A Fine Slice Of SQL Server
Steven Sanderson puts a halt to things:
When working with time series data, one common challenge is dealing with non-stationary data. Non-stationary time series can be a headache for analysts, but fear not, because we have a handy tool to make your life easier. Say hello to the
auto_stationarize()function from the {healthyR.ts} package.
Read on to learn why you want stationary data for time series analysis and how the auto_stationarize() function works.
Steven Sanderson isn’t just spinning in place:
Before we delve into the
ts_adf_test()function, let’s understand the concept behind it. The Augmented Dickey-Fuller (ADF) test is a crucial tool in time series analysis. It’s like the Sherlock Holmes of time series data, helping us detect whether a series is stationary or not. Stationarity is a fundamental assumption in time series modeling because many models work best when applied to stationary data.So, why “Augmented”? Well, it’s an extension of the original Dickey-Fuller test that accounts for more complex relationships within the time series data.
Click through to see how you can use the ts_adf_test() function to get a better feel for whether a time series is stationary.
Michael Mayer has a new package:
The current version offers:
- H statistics per feature, feature pair, and feature triple
- multivariate predictions at no additional cost
- a convenient API
- other important tools from explainable ML:
- performance calculations
- permutation importance (e.g., to select features for calculating H-statistics)
- partial dependence plots (including grouping, multivariate, multivariable)
- individual conditional expectations (ICE)
- Case-weights are available for all methods, which is important, e.g., in insurance applications.
Click through for an example of how it works, followed by some simple benchmarking to give you an idea of how it performs compared to similar tools.
Comments closedSteven Sanderson flattens the legend:
Creating a horizontal legend in base R can be a useful skill when you want to label multiple categories in a plot without taking up too much vertical space. In this blog post, we’ll explore various methods to create horizontal legends in R and provide examples with clear explanations.
Read on for two demos, one with a single legend and one which creates two legends. I’m not so sure about how valuable the latter is (because you’re splitting valuable information into two places, losing some of the glanceability of a chart along the way), but it is interesting that you can do it.
Comments closedBefore diving into code examples, let’s understand the basics. In R, legends are essential for explaining the meaning of different elements in your plot, such as colors, lines, or shapes. Legends help your audience interpret the data effectively.
In most cases, R’s base plotting system provides you with control over the legend’s size. The key functions we’ll explore are
legend()andguides(). We’ll also delve into how to modify legend size in popular plotting packages likeggplot2.
Click through for those demonstrations.
Comments closedTomaz Kastrun says reading is fundamental:
Trick your brain into faster reading with the help of Bionic Reading. With the help of highlighting part of the words, it “guides your eyes over the text and the brain remembers previously learned words more quickly.” (source: br-about)
Here is a beautiful example of how text with the use of opacity, colours, size and many other elements can be quickly achieved for faster reading.
Click through for an example and how to implement it in R.
Comments closedSteven Sanderson has radar love:
Radar charts, also known as spider, web, polar, or star plots, are a useful way to visualize multivariate data. In R, we can create radar charts using the fmsb library. Here are several examples of how to create radar charts in R using the fmsb library:
Radar charts are a guilty pleasure of mine. They are rarely the right choice, but when they are, I love it so much.
Comments closedSteven Sanderson is not limited to one axis:
Boxplots are a great way to visualize the distribution of a numerical variable. They show the median, quartiles, and outliers of the data, and can be used to compare the distributions of multiple groups.
Horizontal boxplots are a variant of the traditional boxplot, where the x-axis is horizontal and the y-axis is vertical. This can be useful for visualizing data where the x-axis variable is categorical, such as species or treatment group.
Click through for an example using base R and ggplot2.
Comments closedSteven Sanderson builds a tree:
Decision trees are a powerful machine learning algorithm that can be used for both classification and regression tasks. They are easy to understand and interpret, and they can be used to build complex models without the need for feature engineering.
Once you have trained a decision tree model, you can use it to make predictions on new data. However, it can also be helpful to plot the decision tree to better understand how it works and to identify any potential problems.
In this blog post, we will show you how to plot decision trees in R using the
rpartandrpart.plotpackages. We will also provide an extensive example using the iris data set and explain the code blocks in simple to use terms.
Read on to see an example of how to do this.
Comments closedSteven Sanderson wants things in the right order:
The first example shows how to order the boxplots based on a specific order for the variable being plotted. We will use the built-in airquality dataset in R. The following code shows how to order the boxplots based on the following order for the Month variable: 5, 8, 6, 9, 7.
Click through to see that result, as well as two other examples.
Comments closed