Project 1 Lesson: Exploratory Data Story with Gapminder

Purpose of Project 1

Project 1 is designed to help you practice the core workflow of this course before working with complex health datasets.

This project focuses on:

  • working comfortably in R and Quarto
  • exploring a dataset using descriptive statistics
  • creating clear data visualizations
  • writing interpretations in plain public health language
  • using inline R code to report values reproducibly

This is not a modeling project. The goal is exploration, clarity, and workflow.

Why we use Gapminder for Project 1

The Gapminder dataset is:

  • clean and well-documented
  • structured in tidy format
  • widely used in teaching data science
  • rich enough to tell meaningful public health stories

Using Gapminder allows you to focus on skills, not data complexity. These same skills will transfer directly to NHANES later in the course.

What you are expected to demonstrate

By the end of Project 1, you should be able to:

  • work entirely in a Quarto document
  • load and explore a dataset
  • compute descriptive summaries
  • create at least two well-labeled figures
  • interpret patterns in writing
  • use inline code for reported values

Effort, clarity, and reproducibility matter more than sophistication.

Project setup

Create a new Quarto document named:

project1-lastname.qmd

At the top of your document, include a clear title and your name.

Loading the data

Install packages if needed (only once):

# install.packages("tidyverse")
# install.packages("gapminder")

Load packages each session:

library(tidyverse)
library(gapminder)

Load the dataset:

gapminder
# A tibble: 1,704 × 6
   country     continent  year lifeExp      pop gdpPercap
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
 1 Afghanistan Asia       1952    28.8  8425333      779.
 2 Afghanistan Asia       1957    30.3  9240934      821.
 3 Afghanistan Asia       1962    32.0 10267083      853.
 4 Afghanistan Asia       1967    34.0 11537966      836.
 5 Afghanistan Asia       1972    36.1 13079460      740.
 6 Afghanistan Asia       1977    38.4 14880372      786.
 7 Afghanistan Asia       1982    39.9 12881816      978.
 8 Afghanistan Asia       1987    40.8 13867957      852.
 9 Afghanistan Asia       1992    41.7 16317921      649.
10 Afghanistan Asia       1997    41.8 22227415      635.
# ℹ 1,694 more rows

Choosing a focus

You will be assigned a country and you will be comparing all results to the United States.

Once selected, filter the dataset.

country_df <- gapminder %>%
  filter(country == "United States")

country_df
# A tibble: 12 × 6
   country       continent  year lifeExp       pop gdpPercap
   <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
 1 United States Americas   1952    68.4 157553000    13990.
 2 United States Americas   1957    69.5 171984000    14847.
 3 United States Americas   1962    70.2 186538000    16173.
 4 United States Americas   1967    70.8 198712000    19530.
 5 United States Americas   1972    71.3 209896000    21806.
 6 United States Americas   1977    73.4 220239000    24073.
 7 United States Americas   1982    74.6 232187835    25010.
 8 United States Americas   1987    75.0 242803533    29884.
 9 United States Americas   1992    76.1 256894189    32004.
10 United States Americas   1997    76.8 272911760    35767.
11 United States Americas   2002    77.3 287675526    39097.
12 United States Americas   2007    78.2 301139947    42952.

Your entire project should focus on one country and the clearly defined comparison.

Section 1: Introduction

In your Introduction section, briefly describe:

  • the country you are examining
  • why it is of interest from a public health perspective
  • what outcomes you plan to explore

This section should be written in complete sentences and should not include code.

Section 2: Descriptive exploration

Start by computing basic descriptive statistics.

mean_lifeExp <- mean(country_df$lifeExp)
sd_lifeExp <- sd(country_df$lifeExp)
n_years <- length(unique(country_df$year))

Report values using inline code, not by typing numbers manually.

Example:

Across 12 years of available data, the mean life expectancy was 73.5 years (SD = 3.3).

Section 3: Visualizations

You must include at least two figures.

Required figure: trend over time

ggplot(country_df, aes(x = year, y = lifeExp)) +
  geom_line() +
  geom_point() +
  labs(
    title = "Life Expectancy Over Time",
    x = "Year",
    y = "Life Expectancy (years)"
  )

Optional second figure (examples)

You may choose one of the following:

  • GDP per capita vs life expectancy
  • population trends over time
  • comparison with another country

Example:

ggplot(country_df, 
       aes(
         x = gdpPercap, 
         y = lifeExp)
       ) +
  geom_point() +
  scale_x_log10() +
  labs(
    title = "Life Expectancy vs GDP per Capita",
    x = "GDP per Capita (log scale)",
    y = "Life Expectancy (years)"
  )

Writing interpretation paragraphs

Every figure must be followed by a short interpretation paragraph.

A strong interpretation includes:

  • what the plot shows
  • notable trends or changes
  • a public health interpretation
  • a brief limitation

Example structure:

  • What do you observe?
  • When do changes occur?
  • Why might this matter for population health?

Do not describe code or aesthetics. Focus on meaning.

Section 4: Data quality and limitations

Include a short section discussing:

  • missingness (if any)
  • time coverage of the data
  • what the data cannot tell us

This helps build good scientific habits early.

Section 5: Reflection

Conclude with a brief reflection addressing:

  • what you learned from exploring the data
  • what surprised you
  • what questions you would explore next

This section is about thinking, not technical skill.

Formatting and reproducibility requirements

Your project must:

  • be written entirely in Quarto
  • render without errors
  • use inline code for reported values
  • include labeled figures
  • avoid screenshots of plots
  • be clearly organized with headings
  • have peer-reviewed sources (at least 8)
Warning

Projects that do not render cleanly cannot be graded.

Submission instructions

Submit:

  • the rendered document (word)
  • the .qmd source file
  • 3MT-style presentation slide

Both are required.

How this project prepares you for the rest of the course

Project 1 builds the foundation for:

  • Project 2 (cleaning NHANES data)
  • Project 3 (modeling and inference)
  • Project 4 (final manuscript)

The workflow you practice here will be reused all semester.

Key takeaways

Project 1 is about learning how to explore data, write clearly, and work reproducibly in Quarto. Focus on effort, clarity, and honest interpretation. You are not expected to be an expert — you are expected to engage.

Looking ahead

After Project 1, we will transition to real U.S. health data and begin preparing datasets for modeling and inference.