r-style-guide: Design & UI for AI Agents | OpenAgentSkill

Overview

R style guide covering naming conventions, spacing, layout, and function design best practices. Use when writing R code.

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R Style Guide & Function Writing Best Practices

Consistent naming, spacing, structure, and function design for R code

Function Writing Best Practices

Structure and Style

# Good function structure
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE, finite = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}

# Use type-stable outputs
map_dbl()   # returns numeric vector
map_chr()   # returns character vector
map_lgl()   # returns logical vector

Naming and Arguments

# Good naming: snake_case for variables/functions
calculate_mean_score <- function(data, score_col) {
  # Function body
}

# Prefix non-standard arguments with .
my_function <- function(.data, ...) {
  # Reduces argument conflicts
}

Style Guide Essentials

Object Names

Use snake_case for all names
Variable names = nouns, function names = verbs
Avoid dots except for S3 methods

# Good
day_one
calculate_mean
user_data

# Avoid
DayOne
calculate.mean
userData

Spacing and Layout

# Good spacing
x[, 1]
mean(x, na.rm = TRUE)
if (condition) {
  action()
}

# Pipe formatting
data |>
  filter(year >= 2020) |>
  group_by(category) |>
  summarise(
    mean_value = mean(value),
    count = n()
  )

Assignment

# Good - Use <- for assignment
x <- 5

# Avoid - = for assignment (use only for function arguments)
x = 5  # Less clear intent

Indentation and Line Length

Use 2 spaces for indentation (never tabs)
Keep lines under 80 characters when possible
For long function calls, put each argument on its own line

# Good - Long function call
do_something_complicated(
  data = my_data,
  arg_one = value_one,
  arg_two = value_two,
  arg_three = value_three
)

# Good - Long pipe chain
result <- data |>
  filter(year >= 2020) |>
  mutate(
    new_var = old_var * 2,
    another_var = str_to_lower(text_var)
  ) |>
  summarise(
    mean_value = mean(value),
    .by = category
  )

Comments

# Good - Comments explain WHY, not WHAT
# Calculate running average to smooth noise in sensor data
running_avg <- zoo::rollmean(values, k = 5)

# Avoid - Comments that just repeat the code
# Add 1 to x
x <- x + 1

File Organization

# 1. Load packages at the top
library(dplyr)
library(ggplot2)

# 2. Source any helper files
source("R/helpers.R")

# 3. Define constants
MAX_ITERATIONS <- 1000
DEFAULT_THRESHOLD <- 0.05

# 4. Define functions
process_data <- function(data) {
  # ...
}

# 5. Main script logic (if not a package)
main <- function() {
  data <- read_csv("data/input.csv")
  result <- process_data(data)
  write_csv(result, "data/output.csv")
}

Function Design Guidelines

Single Responsibility

# Good - Each function does one thing
read_and_validate <- function(path) {
  data <- read_csv(path)
  validate_columns(data)
  data
}

validate_columns <- function(data) {
  required <- c("id", "value", "date")
  missing <- setdiff(required, names(data))
  if (length(missing) > 0) {
    stop("Missing columns: ", paste(missing, collapse = ", "))
  }
}

# Avoid - Function does too many things
do_everything <- function(path, output_path, ...) {
  # Reads, validates, transforms, models, plots, writes...
}

Return Values

# Good - Explicit return for complex functions
calculate_metrics <- function(data) {
  metrics <- list(
    mean = mean(data$value),
    sd = sd(data$value),
    n = nrow(data)
  )
  return(metrics)
}

# Good - Implicit return for simple functions
square <- function(x) {
  x^2
}

# Avoid - Return in the middle without good reason
process <- function(x) {
  if (is.null(x)) return(NULL)  # OK - early exit
  # ... more code
  result  # Implicit return at end
}

Error Handling

Prefer cli::cli_abort() over stop() for user-facing errors. Structure messages as a problem statement followed by context bullets.

