In our last post, we implemented our own k-means clustering algorithm in R! Today, we'll explore k-means clustering some more with a Code Lab to see how we can use the algorithm we coded up last time to cluster pixels in an image!
So far, we've been working with single numbers in our posts. Many kinds of data, however, can be represented by matrices. In order to discuss and learn about methods designed for data stored in matrices, today's post is a quick tutorial on getting started with linear algebra in R!
In our first code lab on estimating pi with dart throwing, we talked briefly about algorithms. If you're new to algorithms, we can think of them as recipes, or instructions, for doing or computing something. Today, we'll dive a little deeper into some aspects of algorithms.
In our last two posts, we went over how to start making data visualizations in R with ggplot2. Now that we've finished that series, let's work on a Code Lab featuring exploratory data analysis! Today, we’ll be exploring patterns in urban bike share usage...
In our last post on Getting Started with Data Visualizations in R, we went over how to start using ggplot2 in R. We learned how to set up a basic scatterplot and how to change the colors of the points via a variety of methods. We also learned how to update our plot for categorical variables, and how to add labels, change fonts, and alter the legend.
Have you ever wondered how to make colorful and interesting plots and charts for data visualization? Today’s post is Part 1 of a two-part series on getting started with data visualizations in R! Throughout this tutorial, we’ll be using ggplot2, a very useful R package that we can use to make some really great and professional-looking plots and figures for visualizing data.
This is the first post in a new Career Chat series featuring interviews with mathematicians, statisticians, data scientists, and people who work with math, statistics, and data.
In our last post, we approximated the mathematical constant \(\pi\) with simulated dart throwing in R. We also saw that, in general, as we increased the number of darts we threw, our estimate for \(\pi\) generally became more accurate. In this post, we'll see how can we can perform uncertainty quantification with the Central Limit Theorem for our \(\pi\) estimate. We’ll refer to the estimate that we compute for \(\pi\) as \(\hat{\pi}\).
Now that we’ve had several posts on getting started with coding in R (see Part 1, Part 2, and More Resources) we’re ready to get started with our first Code Lab! In this post, we’ll see how we can estimate pi with dart throwing in R!
When we shop online, we often get recommendations for other products that are similar to ones we've been looking at. Systems that recommend related products and services are frequently referred to as recommendation systems. In today's Code Lab, we'll code a simple recommendation system using something called cosine similarity!
In our last two posts, we went over how to start making data visualizations in R with ggplot2. Now that we've finished that series, let's work on a Code Lab featuring exploratory data analysis! Today, we’ll be exploring patterns in urban bike share usage...
Now that we’ve had several posts on getting started with coding in R (see Part 1, Part 2, and More Resources) we’re ready to get started with our first Code Lab! In this post, we’ll see how we can estimate pi with dart throwing in R!
