# Top Data Analytics Tools – R vs SAS vs SPSS

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In this data analytical tools tutorial, we are going to learn the evolution of various analytical approaches and categories of Big Data analytics tools. We will then overview the three important tools for data analytics – * R, SAS and SPSS*. We will also discuss the importance of each of these tools, their features and perform a comparison between them. This will give you a clear understanding of the three tools, enabling you to decide the best tool.

### What are Data Analytics Tools?

Analytic professionals have used a range of tools over the years, which enabled them to* prepare data for analysis, execute analytic algorithms, and assess the results.* These tools have evolved over time which has added to their functionality. Apart from the robust user interfaces, tools can now be used for **automating and streamlining mundane tasks**. As a result, analytic professionals end up with more time to focus on analysis. These combinations of new tools are bolstered by efficient and scalable processes that allow the organisations to tame Big Data.

### Evolution of Data Analytic Approaches

In this section, we will discuss the evolution of the data analytic approaches.

Over the years, many data analytical and statistical techniques have been in use. Some of these techniques and approaches such as *regression, classification, clustering* have been effectively used to solve the data problems. Previously, there were constraints on tool availability and even scalability. And, it required much more simpler models and data.

The growth in technology has seen an emerge in Big Data. This data is present in large volumes and requires advanced statistical as well as data manipulation techniques. Furthermore, there is a need for development for scalable models that can not only handle such a large volume of data but do it efficiently and without any fault.

The traditional statistical techniques have evolved over the years to accommodate large volumes of data. Today, we have advanced * machine learning algorithms* that are able to draw accurate predictions with a large amount of data. Deep Learning is one of those tools that perform accurate predictions with an increase in data. Therefore, it is apt for dealing with such a surplus volume of data.

Some of the analytical methods are as follows:

#### 1. Ensemble Methods

The key principle behind Ensemble Methods is the combination of multiple base models to strengthen the overall performance of the combined model. There are several methods in Ensemble Learning – BAGGING and Random Forest Models. The power of ensemble models stems from different techniques that pose varying strengths and weaknesses.

#### 2. Commodity Modeling

The aim of commodity modeling is not the development of an accurate model but a model that will help us to obtain better results. A commodity model provides us with a lower bar that all the other models have already cleared. This model halts when it obtains better results. While quantifying our commodity model, the primary concern is to lead yourself to better results.

#### 3. Text Data Analysis

Technology is evolving rapidly!

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Text data is an unstructured data. This form of data is everywhere on social media, telephonic logs, voice messages, etc. Companies and organisations analyse the text data to *unearth hidden information, customer sentiment, dissatisfaction,* etc. Semantic Mining is one of the most used techniques in Text Analysis. With this, companies are able to assess the meaning of user posts and review it without manually going through them. This allows them to obtain the overall customer report, allowing them to make the necessary decisions.

### Categories of Data Analytics Tools

There are two types of tools in data analytics:

#### 1. Statistical Data Analysis Tool

The modern commercial data tools consist of GUIs that enable the user to implement their code with minimal lines of code. As a result, the utility has become a major area of focus for the organisations. With the help of various pre-defined and pre-processed packages and functions, we can achieve the various tasks very easily without any hassle of writing long pages of code.

With the assistance of robust GUIs, users are able to *perform rapid prototyping and obtain analytical results* at a fast pace. As a result, analytics professionals are able to perform jobs quickly with accurate results. GUI tools are apt at optimising the time of these professionals as they are able to focus on the statistical and analysis methodologies and spend less time on writing the code.

#### 2. Data Visualisation Tool

The results obtained from the analysis of the data need to be represented in the forms that are useful for the user. Data Analytics professionals are able to* create interactive, appealing and aesthetic visual analytics* using visualisation tools. The complex analytical results need to be explained in a lucid manner by the analytic professional routinely. Anything that can help this to be done more effectively is a good thing. Data visualisation falls into this category. Considering the complication of data analytics results, the clients often understand the clear data depictions through charts and graphs. This is where visualisation helps.