# Good - cli::cli_abort() with structured bullets
# Bullet types: x = error detail, i = info/hint, ! = warning
validate_input <- function(x, threshold = 0) {
  if (!is.numeric(x)) {
    cli::cli_abort(c(
      "{.arg x} must be numeric.",
      x = "You supplied {.cls {class(x)}}.",
      i = "Convert with {.fn as.numeric} first."
    ))
  }
  if (any(x < threshold)) {
    cli::cli_abort(c(
      "{.arg x} must be >= {threshold}.",
      x = "{sum(x < threshold)} value{?s} below threshold.",
      i = "Set {.arg threshold} to adjust the lower bound."
    ))
  }
}

# Good - reference argument names, functions, and classes with inline markup
cli::cli_abort(c(
  "{.fn my_func} requires a data frame.",
  x = "{.arg data} is {.cls {class(data)}}, not {.cls data.frame}.",
  i = "Did you mean to call {.fn as.data.frame}?"
))

# Avoid - stop() with string concatenation
stop("`x` must be numeric, not ", typeof(x), call. = FALSE)

Inline markup tokens:

{.arg x} — argument name (backtick-formatted)
{.fn foo} — function name
{.cls {class(x)}} — class name
{.val {value}} — literal value
{?s} — pluralisation (value{?s} → "value" or "values")

Default Arguments

# Good - Sensible defaults
summarise_data <- function(data, na.rm = TRUE, digits = 2) {
  # ...
}

# Good - NULL default for optional arguments
filter_data <- function(data, min_value = NULL, max_value = NULL) {
  if (!is.null(min_value)) {
    data <- filter(data, value >= min_value)
  }
  if (!is.null(max_value)) {
    data <- filter(data, value <= max_value)
  }
  data
}

Tidyverse API Conventions

Data-First Argument

# Good - Data as first argument for piping
my_transform <- function(data, var, threshold = 0.5) {
  data |>
    filter({{ var }} > threshold)
}

# Usage
data |> my_transform(value, threshold = 0.8)

Prefixed Non-Standard Arguments

# Good - Prefix with . to avoid conflicts
group_summary <- function(.data, ..., .by = NULL) {
  .data |>
    summarise(..., .by = {{ .by }})
}

Consistent Return Types

# Good - Always return tibble
my_function <- function(data) {
  result <- data |>
    # processing...
    filter(!is.na(value))

  tibble::as_tibble(result)
}

Common Style Mistakes

Avoid These Patterns

# Avoid - Inconsistent spacing
x<-1+2  # No spaces
x <- 1 + 2  # Correct

# Avoid - Unnecessary parentheses
if ((x > 0)) {}  # Extra parens
if (x > 0) {}    # Correct

# Avoid - Using T/F instead of TRUE/FALSE
if (x == T) {}     # T can be overwritten
if (x == TRUE) {}  # Correct

# Avoid - Semicolons to separate statements
x <- 1; y <- 2  # Hard to read
x <- 1          # Correct
y <- 2

# Avoid - attach() - creates ambiguity
attach(mtcars)
mean(mpg)  # Which mpg?
detach(mtcars)

# Correct - Be explicit
mean(mtcars$mpg)
# or
with(mtcars, mean(mpg))
# or
mtcars |> pull(mpg) |> mean()

View source ↗

File metadata

name: r-style-guide
description: R style guide covering naming conventions, spacing, layout, and function design best practices. Use when writing R code.

View original text

---
name: r-style-guide
description: R style guide covering naming conventions, spacing, layout, and function design best practices. Use when writing R code.
---

# R Style Guide & Function Writing Best Practices

*Consistent naming, spacing, structure, and function design for R code*

## Function Writing Best Practices

### Structure and Style

```r
# Good function structure
rescale01 <- function(x) {
  rng <- range(x, na.rm = TRUE, finite = TRUE)
  (x - rng[1]) / (rng[2] - rng[1])
}

# Use type-stable outputs
map_dbl()   # returns numeric vector
map_chr()   # returns character vector
map_lgl()   # returns logical vector
```

### Naming and Arguments

```r
# Good naming: snake_case for variables/functions
calculate_mean_score <- function(data, score_col) {
  # Function body
}

# Prefix non-standard arguments with .
my_function <- function(.data, ...) {
  # Reduces argument conflicts
}
```

## Style Guide Essentials

### Object Names

- **Use snake_case for all names**
- **Variable names = nouns, function names = verbs**
- **Avoid dots except for S3 methods**

```r
# Good
day_one
calculate_mean
user_data

# Avoid
DayOne
calculate.mean
userData
```

