If you’re into statistics and data science, you will be familiar with R. This programming language helps you visualize data faster and easier. If you’re interested in this open-source software, then one of the things you should know is if it runs better on Windows or macOS.
R on Windows and macOS is essentially the same. There are slight differences, such as R for Windows having a 32-bit version and having a simple command-line interface. But these are not enough to make one version significantly better than the other.
If you’re looking to get started with R programming, then read on as we delve into the differences between R for Windows and R for macOS.
Important Sidenote: We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and identified 6 proven steps to follow for becoming a data scientist. Read my article: ‘6 Proven Steps To Becoming a Data Scientist [Complete Guide] for in-depth findings and recommendations! – This is perhaps the most comprehensive article on the subject you will find on the internet!
Table of Contents
What Is R?
According to a report published by CNBC, Data scientists have the third-best job in 2020. For four years before that, data science held the top spot. In short, it’s one of those careers that offer a lot of opportunities, enviable benefits, and pay. And R is one of the programming languages that they should know.
R is a language that is used for statistical graphics and computing. If you need to do linear modeling, nonlinear modeling, statistical tests, clustering, classification, time-series analysis, and other statistical and graphical tasks, then R is what you need.
R makes it easier and faster to create well-designed plots, complete with formulas and mathematical symbols where it is needed. What’s more, R is free to use and can run on UNIX systems, as well as macOS and Windows.
How Different Is R When Running on Mac or Windows Machines?
The short answer to how different R on macOS and on Windows is, “Not much.” For the most part, R for Windows and R for macOS is the same, even in areas where you’d expect software for these two platforms to be different, such as update schedules and shortcuts. However, you can see some slight differences between the two platforms.
Versions
R for macOS is only available in 64-bit versions, while the one for Windows machines has both 32- and 64-bit versions. That means there is not much confusion about which version you’d need to install on your Mac. What’s more, external software development is focused on 64-bit machines.
With R for Windows, they recommend that you run whichever native version applies to your machine. For example, install the 32-bit version if you have a 32-bit computer, and get the 64-bit version for compatible devices.
When you install the native 64-bit version, you have more address spaces to work with. Instead of using only four gigabytes, you can use up to eight terabytes. The memory manager will also be able to handle bigger objects with ease.
However, the problem with the 64-bit version is that even small objects are larger. But because 64-bit software often exploits the fact that x86 and x64 chips have more registers, SSE2/3 commands, and other additional capabilities, the code will execute faster even if the pointers are bigger.
External Software
Another disadvantage: the 64-bit version also has fewer external software available. There are quite a few contributed packages that only work with 32-bit R for Windows.
That means that you may be limited with the slower 32-bit environment while working on larger datasets just because you won’t have access to external software written for that particular system.
Shortcuts
It’s quite a feat that both the Windows and macOS versions of R use the same shortcuts. For the most part, you will use the same keys for many functions (replacing the CTRL key on Windows with the Command, or ⌘ key on macOS machines, and the ALT key with the Option key).
For instance:
- CTRL or ⌘ + O will open a document
- CTRL or ⌘ + W will close the active document
- CTRL or ⌘ + Shift + R allows you to insert a code section
- CTRL or ⌘ + ALT or Option + T will run the current code section
Updates
When talking about the advantages of using a program on either macOS or Windows, the subject of updates does come up. Some developers sometimes roll out macOS updates first and then wait a while before updating their Windows version. The opposite is true for others, where Windows users get first dibs on new features while macOS users wait.
It seems that this is not the case with R. When updates and upgrades are made available to the base R, it’s released along with the binaries for macOS and Windows.
For instance, R 3.6.3 came out on February 29, 2020, and its binaries for macOS, Windows, and Unix were available on the same day. The same thing happened when R 4.0.0 was released in April 2020.
Command Line
R works on both a graphical user interface and the command line. macOS, or its predecessor OS X, is primarily based on BSD UNIX. When it comes to programming and command lines, Mac machines often have an advantage because it runs on a Unix-based platform.
Meanwhile, on Windows, command-line editing is a little bit simpler than what you will find on Unix, which is Readline based. Readline allows keyboard shortcuts, moving the cursor to where you want it, searching for the command history, and having a better copy and paste clipboard. It also allows you to use the TAB key to complete text on a line.
