16. Synthesis

Author

Affiliation

Smit, A. J.

University of the Western Cape

“The statistician’s task is not to discover the truth, but to measure uncertainty.”

— Bradley Efron

“Somewhere, something incredible is waiting to be known.”

— Carl Sagan

1 Workshop Recap, Assessment Alignment, and What Comes Next

Over the course of this workshop, you have learned not just how to use R, but how to think in a tidy, reproducible, and analytical way. These skills are foundational for all subsequent assessments and for the Biostatistics component that follows.

2 The One Workflow You Will Use Forever

Every analysis you do is a variation of the same loop:

Import → Inspect → Tidy → Transform → Visualise → Summarise → Communicate → Repeat

If you can internalise that sequence, you can learn any new package or domain-specific dataset. The tools will change but the loop will not.

3 Debugging Is a Core Skill

What breaks is not your fault. Debugging is part of the job. Use this simple routine:

1. Read the last line of the error (it usually says what failed).
2. Identify the function that failed (the first line after “Error in …”).
3. Check object class and structure with str() or glimpse().
4. Reproduce with a minimal example (smallest input that still fails).

Common failures to watch for:

• factors vs characters
• NA propagation
• silent recycling (length mismatch)
• grouping that persists too long

4 Predict Before You Execute

Before you run a pipeline, ask:

• “How many rows should I have now?”
• “What changed conceptually?”
• “What stayed the same?”

This habit is the difference between button-pushing and analysis.

5 Object Hygiene and Naming Discipline

Your objects are your memory so treat them carefully.

• Overwrite when you are confident you no longer need the old version.
• Create new objects when you are exploring or unsure.
• Use names that scale (avoid df2, final_final, test).
• Periodically restart R and run your script top-to-bottom. If it fails, your workflow is not yet reproducible.

6 Reproducibility Beyond Quarto

Reproducibility is a mindset:

• Scripts must run from a clean session.
• Relative paths are scientific hygiene.
• Results without code are not evidence.

If you cannot re-run it in six months, it does not exist.

7 Light-Weight Statistical Instincts

Even before formal statistics, cultivate these instincts:

• Variability vs central tendency (do not trust means alone).
• Sample size matters (n() is critical to understanding the power of your data).
• Plots are models and offer first insights into your data.

8 One Narrative Question

We have been asking the same question all along:

How does coastal temperature vary in space, time, and depth?

You saw this question early in simple plots, and later in grouped summaries and spatial maps. The question did not change but your ability to answer it did.

9 Alignment with Assessments

The workshop has been structured to map directly onto your assessed work. Each assessment assumes that you can independently apply the following skills:

• Assessment readiness

  • Import, inspect, and tidy real datasets
  • Apply a coherent workflow from raw data to final output
  • Write readable, reproducible R code using tidyverse principles
  • Produce publication-quality figures using ggplot2
  • Manipulate data confidently using dplyr verbs and pipes

• What assessors will look for

  • Logical data workflows (not trial-and-error code)
  • Clear transformation steps (filter(), mutate(), summarise(), group_by())
  • Appropriate visualisation choices
  • Evidence that results were derived, not manually curated
  • Liberal application of comments to document your workflow, i.e., code that is understandable by someone else (including your future self)

If you can reproduce the analyses and figures from this workshop without following along line-by-line, you are well prepared for the assessments.

10 Concept Map: How the Chapters Fit Together

You should be able to combine lessons learned in each chapter, because they play specific roles in a single, coherent analytical framework/workflow:

1. R and RStudio Orientation — learning the environment, tools, and expectations of working in R.

2. Working with Data and Code Foundations — understanding scripts, objects, and how R thinks.

3. R Markdown and Quarto Reproducibility — integrating code, results, and narrative into a single document.

4. Data Classes and Structures Literacy — knowing what your data is before deciding what to do with it.

5. R Workflows Discipline — structuring analyses so they are repeatable and scalable.

6. Graphics with ggplot2 Visual reasoning — learning to explore and communicate data visually.

7. Faceting Figures Comparison — revealing patterns across groups and conditions.

8. Brewing Colours Clarity and accessibility — making figures interpretable and professional.

9. Mapping with ggplot2 Spatial thinking — extending tidy principles to geographic data.

10. Mapping with Style Polish — producing maps suitable for reports and publications.

11. Mapping with Natural Earth / Applied Examples Integration — combining data sources, projections, and styling.

12. Tidy Data Structure — learning the rules that make analysis possible.

13. Tidier Data Transformation — filtering, mutating, selecting, and summarising.

14. Tidiest Data Power — grouping, pipelines, and complex workflows.

15. Synthesis Synthesis — seeing the workflow as a single analytical language.

Together, these chapters teach you how to move from messy reality → structured data → insight → communication.

11 What You Now Are

You are now someone who can:

• Read unfamiliar R code
• Tidy real-world data
• Ask questions of data (not just plot it)
• Learn new packages independently

12 Prelude to Biostatistics

The Biostatistics component builds directly on everything you have learned here. For those of you taking BCB743 as an elective, this work will also be foundational.

In this workshop, you focused on:

• How to prepare data
• How to explore patterns
• How to visualise structure and variation

In Biostatistics, you will now ask:

• Are these patterns meaningful?
• How much uncertainty is there?
• What conclusions are supported by the data?

The transition looks like this:

• Tidy data → prerequisite for valid statistics
• Grouping and summarising → foundation of statistical models
• Visual exploration → guides hypothesis formulation
• Reproducible workflows → ensures transparent inference

Statistical tests, models, and confidence intervals only make sense when applied to well-structured, well-understood data. You now have the tools to ensure that this condition is met.

Think of this workshop as learning the grammar of data analysis. Biostatistics is where you begin to write arguments.

13 Final Note

You are not expected to memorise functions — that is what the help files are for. You are expected to know and implement workflows, patterns, and logic.

Confidence in R comes from practice, patience, and clarity.

If your code reads like a story of what you did and why — you are doing it right.

14 Session Info

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