Constructing a reproducible blog post using zzcollab tools

A template for reproducible analysis

I didn’t really know much about [topic] until I tried to [implement/understand] it myself. Here’s what I learned along the way.

Engaging hero image that introduces your topic visually

Photo caption with attribution if needed. This image sets the visual tone for your entire post.

1 Introduction

I didn’t really know much about [topic] until I [encountered situation/tried to implement it/needed it for project]. Like many data scientists, I thought [initial misconception or assumption]. Turns out, [what you actually discovered].

[Brief context: Why did you need this? What problem were you trying to solve? Keep it personal and specific.]

Here’s what I set out to understand:

1.1 Motivations

Why explore [topic]? - [Personal reason 1: specific problem you faced] - [Practical need 2: gap in your workflow] - [Learning goal 3: skill you wanted to develop] - [Curiosity 4: interesting question you had]

1.2 Objectives

What I wanted to accomplish: 1. [Specific, measurable objective 1] 2. [Specific, measurable objective 2] 3. [Specific, measurable objective 3] 4. [Stretch goal or advanced concept]

Disclaimer: I’m documenting my learning process here. If you spot errors or have better approaches, please let me know.

Atmospheric image to maintain visual engagement - replace with relevant scene

2 Prerequisites and Setup

Here’s what you’ll need to follow along:

# Install packages if needed (renv should have handled this)
# But just in case:
install.packages(c("tidyverse", "broom", "knitr", "patchwork"))

# Load libraries
library(tidyverse)
library(broom)
library(knitr)
library(patchwork)
source("R/plotting_utils.R")  # Load custom utility functions

# Setup theme and colors
setup_plot_theme()
colors <- get_analysis_colors()

# Load PREPARED data (generated by 01_prepare_data.R)
# This data includes derived variables and transformations
mtcars_clean <- read_csv("data/derived_data/mtcars_clean.csv", show_col_types = FALSE)

Background: Basic R and ggplot2 familiarity helpful but not required. I’ll explain concepts as we go!

Foundational concepts and theoretical frameworks

3 What is [Topic/Concept]?

Before diving into code, let’s clarify what [topic] actually means. [Simple, plain-language explanation of the concept. Use an analogy if helpful.] In practice, this means [concrete example or application].

4 Getting Started: Initial Exploration

# Display structure of prepared data
glimpse(mtcars_clean)

Okay, so we have 32 cars with 11 variables. Let’s examine the data characteristics.

# Key summary stats
summary_table <- mtcars_clean %>%
  summarise(
    n = n(),
    mpg_mean = round(mean(mpg), 1),
    mpg_sd = round(sd(mpg), 1),
    hp_mean = round(mean(hp), 0),
    hp_sd = round(sd(hp), 0)
  )

kable(summary_table,
      col.names = c("N", "MPG Mean", "MPG SD", "HP Mean", "HP SD"),
      caption = "Summary Statistics: Motor Trend Car Data")

Not too shabby! Average fuel efficiency is 20.1 MPG with quite a bit of variation (SD = 6.0).

5 Exploring the Data

Let’s visualize these patterns. I pre-generated these figures using analysis/scripts/03_generate_figures.R:

knitr::include_graphics("figures/eda-overview.png")

Distribution of fuel efficiency across the dataset. Left: Histogram showing MPG distribution. Right: Boxplots of MPG by cylinder count reveal engines with more cylinders tend to have lower fuel efficiency.

Wow, that’s a clear pattern! Cars with fewer cylinders are way more fuel-efficient.

Visual break - another atmospheric image maintaining engagement

5.1 Looking for Relationships

# Find strongest correlations with MPG
correlations <- cor(mtcars_clean %>% select(where(is.numeric))) %>%
  as.data.frame() %>%
  rownames_to_column("var1") %>%
  pivot_longer(-var1, names_to = "var2", values_to = "correlation") %>%
  filter(var1 == "mpg", var2 != "mpg") %>%
  arrange(desc(abs(correlation)))

# Display top 5
kable(correlations %>% head(5),
      caption = "Top 5 Correlations with MPG (fuel efficiency)")

🔍 Weight has the strongest correlation with MPG (r = -0.87). Let’s visualize that relationship:

knitr::include_graphics("figures/correlation-plot.png")

Strong negative relationship between vehicle weight and fuel efficiency. Heavier cars consistently get worse mileage, regardless of cylinder count. The fitted regression line (dashed) shows the overall trend.

