Statistical programming is a big part of the Life Science industry.
It allows for the automation of complex or time-consuming tasks, as well as the ability to work with large records that would be unachievable to analyse manually.
It is integral that data is processed correctly.
With that in mind, which software is the best?
We’re going to take you on a journey through the positives and potential drawbacks of each one.
Grab yourself a strong brew, a couple of biccies and get ready to learn!
SAS – The Basics
A software suite used for advanced analytics, business intelligence, data management, and predictive modeling. SAS programming is commonly used in industries such as healthcare, finance, and government, where large amounts of data need to be analyzed and processed quickly and accurately.
R – The Basics
A programming language and software environment for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and data analysis.
What are some of the positives?
- User-friendly interface: SAS has a graphical user interface (GUI) that makes it easy for users to navigate and perform tasks.
- Comprehensive data management capabilities: SAS provides tools for data import, cleaning, and transformation, which makes it useful for data preparation and management.
- Advanced analytics: SAS offers a wide range of statistical and analytical procedures, including regression analysis, time-series analysis, and multivariate analysis.
- SAS can handle large amounts of data, making it useful for big data projects.
- Support for various data sources: SAS can connect to and import data from a variety of sources, including databases, spreadsheets, and text files.
- Industry standard: SAS is widely used in a variety of industries, such as finance, healthcare, and retail, and is considered as an industry standard.
- Support and Resources: SAS Institute, company behind SAS software, providing a wide range of training, documentation, webinars and consultation.
- SAS is the only accepted software for statistical analysis, by agencies such as the FDA.
- R is open-source software, which means it is free to use, distribute, and modify.
- It has a large and active community of users and developers, which means there are many resources available, including tutorials, documentation, and forums.
- Extensive collection of packages, or libraries, that provide specialised functionality for tasks such as data visualization, machine learning, and natural language processing.
- R can be used for a wide range of tasks, including data exploration, statistical analysis, and data visualisation.
- Excellent visualisation capabilities: R provides various libraries and packages, like ggplot2, lattice and others which are used to create high-quality data visualization.
- High-performance: R uses C and Fortran libraries underneath, which makes it much faster than in-memory operations and supports parallel computing.
- R can easily interoperate with other software such as Python, SQL, and Hadoop.
- It is widely adopted in academic and research institutions and is becoming more popular in industry too.
Now that we have done the basic outline and positives of each software, let’s delve a bit deeper and look at the potential drawbacks for each.
There are a few potential drawbacks to investing in SAS, the most prevalent is that it is a very expensive system to have. Especially for large organisations that may need to purchase multiple licenses, or for those that want to access advanced features and tools. For the most basic user license you are looking at around $8,700 per user per month. With the licenses, there may be more restrictions for usage and distribution, compared to open-source options.
SAS can be very complex and difficult to learn. This could especially be true for people who are less experienced in statistical programming. So, look at your team – would it benefit them to learn something new or more to the point, are they capable of learning the software without too much time being used up to learn it.
It’s not always well-integrated with other data management systems or programming languages, meaning that it could make it difficult to use in some contexts.
Compared to its competitors, SAS performance can decrease when dealing with large amounts of data.
Where SAS has been around longer than most, a lot of organisations are unable to move to different software. For example, if SAS is being used to collate data for clinical trials, it would be near-on impossible for them to transfer their findings to another platform due to the inability to connect with others. Some clinical trials can take years to complete so it would not be possible for them to “jump ship”.
As with any software, R also has some downfalls.
Due to its many packages and libraries, it could be difficult for users to learn, especially those without programming experience. It can take some time to become proficient in the language. With its vast array of packages for specialised fields, diving deep into those packages may require a steep learning curve for certain users.
R can be slow when working with large datasets and intricate models. It’s not designed for parallel computing out of the box, which can be a disadvantage when compared to other languages.
There could potentially be a lack of stability and compatibility with R, this is due to frequent updates to packages and libraries
As a free and open-source platform, R may have a lack of standardisation in terms of coding conventions and naming conventions, thus meaning it could make it difficult to read and maintain code written by other people.
Potential disadvantages outlined, what have we learnt?
All technologies and software have their drawbacks, no one is perfect. Ultimately, the choice of software will depend on the organisation’s needs and expertise.
If you are currently looking for Statistical Programmers proficient in SAS or R to join your team, give Focus on Life Science a call today.