Journal of Statistics Applications & Probability

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An efficient strategy for managing data (exchange and manipulation) is essential for organization to run its data operations. In current scenario, when open-source computation platforms are widely used. The strategy guideline is need of the hour. It makes data flow efficient and fast in an information system. The work focused on benchmarking data exchange activities between data (excel and flat files) and R under the light of major computational frameworks native R [6], readr [7] and data.table [8]. It was concluded that for reading and writing excel files readxl [11] and writexl [10] are most efficient frameworks while for working with flat files data.table become un-disputed leader for both import and export exercises. These frameworks have out-performed when compared to its competitive frameworks like writexl and openxlsx [9] for readxl and writexl. data.table” found superior than native R implementation and data.table. Organization can consider these findings as guidelines and implement in their standard operating procedures so that data operations could me robust and more efficient. This will result into cost saving and optimum utilization of man-power and resources/hardware

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