Interesting! Heavier cars consistently get worse mileage. Makes sense when you think about it 🚗

Statistical modeling and quantification approaches

6 Building a Model

Let me fit a simple linear model to quantify this relationship:

# Load pre-computed model results from 02_fit_models.R
model_coef <- read_csv("data/derived_data/model_coefficients.csv",
                       show_col_types = FALSE)
model_metrics <- read_csv("data/derived_data/model_metrics.csv",
                         show_col_types = FALSE)

# Display model coefficients
kable(model_coef %>% select(term, estimate, std.error, p.value, conf.low, conf.high),
      digits = 4,
      caption = "Linear Regression Results: MPG ~ Weight")

# Display fit metrics
kable(model_metrics %>% select(r.squared, adj.r.squared, statistic, p.value, df.residual),
      digits = 4,
      caption = "Model Fit Metrics")

The model explains 75% of the variance (R² = 0.75). This is a reasonably strong fit. For every 1,000 lbs of weight, we lose about 5.3 MPG (95% CI: [-6.5, -4.1]).

knitr::include_graphics("figures/model-plot.png")

Linear regression fit showing the relationship between vehicle weight and fuel efficiency. The gray band represents the 95% confidence interval around the fitted line. The fit is quite good, explaining 75% of the variance in MPG.

6.1 Making Predictions

Let me make some predictions to see how this works in practice:

# Predict MPG for different weights
new_data <- tibble(wt = c(2, 3, 4))
model <- readRDS("data/derived_data/simple_model.rds")
predictions <- predict(model, newdata = new_data, interval = "confidence")

cbind(new_data, predictions) %>%
  kable(digits = 2,
        caption = "Predicted MPG for Vehicles of Different Weights")

📝 So a 2,000 lb car gets ~30 MPG, while a 4,000 lb car only gets ~15 MPG. That’s quite a difference!

7 Checking Our Work

Before we trust these results, let’s check if our model assumptions hold up:

# Load pre-computed diagnostics
diagnostics <- read_csv("data/derived_data/model_diagnostics.csv",
                       show_col_types = FALSE)

# Summary
outlier_count <- sum(diagnostics$is_outlier)
cat("Outliers found (>2.5 SD):", outlier_count, "\n")
cat("Residual SE:", round(sqrt(mean(diagnostics$residuals^2)), 2), "MPG\n")

Diagnostic checks: Found 2-3 potential outliers (>2.5 SD) when running the analysis. These merit investigation but don’t substantially affect the overall model fit.

Now let’s visualize the residuals to check for patterns:

knitr::include_graphics("figures/diagnostics-plot.png")

Residual diagnostic plot showing standardized residuals vs fitted values. The red dashed lines mark ±2 standard deviations. A good model should show residuals randomly scattered around zero with no patterns. We have a few potential outliers but overall the fit looks reasonable.

Looks pretty good! No major patterns in the residuals, though we have a couple of potential outliers worth investigating 🔍

7.1 Things to Watch Out For

A few gotchas I encountered while working on this:

1. Don’t extrapolate too far - This model works for weights between 1.5-5.5 thousand lbs. Predicting outside that range? Risky!

2. Correlation ≠ Causation - Weight correlates with MPG, but there are confounding variables (engine size, aerodynamics, etc.)

