---
title: "Lab 5"
author: "Your Name Here"
date: ""
output: html_document
---

### Package loading

```{r}
library(tidyverse)
```

### Problems

We'll begin by doing all the same data processing as in lecture.
```{r}
# Load data from MASS into a tibble
birthwt <- as_tibble(MASS::birthwt)

# Rename variables
birthwt <- birthwt %>%
  rename(birthwt.below.2500 = low, 
         mother.age = age,
         mother.weight = lwt,
         mother.smokes = smoke,
         previous.prem.labor = ptl,
         hypertension = ht,
         uterine.irr = ui,
         physician.visits = ftv,
         birthwt.grams = bwt)

# Change factor level names
birthwt <- birthwt %>%
  mutate(race = recode_factor(race, `1` = "white", `2` = "black", `3` = "other")) %>%
  mutate_at(c("mother.smokes", "hypertension", "uterine.irr", "birthwt.below.2500"),
            ~ recode_factor(.x, `0` = "no", `1` = "yes"))
```

#### 1. Some table practice

**(a)** Create a summary table showing the average birthweight (rounded to the nearest gram) grouped by race, mother's smoking status, and hypertension.

```{r}
# Edit me
```

**(b)** How many rows are there in the summary table?  Are all possible combinations of the three grouping variables shown?  Explain.

> Replace this text with your solution.

<br>

**(c)** Repeat part (b), this time adding the argument `.drop = FALSE` to your `group_by()` call.  What happens?

```{r}
# Edit me
```

#### 2. Plotting the diamonds data

**(a)** Construct a violin plot of showing how the distribution of diamond prices varies by diamond `cut`.

```{r}
# Edit me
```

**(b)** Use `facet_grid` with `geom_historam` to construct 5 histograms showing the distribution of price within every category of diamond `color`. 

```{r}
# Edit me
```