R for data analytics
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R was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand to teach introductory statistics. It is an open source software environment excellent for data analysis and graphics using functions in an interpreter. R is used across many industries and can analyze both structured and unstructured data to explore datasets and build predictive models.
R for data analytics
- 2. www.Cjayss.Com Introduction: R was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. They created R as a language to help teach introductory statistics to their students. They based R on the S language that was developed earlier at Bell Labs in the 1970s. After some time they made R available as an open source GNU project. A very active R community now exists around the world. History of R
- 3. R is a software environment that is excellent for data analysis and graphics. R is considered a Domain Specific Language as it was designed primarily for data analysis. R programs are typically created using functions and the programs are executed by an R interpreter. R is not just a programming language as it has native support for creating high quality data visualizations. R is used across many industries retail, financial, Insurance, Healthcare etc. R can be used to analyze both structured and unstructured datasets. R can help you explore a new dataset and perform descriptive analysis. R is also excellent at building predictive models. www.Cjayss.Com What is R
- 4. Data Analyst or Data Scientist R can be used to dig deeper into your data than is possible using spreadsheet- based tools alone Software developer R can enable data analytics computations and graphics into new or existing applications with minimal effort. Big Data usage- there are many new scenarios where using R is an excellent choice to help meet user demands. www.Cjayss.Com Who can Use
- 5. For Data Analysts: R can be used to perform classical statistical tests and predictive models. R also has native support for handling time- series datasets. Classification and clustering models can be used to better detect patterns. www.Cjayss.Com Who can Use
- 6. For Developers: Since R scripts are interpreted because it is INTERPRETER based language it encourages an interactive approach to development. R scripts are typically written using expressions and built-in functions. R provides native support for many useful types of data structures. External libraries can be used to extend the capabilities of R. You can define your own functions and possibly new Classes to meet the demands of your users. www.Cjayss.Com Who can Use