In mkearney/mizzourahmd: A clean and stylish template for rmarkdown

knitr::opts_chunk$set(echo = TRUE, collapse = TRUE, comment = "#>")

This is a reproducible Rmarkdown document that describes a data analysis project. It proceeds in four sections. First, the setup section contains a list of required packages and version numbers. Next, the data section provides a description of the information of interest, which is then analyzed and reported in the results section. Finally, the discussion section concludes with limitations and broader implications.

Setup

All data wrangling and analysis is done using the R environment with the help of the ggplot2 and broom packages.

## install ggplot2 if not already
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}

## load ggplot2 and print version number
library(ggplot2)
packageVersion("ggplot2")

## install broom if not already
if (!requireNamespace("broom", quietly = TRUE)) {
  install.packages("broom")
}

## print broom's version number (namespace will be explicitly qualified later)
packageVersion("broom")

Data

The data set, mpg, comes from the ggplot2 package.

## preview data set
head(mpg)

Results

An Ordinary Least Squares (OLS) model was used to estimate highway fuel efficiency as a function of engine weight, year, number of cylinders, and vehicle class.

## regression model
m1 <- lm(hwy ~ displ + year + cyl + class, data = mpg)

Model fit information is provided below.

## model fit information
data.frame(
  statistic = row.names(t(broom::glance(m1))), 
  value = sprintf("%.2f", t(broom::glance(m1))), 
  row.names = NULL)

And here are the regression coefficients.

## view output
broom::tidy(m1)

It's clear that vehicle class makes a big difference. A boxplot is provided below to visualize this difference.

## plot to visualize relationship between hwy and class
ggplot(mpg, aes(x = class, y = hwy, fill = class)) +
  geom_boxplot(outlier.shape = NA, alpha = .6) + 
  geom_jitter(shape = 21, size = 2, alpha = .5) + 
  theme_minimal(base_family = "Roboto Condensed") + 
  theme(legend.position = "none", 
    axis.title = element_text(hjust = .95, face = "italic"),
    plot.title = element_text(face = "bold")) + 
  labs(x = NULL, y = "Highway Fuel Efficiency", 
    title = "Highway fuel efficiency by vehicle class", 
    subtitle = "Jittered points overlaying boxplots of fuel efficiency by class")