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RStudio is a Certified B Corporation, which means that our open-source mission is codified into our charter. Products Our enterprise-ready professional software products deliver a modular platform that enables teams to adopt open-source data science at scale. The latest release is JAGS 4.3.0. It was released on July 18 2017. Downloads To download JAGS, please visit the files page of the mcmc-jags project at sourceforge. You will find the source for JAGS there as well as binary packages for Mac OS X (Thanks to Matt Denwood and the pioneering work of Bill Northcott) and Windows. Click on your operating system (Windows, MAC, Linux) and follow directions. If Windows, click on 'base' and then on Download R 3.4.1 for Windows. (Note that 3.4.1 is the current version as this is being written, but use whatever shows up as current.) You may need to save the file 'R-3.4.

3.1 What is R?

In Chapter 2, I discussed many of the reasons why you should begin doing your analyses (especially those of the data type) using R. If you skipped over that chapter in the hopes of just diving in to learning about R, I suggest you go back and read it over carefully. As you begin building fluency in working with R, it is especially important to review that introductory chapter from time to time.

Install R For Rstudio

3.1.1 R beginnings

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R was developed by a group of statisticians who wanted an open-source alternative to the costly proprietary options that were (and still are) popular. Because it was created by statisticians (instead of computer scientists), R has some quirky aspects to it that take some time to get used to. We'll see that many packages have been developed to help with this, and these days, you don't need an advanced degree in statistics to work with R.

Getting back to the development of R… R was created by Ross Ihaka and Robert Gentleman in New Zealand at the University of Auckland. It is a spin-off of the S programming language and was named partly after the first names of its developers (as you can see from the emphasis above). The beginning ideas for creating R came in 1992, and the first version of R was released in 1994. You can find much more about the background of R, its features, and its connections to the S language on Wikipedia.

3.1.2 R packages

I first learned to use R as a graduate student at Northern Arizona University from Dr. Philip Turk in 2007. At the time, I never thought that R could have exploded in users as we have seen since 2011. I never would have thought that students taking an introductory statistics course would be encouraged to learn to use R.

In 2007, R was still largely an esoteric and tricky language used by statisticians to do analyses. Getting used to the syntax for producing plots and working with data was especially tricky for those with little to no programming experience. So what has changed since 2007 about learning R?

I believe one of the biggest developments has been the creation of packages to make R easier to work with for newbies. Packages are add-ons created by users of R to increase the functionality of the base R installation. Packages created by Hadley Wickham and others recently have greatly expanded the capabilities of R, while also working to make beginning with R simpler. As of April 2017, more than 10,400 packages were available on common R repositories.¹

Another great development is the graphical user interface called RStudio and a package developed by RStudio, Inc. called rmarkdown. We will discuss rmarkdown (also referred to as R Markdown) in a Chapter 4, and will now focus on discussing RStudio.

• 3 Install R in mac OS
• 5 R basic commands

Installing R. The R project

The first step for becoming a Data Scientist or Data Analyst is having R installed on your computer. R can be freely downloaded in its official page. Note that installation requires up to 150 Mb of disk space available. The official web page of the R project is the following:

In the previous link you can find interesting information about the R project, the R Foundation, the releases, documentation and other interesting links.

Install R on Windows

In order to install R on Windows, you can go through the following link and click on 'Download R x.x.x for Windows' to start downloading the latest R version available.

Once downloaded, open it, select the preferred language and click Next to all dialogs.

Remember to select the path where you want to install R:

Note that you can uncheck the bit files that not correspond to your PC if desired.

You can also specify to customize some startup options:

When the installation finishes, you will be able to start using R base on Windows.

Install R in mac OS

3.1 What is R?

In Chapter 2, I discussed many of the reasons why you should begin doing your analyses (especially those of the data type) using R. If you skipped over that chapter in the hopes of just diving in to learning about R, I suggest you go back and read it over carefully. As you begin building fluency in working with R, it is especially important to review that introductory chapter from time to time.

Install R For Rstudio

3.1.1 R beginnings

Install Rstudio For Windows

R was developed by a group of statisticians who wanted an open-source alternative to the costly proprietary options that were (and still are) popular. Because it was created by statisticians (instead of computer scientists), R has some quirky aspects to it that take some time to get used to. We'll see that many packages have been developed to help with this, and these days, you don't need an advanced degree in statistics to work with R.

Getting back to the development of R… R was created by Ross Ihaka and Robert Gentleman in New Zealand at the University of Auckland. It is a spin-off of the S programming language and was named partly after the first names of its developers (as you can see from the emphasis above). The beginning ideas for creating R came in 1992, and the first version of R was released in 1994. You can find much more about the background of R, its features, and its connections to the S language on Wikipedia.

