Data Science for Beginners: Introduction to R, Workspaces
As a data scientist, from discussions on the same over at runrex.com, you need to have a strong understanding of R, as well as what a workspace is in R. If you are starting as a data scientist, this article will look to try and help by giving a brief overview of what R is as well as what workspaces are in R programming.
Let us start by giving a brief introduction of what R is, and as is outlined over at guttulus.com, R is a programming language and free software that was developed by Ross Ihaka and Robert Gentleman in 1993. It possesses an extensive catalog of statistical and graphical methods and includes machine learning algorithms, linear regression, time series, statistical inference, among others, hence why it is such an important programming language for data scientists according to the subject matter experts over at runrex.com. While most of the R libraries are written in R, for heavy computational tasks, C, C++, and Fortran codes are preferred. R is a popular programming language, used by many companies including Uber, Airbnb, Facebook, Google, and many others.
As part of the introduction to R, it is important as a data scientist to know how data analysis with R is done. This is done in a series of steps which are:
- Programming- This is made possible by the fact that R is a clear and accessible programming tool as explained over at guttulus.com.
- Transforming- This will be made possible by the fact that R is made up of a collection of libraries designed specifically for data science which will help you at this step.
- Discovering- As is revealed in discussions on the same over at runrex.com, this is the stage where you investigate the data, refine your hypothesis, and then analyze them.
- Modeling- At this step of data analysis, R provides you with a wide array of tools that allows you to capture the right model for your data.
- Communication- The final step of data analysis with R is the communication of results which involves the integration of codes, graphs, and outputs to a report with R Markdown or building apps to share with the world, with more on this to be found over at guttulus.com.
It is also important to highlight what R is used for, and according to discussions on the same over at runrex.com, R is used for statistical inference, machine learning algorithm, and data analysis, which also goes to show just how important it is to data scientists. R is, therefore, the first-choice programming language in the healthcare industry. Now that you know what R is used for, another question that may crop is why use R? To answer this question, it is important to point out how data science is shaping the way organizations and companies run their businesses. The reason why you should use R is that it will help you as a data scientist give the best insight from the data you will be working with. This is because, unlike Python which is an excellent tool to deploy machine learning and AI but lacks communication features, R is a good trade-off between data analysis and implementation. It is also important to point out that learning statistical modeling and algorithms is more important than learning a programming language for a data scientist since the most important task in data science is how you deal with the data: importing, cleaning prepping, feature engineering, and feature selection. Data scientists are not programmers as their job is to understand and manipulate data to expose the best approach. This is why you should learn R as it brings it all together and allows you to communicate as a data scientist.
Next up we are going to look at R workspaces, and as explained over at guttulus.com, the workspace is your current R working environment and it includes any user-friendly objects such as vectors, data frames, matrices, functions, and lists. At the end of an R session, the user can save an image of the current workspace, which will then automatically be reloaded the next time R is started. The following are some of the commands that you can carry out in your R workspace:
- List workspace objects
This command will let you know the objects you have in memory. If you want to do this, all you have to do is call the Is function and the objects in the memory will be displayed.
- Clear workspace in R
This will allow you to clear your session, whether you just want to clear your current workspace and start a new one or want to do it to avoid overriding some R objects. If you want to clear your workspace in R, you can use the rm function and specify it to clear just one object. There is also a code that will allow you to clear the full workspace.
- Save R workspace image
This is relatively easy since when you call the Is function, you can use the save.image function and the data in your workspace will be saved in a file of type RData, also known as rda.
- Load workspace in R
Once you save your workspace, you can now load it when you need to which means that you won’t need to run the code to obtain those objects again. To load the RData object, you can use the load function and call the file name.
- R command history
The code execution history is also related to the workspace. Here, you can recover instruction lines introduced before with the top arrow of the keyboard when the focus is on the command line, just in case you want to run some code again or modify something. Additionally, you can use the history function which will show you the latest used commands.
For more information on the above discussion, don’t hesitate to check out the highly regarded runrex.com and guttulus.com.