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## How to Create Repeat Loop in R Programming

In this tutorial, we will learn to make a repeat loop with the use of R programming.

A repeat loop is used to iterate over a block of code over several number of times.

In case of a repeat loop, there is no condition to check in for exiting repeat loop.

Hence, we must ourselves put a condition explicitly within a repeat loop body and make use of the break statement to exit the loop. Failing to do so will result into an infinite loop.

#### Syntax of repeat loop

```repeat {
statement
}
```

When in the statement block, we must use the statement ‘break’ to exit the loop.

#### Example: repeat loop

``````x <- 1

repeat {
print(x)
x = x+1
if (x == 6){
break
}
}``````

#### Output

```[1] 1
[1] 2
[1] 3
[1] 4
[1] 5
```

Note that in the example above, we have only made use of a condition to check and exit the loop when x equals the value of 6.

That is why we see in our output that only values from 1 to 5 get printed.

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This post originally appeared onwww.datamentor.io/r-programming/repeat-loop

## Role of R In Business Intelligence

To put it simply Business Intelligence is the action of extracting and to derive information that may be of use from the available data. As might be evident the process is a broad one where the quality and the source of the data structure is variable. Transformations like this might in technical terms be described as ETL or extract, transform and load in addition to the presentation of information that is of use.

### R Programming in Business Intelligence

Some R Programming Experts hold that R is fully able to take on the role of the engine for processes related to BI. Here we will focus only on the BI function of R i.e. to extract, transform load and present information and data. The following packages correspond to indicated processes in Business Intelligence.

Extract

Extraction

•  RODBC
• DBI
• RJDBC

In addition to these, there are several other packages that support data in a variety of formats.

Transform

• data.table
• dplyr

• DBI
• RODBC
• RJDBC

Prsentation

Presenting data is a wholly different ball game than the previously mentioned process of ETL. Never fear, it may be outsourced with ease to tools of BI dashboard with ease by populating the structure of data according to the expectations of the particular data tool. R is able to create a dashboard of a web app directly from within itself through packages like:

•  shiny
• httpuv
• opencpu
• rook

These packages let you play host to interactive web apps. They have the ability to query the data in an interactive manner and generate interactive plots. The basis for all of these is an R session engine and is able to execute all functions of R and may leverage the capabilities of statistics of all packages in R.

Extras

• db.r
• ETLUtils
• Sqldf
• Dplyr
•  shinyBI
• dwtools

###### The following factors are critical while R is adopted by businesses:

• Performance and scalability
• Presentation
• Support and licensing

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## New R Packages- 5 Reasons for Data Scientists to Rejoice

One of the fundamental advantages of the ecosystem related to R and the primary reason that lie behind the phenomenal growth of R is the practice and facility to contribute new packages to R. When this is added to the highly stable CRAN which happens to be the primary repository of packages of R,gives it a great advantage. The effectiveness of CRAN is further enhanced by the ability of people with sufficient technical expertise and to contribute packages through a proper system of submission.

It is only with sufficient effort and time that one realizes the system of packages submitted through proper procedures can yield integrated software of high quality.Even those who are relatively new to R Programming the process of discovering the packages that serves as the bedrock of R language growth. Such packages add value to the language in a reliable way.

• #### AzureML V0.1.1

Cloud computing is and will continue to be of great interest to all data scientists. The AzureML provides Python and R Programmers a rich environment for machine learning. If you are yet to be initiated to Azure as a user this package will go long ways in helping you get started. It provides functions that let you push R code from your local system to the Azure cloud in addition to publishing models and functions as web services.

• #### Distcomp V0.25.1

Using distributed computing when dealing with large sets of data is invariable an irksome problem. This is truer in cases where sharing data amongst collaborators is difficult or simply not possible. The distcomp package implements a crafty partial likelihood algorithm which lets users build statistical models of complexity and sophistication on data sets that are not aggregated.

• #### RotationForest V0.1

If there is any primary ensemble method that performs well on diverse sets of data on a constant basis is the forests algorithm. This particular variety performs principal analysis of components on subsets taken at random in the feature space and holds great promise.

• #### Rpca V0.2.3

In case there is a matrix that forms a superposition of a component that is lowly ranked along with a sparse component, rcpa calls in a robust PCA method that recovers all of these components. The algorithm was publicized by the data scientists at Netflix.

• #### SwarmSVM V0.1

One of the primary machine learning algorithm happens to be the support vector machine. SwarmSVM has for its basis an approach that may be said to be as a clustering approach and makes provisions for 3 different ensemble methods that train support vector machines. A practical introduction to this particular method is also attached with the vignette that comes with the package.

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