3.1.2 R packages

I first learned to use R as a graduate student at Northern Arizona University from Dr. Philip Turk in 2007. At the time, I never thought that R could have exploded in users as we have seen since 2011. I never would have thought that students taking an introductory statistics course would be encouraged to learn to use R.

In 2007, R was still largely an esoteric and tricky language used by statisticians to do analyses. Getting used to the syntax for producing plots and working with data was especially tricky for those with little to no programming experience. So what has changed since 2007 about learning R?

I believe one of the biggest developments has been the creation of packages to make R easier to work with for newbies. Packages are add-ons created by users of R to increase the functionality of the base R installation. Packages created by Hadley Wickham and others recently have greatly expanded the capabilities of R, while also working to make beginning with R simpler. As of April 2017, more than 10,400 packages were available on common R repositories.¹

Another great development is the graphical user interface called RStudio and a package developed by RStudio, Inc. called rmarkdown. We will discuss rmarkdown (also referred to as R Markdown) in a Chapter 4, and will now focus on discussing RStudio.

• 3 Install R in mac OS
• 5 R basic commands

Installing R. The R project

The first step for becoming a Data Scientist or Data Analyst is having R installed on your computer. R can be freely downloaded in its official page. Note that installation requires up to 150 Mb of disk space available. The official web page of the R project is the following:

In the previous link you can find interesting information about the R project, the R Foundation, the releases, documentation and other interesting links.

Install R on Windows

In order to install R on Windows, you can go through the following link and click on 'Download R x.x.x for Windows' to start downloading the latest R version available.

Once downloaded, open it, select the preferred language and click Next to all dialogs.

Remember to select the path where you want to install R:

Note that you can uncheck the bit files that not correspond to your PC if desired.

You can also specify to customize some startup options:

When the installation finishes, you will be able to start using R base on Windows.

Install R in mac OS

The process of installing R in a mac is analogous to installing it in Windows. You can download the binary package (for Mac OS X 10.6 and above) from the following URL and then install it.

If for some reason you are using Mac OS 8.6 to 9.2 (and Mac OS X 10.1) versions here you can find the old binary packages. For older Mac OS X systems and PowerPC Macs, you must use this repository to download R.

Install R with Homebrew

An alternative to install R in macOS is to use the Homebrew package manager. For that purpose you just need to type the following in the corresponding brew terminal:

Install RStudio

RStudio is the most popular IDE for R, providing a clean and useful graphic interface for developing in R language. The main advantages of this IDE are the pane layout for getting help and displaying plots (even interactive), creating RMarkdown and Sweave documents, among others. You can download RStudio from the following link:

Once downloaded, open the setup wizard and follow the simple steps.

R basic commands

After downloading R and RStudio open one and you will see the corresponding IDE (Integrated Development Environment). First, try to write direct commands in the commands window, where you can see the symbol > for start writing. Write the following and press Enter in the command line.

Those were your first R commands. Amazing!

You can also write in the script window and execute the code when you want. If this window doesn't appear at first glance go to File → New File → R Script or press Ctr + Shift + n or Cmd + Shift + n in Mac if you are using RStudio.

Once opened, copy and paste the code to the script, select it and press Ctr + r or Cmd + r in R base or Ctr + Enter or Cmd + Enter in RStudio (you can customize this). In the following block there are several examples you can try.

It should be noted that you can use #for comments that won't be executed.

You can also write several instructions within the same line, using a semicolon (;) as separator, although this practice is not recommended.

If you want to store the values in variables you can use the = or <- sign. In R, it is more recommended using the arrow <-. If you want to print the stored variables in the console call the name or call the assignation between brackets.

A more detailed example

Let's see a more advanced example. We are going to simulate a normal random variable of size 200 with mean 105 and standard deviation of 2. Then, we make a summary and its corresponding histogram and boxplot.

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Check where is R installed

Sometimes it is useful to know the R installation path. The R.home function will output the path where you have R installed in your computer. Open R and type the following in the console if you want to check which is the installation path.

Check R version

You can also check you have downloaded and installed the correct version typing:

In this case the output shows that we have installed the R version 4.0.0. You can have additional information about the version just typing R.Version().

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How to change the language in R GUI?

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By default, R will show the graphic user interface in the language of your operating system. To override this behavior and change the language permanently you can follow this steps:

• Go to the R installation path. Recall to use the R.home() function if you don't remember where it is.
• Open the installation folder.
• Open the etc folder.
• Open the RConsole archive with a notepad.
• Change the line language = for language = en if you want to set the language to English, or specify the language code you prefer.