Does R Project Vector Computing Capability Beat Esproc?

Do you find Vector Computing tiresome while using statistical computing tools

? Here we go for a Vector Computing Comparison: R Language vs. esProc. To me, one of the most attractive features of R language and esProc is that their codes are both agile, that is, only requiring a few lines of codes to implement plentiful functions. For example, both of them allow for composing Vector Computing expression, simplify the judgment statements, extend the basic functions to the advanced ones, and support the generic type. In which, regarding the vector computing, they are characterized with the massive data processing through functions and operators, so as to avoid the loop statement. Users can benefit from 2 resulting advantages: first, easy to grasp for business experts and keep the learning cost low; second, easy to implement the parallel computation and improve the performance.

In order to show users the subtle differences between R and esProc on vector computing, we will go on with several examples below.

Firstly, let's check the most basic functions like vector value getting and assigning. For example, get 5 values of vectors whose subscripts are from 5 to 10, and replace them with another 5 values.

R solution:

01A1

by: Datakeyword

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