The Bottom Line
R for Windows and R for macOS doesn’t differ too much from one another. If pressed for a reason to prefer one over the other, the macOS flavor is slightly advantageous because it only has the x64 version. That means that development and support, such as external software and community contributions, are focused on just one version, unlike with R for Windows, where you have two versions – 32- and 64-bit.
Additionally, when you’re using the 64-bit system, you gain the advantage of having more memory addresses to work with. However, some external software may be available for the 32-bit version that’s not available for the 64-bit.
Zooming Out: Looking at the Operating Systems
So if R on Windows doesn’t differ that much from R running on a Mac, are there advantages for each operating system that could sway you to go for one over the other?
If you’re working with data, you will probably need a computer with many resources to spare. Large data sets would always require more memory in the range of eight to 16 gigabytes.
Faster storage speed, which is guaranteed by using solid-state drives, is also essential. After that, you’d probably need a fast processor and graphics processing units that deliver.
Windows 10 gives you a lot of hardware choices. For the same price as expensive Mac computers, you can configure it to have a faster processor, a more kick-ass graphics card, and more storage. Not to mention that you can get more peripherals for Windows PCs, including Raspberry Pi, VR headsets, and other non-traditional devices.
In short, Windows PCs give you more bang for your buck, and that can mean a faster and more capable computer whose performance can make your jaw drop.
However, macOS is more secure and stable than Windows 10. So if you’re handling confidential and sensitive data, you don’t want to risk it on Windows machines. For one, Windows computers have been plagued with a lot more malware than Apple’s line of computers.
R for Windows and R for macOS: Which Should You Choose?
R is probably one of those software that gives you true freedom. You simply can’t go wrong going with both R for Windows and for macOS. Is one version better than the other? No, not really.
If you’re still starting and you haven’t bought a computer yet, then you might want to consider how you can get a Windows PC with better specs at cheaper or the same prices as a comparable macOS machine. Or you might want to play it safe and just go for a macOS computer, which is pretty eye-catching.
Author’s Recommendations: Top Data Science Resources To Consider
Before concluding this article, I wanted to share few top data science resources that I have personally vetted for you. I am confident that you can greatly benefit in your data science journey by considering one or more of these resources.
- DataCamp: If you are a beginner focused towards building the foundational skills in data science, there is no better platform than DataCamp. Under one membership umbrella, DataCamp gives you access to 335+ data science courses. There is absolutely no other platform that comes anywhere close to this. Hence, if building foundational data science skills is your goal: Click Here to Sign Up For DataCamp Today!
- IBM Data Science Professional Certificate: If you are looking for a data science credential that has strong industry recognition but does not involve too heavy of an effort: Click Here To Enroll Into The IBM Data Science Professional Certificate Program Today! (To learn more: Check out my full review of this certificate program here)
- MITx MicroMasters Program in Data Science: If you are at a more advanced stage in your data science journey and looking to take your skills to the next level, there is no Non-Degree program better than MIT MicroMasters. Click Here To Enroll Into The MIT MicroMasters Program Today! (To learn more: Check out my full review of the MIT MicroMasters program here)
- Roadmap To Becoming a Data Scientist: If you have decided to become a data science professional but not fully sure how to get started: read my article – 6 Proven Ways To Becoming a Data Scientist. In this article, I share my findings from interviewing 100+ data science professionals at top companies (including – Google, Meta, Amazon, etc.) and give you a full roadmap to becoming a data scientist.
Conclusion
R is meticulously maintained and updated. Aside from having a 32-bit version and external software availability, using R for Windows allows you to choose a computer with better specs for the same amount of money you spend on a macOS device, but Macs are more secure. In the end, the choice is really what you prefer.
BEFORE YOU GO: Don’t forget to check out my latest article – 6 Proven Steps To Becoming a Data Scientist [Complete Guide]. We interviewed numerous data science professionals (data scientists, hiring managers, recruiters – you name it) and created this comprehensive guide to help you land that perfect data science job.
Affiliate Disclosure: We participate in several affiliate programs and may be compensated if you make a purchase using our referral link, at no additional cost to you. You can, however, trust the integrity of our recommendation. Affiliate programs exist even for products that we are not recommending. We only choose to recommend you the products that we actually believe in.
