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R vs SQL - Which Should I Learn?

Find out everything you need to know about R and SQL, helping you choose which one is best to learn for your needs.
Dec 2022

Learning a new language can sometimes be a daunting prospect. Whether it’s a spoken language or a programming language, you’ll want to make sure you’re learning something that’s worthwhile, accessible, and something you’ll persist with. If you’ve narrowed down your choice to R vs SQL, this article will help you choose which one to learn first. 

We’ll cover everything you need to know about the two languages and their uses, as well as the advantages of each, what you can use them for, and how the two compare on some key factors. 

What Are R and SQL?

Let’s start with the basics, what are these two languages? R Programming is a programming language and software environment used mainly for statistical computing and creating graphics. This system is comprised of two parts: the R language itself (which is what most people mean when they talk about R) and a run-time environment.

On the other hand, SQL (Structured Query Language) is a programming language sued mainly to ask questions (queries) of databases. We use SQL to communicate with relational databases. With SQL's help, we can access, retrieve, sort, and update information. It's used with many well-known relational database management systems (RDBMS), such as Oracle, Microsoft SQL Server and, MySQL. 

Overview of Differences and Similarities Between R and SQL 

We’re going to explore the merits of these two languages in detail throughout this post, but we’ll start by covering the main similarities and differences between SQL and R in brief.

Purpose 

R and SQL are both programming languages, but they are used for different purposes. R is a programming language and software environment for statistical computing and graphics, while SQL is a programming language for managing and manipulating data stored in relational databases.

Data types

R is designed to work with complex, structured data sets, such as those commonly used in statistical analysis. In contrast, SQL is designed to work with data stored in relational databases. This means that R is better suited for tasks such as data visualization and statistical modeling, while SQL is better suited for tasks such as querying and updating data in a database.

How they’re used

R is a general-purpose programming language, which means it can be used to perform a wide variety of tasks, including data manipulation, statistical analysis, and data visualization. In contrast, SQL is a domain-specific language, which means it is designed specifically for working with data in relational databases. This means that SQL is typically used for tasks such as querying and updating data in a database, while R is used for more general data analysis tasks.

Users

Both languages are widely used by data analysts and other professionals working with data, and both languages are designed to be relatively easy to learn and use. Additionally, both R and SQL are supported by a large and active community of users and developers, which makes it easy to find support and resources for working with these languages.

Advantages of R

There are several advantages to choosing to learn R Programming. As a powerful and widely used open-source programming language for data analysis, there are some fairly clear advantages to consider. These include: 

  1. It’s a highly versatile and flexible language, allowing users to perform various data manipulation, statistical analysis, and graphical visualization tasks.
  2. There is a large, active community of R users, which means there is a wealth of online resources, documentation, and support available to help you learn and use the language effectively.
  3. R is also widely used in both academia and industry, so learning and using R can be beneficial if you’re hoping to pursue a career in data science, research, or other related fields.
  4. R has several built-in functions and packages that make it easier to perform complex data analysis tasks, such as machine learning, without having to write lengthy and complex code from scratch.
  5. The language is platform-independent, so it can be used on any operating system, making it a versatile tool for data analysis.

Advantages of SQL

We’ll see that many of the advantages of learning SQL are similar to those of learning R: 

  1. SQL is essential for anyone working with databases. Whether you want to become a data analyst, a data scientist, or a web developer, a strong understanding of SQL is essential for working with data.
  2. It’s a widely-used language, and learning it can open up job opportunities in many different industries. Many companies, from small startups to large enterprises, use SQL to manage and analyze their data.
  3. It is a versatile language that you can use to perform various tasks, including creating and modifying databases, inserting and updating data, and querying and analyzing data.
  4. SQL is relatively easy to learn compared to other programming languages. Many people who are new to programming can learn the basics of SQL in a short amount of time.
  5. SQL can help you to work more efficiently and effectively with data. With SQL, you can quickly and easily retrieve, manipulate, and analyze data, saving you a lot of time and effort.

