library(tidyverse)
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### Problems

We’ll begin by doing all the same data processing as in lecture.

# 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.

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

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

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#### 2. Plotting the diamonds data

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

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(b) Use facet_grid with geom_historam to construct 7 histograms showing the distribution of price within every category of diamond color.

# Edit me