I’ll never forget the panic I felt when my manager asked me to update last quarter’s sales analysis. I opened my old project and nothing worked. Packages had updated and broken my code, the data format had changed, and I couldn’t remember how I’d calculated some key metrics. That’s when I realized: if you can’t reproduce your own work, you’re building on sand.
Reproducibility isn’t about academic purity—it’s about creating analysis that stands up to real-world demands. Here’s how to build work that doesn’t let you down when it matters most.
Create a Home for Your Project
Think of your project like a well-organized workshop. Every tool has its place, and you can find what you need even after months away.
text
sales_analysis_2025/
├── data/
│ ├── raw/ # Original data, untouched
│ │ ├── sales_q1.csv # From CRM export on 2025-04-01
│ │ └── customers.csv # From database snapshot
│ ├── processed/ # Cleaned and transformed
│ └── external/ # Third-party data sources
├── scripts/
│ ├── 01_import_clean.R
│ ├── 02_calculate_metrics.R
│ ├── 03_build_models.R
│ └── functions/ # Reusable pieces
│ ├── calculate_growth.R
│ └── segment_customers.R
├── analysis/
│ ├── exploratory.qmd # Your thinking space
│ └── final_report.qmd # Polished version
├── outputs/
│ ├── figures/ # Charts and graphs
│ ├── models/ # Saved model objects
│ └── tables/ # Summary statistics
└── project_guide.md # How everything works
The magic happens when this structure becomes muscle memory. New team members should be able to find their way around without asking you.
Make Git Your Project’s Diary
Version control isn’t just for programmers—it’s your project’s memory.
bash
# Tell the story of your changes
git commit -m “Add regional sales breakdown
– Implement new territory mapping from sales ops
– Fix currency conversion for international sales
– Add validation to catch negative sales amounts
– Update documentation with new methodology”
# Tag important milestones
git tag -a “v1.0-q1-results” -m “Q1 2025 final analysis”
Bad commit messages like “fixed stuff” are useless. Good ones let you understand your own thinking six months later.
Freeze Your Packages in Time
I once had a dplyr update change how group_by worked, breaking three months of analysis. Never again.
r
# Start every project like this
library(renv)
renv::init() # Creates your project’s environment
# Work normally – install packages, write code
install.packages(“fancy_new_analysis_package”)
# When everything works, take a snapshot
renv::snapshot() # Creates renv.lock
# Your future self (or colleagues) can recreate everything
renv::restore() # Recreates your exact environment
The renv.lock file is like a recipe card for your computational environment. It remembers every package version so your analysis doesn’t break when packages update.
Automate Your Workflow
Manual steps introduce errors. I once forgot to run a data cleaning script and published analysis with test accounts included. Automation prevents these mistakes.
r
library(targets)
list(
# Data preparation
tar_target(
raw_sales,
read_csv(“data/raw/sales_q1.csv”),
format = “file” # Tracks the actual file
),
tar_target(
clean_sales,
{
# Validate data first
validate_sales_data(raw_sales)
# Then clean
clean_sales_data(raw_sales)
}
),
# Analysis
tar_target(
sales_trends,
calculate_monthly_trends(clean_sales)
),
tar_target(
customer_segments,
identify_customer_segments(clean_sales)
),
# Reporting
tar_target(
quarterly_report,
{
rmarkdown::render(
“analysis/quarterly_report.qmd”,
output_file = paste0(“reports/sales_q1_”, Sys.Date(), “.html”)
)
},
format = “file”
)
)
Run tar_make() once and everything happens in the right order. The system knows what depends on what, so it only rebuilds what’s necessary when things change.
Create Reports That Adapt
Don’t copy-paste the same analysis for different regions or time periods.
yaml
—
title: “Regional Performance: `r params$region`”
params:
region: “Northeast”
start_date: “2025-01-01”
end_date: “2025-03-31”
—
# `r params$region` Sales Review
## Performance from `r params$start_date` to `r params$end_date`
“`{r load-regional-data}
regional_sales <- sales_data %>%
filter(region == params$region,
between(sale_date,
as.Date(params$start_date),
as.Date(params$end_date)))
The r params$region region generated $r format(sum(regional_sales$amount), big.mark=”,”) in revenue during this period.
text
Now generate customized reports effortlessly:
“`r
# For the sales team
quarto::quarto_render(“regional_report.qmd”,
output_file = “northeast_sales.html”,
execute_params = list(region = “Northeast”))
# For executives
quarto::quarto_render(“regional_report.qmd”,
output_file = “national_summary.html”,
execute_params = list(region = “All”))
Build Safety Nets
Assume things will go wrong, and catch them early.
