How to Replicate Any Plot with R for Your Statistics Assignments

• Develop a deeper understanding of how visual encoding works (axes, scales, color).
• Reverse-engineer journalistic and scientific visualizations.
• Improve your R plotting skills and debugging ability.
• Enhance the clarity and credibility of your own data analysis work.

If you're working on a statistics assignment where the instructions mention "visualize the findings," "compare trends," or "replicate published graphs," the following examples will give you a big head start.

Example 1: Gun-Related Homicides in G8 Countries (ABC News)

In this example, we take a chart from ABC News showing gun-related homicide rates across G8 nations and replicate it using ggplot2, ggflags, and countrycode.

The result is a polished and informative plot with flags as icons, clearly labeled bars, and a source citation—all features that elevate the visual storytelling.

What Students Learn:

• How to use ggflags for flag icons.
• Customizing labels and themes.
• Controlling axis limits and annotation text.

Example 2: International Gun Murders (Everytown.org)

This chart shows gun murder rates per 100,000 residents across 23 developed countries.

Here, we create a horizontal bar plot, a useful layout when comparing many categories. The flip helps avoid clutter and enhances readability.

Assignment Tip:

When you're tasked with comparing several countries, always consider using coord_flip() if the country names are long or numerous.

Example 3: Measles Rate Over Time by State (Wall Street Journal)

This is a heatmap showing measles incidence per 10,000 people across U.S. states over several decades.

With geom_tile(), we produce a visually engaging heatmap, adding geom_vline() to mark the year vaccines were introduced.

Skills Gained:

• Data wrangling with dplyr
• Color palettes and gradient control with colorRampPalette
• Annotating plots with vertical lines and text

Example 4: Regents Exam Scores in NYC (New York Times)

This visualization replicates a bar plot showing how many students achieved particular test scores, with an annotation to highlight the passing threshold.

By layering annotate("rect"), geom_bar(), and annotate("text"), we guide the viewer's attention to key score thresholds. This is a great technique to use in educational or exam-based data assignments.

What Students Can Apply:

• Adding custom zones (e.g., passing/failing ranges) with annotate("rect")
• Right-aligning axis scales for clarity
• Minimalist themes that enhance the data story

Example 5: Election Forecast Uncertainty (FiveThirtyEight)

This is a more advanced chart—essentially a set of probability distributions for election forecasts, separated by candidate.

We create ribbon plots using geom_ribbon() with custom gamma distributions and overlay them with geom_point() and geom_text() for annotations.

Concepts Reinforced:

• Understanding distributions visually (gamma, in this case)
• Custom plotting with facet_grid()
• Use of statistical functions like dgamma() and qgamma() in context

Final Thoughts: Why This Matters for Your Assignments

Most students are familiar with creating basic bar charts, scatterplots, or histograms. But replicating professional plots teaches you how to:

• Think like a data journalist or analyst
• Match visual elements to story goals
• Use annotations, themes, and layout tricks to guide the reader's eye

At Statisticshomeworkhelper.com, we often help students complete assignments that require:

• Replicating figures from articles or reports
• Customizing plots based on professor requirements
• Interpreting what the graph implies statistically

These five examples cover most of the graphical challenges you'll encounter in coursework or applied projects.