There is a lot of discussion about Tableau vs R in the data community. Tableau is a great tool for discovering data for data visualization and should be used when you want to play with data and see if there are any patterns. R is a framework with which you will have access to several libraries including data manipulation, data visualization, interactive Google charts, machine learning, text mining, financial analysis, and web apps. Read on to learn more about the differences between the two:
Tableau is one of the most powerful and fastest-growing tools used for data visualization in the business intelligence industry. With it, you can simplify raw data in a format that is easily understandable by all. Over 2.5 quintillion bytes of the data is created every day and Tableau helps in creating data that everyone in the organization, regardless of their skills, can understand. Even non-technical users can use this tool for creating a customized dashboard. Also, data analysis is quite fast in Tableau and you can use worksheets and dashboards for creating visualizations. However, the Tableau software is best known for its data collaboration, data blending, and real-time analysis skills. And, the great thing about using this software is that you don’t need any programming or technical skills to operate it. This is also the reason why it is quite popular in all sectors including research, business, etc. There are several courses available online where you can learn Tableau for free. So, enrol yourself in one and start your journey in the field of data science.
Tableau comes with some great out-of-the-box connecting capabilities. For example, it can consume multiple file types immediately, has pre-built connections to several services, and can connect to different types of databases. The range of flexibility it offers is the reason why it is the first choice for many professionals. There are certain scenarios in which Tableau software works best, including the following:
- For geospatial data types, you should use Tableau. It can handle longitude/latitude, even create them based on an address, and easily map everything.
- It can also handle ShapeFile (.shp) files easily which makes mapping easy. Just drag and drop and everything else will be taken care of by the Tableau software.
However, there are certain situations where you should avoid using Tableau:
- When you are working with web scraping, NoSQL databases, nested data sources, or anything requested via https, using Tableau might be troublesome as the platform hasn’t been created for these things. Over the years, there have been several upgrades to incorporate these, but you might have to spend a lot of time trying to figure out how to convert your data into a format that is usable.
- Tableau isn’t free, in fact, can be expensive to some.
- If you are working with nested JSON data, using and interpreting it in Tableau might get difficult. If you do some research, you might find a way to get the results you want, but it won’t be a trivial task.
R is more than just a programming language, it is an environment for graphics and statistical computing. This GNU project is quite similar to the S language that was developed at Bell Laboratories by John Chambers. R is a different implementation of S. And even though there are some major differences between the two, there is a lot of code from S that runs unchanged under R.
With R, you will get access to a wide range of graphical and statistical techniques (classical statistical tests, classification, clustering, time-series analysis, linear and non-linear modelling). It is a highly extensible language that provides an open-source route. One of the strengths of this environment is the ease it offers for producing well-designed publication-quality plots, including mathematical formulae and symbols wherever needed. There are a lot of default options for minor design choices, but you will have full control as the user. Under the Free Software Foundation’s GNU General Public License terms, R is available for free in source code form. It can compile and run on several UNIX platforms and similar symptoms, macOS, and Windows.
The R environment is a suite integrated with software facilities for manipulating data, calculating, and displaying it graphically. It includes the following:
- A suite of operators that can be used for arrays’ calculation, in particular, matrices
- Effective storage and data handling facility
- Graphical facilities to use for data analysis and displaying data
- A coherent, integrated collection of tools for data analysis
- A simple, effective, and well-developed programming language that includes loops, conditions, input and output facilities, and user-defined recursive functions.
The reason why the term ‘environment’ is used in context to R is for characterizing it as a planned and coherent system, instead of an incremental accretion of inflexible and specific tools, which is usually the case with other software used for data analysis. Here are some cases in which R works best:
- Just like S, R was designed around a computer language that allows users to define new functions and add additional functionality. For computationally-intensive tasks, you can link Fortran, C, and C++ code and call it at run time. If you are an advanced programmer, you can write C code for directly manipulating R objects.
- Some believe that R is a statistics system while others believe that it is an environment in which you can implement statistical techniques. The best part is that it is possible to extend R using packages. With the R distribution, you will be supplied eight packages and you can get more through the CRAN family of sites that cover several modern statistics.
- R comes with a LaTeX-like documentation format that can be used for supplying comprehensive documentation, both in hard copy and online in a wide range of formats.
Just like Tableau, there are some cases where you shouldn’t use R:
- If you have to clean your data before you can store it, you should avoid R. For example, while you are scraping a website to build a dataset that can be used for analysis. And, while you can use RVest or RCurl for this, it will be more time-consuming and difficult. Instead, you can use Python libraries like Scrapy that are built for automation.
- Since everything in R is stored in memory, working with Big Data might get trickier. If you have to work with R, you can always try using a subset to get an idea of exactly what you have to deal with.
Both Tableau and R have their own benefits and are suited for certain cases. Regardless of their differences, both of them are amazing tools that have earned quite a name for themselves in the data community.