### What is R?

R is an open-source software environment and strong programming language created primarily for statistical computation and data analysis. R is a widely used statistical and graphical toolkit that was developed by Ross Ihaka and Robert Gentleman at the University of Auckland in New Zealand. R is very well known among data scientists, statisticians, researchers, and analysts.

The extensive array of packages and libraries that a thriving developer community has provided to R is one of its greatest assets. For diverse statistical studies, machine learning techniques, data visualisation, and data manipulation activities, these programmes offer specialised features. These packages are simple to install and load, allowing users to increase R’s functionality and cater to certain analytical requirements.

R is a good choice for exploratory data analysis because of its interactive features and command-line interface, which enable users to explore, alter, and analyse data in real-time. Additionally, R has strong visualisation tools that let users design excellent plots and charts to efficiently visualise their data.

R has become a standard tool for academic research, data science initiatives, and business applications because of its adaptability and flexibility. Its versatility in handling many data formats, the strength of its statistical tools, and the support of an open-source community have all led to its enduring appeal and extensive use in the data analysis and research sectors.

#### Advantages of R:

1. R is entirely open-source. Therefore, you can utilise this tool without any requirement for a licence. You can also work towards the development of the R language by* developing packages, customising its code and through the resolution of its existing problems.* Furthermore, you can contribute towards the development of R by customising its packages, developing new ones and resolving issues.

2. R is the most popular language because of its **data-wrangling facilities**. With the help of packages like *dplyr, readr,* R is capable of performing data wrangling.

3. R has a colossal repository of packages. There are over 10,000 packages in the CRAN repository and this number is growing at a constant rate. Furthermore, these packages are of utilisation by all the areas of industry.

4. With the help of R, you can *delineate visually appealing plotting and graphing.* There are various popular libraries like **ggplot2** and **plotly** that are used heavily for the aesthetic creation of graphs.

5. R is platform independent and holds cross-platform compatibility on Windows, Linux and Mac.

#### Limitations of R

1. R was developed from the much older programming language called S. The architecture of R therefore, is much older that does not advocate for dynamic and 3D graphics.

2. R stores its objects in a physical memory. This is a problem when the data is much larger and the memory is less. R also utilises a lot of memory for its execution of statistical models. It loads all of its data into one single place and hence it is not ideal when dealing with large data sets.

3. R is not secure. This is in contrast with other tools like SAS and SPSS where security is the most quintessential feature.

4. R has a steep learning curve. It is not an ideal programming language for people who are beginners in programming.

### What is SAS?

*SAS stands for Statistical Analysis System.* It was developed by the SAS Institute with a sole purpose of efficient **statistical modeling.** SAS has a variety of applications in the field of statistical modeling. It is popular for *predictive analytics, business intelligence, data management, multivariate analysis,* etc. At the North Carolina State University, SAS developed as a rival to IBM’s SPSS. It has now evolved into a primary and a major tool for statistical modeling.

SAS has been a power player in the world of analytics and enterprise market. It facilitates various functionalities like *data mining, updation, data extraction and data management*. We apply these methods for statistical analysis after data extraction and processing is carried out. You can perform these actions using the SAS programming environment – SAS Studio.

#### i. Advantages of SAS:

1. SAS offers high security to its users. Due to this, it has become a trusted name in the enterprise industry.

2. It comprises of a wide range of statistical libraries that allow the organisations to implement these techniques on all types of data.

3. It provides a scalable and stable software that allows the companies to load large volumes of data and also facilitates ease of extension with various Big Data platforms.

4. SAS facilitates interaction with the data files that other statistical tools like* Excel, SPSS, Stata,* etc generate. All the external data files can be easily converted into the SAS format.

5. SAS has an active and dedicated support centre. It is helpful when you are dealing with any form of error, either in regards to the installation or any bug that you encountered during the execution.

#### ii. Limitations of SAS

1. SAS is a closed source software. It means that you have to buy a licence for using it. The cost of this licence is very expensive that individuals or small-scale enterprises cannot afford.

2. SAS lacks most features in graphical visualisations. It falls behind in these areas when compared to an open-source tool like R.

3. Most of the features in SAS are very limited. In order to use statistical techniques or machine learning models, you will have to purchase other versions of R that can add up to the overall costs.

