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r-language-api
R interfaces to other languages. Use for calling Python, Java, JavaScript, and other languages from R.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
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R interfaces to other languages. Use for calling Python, Java, JavaScript, and other languages from R.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | r-language-api |
| description | R interfaces to other languages. Use for calling Python, Java, JavaScript, and other languages from R. |
Call other programming languages from R.
library(reticulate)
# Use Python
py_run_string("x = 1 + 1")
py$x
# Import modules
np <- import("numpy")
pd <- import("pandas")
# Call Python functions
np$array(c(1, 2, 3))
pd$DataFrame(list(a = 1:3, b = 4:6))
library(rJava)
# Initialize JVM
.jinit()
# Create Java objects
str <- .jnew("java/lang/String", "Hello")
# Call methods
.jcall(str, "I", "length")
.jcall(str, "S", "toUpperCase")
library(V8)
# Create context
ctx <- v8()
# Run JavaScript
ctx$eval("var x = 1 + 1")
ctx$get("x")
# Call functions
ctx$eval("function add(a, b) { return a + b; }")
ctx$call("add", 1, 2)
library(Rcpp)
# Inline C++
cppFunction('
int add(int x, int y) {
return x + y;
}
')
add(1, 2)
# Source C++ file
sourceCpp("functions.cpp")
# R to Python
py$df <- r_to_py(mtcars)
# Python to R
r_df <- py_to_r(py$df)
# Automatic conversion
reticulate::py_config()
# 1. Check availability
reticulate::py_available()
rJava::.jcheck()
# 2. Handle errors
tryCatch({
py_run_string("import nonexistent")
}, error = function(e) {
message("Python error: ", e$message)
})
# 3. Clean up resources
# Java: .jgc()
# V8: ctx$reset()
R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".
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R iml package for interpretable ML. Use for model-agnostic interpretability methods.
R lime package for local explanations. Use for explaining individual predictions with local interpretable models.
R packages for ML interpretability. Use for explaining and interpreting machine learning models.
R vip package for variable importance. Use for computing and visualizing variable importance scores.