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Comparison Table : Attribute R ( ) Python Rapidminer?

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(4.4) Comparison Table
Attribute R( ) Python RapidMiner purpose R emphases on data analysis, statistics and graphical models Python focuses on efficiency and code readability It offers a unique mix of power, analytics capabilities and versatility
Used By R is more preferred by statistician and data scientist Python is preferred by programmers who want to analysis or perform statistical techniques RapidMiner is preferred by who neither statistician nor programmer. Mostly business analytics use this.
Public R is more adopted by researchers, data scientists, statisticians, quants Python is more adopted by software developers and programmers RapidMiner is more adopted by …show more content…

accessibility Use to rPython package to run python code.Get python data from python , call python functions or methods Use the RPy2 library to run R code within python. It provides a low level interface from python to R From marketplace Python Scripting and R Scripting operators can be downloaded by dragging those operators process can be run
Usage R is basically used when the data analysis tasks needs individual computing or analysis on discrete server Python is basically used when the data analytics task need to be combined with web apps or if statistics program needs to be combined into a production database RapidMiner basically used in business purpose for data analysis
Task Statistical models can be transcribed with a few lines of codes and experimental work R is very easy for beginners Python is good tool for implement algorithms for invention use RapidMiner has good balance between statistics and natural language and create a plan to prepare the data automatically
Scripting difference Boolean true/false values are written in all caps in R (TRUE/FALSE) and NONE while Python uses first letter capitalisation (True/False) And None Interactive code editor and syntax high-lighting
Coding difference Base R uses nested tasks, where each function is called with “function_name()” and we specify columns with “object_name$column_name”. Python concatenates the object name (“df) with the column name and the functions that we want to run on this

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