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Python Environment

library(TextAnalysisR)

tokens <- quanteda::tokens(SpecialEduTech$abstract[1:5])
dispersion <- calculate_lexical_dispersion(
  tokens,
  terms = c("learning", "instruction")
)
head(dispersion)
##   doc_id        term  position doc_length
## 1  text2    learning 0.5200000         25
## 2  text3    learning 0.3714286         35
## 3  text3 instruction 0.3142857         35
## 4  text3 instruction 0.6857143         35
## 5  text5    learning 0.7904762        105

Python enables features: NLP with spaCy, embeddings, and neural sentiment analysis.

Quick Setup

setup_python_env() automatically:

  1. Creates virtual environment textanalysisr-env
  2. Installs spacy and pdfplumber
  3. Downloads spaCy English model (en_core_web_sm)

Uses virtualenv (or conda if available).

Check Status

Run check_python_env() to verify the environment.

Common Issues

“Another Python already initialized”

Set preferred environment in .Rprofile with Sys.setenv(RETICULATE_PYTHON_ENV = "textanalysisr-env"), then restart R.

Environment in OneDrive

Avoid OneDrive paths. Use setup_python_env(envname = "textanalysisr-env").

spaCy Models

The default en_core_web_sm model is installed automatically. For word vectors (similarity), the medium model is 91 MB and the large model is 560 MB:

python -m spacy download en_core_web_md
python -m spacy download en_core_web_lg

Deep Learning (Optional)

For embeddings and neural sentiment:

pip install sentence-transformers transformers torch

Diagnostics

Use reticulate::py_config() and reticulate::virtualenv_list() to inspect the active Python.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.