Using R for Statistical and Data Analysis

Companies are investing a lot of money and resources in analyzing data. These data are gathered from their customers, website traffic and online sales. The amount of information collected is called big data. Keeping the report is called data warehousing, and going through the data is called data mining.

Through all of these is the evolution of the necessary tools. Data warehousing and mining initially used mainframes and expensive software like SAS and Teradata, with the data stored in large databases using Oracle. New tools are now available to run on workstations and PCs.

One of the most popular computer statistical tools is R, based on S. It is an interpreted language developed for statistics, data science, analytics and visualization. It is a powerful language that can be integrated or extended with other languages and tools. 

Advantages of R

The extensibility of the language and the multi-platform implementation make it a welcome tool for all levels of users. It can be installed on a laptop, workstation, or mainframe. It has versions for Unix, Linux, Windows, Mac OS X, and mainframe operating systems as an open-source project. It has been integrated with other tools to access information as a statistical system. It can connect with Oracle, non-relational, and text or raw files.

R is not a database system. Instead, like all statistical analysis systems, it uses a flat file that already exists. This requires either extending the language to access the data straight from another system or for the source system to export the data to a flat file. Fortunately, exporting flat files is a common feature of almost all databases, archives and repositories. Using a flat file is necessary for R because it allows for faster analysis of very large amounts of data. A flat file is smaller than any other data format without any index, formatting, or embedded data. 

Systems that generate big data choose flat files because it is faster to write and easier to keep in one place. Telecommunications companies, consumer manufacturers, eCommerce, researchers and big companies generate large amounts of data quickly. Typically, they end up with black hole storage, where the data comes in and does not. Sifting through these data requires a fast system for analysis.

Time series analysis, machine learning, trending, and surveys use R to make sense of the data. 

The development of R followed the open-source system and resulted in faster user adoption. Anyone can develop an R system for any computer if the implementation follows the standards. As an open-source system, users can review the code. Companies that do surveys can be sure that the data analysis is correct because they can follow the R code. They do not have to use it blindly. In addition, the open source community allows users to add or revise code and contribute to the further development of the language.

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Small Footprint

Statistics are math-heavy and coupled with a large amount of data. Understandably, the most popular statistical systems are also large, complex and comprehensive. The open-source development of S and R has created a revolution of sorts, and a small footprint is an advantage for a statistical package system.

Understandably, R is easy to learn and downloadable and free to use. If you want to learn how to use R, you can do so in the confines of your home. This is very convenient for newbies who don’t know anything about statistics or data analytics. Students and beginners can focus on the task at hand rather than on the science of statistics.

The science of statistics is hard enough to understand and requires a lot of knowledge and appreciation of math. With the proper tools, a data analyst can skip the theoretical knowledge and go straight to the task. Knowing the tools is different from knowing the theories behind the tools. However, knowing the tools well can jumpstart a data analysis project.

R covers a full suite of statistical tools and packages. Since it is small enough to run on a laptop or desktop, users and beginners can have it running almost immediately. The documentation is complete, readable and digestible. Experienced users and programmers know that documentation is the most important part of any package or library. Users can dive in and research the functions they need at that time.

Efficiency, quick implementation and repeatability are necessary for research or analysis. R users are happy to create elegant processes on the hardware available. A lot of data is being generated daily, and understanding what the data means requires a tool that can dive deep and quickly.

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