Outcome = as.factor(model_predict_Outcome))

ggplot(data=test_data) +
  geom_bar(aes(x=model_predict_Outcome))

```


```{r}
Confusion_mat = caret::confusionMatrix(test_data$model_predict_Outcome, test_data$Outcome,positive='1')
Confusion_mat
draw_confusion_matrix(Confusion_mat)

```

# Model without hypertension
```{r}

modelW <- glm(Outcome ~ LevelCho + Diabetes + HeartDisease + LevelBmi + PhysicalActivityLevel + AlcoholConsumption + GeneticRisk +AnnualCheckups + Age , family = "binomial", data = train_data)
modelW

```






```{r}
test_data$model_prob <- predict(modelW, test_data, type = "response")
test_data <-test_data %>%
  mutate(model_predict_Outcome = case_when(model_prob>0.50~1,TRUE ~0))

test_data <- test_data %>% 
  mutate(model_predict_Outcome = as.factor(model_predict_Outcome))

ggplot(data=test_data) +
  geom_bar(aes(x=model_predict_Outcome))
```


```{r}
Confusion_mat = caret::confusionMatrix(test_data$model_predict_Outcome, test_data$Outcome,positive='1')
Confusion_mat
draw_confusion_matrix(Confusion_mat)
```

```{r}
modelT <- glm(Outcome ~ Systolic+ Diastolic+LevelCho + Diabetes + HeartDisease + LevelBmi + ExerciseFrequency + PhysicalActivityLevel + AlcoholConsumption + GeneticRisk +AnnualCheckups+SmokingStatus + Age+ Systolic* Diastolic, family = "binomial", data = train_data)
modelT

```


```{r}
test_data$model_prob <- predict(modelT, test_data, type = "response")
test_data <-test_data %>%
  mutate(model_predict_Outcome = case_when(model_prob>0.50~1,TRUE ~0))

test_data <- test_data %>% 
  mutate(model_predict_Outcome = as.factor(model_predict_Outcome))

ggplot(data=test_data) +
  geom_bar(aes(x=model_predict_Outcome))

```


```{r}
Confusion_mat = caret::confusionMatrix(test_data$model_predict_Outcome, test_data$Outcome,positive='1')
Confusion_mat
draw_confusion_matrix(Confusion_mat)

```

###  F1
```{r}
modelS <- glm(Outcome ~ Systolic+LevelCho + Diabetes + LevelBmi + PhysicalActivityLevel + AlcoholConsumption + GeneticRisk +AnnualCheckups+SmokingStatus + Age, family = "binomial", data = train_data)
modelS

test_data$model_prob <- predict(modelS, test_data, type = "response")
test_data <-test_data %>%
  mutate(model_predict_Outcome = case_when(model_prob>0.50~1,TRUE ~0))

test_data <- test_data %>% 
  mutate(model_predict_Outcome = as.factor(model_predict_Outcome))

ggplot(data=test_data) +
  geom_bar(aes(x=model_predict_Outcome))

Confusion_mat = caret::confusionMatrix(test_data$model_predict_Outcome, test_data$Outcome,positive='1')
Confusion_mat
draw_confusion_matrix(Confusion_mat)


```
```{r}
model0 <- glm(Outcome ~ 1 , family = "binomial", data = train_data)
model0

```


```{r}
exp(-1.086e-18) ## odd = 1  p = .5
```