R vs SQL Common Use Cases

Now that we know a bit about these languages, let’s look at what each is used for and where they overlap. You can read in more detail about what SQL is used for and what you can do with R in separate posts. 

Data analysis

R and SQL are both languages that are commonly used for data analysis. The main difference between the two is that R is a programming language that is specifically designed for statistical computing and data analysis, while SQL is a language that is used for managing and querying data stored in relational databases. 

R is a powerful tool for performing complex statistical analyses and creating visualizations, while SQL is better suited for working with large datasets and performing more basic operations like filtering, sorting, and aggregating data. Ultimately, they’re both tools that are incredibly useful for data analysis. 

Data manipulation

Both of these languages are useful for data manipulation, the process of extracting information and changing it to make it easier to interpret or organize. With R, you can perform data manipulation in various ways, such as using functions like subset(), with(), and within(), as well as packages like data. table, ggplot2, reshape2, and readr.

When it comes to manipulating relational data, SQL is one of the most useful tools out there. Operations such as Limit, Distinct, Aggregation, Groupby, and more can help you organize your data across different databases. 

Comparison of SQL vs R

Let's look at a comparison of how these two languages perform at certain tasks:

Data management

Databases are at the heart of data management, which makes SQL the obvious choice for this task. It’s the underlying language that allows us to build and interact with relational databases through queries. Although R is typically used for data analysis, statistics, and visualization, it’s becoming increasingly popular as a tool to manage data and files.

Data manipulation speed

When comparing the speeds of various functions, you’ll find various differences between R and SQL. In an analysis of various performance benchmarks, R performed significantly quicker on some tasks, such as basic database queries, while it was quite a lot slower when creating a temporary variable and performing an aggregate based on a condition. So, while R is incredibly quick at some data manipulation tasks, SQL is stable across all tasks. 

Graphical capabilities

R is specifically designed for graphical data analysis. As such, it’s easy to create a host of visualizations for you data, such as histograms, bar charts, scatter plots, and more. In comparison, SQL by itself isn’t really a graphical tool, focused more on organizing data. But that doesn’t mean there aren’t various tools that can help you transform your SQL data into powerful visualizations.  

R vs SQL - Which Should You Learn?

So, when it comes to deciding which to learn, how can you decide? As we’ve seen, while there are some overlaps, the two tools have different primary uses. R is designed for working with statistical data and performing data analysis. On the other hand, SQL is primarily designed for managing and manipulating data in databases. 

That being said, R and SQL can be used together in a number of ways. For example, you can use SQL to extract data from a database and then use R to analyze and visualize that data. You can also use R to create and manage databases or to connect to existing databases and run SQL queries directly from within R. There are many R packages that make it easy to work with databases using SQL, so if you are interested in using both R and SQL together, it is definitely possible.

Ultimately, the choice between R and SQL will depend on your specific needs and interests. If you are interested in doing statistical analysis and data visualization, then R would be a good choice. If you are interested in working with databases, then SQL would be a better choice. If you are unsure which one to choose, you could consider learning both, as they can be used together in many different ways.

You can get started with both here at DataCamp. Check out our Introduction to SQL and Introduction to R courses.

R vs SQL FAQs

What is R?

R is a programming language and software environment for statistical computing and graphics. It is commonly used for data analysis and machine learning.

What is SQL?

SQL (Structured Query Language) is a programming language used to manage data stored in relational databases. It is used to create, modify, and query databases.

What are the differences between R and SQL?

Some key differences between R and SQL include:

  • R is a programming language and software environment, while SQL is a programming language specifically designed for managing data in relational databases.
  • R is primarily used for statistical analysis and data visualization, while SQL is used for managing and querying data in databases.
  • R has a wide variety of built-in functions and packages for data analysis, while SQL has a more limited set of functions specifically designed for working with relational data.

Can R and SQL be used together?

Yes, R and SQL can be used together. For example, you can use SQL to clean and prepare data in a database and then use R to perform more complex analysis and visualization on the cleaned data.

Introduction to R

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Master the basics of data analysis in R, including vectors, lists, and data frames, and practice R with real data sets.
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Reporting in SQL

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