--- title: "Lab 6" 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. > Your answer here
**(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 7 histograms showing the distribution of price within every category of diamond `color`. ```{r} # Edit me ```