# what is an r package

R packages areextensions to the R statistical programming language. R packages contain code,data,and documentation in a standardised collection format that can be installed by users of R,typically via a centralised software repository such as CRAN (the Comprehensive R Archive Network).

## How do I install an your package from source?

change directories to the parent directory of the source code for the R package you want to install. Start R by typing R at the command prompt. If you want to build an R package to distribute as a binary for other Windows users, use R CMD INSTALL.

## What are the essential are packages?

R is the most popular language for Data Science. There are many packages and libraries provided for doing different tasks. For example, there is dplyr and data.table for data manipulation, whereas libraries like ggplot2 for data visualization and data cleaning library like tidyr.Also, there is a library like ‘Shiny’ to create a Web application and knitr for the Report generation where finally …

## What type of object is an your package?

These objects can be almost anything, from a single number or character string (like a word) to highly complex structures like the output of a plot, a summary of your statistical analysis or a set of R commands that perform a specific task.

## How to develop an your package?

Unloads any existing version of the package (including shared libraries if necessary).Builds and installs the package using R CMD INSTALL.Restarts the underlying R session to ensure a clean environment for re-loading the package.Reloads the package in the new R session by executing the library function.

## What is tidymodels framework?

The tidymodels framework is** a collection of packages for modeling and machine learning using tidyverse principles. **

## What is tidyverse in R?

The tidyverse is** an opinionated collection of R packages designed for data science. ** All packages share an underlying philosophy and common APIs.

## What is tidyr data?

tidyr** makes it easy to “tidy” your data. ** Tidy data is data that’s easy to work with: it’s easy to munge (with dplyr), visualise (with ggplot2 or ggvis) and model (with R’s hundreds of modelling packages).

## What is purrr in R?

purrr enhances** R’s functional programming (FP) toolkit by providing a complete and ** consistent set of tools for working with functions** and ** vectors.

## What is reticulate package?

The reticulate package provides** a comprehensive set of tools for interoperability between Python and R. **

## What is FlexDashboard used for?

Use flexdashboard to** publish groups of related data visualizations as a dashboard. **

## What is R packages?

An R package is** an extension of R containing data sets and specific functions to solve specific questions. **

## What is GitHub in R?

GitHub is** a repository useful for all software development and data analysis, including R packages. ** It makes sharing your package easy. You can read more about GitHub here: Git and GitHub, by Hadley Wickham. To install a package from GitHub, the R package devtools (by Hadley Wickham) can be used.

## What is the R/Bioconductor package?

Bioconductor contains packages for analyzing biological related data. In the following R code, we want to install the R/Bioconductor package limma, which is dedicated to analyse genomic data.

## What is read_tsv in readr?

The function read_tsv () [in readr] can be** used to import a tab separated . **txt file:

## What is the function install.packages?

The function install.packages () is** used to install a package from CRAN. ** The syntax is as follow:

## How to use a specific function in R?

To use a specific function available in an R package, you** have to load the R package using the function library (). **

## What to do after installation of a package?

After installation, you must** first load the package for using the functions in the package. **

## What Is R and What Are the Advantages?

**The R programming language has a lot going for it. ** Here is a list of some of its major strong points:

## What is R Used For?

Although R is a popular language used by many programmers, it is especially effective when used for

## What Are the Most Popular R Packages?

R packages are defined as collections of R functions, sampled data, documentation, and compiled code. These elements are stored in a directory called “library” within the R environment and are installed by default during installation.

## What Is R and Which Language Is Better: Python or R?

According to StatisticsTimes, C is the top programming language as of August 2021 (R is in fourth place on the list). But both** Python ** and R are popular and have their share of adherents. But which one is the best?

## How Would You Like to Become a Data Scientist?

Could you be one of them?** Simplilearn’s Data Science With R Certification Course covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. ** You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

## How many packages are there in R?

It has lots of packages. For example, the R language has** more than 10,000 ** packages stored in the CRAN repository, and the number is continuously increasing.

## What is a graphic facility?

Graphical facilities** for data analysis and display that work either for on-screen or hardcopy **

## What is xtable in R?

xtable – The** xtable function takes an R object (like a data frame) and returns the latex or HTML code you need to paste a pretty version of the object into your documents. ** Copy and paste, or pair up with R Markdown.

## What is tidymodels?

tidymodels –** A collection of packages for modeling and machine learning using tidyverse principles. ** This collection includes rsample, parsnip, recipes, broom, and many other general and specialized packages listed here.

## What is tidyverse in R?

tidyverse –** An opinionated collection of R packages designed for data science that share an underlying design philosophy, grammar, and data structures. ** This collection includes all the packages in this section, plus many more for data import, tidying, and visualization listed here.

## What is dplyr in data?

dplyr –** Essential shortcuts for subsetting, summarizing, rearranging, and joining together data sets. ** dplyr is our go to package for fast data manipulation.

## What is DBI in R?

DBI –** The standard for for communication between R and relational database management systems. ** Packages that connect R to databases depend on the DBI package.

## What is R markdown?

R Markdown –** The perfect workflow for reproducible reporting. ** Write R code in your markdown reports. When you run render, R Markdown will replace the code with its results and then export your report as an HTML, pdf, or MS Word document, or a HTML or pdf slideshow. The result? Automated reporting. R Markdown is integrated straight into RStudio.

## What is stringr?

stringr –** Easy to learn tools for regular expressions and character strings. **

## 1.5.1 CRAN packages

See this video for step-by-step instruction on how to install, use and update packages from CRAN

## 1.5.2 Bioconductor packages

To install packages from Bioconductor the process is a little different. You first need to install the BiocManager package. You only need to do this once unless you subsequently reinstall or upgrade R

## 1.5.3 GitHub packages

There are multiple options for installing packages hosted on GitHub. Perhaps the most efficient method is to use the install_github () function from the remotes package (you installed this package previously ). Before you use the function you will need to know the GitHub username of the repository owner and also the name of the repository.

## 1.5.4 Using packages

Once you have installed a package onto your computer it is not immediately available for you to use. To use a package you first need to load the package by using the library () function. For example, to load the remotes package you previously installed

## What is the environment in data analysis?

The term “environment” is intended to characterize it as** a fully planned and coherent system, rather than an incremental accretion of very specific and inflexible tools, ** as is frequently the case with other data analysis software.

## What is R in computing?

R is** a language and environment for statistical computing and graphics. ** It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S.

## What is R in statistics?

R provides** a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ** …**) and graphical techniques, ** and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

## What is R software?

R is** an integrated suite of software facilities for data manipulation, calculation and graphical display. ** It includes

## What are the strengths of R?

One of R’s strengths is** the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. ** Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control.

## Is R a statistical system?

**Many users think of R as a statistics system. We prefer to think of it as an environment within which ** statistical techniques are implemented. R can be extended (easily) via packages. There are about eight packages supplied with the R distribution and many more are available through the CRAN family of Internet sites covering a very wide range of modern statistics.

## Is R a free software?

**R is available as Free Software ** under the terms of the Free Software Foundation ’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS.