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R natural language processing packages. Use for text mining, sentiment analysis, topic modeling, tokenization, and text vectorization.
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
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".
R DALEX package for model explanations. Use for explaining complex machine learning models.
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.
| name | r-nlp |
| description | R natural language processing packages. Use for text mining, sentiment analysis, topic modeling, tokenization, and text vectorization. |
| Sub-skill | Description |
|---|---|
| r-nlp-text | tidytext, quanteda, text2vec |
| r-nlp-topic | LDA, STM, topic modeling |
| r-nlp-sentiment | sentimentr, syuzhet, lexicons |
Natural Language Processing and text mining in R.
| Package | Description |
|---|---|
| tidytext ★ | Tidy text mining (tidyverse style) |
| quanteda ★ | Quantitative text analysis |
| tm | Comprehensive text mining framework |
| text2vec ★ | Fast vectorization & word embeddings |
| NLP | Basic NLP functions |
| openNLP | Apache OpenNLP interface |
| Package | Description |
|---|---|
| stringr | Consistent string manipulation |
| stringi | ICU-based string processing |
| SnowballC | Snowball stemmers |
| koRpus | Text analysis package |
| utf8 | UTF-8 text handling |
| Package | Description |
|---|---|
| syuzhet | Sentiment extraction (3 dictionaries) |
| sentimentr | Sentence-level sentiment |
| tidytext | Sentiment lexicons (AFINN, Bing, NRC) |
| Package | Description |
|---|---|
| topicmodels | LDA and CTM topic models |
| LDAvis | Interactive topic model visualization |
| stm | Structural topic models |
| Package | Description |
|---|---|
| zipfR | Word frequency distributions |
| MonkeyLearn | MonkeyLearn API interface |
| corporaexplorer | Dynamic text collection exploration |
# tidytext workflow
library(tidytext)
library(dplyr)
# Tokenize
df %>%
unnest_tokens(word, text) %>%
anti_join(stop_words) %>%
count(word, sort = TRUE)
# Sentiment analysis
df %>%
unnest_tokens(word, text) %>%
inner_join(get_sentiments("bing")) %>%
count(sentiment)
# TF-IDF
df %>%
unnest_tokens(word, text) %>%
count(document, word) %>%
bind_tf_idf(word, document, n)
# quanteda
library(quanteda)
corpus <- corpus(texts)
tokens <- tokens(corpus, remove_punct = TRUE)
dfm <- dfm(tokens) %>%
dfm_remove(stopwords("en")) %>%
dfm_trim(min_termfreq = 5)
# Topic modeling
library(topicmodels)
dtm <- cast_dtm(df, document, word, n)
lda <- LDA(dtm, k = 5, control = list(seed = 1234))
topics <- tidy(lda, matrix = "beta")
# text2vec word embeddings
library(text2vec)
it <- itoken(texts, tokenizer = word_tokenizer)
vocab <- create_vocabulary(it)
vectorizer <- vocab_vectorizer(vocab)
tcm <- create_tcm(it, vectorizer, skip_grams_window = 5)
glove <- GloVe$new(rank = 50)
word_vectors <- glove$fit_transform(tcm, n_iter = 10)
library(tidytext)
library(dplyr)
library(stringr)
clean_text <- df %>%
mutate(
text = str_to_lower(text),
text = str_remove_all(text, "[[:punct:]]"),
text = str_remove_all(text, "[[:digit:]]"),
text = str_squish(text)
) %>%
unnest_tokens(word, text) %>%
anti_join(stop_words) %>%
mutate(word = SnowballC::wordStem(word))
# tidytext to DTM
dtm <- df %>%
unnest_tokens(word, text) %>%
count(doc_id, word) %>%
cast_dtm(doc_id, word, n)
# quanteda DFM
dfm <- corpus(texts) %>%
tokens() %>%
dfm()