.. _ts-getting-started-with-r:

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Getting Started with R
========================================================================

**Stage 1 · 🧭 Orientation**  ·  Lesson 02 of 18  ·  *beginner*

:doc:`◀ Previous · What Are Time Series, and How Are They Used? <01-what-are-time-series-and-how-are-they-used>`   ·   :doc:`Next · A Gentle Introduction to Stationarity ▶ <03-a-gentle-introduction-to-stationarity>`   ·   :doc:`↑ Section <index>`


.. important::

   **✨ AI-generated content.** This page was written with the assistance of an
   AI language model and is provided as a learning aid. Despite careful
   review, it may still contain mistakes, omissions, or out-of-date
   information. Whether you are new to the topic, a team lead, or a senior
   practitioner, treat it as a starting point rather than an authoritative
   reference: read it critically and independently verify anything you act on
   (code, commands, figures, and factual claims) against official
   documentation and primary sources before relying on it.

The toolkit
------------

The source course is taught in **R**, the classic Box–Jenkins environment. This reference
reframes the same ideas in **Python**, using the standard scientific stack: **NumPy** for arrays,
**pandas** for time-indexed data, **statsmodels** for the models (AR / ARMA / ARIMA / SARIMAX,
the ACF / PACF, and the ADF / KPSS stationarity tests), and **Matplotlib** for plots. The
mathematics is identical in either language; only the syntax differs.

Loading a series
------------------

In Python a time series is a ``pandas.Series`` (or a DataFrame column) carrying a
**DatetimeIndex**, so pandas knows the spacing and can resample, align and difference for you:

.. code-block:: python

   import pandas as pd

   s = pd.read_csv("sales.csv", parse_dates=["date"], index_col="date")["value"]
   s = s.asfreq("MS")          # pin an explicit monthly-start frequency
   s.plot(title="Monthly sales")

Setting an explicit frequency (``asfreq``) matters: many models need to know the season length.

From R to Python
------------------

The classic R verbs map cleanly onto the Python stack:

* ``ts()`` → a ``pandas.Series`` with a ``DatetimeIndex``;
* ``acf()`` / ``pacf()`` → ``statsmodels.graphics.tsaplots.plot_acf`` / ``plot_pacf``;
* ``arima()`` / ``Arima()`` → ``statsmodels.tsa.arima.model.ARIMA``;
* ``forecast()`` → ``results.get_forecast(steps=...)``;
* ``auto.arima()`` → ``pmdarima.auto_arima``.

Reach for whichever environment you like; this course uses the Python calls throughout.

.. hint::

   **Related lessons:** :doc:`What Are Time Series, and How Are They Used? <01-what-are-time-series-and-how-are-they-used>`  ·  :doc:`A Gentle Introduction to Stationarity <03-a-gentle-introduction-to-stationarity>`  ·  :doc:`Sample ACF and Sample PACF <10-sample-acf-and-sample-pacf>`

.. seealso::

   **Source article** Adapted (context, re-expressed) in our own words from: `https://insightful-data-lab.com/2026/01/17/getting-started-with-r/ <https://insightful-data-lab.com/2026/01/17/getting-started-with-r/>`__ (insightful-data-lab.com).

.. tags:: purpose: reference, topic: time series, level: beginner