Introduction

Formatting citation numbers into compact ranges is a common text-processing task: convert 1,2,3,5,7,8 into 1-3,5,7-8. In R, this is easiest by sorting unique integers, grouping consecutive runs, and rendering each run as single number or range.

A robust solution must handle duplicates, unsorted input, and missing values.

Core Sections

1. Core algorithm

Steps:

1. remove NA
2. unique + sort
3. find run boundaries where diff != 1
4. format each run

1format_citations <- function(x) {
2  x <- sort(unique(x[!is.na(x)]))
3  if (length(x) == 0) return("")
4
5  breaks <- c(TRUE, diff(x) != 1)
6  grp <- cumsum(breaks)
7
8  parts <- tapply(x, grp, function(run) {
9    if (length(run) == 1) as.character(run)
10    else paste0(min(run), "-", max(run))
11  })
12
13  paste(parts, collapse = ",")
14}
15
16format_citations(c(3,2,1,5,8,7,7,NA))
17# "1-3,5,7-8"

2. Preserve original order variant

If you must preserve first appearance order instead of numeric sorting, logic changes and range semantics become less standard for citations.

3. Vectorized usage in data frames

1library(dplyr)
2
3df %>%
4  mutate(citation_text = sapply(citation_list_col, format_citations))

Where citation_list_col contains integer vectors/lists.

4. Validation tests

Create tests for edge cases:

• single value
• all sequential
• no sequential
• duplicates
• empty vector

5. Output style customization

You can change separators (; vs ,) or range symbol (– en dash) by parameterizing format function.

Common Pitfalls

• Formatting unsorted values directly and creating broken ranges.
• Forgetting to remove duplicates before range grouping.
• Treating gaps larger than 1 as sequential runs.
• Failing on empty vectors or all-NA input.
• Hardcoding separators and range symbols when output style differs by journal.

Summary

In R, citation range formatting is a straightforward run-length grouping problem on sorted unique integers. By identifying consecutive runs and rendering singletons/ranges appropriately, you get compact strings like 1-3,5,7-8. Add edge-case handling and configurable separators to make the formatter reusable across publication styles.

A practical way to keep this guidance valuable over time is to convert it into an executable runbook rather than treating it as static prose. The runbook should include exact prerequisites, supported tool versions, expected environment settings, and a concise verification sequence that can be run from a clean machine. For each step, include a brief expected output and one common failure signature so engineers can quickly determine whether they are on a known-good path or a known-bad path. This reduces guesswork during incidents and shortens time-to-resolution when teams rotate ownership frequently.

It also helps to maintain one minimal reproducible fixture in source control for the specific scenario covered by the article. The fixture can be a tiny script, focused test case, sample dataset, or minimal manifest depending on topic. The point is to have an artifact that demonstrates both successful behavior and a realistic failure condition in isolation. When dependency versions or infrastructure behavior change, teams can run the fixture quickly and identify whether the regression is caused by environment drift, configuration mismatch, or application logic changes. This dramatically improves debugging speed compared to investigating only full production workflows.

For long-term reliability, add one lightweight CI guardrail that targets the most failure-prone step in the flow. Good examples include schema checks, startup smoke tests, deterministic unit tests, API contract assertions, and compatibility probes. Keep guardrails fast and specific so they run on every change and produce actionable failures. If a class of issue appears repeatedly, promote the manual troubleshooting step into automation so regressions are caught before deployment. Over time, this shifts effort from reactive debugging to preventive quality control and keeps operational knowledge aligned with real-world delivery practices.

As an additional safeguard, schedule periodic verification in a clean ephemeral environment and store the results as part of release evidence. This keeps assumptions current as dependencies evolve and helps detect subtle regressions before they reach production.