Should We Use R or Excel for Linear Programming?
Linear programming is the technique of analyzing a number of linear inequalities relating to a phenomenon and identifying the “best” value that can be obtained under certain conditions. In the real world, linear programming is used to determine the most effective way to utilize limited resources to attain a given goal. Both R and Excel can be used to solve linear programming problems. In this article, our R programming assignment helpers explore how these two applications are used in linear programming to find out which one is a better option.
Elements of linear programming problem
According to our linear programming assignment help providers, for linear programming to be applied in data analysis, the problem being solved must contain the following elements:
- Decision variables
When approaching a problem, the decision variables are usually unknown. These variables contain values that minimize or maximize the objective function and one must identify them before performing linear programming.
- Objective function
Objective functions are statistical functions that use decision variables to express the goal (s) that one hopes to achieve from the data being analyzed.
These can be defined as statistical functions that use decision variables to show boundaries on the expected solutions.
- Variable bounds
Since decision variables are not allowed to contain any values, they usually have bounds, for example, ≥ 0.
Using R for linear programming
R is both a programing language and a statistical environment. The environment comes fully packed with features and tools for calculating, manipulating, and displaying data. Among these is IpSolve, one of the most commonly used tools for linear programming. The IpSolve is called in R using a module or extension. As such, it looks like the tool is completely integrated with R.
With IpSolve, you can transfer matrices between the R environment to IpSolve and vice versa. The IpSolve interface is written in C to ensure ease of navigation and maximum performance. The tool itself offers high-level functions for solving both basic and complex integer (linear) programming problems. It also comes with an R API mirroring feature that increases the performance of these functions. However, this feature has a steeper learning curve. To find out more about the tools that make it possible for data analysts to perform linear programming in R, connect with our R programming assignment helpers.
Using Excel for linear programming
MS Excel has a plugin called the Solver that makes it possible to perform calculations involving linear programming equations or inequalities. If the Solver is not enabled in the Excel Add-in section, you will not be able to solve linear equations. There are two methods through which one can find solutions for linear equations in Excel. One uses constraints while the other one uses tables with defined rows and columns within the worksheet. Data analysts prefer using constraints because it is a much faster method. To understand how Excel Solver works, liaise with our Excel homework helpers.
Which one is better?
Whether to choose R or Excel for linear programming depends on personal preferences. Both programs are fully equipped with the tools necessary to solve linear programming problems. R, however, has a steeper learning curve and may not be the best option for a person who is just getting started with linear programming. The Excel Solver is much easier to use and can be used by data analysts of all skill levels.