The ggplot2 package is an essential tool in every data scientists toolkit. Today we show you how to use ggplot2 to make a professional heatmap that organizes customers by their sales purchasing habits.

Reading many CSV files is a common task for a data scientist. In this free tutorial, we show you 3 ways to streamline reading CSV files in Python.

furrr is a critical package to speed up iterative calculations using tidyverse purrr syntax.

I'm super excited to introduce the experimental feature for performing iterative forecasting.

ggalt is a ggplot2 extension that adds many new ggplot geometries. In this tutorial, we'll learn how to make lollipop plots for comparing categories within our data using geom_lollipop().

ggalt is a ggplot2 extension that adds many new ggplot geometries. In this tutorial, we'll learn how to make dumbbell plots for visualizing change within our data using geom_dumbbell().

SweetViz is a Python library that makes exploratory data analysis (EDA) fast and effective. Learn how to investigate feature relationships using correlation and associations in the automated SweetViz report.

ggforce is a ggplot2 extension that adds many exploratory data analysis features. In this tutorial, we'll learn how to make hull plots for visualizing clusters or groups within our data.

The ggdist package is a ggplot2 extension that is made for visualizing distributions and uncertainty. We'll show see how ggdist can be used to make a raincloud plot.

I'm super excited to introduce the new panel data forecasting functionality in modeltime. It's perfect for making many forecasts at once without for-loops.

The easystats performance R package makes it easy to investigate the relevant assumptions for regression models. Simply use the check_model() function to produce a visualization that combines 6 tests for model performance.

Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem.