R Libraries Every Data Scientist Should Know

I have been using R for the longest time in my professional life, I realized that R outclasses Python in several use cases, particularly for statistical analyses. As well, R has some powerful packages that were built by the world’s biggest tech companies, and they aren’t in Python! And so, in this article, I wanted to go over three R packages that I highly recommend that you take the time to learn and add to your arsenal of tools because they are seriously powerful tools. Without further ado, here are three R packages that every data scientist should know:

R Libraries Every Data Scientist Should Know
R Libraries Every Data Scientist Should Know

1. Causal Impact (Google)

The package is designed to make a counterfactual inference as easy as fitting a regression model, but much more powerful, provided the assumptions above are met. The package has a single entry point, the function CausalImpact(). Given a response time series and a set of control time series, the function constructs a time-series model, performs posterior inference on the counterfactual, and returns an CausalImpact object. The results can be summarized in terms of a table, a verbal description, or a plot.

2. Robyn (Meta / Facebook)

Robyn is an automated Marketing Mix Modeling (MMM) code. It aims to reduce human bias by means of ridge regression and evolutionary algorithms, enables actionable decision making provides a budget allocator and diminishing returns curves and allows ground-truth calibration to account for causation

3. Anomaly Detection (Twitter)

AnomalyDetection is an open-source R package to detect anomalies that is robust, from a statistical standpoint, in the presence of seasonality and an underlying trend. The anomaly detection package can be used in a wide variety of contexts. For example, detecting anomalies in system metrics after a new software release, user engagement post an A/B test, or problems in econometrics, financial engineering, political and social sciences.

Download: Data Science with R: A Step-by-Step Guide

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