The console window (located at the bottom left panel in RStudio) is where R will listen for your commands. While you can type directly into it, writing out scripted commands is generally more efficient.
A script is a text file with code and comments mixed together, while R provides many bundled packages containing functions for performing various tasks. When starting up a session with RStudio, these packages are installed into your local environment and activated accordingly.
What is R?
R is a programming language that has emerged as a powerful tool for data analysis, visualization and statistical computation. Particularly useful in machine learning scenarios, as it is capable of handling large datasets and performing complex computations. Furthermore, R provides many functions for importing, cleaning and manipulating data sets – useful functions used by data scientists and other professionals conducting research or analyzing large volumes of information such as regression analyses, clustering or classification.
Although R is available with both a command-line interface and a graphical user interface (GUI), its most popular graphical front end is RStudio, an integrated development environment (IDE). This tool provides tools for developing, debugging and managing R scripts and datasets. RStudio can be downloaded free from its website for both 32-bit and 64-bit systems – making it compatible with Windows, Linux and Mac OS X operating systems.
RStudio features several tabs. The Console pane allows users to enter commands directly into the computer, while Environment shows variables used, History contains past commands and Files is where scripts and other files can be saved and renamed; additionally this tab allows the current directory be set using getwd().
R scripts differ from other graphical software in that they don’t rely on clicks and points, instead being written as code statements that anyone can understand and read aloud. This enables more thorough examination of results of work completed as well as identification of mistakes that have occurred – all without needing to rerun your script again, unlike with other software solutions.
R’s collaborative community is another advantage. There are various groups, meetups and conferences held every year to discuss new techniques in the field; this makes it easy to locate someone to answer questions about specific problems or provide advice on how to approach certain challenges. Furthermore, R has an impressive collection of software libraries which can be installed to extend functionality; many come complete with documentation, vignettes or demonstration files so users are supported.
Installing R Studio
Before beginning to use R and RStudio on your computer, they must first be installed on it. The process should be straightforward – scroll down the page until you reach “Installers for Supported Platforms,” click on your operating system (Windows, Mac or Linux), run the installer using that link and provide your password; after installing RStudio open it and follow its on-screen instructions to set up your workspace.
RStudio’s main window comprises multiple panes. On the left is known as the Console; here you enter and view results of commands used with R. For instance, to calculate two plus two enter “2 + 2” into this pane and it will position its pointer to that line delineated by vertical bar.
RStudio provides more than just R programming: its unique features include text editors, graphics windows, debuggers and file managers – these combined make up its integrated development environment that makes learning R possible – but can also be used independently for projects and research.
RStudio includes the Packages tab to make installing new R packages easy. Packages serve as add-ons that expand upon what your code can accomplish; most can be found hosted by CRAN and provide good documentation and testing; however, other non-CRAN packages may also be installed through command line scripting or manually.
Once R and RStudio are installed, Oracle Machine Learning for R is ready for work. Before beginning development and deployment of models, set your working directory using setwd() or directly via GUI clicking “3 Dots in More Tab and Set as Working Directory”. This will ensure you always refer back to files within your project making development and execution much simpler.
Creating a new project
R Studio makes creating new projects easier, by giving you the chance to organize all your work into an organized folder. That means storing data files, scripts and analytical results and figures all in one location – making it easy for others (or future you!) to retrace your analysis and keep track of everything involved with it.
It is generally advised to work on an R Project whenever working with RStudio, as this will ensure your workspace (where R stores its object and result data) starts off fresh each time you launch an RStudio session – thus avoiding potential catastrophe caused by saving and reloading workspace from an earlier session.
Once you select “New Project” in either place, you will be asked to give your project a name and select where the.Rproj file should be saved.
Your project’s naming will dictate where RStudio will look when opening sessions and will also display as the root path in the console. At any point in time you can change this by right-clicking its icon and choosing Edit Project Settings… from its drop-down menu.
Add a README file to your project, outlining its purpose and what you hope to achieve with it, to help others understand it, as well as track changes over time. There are various methods for creating README files; we will be using Git for this tutorial; however you could switch back and forth as needed between working in RStudio or Git when needed.
Creating a new script
Create a new script in RStudio by selecting “New R Script” from the File menu and clicking. Each open script will appear as tabs at the top of your window and any edits made will be marked by an icon resembling a blue floppy disk icon. To save, click on this floppy disk icon which will prompt you to choose where to save your work; RStudio will create an empty folder in that directory for you.
An R script provides results by following written commands instead of clicking through screens, making your analysis code clearer for others and helping them better comprehend your work. Reusing this script with any text editor makes reuse easy; sharing can even be accomplished by saving your file with an extension such as.R that any text editor can open.
When typing commands into the console pane in RStudio, they will be immediately executed by your computer. When entering new commands, RStudio displays a > prompt for you to type it. Once your command has been entered successfully, its results will appear immediately in the Console window with those highlighted in gray being highlighted and those not highlighted displaying as question marks instead.
Ctrl + 1 and Ctrl + 2 shortcuts allow for fast switching between script and console panes, with folder navigation using the Files pane at the bottom of your screen, as well as changing working directory with ease using RStudio’s drop-down menu in the top left corner. It is useful that RStudio remembers your previous folder selection each time you launch a session; however, for optimal use save frequently any datasets and scripts of importance in order to prevent data loss!