A Fine Slice Of SQL Server
Adrian Tam continues a primer on R:
R is a procedural programming language. Therefore, it has the full set of flow control syntax like many other languages. Indeed, the flow control syntax in R is similar to Java and C. In this post, you will see some examples of using the flow control syntax in R.
Read on for examples of flow control (if/else, for, etc.) and creating functions.
Comments closedTomaz Kastrun forgot to remove The Club from his REPL:
Another one from the Leetcode challenge. This time, get the elements (single values) from the matrix in a spiral order with a starting position of [1,1].
So, the basic idea is to retrieve a vector of elements from a matrix in the following order:
Probably not something you’d use with any frequency, but it’s a fun way to learn how to operate within matrices.
Comments closedAdrian Tam continues a series on getting started in R:
The ecosystem in R contains not only the function libraries to help you perform statistical analysis but also the data library that gives you some famous datasets to test out your program. There are a lot of built-in datasets in R. In this post, you will:
- Learn some of the built-in datasets
- Know how to use these datasets
Let’s get started.
Most of these built-in sets are fairly small and able to help you illustrate a specific point.
Comments closedAdrian Tam continues a series on getting started with R:
Vectors in R is the native way of handling data. In addition to the vector operations you saw in the linear algebra textbook, R supports a lot more. In this post, you will learn about:
- How to manipulate a vector
- How to treat vectors as sets
Read on for a few examples of how to work with vectors, including in set comparisons.
Comments closedSteven Sanderson shows us two libraries to plot two histograms:
Histograms are a powerful tool for visualizing the distribution of numerical data. They allow us to quickly understand the frequency distribution of values within a dataset. In this tutorial, we’ll explore how to create multiple histograms using two popular R packages: base R and ggplot2. By the end of this guide, you’ll be able to confidently display multiple histograms on a single graph using both methods.
Click through for more than two examples.
Comments closedSteven Sanderson reviews the shape of the data:
Understanding the distribution of your data is a fundamental step in any data analysis process. It gives you insights into the spread, central tendency, and overall shape of your data. In this blog post, we’ll explore two popular functions in R for visualizing data distribution:
density()andhist(). We’ll use the classic Iris dataset for our examples. Additionally, we will introduce the{TidyDensity}library and show how it can be used to create distribution plots.
Click through for three different functions for visualizing the density of a variable.
Comments closedSteven Sanderson tracks the sixth number of a five-number summary:
Data visualization is a powerful tool for understanding and interpreting data. In this blog post, we will explore how to create box plots with mean values using both base R and ggplot2. We will use the famous iris dataset as an example. So, grab your coding tools and let’s dive into the world of box plots!
Note that this is mean in addition to median in these visuals, not replacing the median.
Comments closedI would like to illustrate a way which omitted variables interfere in logistic regression inference (or coefficient estimation). These effects are different than what is seen in linear regression, and possibly different than some expectations or intuitions.
This is an interesting article and there’s a really good comment helping to explain this effect in epidemiology.
Comments closedAdrian Tam continues a series on core data types in R:
Vectors in R are supposed to be of homogeneous data type. You can use a list as the container if there are mixed data types, such as numbers and strings. The list and data frame are closely related in R. The data frame is probably more useful because it reflects how we usually collect statistics. In this post, you will learn about them. Specifically, you will know:
- What are lists and data frames in R
- How to manipulate lists and data frames
Read on to learn more about these two sorts of collections.
Comments closedSteven Sanderson builds up a box plot:
Are you ready to dive into the world of data visualization in R? One powerful tool at your disposal is the box plot, also known as a box-and-whisker plot. This versatile chart can help you understand the distribution of your data and identify potential outliers. In this blog post, we’ll walk you through the process of creating box plots using R’s
ggplot2package, using theairqualitydataset as an example. Whether you’re a beginner or an experienced R programmer, you’ll find something valuable here.
Click through to learn what kind of information a box plot can provide, as well as how to create one using a variety of R libraries.
Comments closed