Difference between revisions of "R"
Sudhakarst (Talk | contribs) |
Sudhakarst (Talk | contribs) |
||
| Line 4: | Line 4: | ||
'''R''' can be used for simple calculations, matrix calculations, differential equations, optimisation, statistical analysis, plotting graphs, etc. Also, it is useful to anybody who wishes to undertake extensive statistical computations and data visualization. | '''R''' can be used for simple calculations, matrix calculations, differential equations, optimisation, statistical analysis, plotting graphs, etc. Also, it is useful to anybody who wishes to undertake extensive statistical computations and data visualization. | ||
| − | |||
| Line 10: | Line 9: | ||
'''Note:''' Each numbered topic corresponds to a single spoken tutorial. Each bulleted point corresponds to a command or topic that must be covered in the given spoken tutorial. | '''Note:''' Each numbered topic corresponds to a single spoken tutorial. Each bulleted point corresponds to a command or topic that must be covered in the given spoken tutorial. | ||
| + | |||
| + | ==Module 1: Introduction to basics of R == | ||
| + | ==Module 2: Introduction to data frames in R== | ||
| + | ==Module 3: Introduction to RStudio== | ||
| + | ==Module 4: Introduction to R script== | ||
| + | ==Module 5: Working Directories in RStudio== | ||
| + | ==Module 6: Indexing and Slicing Data Frames== | ||
| + | ==Module 7: Creating Matrices using Data Frames== | ||
| + | ==Module 8: Operations on Matrices and Data Frames== | ||
| + | ==Module 9: Merging and Importing Data== | ||
| + | ==Module 10: Data Types and Factors== | ||
Revision as of 15:37, 18 April 2019
R ( http://www.r-project.org/) is an open source software - a well organized and sophisticated package - that facilitates data analysis, modeling, inferential testing and forecasting. It is a user friendly software which allows to create new function commands to solve statistical problems. It runs on a variety of UNIX platforms (and similar systems such as LINUX), Windows and Mac OS.
R is the most preferred open source language for analytics and data science. At Microsoft, R is used by its data scientists, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing, and Finance departments. Twitter has been using R for measuring user-experience. On the other hand, the cross-platform compatibility of R and its capacity to handle large and complex data sets make it an ideal tool for academicians to analyze data in their labs.
R can be used for simple calculations, matrix calculations, differential equations, optimisation, statistical analysis, plotting graphs, etc. Also, it is useful to anybody who wishes to undertake extensive statistical computations and data visualization.
Contents
- 1 Module 1: Introduction to basics of R
- 2 Module 2: Introduction to data frames in R
- 3 Module 3: Introduction to RStudio
- 4 Module 4: Introduction to R script
- 5 Module 5: Working Directories in RStudio
- 6 Module 6: Indexing and Slicing Data Frames
- 7 Module 7: Creating Matrices using Data Frames
- 8 Module 8: Operations on Matrices and Data Frames
- 9 Module 9: Merging and Importing Data
- 10 Module 10: Data Types and Factors
Note: Each numbered topic corresponds to a single spoken tutorial. Each bulleted point corresponds to a command or topic that must be covered in the given spoken tutorial.
