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R-bloggers | R news and tutorials contributed by (750) R bloggers - Software

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R-bloggers | R news and tutorials contributed by (750) R bloggers

02.07.2025

Explore Neural Networks Interactively with Quarto Live! | R-bloggers

What is Quarto Live? Quarto Live combines Quarto with WebR to enable interactive R code execution directly in the browser allowing for: Running R code directly in the web browser Instant feedback when modifying code examples No installation requirements Easy sharing of interactive documents The following code blocks are fully interactive. Feel free to modify parameters and execute the code! Creating a Simple Dataset The first step is to create a dataset for training the neural network: Try this: Modify the set.seed() value to generate different random data Change the sample size from 100 to a different value Add a third feature variable to the dataset Understanding Neural Networks Neural networks are computational models inspired by the human brain. A basic neural network consists of: Input layer: The data features (x1 and x2 in this example) Hidden layers: The intermediate processing layers where learning occurs Output layer: The final prediction layer (the y value) Building a Basic Neural Network Key components: Input nodes (x1, x2) feed data into the network Three hidden neurons process the inputs An output neuron produces the prediction Black lines represent connections, with thickness indicating weight strength Numbers show the actual weight values assigned to each connection Experimenting with Different Architectures Try modifying the hidden parameter and re-running the code snippet! Adding Multiple Layers Neural networks with multiple hidden layers can learn more complex patterns. Feel free to test the following variations: hidden = c(8) for a wide, single-layer network hidden = c(3, 3) for a balanced two-layer network Experiment with hidden = c(2, 2, 2) for a deep, narrow network Visualizing the Decision Boundary Since the sample problem is setup as a classification problem with only 2 input parameters, the decision boundary can be visualised: Evaluating Model Performance This section evaluates the neural network’s performance on unseen test data: Additional Resources The following resources provide further information about Quarto Live and neural networks: Quarto Live Documentation - Comprehensive guide to Quarto Live features WebR Documentation - Technical details about the WebR project Quarto Documentation - Complete information about the Quarto publishing system

R-bloggers | R news and tutorials contributed by (750) R bloggers

30.06.2025

ggplot dotplot using R | geom_dotplot, dot plot using ggplot2 | R-bloggers

Beyond just plotting points on a chart, how can you transform a simple ggplot dotplot into a powerful narrative tool that reveals the hidden stories within your data's distribution, all while avoiding the common pitfalls of visual clutter and misinterpretation? A ggplot dotplot is a powerful data visualisation tool within the R programming language, specifically using the ggplot2 package. It represents individual data points as dots, stacking them in bins to show the distribution of a continuous variable. Unlike histograms, which aggregate data into bars, the geom_dotplot function allows you to visualise the frequency and spread of individual observations, making it invaluable for exploring small to moderate-sized datasets and comparing distributions across multiple groups. Its true power lies in its customizability, allowing you to control dot size, colour, and stacking to create precise and insightful graphics. Table of Contents KeyPoints See Every Single Data Point. Forget averages that hide the truth. A dot plot allows you to visualise every individual customer, score, or sale. This helps you spot the true story, like clusters or gaps in your data, that other charts might miss.Create Your First Plot in Seconds. Getting started is easier than you think. With just one line of R code, you can turn a column of data into an insightful visualization. This simple command is your first step to mastering the ggplot dotplot.ggplot(df, aes(x = CreditScore)) + geom_dotplot()Customise Your Plot to Stand Out. Don't settle for the default look. A few simple tweaks to the fill colour or it dotsize can make your chart much clearer and more professional. It’s how you go from a basic plot to a great one.Easily Compare Groups Side-by-Side. Want to see how different groups compare? Just add fill = YourGroup to your code. This is the most powerful feature of the dot plot, allowing you to instantly compare distributions for different categories, like policy types.ggplot(df, aes(x = Age, fill = PolicyType)) + geom_dotplot()Add Pro-Level Context with Layers. Tell the whole story by adding layers of summary statistics. You can place a transparent overlay geom_boxplot() over your dots to show both the individual data points and the overall summary in one powerful chart. Beyond the Bar Chart with ggplot2 When you need to see the real story behind your data, a simple bar chart doesn't always cut it. While useful, they often hide important details about the distribution of your numbers. This is where the ggplot2 R package comes in. It gives you the power to create a better kind of plot, one that shows every single data point. Moving to a visualisation like the dot plot is a game-changer for analysis. dot plot helps you see not just the "how much" but also the "how it's spread out." This guide will walk you through creating a ggplot dotplot, a crucial skill for anyone serious about data analysis in R. It’s a tool that brings clarity and depth to your work, allowing you to present findings with confidence and precision.Read More »

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