### Spacing and Layout

```r
# Good spacing
x[, 1]
mean(x, na.rm = TRUE)
if (condition) {
  action()
}

# Pipe formatting
data |>
  filter(year >= 2020) |>
  group_by(category) |>
  summarise(
    mean_value = mean(value),
    count = n()
  )
```

### Assignment

```r
# Good - Use <- for assignment
x <- 5

# Avoid - = for assignment (use only for function arguments)
x = 5  # Less clear intent
```

### Indentation and Line Length

- Use 2 spaces for indentation (never tabs)
- Keep lines under 80 characters when possible
- For long function calls, put each argument on its own line

```r
# Good - Long function call
do_something_complicated(
  data = my_data,
  arg_one = value_one,
  arg_two = value_two,
  arg_three = value_three
)

# Good - Long pipe chain
result <- data |>
  filter(year >= 2020) |>
  mutate(
    new_var = old_var * 2,
    another_var = str_to_lower(text_var)
  ) |>
  summarise(
    mean_value = mean(value),
    .by = category
  )
```

### Comments

```r
# Good - Comments explain WHY, not WHAT
# Calculate running average to smooth noise in sensor data
running_avg <- zoo::rollmean(values, k = 5)

# Avoid - Comments that just repeat the code
# Add 1 to x
x <- x + 1
```

### File Organization

```r
# 1. Load packages at the top
library(dplyr)
library(ggplot2)

# 2. Source any helper files
source("R/helpers.R")

# 3. Define constants
MAX_ITERATIONS <- 1000
DEFAULT_THRESHOLD <- 0.05

# 4. Define functions
process_data <- function(data) {
  # ...
}

# 5. Main script logic (if not a package)
main <- function() {
  data <- read_csv("data/input.csv")
  result <- process_data(data)
  write_csv(result, "data/output.csv")
}
```

## Function Design Guidelines

### Single Responsibility

```r
# Good - Each function does one thing
read_and_validate <- function(path) {
  data <- read_csv(path)
  validate_columns(data)
  data
}

validate_columns <- function(data) {
  required <- c("id", "value", "date")
  missing <- setdiff(required, names(data))
  if (length(missing) > 0) {
    stop("Missing columns: ", paste(missing, collapse = ", "))
  }
}

# Avoid - Function does too many things
do_everything <- function(path, output_path, ...) {
  # Reads, validates, transforms, models, plots, writes...
}
```

### Return Values

```r
# Good - Explicit return for complex functions
calculate_metrics <- function(data) {
  metrics <- list(
    mean = mean(data$value),
    sd = sd(data$value),
    n = nrow(data)
  )
  return(metrics)
}

# Good - Implicit return for simple functions
square <- function(x) {
  x^2
}

# Avoid - Return in the middle without good reason
process <- function(x) {
  if (is.null(x)) return(NULL)  # OK - early exit
  # ... more code
  result  # Implicit return at end
}
```

### Error Handling

Prefer `cli::cli_abort()` over `stop()` for user-facing errors. Structure messages as a problem statement followed by context bullets.

```r
# Good - cli::cli_abort() with structured bullets
# Bullet types: x = error detail, i = info/hint, ! = warning
validate_input <- function(x, threshold = 0) {
  if (!is.numeric(x)) {
    cli::cli_abort(c(
      "{.arg x} must be numeric.",
      x = "You supplied {.cls {class(x)}}.",
      i = "Convert with {.fn as.numeric} first."
    ))
  }
  if (any(x < threshold)) {
    cli::cli_abort(c(
      "{.arg x} must be >= {threshold}.",
      x = "{sum(x < threshold)} value{?s} below threshold.",
      i = "Set {.arg threshold} to adjust the lower bound."
    ))
  }
}

# Good - reference argument names, functions, and classes with inline markup
cli::cli_abort(c(
  "{.fn my_func} requires a data frame.",
  x = "{.arg data} is {.cls {class(data)}}, not {.cls data.frame}.",
  i = "Did you mean to call {.fn as.data.frame}?"
))

# Avoid - stop() with string concatenation
stop("`x` must be numeric, not ", typeof(x), call. = FALSE)
```

