I Can Take Joe Biden in a Fist Fight

```{r, results='hide', message=FALSE, warning=FALSE}
library(mdsr)
library(mosaic)
library(tidyverse)
library(gapminder)
library("rmarkdown")
library(dplyr)
library(class)
library(e1071)
library(ROCR)
library(NHANES)
library(rpart)
library(rpart.plot)
library(tidyr)
```

1. If you look at the class notes, you will see that the dataset NHANES has 76 variables. In this homework, you will select a subset of the data targeting a specific group of people doing analysis using the filter command. For example, If we looking at the group of married people, you will use;

```{r, results='hide', message=FALSE, warning=FALSE}
Married1 <- filter(NHANES, MaritalStatus =="Married")

Married1
```

2. Count the percentage of married persons have Diabetes

```{r, results='hide', message=FALSE, warning=FALSE}
tally(~ Diabetes, data= people, format = "percent")
```

3. Using the Married people's data, select columns of Age, Diabetes, BMI, TotChol, PhysActive from the data

```{r, results='hide', message=FALSE, warning=FALSE}
NHANES %>%
    select(c("TotChol","Age","Diabetes","BMI", "PhysActive"))
```

4. Use rpart to generate a decision tree classification model using:

```{r, results='hide', message=FALSE, warning=FALSE}
whoIsDiabetic <-rpart( Diabetes ~ Age +BMI +TotChol +PhysActive, data =people, control =rpart.control(cp =0.005, minbucket = 30))
 rpart.plot(whoIsDiabetic, extra=4)

whoIsDiabetic
```

6. Discuss any difference between this decision tree and the first one decision tree shown in the lecture notes.