---
title: "Lab 2: Describing and Comparing Data in R"
author: "Your name"
format: html
editor: visual
---

## Setup

```{r}
library(dplyr)
library(ggplot2)

load(url("https://raw.githubusercontent.com/GarciaRios/govt_3990/gh-pages/Labs/lab2/Data/cdc.RData"))
```

## Exercise 1: Meet the data

```{r}
glimpse(cdc)
dim(cdc)
names(cdc)
```

What is the unit of observation?

> 

Classify the variables as categorical or quantitative. Where useful, be more specific: ordinal or nominal; discrete or continuous.

> 

## Exercise 2: See the distribution

```{r}
ggplot(cdc, aes(x = age)) +
  geom_histogram(binwidth = 5) +
  theme_minimal()
```

Now create the same histogram with bin widths of 10 and 1.

```{r}

```

What features of the distribution stay the same? What changes as the bin width changes?

> 

Describe the shape, center, spread, and any unusual features of the age distribution.

> 

## Exercise 3: Describe a quantitative variable

```{r}
cdc %>%
  summarise(
    mean_weight = mean(weight),
    median_weight = median(weight),
    sd_weight = sd(weight),
    variance_weight = var(weight),
    IQR_weight = IQR(weight),
    min_weight = min(weight),
    max_weight = max(weight)
  )
```

Create a histogram of `weight` with a reasonable bin width.

```{r}

```

Using both the graph and the numerical summaries, answer:

1. Is the distribution roughly symmetric or skewed?
2. Are the mean and median similar? Why or why not?
3. Which pair would you emphasize for this distribution: mean and SD, or median and IQR?
4. What does the standard deviation tell you in substantive terms?

> 

## Exercise 4: Compare groups

```{r}
cdc %>%
  group_by(exerany) %>%
  summarise(
    n = n(),
    mean_weight = mean(weight),
    median_weight = median(weight),
    sd_weight = sd(weight),
    IQR_weight = IQR(weight)
  )
```

Create a boxplot comparing weight for respondents who did and did not exercise in the previous month.

```{r}

```

Describe the comparison using both the graph and the numerical summaries. Does this establish that exercise causes differences in weight? Explain.

> 

## Exercise 5: Categorical variables and denominators

```{r}
cdc %>%
  count(genhlth) %>%
  mutate(prop = n / sum(n))
```

What is the most common self-rated health category? What proportion reports excellent health? Is `genhlth` nominal or ordinal?

> 

Now compare smoking history by gender.

```{r}
cdc %>%
  count(gender, smoke100) %>%
  group_by(gender) %>%
  mutate(prop_within_gender = n / sum(n))
```

What proportion of respondents in each gender category report having smoked at least 100 cigarettes?

> 

Why is the denominator the number of respondents within each gender category rather than the entire sample?

> 

## Exercise 6: Match the graph to the variables

### Two quantitative variables

```{r}
ggplot(cdc, aes(x = weight, y = height)) +
  geom_point(alpha = 0.2) +
  theme_minimal()
```

Describe the direction, form, strength, and unusual observations.

> 

### Two categorical variables

```{r}
ggplot(cdc, aes(x = gender, fill = smoke100)) +
  geom_bar(position = "fill") +
  theme_minimal()
```

Why is `position = "fill"` useful here?

> 

### One categorical and one quantitative variable

```{r}
ggplot(cdc, aes(x = gender, y = height)) +
  geom_boxplot() +
  theme_minimal()
```

Describe how the distributions differ in center, spread, and overlap.

> 

## Exercise 7: Create BMI

```{r}
cdc <- cdc %>%
  mutate(bmi = (weight / height^2) * 703)

summary(cdc$bmi)
```

Create a boxplot comparing BMI across self-rated health categories.

```{r}

```

Describe the pattern without making a causal claim.

> 

## On Your Own: Build a descriptive comparison

Use the CDC data to investigate one comparison of your choice.

### 1. State your question

> 

### 2. Identify your variables and their types

> 

### 3. Create an appropriate graph

```{r}

```

### 4. Calculate at least two appropriate summary statistics or proportions

```{r}

```

### 5. Interpret what you find in 3–4 sentences

> 

### 6. State one thing these descriptive data cannot establish

>