Pada pembahasan berikut, dapat diberikan kutipan tentang penjelasan singkat terkait pemrograman data sains di RStudio. Menurut Norman Matloff dalam bukunya yang berjudul “The Art of R Programming” (2009) menyatakan bahwa :

R is a scripting language for statistical data manipulation and analysis. It was inspired by, and is mostly compatible with, the statistical language S developed by AT&T. The name S, obviously standing for statistics, was an allusion to another programming language developed at AT&T with a one-letter name, C. S later was sold to a small firm, which added a GUI interface and named the result S-Plus. R has become more popular than S/S-Plus, both because it’s free and because more people are contributing to it. R is sometimes called “GNU S.”

Masih menurut Norman Matloff dalam bukunya yang berjudul “The Art of R Programming”(2009), menyatakan bahwa :

Why use anything else? As the Cantonese say, yauh peng, yauh leng –“ both inexpensive and beautiful. Its virtues :

1. A public-domain implementation of the widely-regarded S statistical language; R/S is the de facto standard among professional statisticians.

2. Comparable, and often superior, in power to commercial products in most sense.

3. Available for Windows, Macs, Linux.

4. In addition to enabling statistical operations, it’s a general programming language, so that you can automate your analyses and create new functions.

5. Your data sets are saved between sessions, so you don’t have to reload each time.

Open-software nature means it’s easy to get help from the user community, and lots of new functions get contributed by users, many of which are prominent statisticians. I should warn you that one submits commands to R via text, rather than mouse clicks in a Graphical User Interface (GUI). If you can’t live without GUIs, you should consider using one of the free GUIs that have been develop for R, e.g. R commander or JGR. Note that R definitely does have graphics –tons of it. But the graphics are for the output, e.g. plots, not for the input.

The term object-oriented can be explained by example, say statistical regression. When you perform a regression analysis with other statistical package, say SAS or SPSS, you get a mountain of output. By 1contrast, if you call lm() regression function in R, the function return an object containing all the results – estimated coefficients, their standard errors, residual, etc. You then pick and choose which parts of that object to extract, as you wish.”

Andrie de Vries dan Joris Meys dalam bukunya yang berjudul “R for Dummies” menyatakan sebagai berikut (2012).

1. R is more a programming language than application. When you download R, you automatically download a console application that’s suitable for your operating system.

2. The R Development Core Team has put a lot of effort into making R available for different types of hardware and software. This means that R is available for Windows, Unix systems (such as Linux), and the Mac.

3. R itself is a powerful language that performs a wide variety of functions, such as data manipulation, statistical modeling, and graphics.”

Lebih lanjut, Andrie de Vries dan Joris Meys dalam bukunya yang berjudul “R for Dummies” menyatakan sebagai berikut (2012).

In order to start working in R, you need to use an editing tool. Which editing tool you use depends to some extent on your operating system, because R does not provide a single graphical editor for all operating systems. The basic R install gives you the following:

1. Windows: A basic editor called RGui.

2. Mac OS X: A basic R editor called R.app.

3. Linux: There is no specific R editor on Linux, but you can use any editor (like Vim or Emacs) to edit your R code.

4. Fortunately, there is an alternative called RStudio, third-party software that provides a consistent user interface regardless of operating system. In addition to demonstrating how to work with the Windows RGui, we also illustrate how to use RStudio.

The one thing that R is not is an application, which means that you have the freedom of selecting your own editing tools to interact with R. In this section we discuss the Windows R editor, RGui (short for R graphical user interface). Since the standard, basic R editors are so, well, basic, we also introduce you to RStudio. RStudio offers a richer editing environment than RGui and makes some common tasks easier and more fun.

As part of the process of downloading and installing R, you get the standard graphical user interface (GUI), called RGui. RGui gives you some tools to manage your R environment – most important, a console window. The console is where you type instructions, or scripts, and generally get R to do useful things for you.

Garrett Grolemund dalam bukunya “Hands-On Programming with R” menyatakan sebagai berikut(2014).

Learning to program is important if you’re serious about understanding data. There’s no argument that data science must be performed on a computer, but you have a choice between learning a graphical user interface (GUI) or a programming language. Both Garrett and I strongly believe that programming is a vital skill for everyone who works intensely with data. While convenient, a GUI is ultimately limiting, because it hampers three properties essential for good data analysis:

1. Reproducibility, The ability to-recreate a past analysis, which is crucial for good science.

2. Automation, The ability to rapidly re-create an analysis when data changes (as it always does).

3. Communication, Code is just text, it is easy to communicate. When learning, this makes it easy to get help - whether it’s with email, Google, Stack Overflow, or elsewhere.

Don’t be afraid of programming! Anyone can learn to program with the right motivation,and this book is organized to keep you motivated. This is not a reference book;instead, it’s structured around three hands-on challenges. Mastering these challengeswill lead you through the basics of R programming and even into some intermediatetopics, such as vectorized code, scoping, and S3 methods. Real challenges are a greatway to learn, because you’re not memorizing functions void of context; instead, you’relearning functions as you need them to solve a real problem. You’ll learn by doing, not by reading.”

Lebih lanjut, Garrett Grolemund dalam bukunya “Hands-On Programming with R” menyatakan sebagai berikut(2014). Not every programmer needs to be a data scientist, so not every programmer will findthis book useful. You will find this book helpful if you’re in one of the followingcategories:

1. You already use R as a statistical tool but would like to learn how to write your ownfunctions and simulations with R.