### What is SPSS?

A popular software package for statistical analysis, data management, and data visualisation is called SPSS, which stands for Statistical Package for the Social Sciences. The software known as SPSS was created in 1968 by Norman H. Nie, C. Hadlai “Tex” Hull, and Dale H. Bent. In 2009, IBM purchased the company and changed the name of the software to IBM SPSS Statistics. Due to its user-friendly interface, comprehensive statistical capabilities, and powerful data processing tools, it is very well-liked among academics, data analysts, and social scientists.

Both descriptive and inferential statistical analysis are supported by a complete set of tools in SPSS. To visualise data, users may run a variety of statistical tests, do regression and factor analysis, and make charts and graphs. It is suitable with many different data sources since it supports a large variety of data types. A further feature of SPSS is syntax-based programming, which enables users to automate monotonous operations and guarantee the repeatability of their findings.

#### 1. Advantages of SPSS:

1. SPSS is easy to use due to its GUI features that facilitate minimal coding to undertake complex tasks.

2. It comprises of efficient data management tools with which the user can have a lot of control.

3. It is popular because of its in-depth data analysis, faster as well as accurate data results.

4. SPSS keeps track and the location of data objects and variables. This allows the user to efficiently manage the model and perform faster data analysis.

5. A separate file stores the SPSS data. This also aids in better management as the users need to no longer worry about file overwriting or mixing of the data.

#### 2. Limitations of SPSS:

1. As compared with SAS, SPSS has a limited data storage facility. Therefore, it is not so apt at handling and processing large datasets.

2. SPSS is also closed source and expensive to purchase. Only large scale enterprises and organisations can afford to purchase this software for their data requirements.

3. It provides a limited syntax and features that are otherwise prevalent in other programming tools like R and SAS.

### R vs SAS vs SPSS

Let us see a comparison between the three Data analytics tools seen above:

#### 1. User Interface

When it comes to interactive GUI, SAS takes the lead followed by SPSS. SAS offers an interactive and user-friendly interface. On the other hand, R is a programming tool that requires the user to code statistical model. Working in R requires knowledge of the programming fundamentals. SAS and SPSS were developed to implement statistical models with minimal code through an extensive interface.

#### 2. Decision Trees

IBM SPSS holds the edge when it comes to the implementation of decision tree algorithms. In the case of the SAS tool, you cannot implement decision trees without purchasing the expensive data mining suite. This limits the capabilities of the base SAS package which is already highly expensive. Furthermore, the decision trees that IBM SPSS supports, are much more diverse than the ones that are distributed by R.

#### 3. Data Management

Data Management is the strongest suite of SPSS. SAS follows this. In data management, SAS has an edge over IBM SPSS and is somewhat better than R. A major drawback of R is that most of its functions load all the data into memory before execution, which sets a limit on the volumes that it can handle. However, some packages are beginning to break free of this constraint. One example is the **biglmpackage** for linear models.

#### 4. Documentation

R provides extensive documentation through various manuals, books, journals as well as the contributed documentation of the CRAN website. SPSS lags behind R, in this feature. On the contrary, SAS has comprehensive technical documentation that covers the depth of SAS programming. One of the strongest suits of R is its community support. The R community organises various seminars, bootcamps to promote its support for programming.

#### 5. Learning Curve

Areas that require utility have a preference for SPSS. It provides various functions that can be pasted into the interface to obtain fast and accurate results. As a result, SPSS has the **easiest learning curve.** And, SAS also follows this. R has the steepest learning curve among all. In R, we perform statistical modeling through programming. Therefore, it is essential to have knowledge of software fundamentals and programming paradigms in R.

#### 6. Data Handling Capability

SPSS’s limitations are mostly its inability to handle a large amount of data. SAS proves to be a powerful tool when it comes to working on a large dataset. It can efficiently slice and splice the data. R, on the other hand, is relatively slow when it comes to data loading and data processing.

### Summary

In the above article, we looked at Data Analytics, its approaches and its evolution. We also looked at the various tools of Data Analytics such as R, SAS and SPSS. We discussed the various advantages and limitations of these tools. Furthermore, we also compared these tools based on several parameters.

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