Category: Visualization

Steven Sanderson shows multiple plots on one image:

Data visualization is a crucial tool in the data scientist’s toolkit. It allows us to explore and communicate complex patterns and insights effectively. In the world of R programming, one of the most powerful and versatile packages for data visualization is ggplot2. Among its many features, ggplot2 offers the facet_grid() function, which enables you to create multiple plots arranged in a grid, making it easier to visualize different groups of data simultaneously.

In this blog post, we’ll dive into the fascinating world of facet_grid() using a practical example. We’ll generate some synthetic data, split it into multiple groups, and then use facet_grid() to create a visually appealing grid of plots.

Read on for the demo script. The text talks about facet_grid() and the demo is facet_wrap(). The two behave very similarly, though they have slightly different use cases.

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Adrian Tam continues a series on R:

One of the most popular plotting libraries in R is not the plotting function in R base, but the ggplot2 library. People use that because it is flexible. This library also works using the philosophy of “grammar of graphics”, which is not to generate a visualization upon a function call, but to define what should be in the plot, and you can refine it further before setting it into a picture. In this post, you will learn about ggplot2 and see some examples. In particular, you will learn:

• How to make use of ggplot2 to create a plot from a dataset
• How to create various charts and graphics with multiple facades using ggplot2

It takes a little while to understand the grammar of graphics approach that ggplot2 takes, but once you do, you realize just how good this library is for generating static images.

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Steven Sanderson shows how we can create a pairs plot using the pairs() function in R:

A pairs plot, also known as a scatterplot matrix, is a grid of scatterplots that displays pairwise relationships between multiple variables in a dataset. Each cell in the grid represents the relationship between two variables, and the diagonal cells display histograms or kernel density plots of individual variables. Pairs plots are incredibly versatile, helping us to identify patterns, correlations, and potential outliers in our data.

Click through for one example, how to interpret it, and how to customize the outputs.

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Steven Sanderson goes frequentist on us:

Linear regression is a fundamental statistical technique used to model the relationship between a dependent variable and one or more independent variables. While fitting a linear model is relatively straightforward in R, it’s also essential to understand the uncertainty associated with our model’s predictions. One way to visualize this uncertainty is by creating confidence intervals around the regression line. In this blog post, we’ll walk through how to perform linear regression and plot confidence intervals using base R with the popular Iris dataset.

Click through to see how, even if you’re a Bayesian who considers confidence intervals to overstate precision in reality.

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Steven Sanderson builds a scatter plot:

Data visualization is a powerful tool for gaining insights from your data. Scatter plots, in particular, are excellent for visualizing relationships between two continuous variables. But what if you want to compare multiple groups within your data? In this blog post, we’ll explore how to create engaging scatter plots by group in R. We’ll walk through the process step by step, providing several examples and explaining the code blocks in simple terms. So, whether you’re a data scientist, analyst, or just curious about R, let’s dive in and discover how to make your data come to life!

Click through for several examples of plot generation.

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Steven Sanderson divvies out buckets for a histogram:

Histograms divide data into bins, or intervals, and then count how many data points fall into each bin. The breaks parameter in R allows you to control how these bins are defined. By specifying breaks thoughtfully, you can highlight specific patterns and nuances in your data.

Click through to see how you can use the breaks parameter in a few different ways to customize your histogram. The default breaks in R are often reasonable, but trying a few different breaks can help you get a better understanding of the actual distribution of the data.

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