r
validate_sales_data <- function(sales_df) {
issues <- c()
# Check for required columns
required_cols <- c(“sale_id”, “amount”, “date”, “region”)
missing_cols <- setdiff(required_cols, names(sales_df))
if (length(missing_cols) > 0) {
issues <- c(issues, paste(“Missing columns:”, paste(missing_cols, collapse = “, “)))
}
# Check for data quality issues
if (any(sales_df$amount < 0, na.rm = TRUE)) {
issues <- c(issues, “Negative sales amounts found”)
}
if (any(sales_df$amount > 1000000, na.rm = TRUE)) {
issues <- c(issues, “Extremely large sales amounts – possible data entry errors”)
}
# Check date range makes sense
date_range <- range(sales_df$date, na.rm = TRUE)
if (date_range[1] < as.Date(“2020-01-01”)) {
issues <- c(issues, “Sales dates before 2020 – check data source”)
}
if (length(issues) > 0) {
stop(“Data validation failed:\n”, paste(“•”, issues, collapse = “\n”))
}
message(“✓ Data validation passed”)
return(TRUE)
}
Document Your Thinking
Good documentation answers questions before they’re asked.
project_guide.md:
markdown
# Sales Analysis 2025
## Getting Started
1. Run `renv::restore()` to install required packages
2. Run `targets::tar_make()` to reproduce the full analysis
3. Open `outputs/final_report.html` for the main findings
## Data Sources
– `data/raw/sales_*.csv`: Export from Salesforce (contact: [email protected])
– `data/raw/customers.csv`: Customer database snapshot
– See `data/README.md` for detailed data lineage
## Key Methodology Decisions
– Sales under $50 are excluded (likely refunds or adjustments)
– Regional assignment uses customer billing address, not shipping
– Growth rates calculated using 30-day rolling averages
## Common Issues & Solutions
– If you get package errors: Run `renv::restore()`
– If sales data is missing: Download latest from Salesforce reports
– If regional mapping fails: Check `scripts/helpers/region_mapping.R`
Track Your Data’s Journey
Know where your data comes from and what you’ve done to it.
data/provenance.md:
markdown
## Sales Data
– **Source**: Salesforce Reports → Export as CSV
– **Contact**: [email protected]
– **Update Frequency**: Weekly
– **Last Updated**: 2025-04-01
– **Hash**: a1b2c3d4… (verify with `tools::md5sum(‘data/raw/sales_q1.csv’)`)
## Processing Steps
1. Remove test accounts (email contains ‘test’ or ‘example’)
2. Standardize regional names using official territory map
3. Convert currencies to USD using daily exchange rates
4. Flag and review sales over $100,000 for validation
## Known Issues
– Sales from new acquisition may be delayed by 24 hours
– International sales converted at month-end rates
Make Collaboration Frictionless
r
# onboard_new_analyst.R
message(“Welcome to the Sales Analysis project!”)
# Check system requirements
if (!requireNamespace(“renv”, quietly = TRUE)) {
install.packages(“renv”)
}
# Restore environment
renv::restore()
# Verify data is available
required_files <- c(“data/raw/sales_q1.csv”, “data/raw/customers.csv”)
missing_files <- required_files[!file.exists(required_files)]
if (length(missing_files) > 0) {
message(“Please download these files from the data warehouse:”)
message(paste(“-“, missing_files, collapse = “\n”))
} else {
message(“✓ All files present”)
message(“Run targets::tar_make() to reproduce the analysis”)
}
Conclusion: Build Analysis You Can Trust
That stressful experience with my sales analysis taught me a valuable lesson. Now when I finish a project, I ask myself one question: “If I had to explain this to a skeptical auditor in six months, could I reproduce every number and defend every decision?”
The answer is now always “yes” because I’ve built reproducibility into my workflow from day one.
Reproducible analysis isn’t about following rules—it’s about creating work that:
- Saves you time when you need to update or explain it
- Builds trust because others can verify your work
- Survives changes in data, packages, and team members
- Scales effortlessly across projects and organizations
- Makes you look professional because you’re prepared for anything
Start your next project with these practices. The initial setup takes minutes, but it saves hours of frustration later. Your future self—and anyone else who needs to understand your work—will thank you.
In the end, reproducibility isn’t just about making your analysis repeatable. It’s about making it reliable, trustworthy, and valuable long after you’ve moved on to other work. And that’s the kind of analysis that truly makes a difference.