SAS SGPLOT already provides the necessary graphical elements for complex visualization. 3D or high-dimensional data can be easily visualized after being projected appropriately. With SAS' strong support for animation features, SAS users can create complicated 3D or high dimension visualizations quickly.
SAS can generate animations in GIF or SVG format. The SAS system option ANIMDURATION can specify the duration of a frame corresponding to the appropriate FPS.
By starting from a set of purely declarative statements, any zebra puzzle with limited constraints can be tagged and solved quickly in the same way without changing any code.
The Missionaries and Cannibals Problem (MCP) is a classic river-crossing logic puzzle. With SAS, we can solve this problem, visualize the solutions with SAS NETDRAW procedure and generate steps for any MCP problems.
This article introduces how to solve the pirate game with a recursive solving algorithm in SAS, and how to analyze and visualize the law behind the complex logic of the pirate game. If you join in a pirate game next time, you can know your destiny ahead of time to reap the benefits and avoid getting killed.
Let's take a look at the design and implementation of SAS functions in financial calculations. We'll do this through examples calculating and analyzing the monthly payment, interest, and principal for CPM/CAM mortgages.
In many programming languages, there is a function named eval() that can be used to evaluate an expression and return the result at run time. For example, in Python, the eval() function parses the expression passed to it and runs a Python expression or code within the program. Even Python
In natural language processing, word vectors play a key role in making technologies such as machine translation and speech recognition possible. Let's talk about how to transform word vector tables from long to wide in SAS, so we can potentially get sentence vectors to process further.
This article discusses how to use SAS to filter variables in a dataset based on the percentage of missing values or duplicate values. The missing value statistics can be implemented by either DATA step programming on your own or reusing the existing powerful PROC FREQ.
With time series data analysis, we can apply moving average methods to predict data points without seasonality. This includes Simple Average (SA), Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Moving Average (EMA), etc. For series with a trend but without seasonality, we can use linear, non-linear and autoregressive