Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
R
Avoid common R mistakes — vectorization traps, NA propagation, factor surprises, and indexing gotchas.
skill openclawclawhub Free
Avoid common R mistakes — vectorization traps, NA propagation, factor surprises, and indexing gotchas.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Vectorization
Loops are slow — use apply(), lapply(), sapply(), or purrr::map() Vectorized functions operate on whole vectors — sum(x) not for (i in x) total <- total + i ifelse() is vectorized — if is not, use ifelse() for vector conditions Column operations faster than row — R is column-major
Indexing Gotchas
R is 1-indexed — first element is x[1], not x[0] x[0] returns empty vector — not error, silent bug Negative index excludes — x[-1] removes first element [[ extracts single element — [ returns subset (list stays list) df[, 1] drops to vector — use df[, 1, drop = FALSE] to keep data frame
NA Handling
NA propagates — 1 + NA is NA, NA == NA is NA Use is.na() to check — not x == NA Most functions need na.rm = TRUE — mean(x) returns NA if any NA present na.omit() removes rows with any NA — may lose data unexpectedly complete.cases() returns logical vector — rows without NA
Factor Traps
Old R converted strings to factors by default — use stringsAsFactors = FALSE or modern R levels() shows categories — but factor values are integers internally Adding new value not in levels gives NA — use factor(x, levels = c(old, new)) as.numeric(factor) gives level indices — use as.numeric(as.character(factor)) for values Dropping unused levels: droplevels() — or factor() again
Recycling
Shorter vector recycled to match longer — c(1,2,3) + c(10,20) gives 11, 22, 13 No error if lengths aren't multiples — just warning, easy to miss Single values recycle intentionally — x + 1 adds 1 to all elements
Data Frames vs Tibbles
Tibble never converts strings to factors — safer defaults Tibble never drops dimensions — df[, 1] stays tibble Tibble prints better — shows type, doesn't flood console as_tibble() to convert — from tibble or dplyr package
Assignment
<- is idiomatic R — = works but avoided in style guides <<- assigns to parent environment — global assignment, usually a mistake -> right assignment exists — rarely used, confusing
Scope
Functions look up in parent environment — can accidentally use global variable Local variable shadows global — same name hides outer variable local() creates isolated scope — variables don't leak out
Common Mistakes
T and F can be overwritten — use TRUE and FALSE always 1:length(x) fails on empty x — gives c(1, 0), use seq_along(x) sample(5) vs sample(c(5)) — different! first gives 1:5 permutation String splitting: strsplit() returns list — even for single string
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
1 Docs
- SKILL.md