**Inline markup tokens:**
- `{.arg x}` — argument name (backtick-formatted)
- `{.fn foo}` — function name
- `{.cls {class(x)}}` — class name
- `{.val {value}}` — literal value
- `{?s}` — pluralisation (`value{?s}` → "value" or "values")

### Default Arguments

```r
# Good - Sensible defaults
summarise_data <- function(data, na.rm = TRUE, digits = 2) {
  # ...
}

# Good - NULL default for optional arguments
filter_data <- function(data, min_value = NULL, max_value = NULL) {
  if (!is.null(min_value)) {
    data <- filter(data, value >= min_value)
  }
  if (!is.null(max_value)) {
    data <- filter(data, value <= max_value)
  }
  data
}
```

## Tidyverse API Conventions

### Data-First Argument

```r
# Good - Data as first argument for piping
my_transform <- function(data, var, threshold = 0.5) {
  data |>
    filter({{ var }} > threshold)
}

# Usage
data |> my_transform(value, threshold = 0.8)
```

### Prefixed Non-Standard Arguments

```r
# Good - Prefix with . to avoid conflicts
group_summary <- function(.data, ..., .by = NULL) {
  .data |>
    summarise(..., .by = {{ .by }})
}
```

### Consistent Return Types

```r
# Good - Always return tibble
my_function <- function(data) {
  result <- data |>
    # processing...
    filter(!is.na(value))

  tibble::as_tibble(result)
}
```

## Common Style Mistakes

### Avoid These Patterns

```r
# Avoid - Inconsistent spacing
x<-1+2  # No spaces
x <- 1 + 2  # Correct

# Avoid - Unnecessary parentheses
if ((x > 0)) {}  # Extra parens
if (x > 0) {}    # Correct

# Avoid - Using T/F instead of TRUE/FALSE
if (x == T) {}     # T can be overwritten
if (x == TRUE) {}  # Correct

# Avoid - Semicolons to separate statements
x <- 1; y <- 2  # Hard to read
x <- 1          # Correct
y <- 2

# Avoid - attach() - creates ambiguity
attach(mtcars)
mean(mpg)  # Which mpg?
detach(mtcars)

# Correct - Be explicit
mean(mtcars$mpg)
# or
with(mtcars, mean(mpg))
# or
mtcars |> pull(mpg) |> mean()
```

Review the source

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Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
AI review approval is missing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 202 stars, 33 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
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Source & usage notes

IndexedStatic Checked

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

Source repository: ab604/claude-code-r-skills
License: MIT
Version: Unknown
Last GitHub push: Sep 17, 2026
Registry updated: Sep 18, 2026
Instruction path: .claude/skills/r-style-guide/SKILL.md @ 529de4fcfe68

Version reported in registry metadata; check source releases before relying on it.

Quality

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      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 202 stars, 33 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution",
      "Review status: AI review approval is missing"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 75,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "AI review approval is missing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 202 stars, 33 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 65,
    "label": "Promising"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "7d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "emilkowalski-apple-design",
      "name": "Apple Design",
      "url": "https://www.openagentskill.com/skills/emilkowalski-apple-design",
      "stars": 34452,
      "install_command": "npx skills@latest add emilkowalski/skills",
      "trust_score": 93,
      "audit_score": 94
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "AI review approval is missing",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use r-style-guide in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 71/100 Manual review",
      "Audit: 75/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "ab604-r-style-guide (r-style-guide)",
      "install_command": "npx skills add ab604/claude-code-r-skills --skill r-style-guide",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "ab604-r-style-guide",
      "task": "Use r-style-guide in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/ab604-r-style-guide",
    "api": "https://www.openagentskill.com/api/agent/skills/ab604-r-style-guide",
    "audit": "https://www.openagentskill.com/skills/ab604-r-style-guide/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=ab604-r-style-guide&task=Use%20r-style-guide%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20r-style-guide%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20r-style-guide%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/ab604-r-style-guide/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/ab604-r-style-guide"
  }
}

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Creator: ab604
Source: ab604/claude-code-r-skills
Indexed by: OpenAgentSkill community index

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