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.