How to use pymovements in R#

This guide shows how to use pymovements from R via the reticulate package.

Install and load reticulate in R#

install.packages("reticulate")

Load the package.

library(reticulate)

Installing pymovements.#

py_install("pymovements")

If this fails, create a dedicated python environment and make sure to point R to it.

Set up a dedicated environment#

Skip this step if you already have an environment containing pymovements.

1. using reticulate functionality in R#

reticulate::install_miniconda()
pymovements_packages <- c(
  "python==3.9",
  "pymovements"
)
reticulate::conda_create("pymovements_env", packages = pymovements_packages, pip = TRUE)

2. using terminal#

If you work with Conda:

conda create -n pymovements_env python=3.9 # supported: 3.9–3.13
conda activate pymovements_env
conda install -c conda-forge pymovements

If you prefer virtualenv:

python -m venv pymovements_env
# Activate the environment:
# macOS/Linux:
source pymovements_env/bin/activate
# Windows:
pymovements_env\Scripts\activate
pip install pymovements

Point R to use your Python environment#

If you used Conda:

use_condaenv("pymovements_env", required = TRUE)

If you used virtualenv:

use_virtualenv("pymovements_env", required = TRUE)

Working with pymovements#

Import pymovements as pm.

pm <- import("pymovements")

Now pymovements should appear as pm under values in your environment

Access functions and data within python modules and classes via the $ operator

To test, you can proceed with the “pymovements in 10 minutes” tutorial, for example this is how you download the ToyDataset:

dataset = pm$Dataset('ToyDataset', path='data/ToyDataset')
dataset$download()

Now let’s load in the dataset into R and display the found files:

dataset$load()
dataset$fileinfo