As anyone with a modicum of exposure to the topic of data analysis will tell you, R is a powerful statistical programming language used widely among statisticians and data scientists for investigating data and developing statistical software.
As we handle increasingly larger datasets, especially in the realm of “big data”, R provides several effective benefits that make it particularly attractive for data analysis.
In this free to access GreyData blog entry, lets look into the 10 key reasons why:
1. COMPREHENSIVE STATISTICAL ANALYSIS CAPABILITIES
R is primarily a statistical software, and it excels in both traditional and cutting-edge statistical methodologies. Whether dealing with regression analysis, time series forecasting, or more complex machine learning algorithms, R has robust capabilities.
The Comprehensive R Archive Network (CRAN), for instance, hosts thousands of packages that extend the functionality of R, allowing users to apply complex data transformations, statistical models, and predictive analytics easily.
2. FLEXIBILITY IN HANDLING DATA
R is designed to handle a variety of data types and structures including vectors, matrices, data frames, and lists. This inherent flexibility makes it particularly easy to manage and manipulate large datasets often encountered in big data projects.
R supports data from different sources and formats like CSV, Excel, or databases including both SQL-based and NoSQL options like MongoDB, making it versatile in data ingestion.
3. INTEGRATION WITH BIG DATA PLATFORMS
Over the years, R has developed strong connectivity with big data tools and platforms. Packages like ‘rmr2’, ‘rhdfs’, and others allow R to interface directly with Hadoop, the popular big data platform, enabling R to be used for processing large data clusters processed through Hadoop.
R can be integrated seamlessly with Apache Spark using sparklyr or SparkR, allowing R to utilise Spark’s capabilities in data processing and machine learning on big data.
4. OPEN SOURCE
R is open source, which means it is free to use, and a large community of users contributes continuously to its development. This aspect benefits big data analysts by reducing the cost of analytical tooling and allowing modifications and customisations to meet specific analytical needs.
It also means that the latest statistical techniques and algorithms often find their way into R quickly, made available via packages created by leading statisticians and data scientists.
5. SCALED AND ACTIVE COMMUNITY
Having a large active community not only helps in troubleshooting but also contributes to an extensive ecosystem of packages and tutorials. For any analytical challenge you might face with big data, it is likely that someone has already tackled it and created a package or written a tutorial about it.
This community support results in an agile environment where solutions are shared, and continuous improvements are made.
6. ADVANCED VISUALISATIONS
Visual data exploration is crucial in understanding big datasets and communicating insights. R shines in its capacity for advanced graphical representations, essential in the era of big data. Tools such as ‘ggplot2‘ allow for sophisticated visualisations, while packages like ‘plotly’ provide a pathway to interactive web-based charts.
The ability to effectively visualise data at scale helps in uncovering patterns and anomalies in large datasets that might otherwise go unnoticed.
7. VECTORISED OPERATIONS
R allows for vectorised operations, which means operations are applied to whole vectors or matrices in one operation, rather than through loop iterations. This capability is incredibly effective for performing computations on large datasets, as it leverages optimised underlying libraries and faster computation capabilities, increasing the efficiency of data processing.
8. REPRODUCIBILITY AND COLLABORATION
In big data projects, ensuring results are reproducible and maintaining a collaborative environment can be challenging.
R addresses this with tools for creating reproducible research, such as R Markdown and Shiny, which allow users to compile analysis reports and create interactive web applications directly from R scripts. This approach enhances the sharing and presentation of insights drawn from big data.
9. SCALABILITY THROUGH PACKAGES
Several packages in R, such as ‘data.table’ and ‘dplyr’, are designed to enhance its performance and ability to handle large datasets efficiently. ‘data.table’, for instance, can handle datasets with hundreds of millions of rows with ease, making it especially suited for big data analytics.
10. STATISTICAL RIGOUR
Given its origins and main focus on statistics, R is equipped with advanced techniques that are essential in deriving valid inferences from complex and large datasets.
The strength of R in areas like inference testing and experimental design is crucial when dealing with the variability and complexity inherent in big data.
Below is a list of businesses that actively use R for data analysis and machine learning:
- Google is known for its investment in data analytics and machine learning, Google uses R for various statistical analyses and predictive modelling, particularly in areas like ad effectiveness and economic forecasting.
- Meta Platforms utilises R for behaviour analysis related to user posts and interactions. They have developed proprietary packages for managing large-scale data analysis tasks with R.
- Microsoft Corporation not only does Microsoft use R internally, but it has also integrated R into several of its products like SQL Server and offers R-based services through Azure Cloud, indicating significant use of R for data analytics.
- As a major player in big data and analytics, IBM uses R for data mining and predictive analytics, integrating it into many of their data platforms.
- Airbnb uses R for scalable data science and to model the economic impact of their services on urban economics.
- Uber Technologies employs R for statistical analysis to optimise their operational models and improve the user experience in areas such as price modelling and supply positioning.
- In the pharmaceutical industry, Pfizer uses R for drug discovery and clinical trials data analysis, helping in predictive modelling of drug interactions and side effects.
- John Deere uses R for forecasting and agronomic data analysis to develop new agricultural products and optimise farming practices.
- Bank of America utilises R in its risk analytics and quantitative financial research to model and predict various economic scenarios and their impacts on banking portfolios.
R’s robust ecosystem, comprehensive statistical capabilities, and flexibility in handling and processing large and complex datasets make it an excellent tool for big data analysis.