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

# BRFSS Report

## Introduction

Introduce the dataset

Why we are doing this analysis?

## Data

```{r}
data <- read.csv("brfss_iowa.csv")
```

## Verify 6227 obs

```{r}
dim(data)

str(data)

nrow(data)
```

From the results above, we can see that there are 6227 obs in this dataset.

## Verify that there are variables WEIGHT2 and HEIGHT3 

```{r}
"WEIGHT2" %in% colnames(data)
```

```{r}
"HEIGHT3" %in% colnames(data)
```

Since we get two TRUEs, we verify that ...

## Find the cor between ht and wt by gender

```{r}
ht_male <- data[data$SEX == 1, "HEIGHT3"]
wt_male <- data[data$SEX == 1, "WEIGHT2"]
cor(ht_male, wt_male)

```

I get NA, I don't know how to get rid of 
NAs for cor 

BUT, I searched a little bit, 

```{r}
cor(ht_male, wt_male, 
    use = "complete.obs")
```

I found that the cor is 

## Step further

We found that those 

The following is to solve this problem

### Solution 1: we found other clean variables

```{r}

```


### Solution 2: data cleaning

```{r}

```


### Comparison

findings: 

## Conclustion

For this analysis, 





