`R`

Programming LanguageData -> Information -> Knowledge -> Wisdom

Muhammad Yaseen

(https://myaseen208.com/)

(https://myaseen208.com/)

- A software developed by Statisticians around the World
- Free software, released under the General Public License (GPL)
- Open source, Very Flexible & makes you think
- 19,366 freely available packages (by 2023-04-18)

- An Elegant & Comprehensive Statistical & Graphical programming language
- Contains Advanced Statistical routines
`R`

&`LaTeX`

work together - seamlessly- Automated & Reproducible Research
- Good online documentation, robust & vibrant community

`R`

is a dialect of the`S`

language`S`

language was developed in 1976 by John Chambers*et al.*at Bell Laboratories

`R`

was created in 1991 in New Zealand by Ross Ihaka & Robert Gentleman (R & R).Their experience developing

`R`

is documented in a 1996 JCGS paper.Martin Machler convinced Ross & Robert to use the GNU General Public License (GPL) to make

`R`

free software.Since 1997, International

`R`

-core team & 1000s of code writers and Statisticians happy to share their libraries! Awesome!

`R`

Core Team`R`

Core TeamDouglas Bates | Ross Ihaka | Thomas Lumley | Brian Ripley |

John Chambers | Tomas Kalibera | Martin Maechler | Deepayan Sarkar |

Peter Dalgaard | Michael Lawrence | Sebastian Meyer | Duncan Temple Lang |

Robert Gentleman | Friedrich Leisch | Paul Murrell | Luke Tierney |

Kurt Hornik | Uwe Ligges | Martyn Plummer | Simon Urbanek |

`R`

The main web site for the

`R`

Project: www.r-project.orgComprehensive R Archive Network (CRAN) primary site: https://cran.r-project.org/

`R`

`R`

Provides a wide variety of statistical & graphical techniques, including:

State-of-the-art & Publication quality graphs

Classical Statistical Tests, ANOVA & Regression Analysis

Generalized, Linear, Mixed & Nonlinear Models

Time Series, Panel Data Analysis & Financial Data Modeling

Multivariate Analysis & Structural Equation Modeling (SEM)

Geographic Information System (GIS) & Spatial Analysis

Bayesian Methods & many more.

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Once `R`

is installed, there is a comprehensive built-in help system. At the program’s command prompt you can use any of the following:

`R`

`R`

There are many ways to get data into `R`

. Few of them are:

Entering directly through console

Importing from an external file through

`R`

commands`read.table`

`read.csv`

`read_excel`

etc.

Operator | Description |
---|---|

`+` |
Addition |

`-` |
Subtraction |

`*` |
Multiplication |

`/` |
Division |

`^` or `**` |
Exponentiation |

`x%y` |
Modulus (x mod y) `5%2` is 1 |

`x%/%y` |
Integer Division `5%/%2` is 2 |

`R`

```
'lm' is used to fit linear models, including multivariate ones. It can
be used to carry out regression, single stratum analysis of variance
and analysis of covariance (although 'aov' may provide a more
convenient interface for these).
```

\[\\[0.2in]\]

`R`

```
'glm' is used to fit generalized linear models, specified by giving a
symbolic description of the linear predictor and a description of the
error distribution.
```

\[\\[0.2in]\]

```
glm(formula, family = gaussian, data, weights, subset,
na.action, start = NULL, etastart, mustart, offset,
control = list(...), model = TRUE, method = "glm.fit",
x = FALSE, y = TRUE, singular.ok = TRUE, contrasts = NULL, ...)
glm.fit(x, y, weights = rep.int(1, nobs),
start = NULL, etastart = NULL, mustart = NULL,
offset = rep.int(0, nobs), family = gaussian(),
control = list(), intercept = TRUE, singular.ok = TRUE)
## S3 method for class 'glm'
weights(object, type = c("prior", "working"), ...)
```

`R`

\[\\[0.2in]\]

`R`

```
Fit a generalized linear mixed-effects model (GLMM). Both fixed
effects and random effects are specified via the model 'formula'.
```

\[\\[0.2in]\]

R Core Team (2023). *R: A Language and Environment for Statistical Computing*. R Foundation for Statistical Computing. Vienna, Austria. URL: http://www.R-project.org/.

Vance, A. (2009). “Data analysts captivated by R’s power”. In: *New York Times* 6.

Venables, W. N., D. M. Smith, R. D. C. Team, et al. (2002). *An introduction to R*.

Wickham, H. (2016). *ggplot2: elegant graphics for data analysis*. Springer-Verlag New York. ISBN: 978-3-319-24277-4. URL: http://ggplot2.org.

\(\color{green}{\textit{Muhammad Yaseen, PhD (Statistics, UNL-USA)}}\), (\(\color{red}{https://myaseen208.com/}\))