SQL vs R – Which to use for Data Analysis?

Data Analysis, as the name suggests, means the evaluation or examination of the data, in Layman’s terms. The answer to the question as to why Data Analysis is important lies in the fact that deriving insights from the data and understanding them are extremely crucial for organizations and businesses across the globe for profits. 

Data Analysis essentially comprises 5 steps which include:

1. Defining the problem statement for Data Analysis
2. Collection of the pertinent data 
3. Cleaning the data
4. Analyzing the data
5. Interpreting the results

Data Analysis can be further classified as Text Analysis, Predictive Analysis, Statistical Analysis, etc., based on the nature of the data and what type of analysis is to be done on the data. There are numerous tools available for Data Analysis, like R, SQL, MATLAB, Python, etc.

To conclude as to which is a better language for data analysis, we will first have to understand SQL and R and compare them based on their individual features. 

What is SQL?

SQL(Structured Query Language) is a language used for data management. SQL was introduced in the 1970s, by Raymond Boyce and Donald Chamberlin. It is primarily used for interacting with databases, and performing CRUD(Create, Read, Update, Delete) operations on databases. By using SQL, we can easily retrieve the data required, very easily.

Commonly used SQL commands – CREATE, SELECT, UPDATE, DELETE, INSERT, etc.

Advantages of SQL:

• SQL is easy to learn and is widely used for dealing with data.
• SQL is used to obtain useful data insights for businesses to increase revenues.
• It offers high-speed query processing.
• SQL undoubtedly is one of the best languages for the purpose of data management. 
• SQL tends to show good speed for querying and performing data aggregation.
• It is a very flexible language, used for performing multiple operations on the database, like, creating the database, updating the database, inserting records, deleting records in a database, etc.
• Just with the help of SQL queries, the required data can be retrieved/obtained from the database.

What is R?

R is a programming language that is used for statistical evaluation and analysis. R is built on the programming language ‘S’, which was introduced in the 1970s. R is mainly used for data analysis, statistical analysis, data visualizations, etc. It is capable of running on various operating systems, including Windows, Linux, UNIX, etc. R is also used for running Machine Learning algorithms, including Classification problems and Regression problems.

Commonly used R commands – ls(), rm(list=ls()), max(), min(), mean(), plot() etc. 

Advantages of R:

• With R, users can perform Machine Learning, Statistical Computing and Analysis, Data Analysis, Data Visualization, Data Wrangling, and much more.
• R is used extensively for data visualization, it performs graphical analysis of data by means of bar charts, pie charts, histograms, scatter plots, box plots, etc. 
• R’s libraries enable users to get excellent insightful plots and graphs.
• There are numerous packages available in R for data analysis like ggplot2, dplyr, plotly, Shiny, etc. SQL, on the other hand, has lesser packages for data analysis. 
• In terms of speed, R is fast in data querying but is slower than SQL when it comes to data aggregation and complex data operations. 
• Modern businesses require Statistics to analyze their performance and to devise ways in which they can increase their revenues. R, being a statistical tool, helps businesses significantly for this purpose.
• As mentioned above, R can run on many platforms like Mac, Windows, Linux, UNIX, etc.
• R is also compatible with other programming languages like Python, C++, Java, etc.

Below is a tabular comparison of SQL and R on the basis of the points mentioned above:

SQL	R
SQL is used for handling databases and performing database-related operations.	R is widely used for Statistical Computing, Data Visualisation, and Data Analysis.
SQL is better at Data Management than R.	R is better at Data Visualization than SQL.
For data aggregation and complex data operations, SQL is way quicker than R.	R is quicker than SQL for performing basic data querying and data manipulation tasks. Overall, SQL is a better language in terms of speed.
SQL has fewer packages for data visualization in comparison to R.	R has many data visualization packages, including ggplot2, data.table, dplyr, Shiny, etc.
Commonly used commands in SQL – CREATE, SELECT, UPDATE, DELETE, INSERT, etc.	Commonly used R commands – ls(), rm(list=ls()), max(), min(), mean(), plot() etc.
SQL is used in the domains of Software Development, Data Science, Financial Services, Database Administration, etc.	R is used in fields like Finance, Banking, Healthcare, E-Commerce, etc.

SQL or R – Which to use for Data Analysis?

Coming to the main question, both SQL and R are programming languages that can be used for Data Analysis. However, a comprehensive comparison of both of them leads us to the conclusion that R can be considered a better programming language for Data Analysis.

This is because SQL is mainly a query language that is used for performing operations on databases. SQL is used for creating, managing, updating, and retrieving data. On the other hand, R is a widely used statistical tool for analyzing and deriving insights from the data. This is the reason why businesses use statistical tools like R for making well-informed business decisions. Data visualization is also made possible with R, employing highly intuitive graphs and plots. 

Therefore, the bottom line is that both SQL and R can be used for Data Analysis, but, R can be thought of as a better programming language as compared to SQL when it comes to Data Analysis.