3. Check your assumptions - Always plot residuals! A good R² doesn’t guarantee your model is appropriate.

4. Small sample size - We only have 32 cars. Take the confidence intervals seriously!

8 What Did We Learn?

8.1 Lessons Learnt

Here’s what I took away from this exploration:

Conceptual Understanding: - Vehicle weight is a strong predictor of fuel efficiency (R² = 0.75) - Each 1,000 lbs reduces MPG by ~5.3 miles (95% CI: [-6.5, -4.1]) - Cylinder count effects are partially mediated through weight - Simple models can be surprisingly effective with the right predictor

Technical Skills: - Using broom::tidy() for clean model output formatting ✅ - Calculating and interpreting confidence intervals for predictions - Creating diagnostic plots to validate regression assumptions - Combining multiple ggplot visualizations with patchwork

Gotchas and Pitfalls: - Always check residual plots - R² alone isn’t enough! - Extrapolation beyond data range is dangerous - Small sample sizes (n=32) require cautious interpretation - Correlation doesn’t prove causation (confounding variables matter)

8.2 Limitations

This analysis has several limitations to keep in mind:

• Old data: mtcars is from 1974 - modern vehicles (hybrids, EVs) behave differently
• Small sample: Only 32 observations limits statistical power
• Missing variables: Doesn’t account for aerodynamics, transmission type, engine tech
• Simple model: Single predictor ignores important confounders
• Limited scope: Only passenger cars; may not generalize to trucks/SUVs

8.3 Opportunities for Improvement

If I had more time, here’s what I’d explore next:

1. Multiple regression - Add cylinder count, horsepower, transmission type
2. Interaction effects - Does weight impact differ by number of cylinders?
3. Modern data - Replicate with 2020+ vehicle data to see how relationships changed
4. Non-linear models - Try polynomial regression or splines for better fit
5. Machine learning comparison - How does linear regression compare to random forest?
6. Causal inference - Use techniques to establish causality, not just correlation

9 Wrapping Up

So that’s my journey exploring [topic]! We saw that vehicle weight is a powerful predictor of fuel efficiency, accounting for 75% of the variance. The model is simple but effective, though it has limitations worth keeping in mind.

Main takeaways: - Weight strongly predicts MPG (R² = 0.75, β = -5.3) - Always check model assumptions with diagnostic plots - Confidence intervals matter, especially with small samples - Simple models can be surprisingly powerful

I learned a lot working through this, especially about [specific technical skill you gained]. There’s definitely room for improvement—adding more predictors, trying non-linear models, and using modern data would all be interesting extensions.

If you’re trying this yourself: - Start with exploration before modeling - Plot your residuals! - Don’t trust high R² blindly - Report confidence intervals alongside point estimates

Thanks for following along.

10 See Also

Related posts and resources:

• [Link to related post 1]
• [Link to related post 2]
• [Link to related resource]

Key Resources: - R for Data Science - Free book on tidyverse - Introduction to Statistical Learning - Free textbook with R code - broom package docs - Tidy model outputs - Cross Validated - Stats Q&A community

---

11 Reproducibility

Data: mtcars (built-in R dataset, loaded by analysis/scripts/01_prepare_data.R)

Analysis Pipeline:

make docker-build
make docker-post-render

Or step-by-step:

Rscript analysis/scripts/01_prepare_data.R
Rscript analysis/scripts/02_fit_models.R
Rscript analysis/scripts/03_generate_figures.R
quarto render index.qmd

All Reproducible Code: - analysis/scripts/01_prepare_data.R - Data preparation - analysis/scripts/02_fit_models.R - Model fitting - analysis/scripts/03_generate_figures.R - Figure generation - R/plotting_utils.R - Reusable utility functions - analysis/paper/index.qmd - This blog post (narrative only)

Session Information:

R version 4.5.2 (2025-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.6.1

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.5.2    fastmap_1.2.0     cli_3.6.5        
 [5] tools_4.5.2       htmltools_0.5.9   parallel_4.5.2    yaml_2.3.11      
 [9] rmarkdown_2.30    knitr_1.50        jsonlite_2.0.0    xfun_0.54        
[13] digest_0.6.39     rlang_1.1.6       png_0.1-8         evaluate_1.0.5   

---

12 Let’s Connect!

Have questions, suggestions, or spot an error? Let me know!

• Twitter/X: @rgt47
• Mastodon: @your_mastodon
• GitHub: rgt47
• Email: Contact form

Please reach out if you: - Spot errors or have corrections - Have suggestions for improvement - Want to discuss the approach - Have questions about implementation - Just want to connect!

---