Digital Marketing

An Introduction To Using R For SEO

Predictive analysis refers to the use of historical data and analysis using statistics to predict future events.

It takes place in seven steps, namely: project identification, data collection, data analysis, statistics, modeling, and model monitoring.

Many companies rely on predictive analysis to determine the relationship between historical data and predict a future trend.

These patterns help companies with risk analysis, financial modeling, and customer relationship management.

Predictive analysis can be used in almost all sectors, for example, healthcare, telecom, oil and gas, insurance, travel, retail, financial services, and pharmaceuticals.

Many programming languages ​​can be used in predictive analysis, such as R, MATLAB, Python, and Golang.

What is R and why is it used for SEO?

R is a package of free software A programming language developed by Robert Gentleman and Ross Ihaka in 1993.

It is widely used by statisticians, bioinformatics scientists, and data miners for statistical software development and data analysis.

R consists of an extensive graphical and statistical catalog supported by the R Foundation and the R Core Team.

It was originally created for statisticians but has grown into a powerhouse for data analysis, machine learning, and analytics. It is also used for predictive analysis due to its data processing capabilities.

R can handle various data structures such as lists, vectors, and arrays.

You can use the R language or its libraries to implement classical statistical tests, linear and nonlinear modeling, clustering, spatial and time series analysis, classification, etc.

Besides, it is an open source project, which means that anyone can improve their code. This helps fix bugs and makes it easier for developers to build applications in their own framework.

What are the benefits of R vs. MATLAB, Python, Golang, SAS, and Rust?

R vs. matlab

R is an interpreted language, while MATLAB is a high level language.

For this reason, they work in different ways to use predictive analysis.

As a high level language, most of the current MATLAB is faster than R.

However, R has a general advantage, because it is an open source project. This makes it easy to find materials online and support from the community.

MATLAB is a paid program, which means availability can be an issue.

The verdict is that users looking to solve complex things with a bit of programming can use MATLAB. On the other hand, users who are looking for a free project with strong community support can use R.

R vs. Python

It is important to note that these two languages ​​are similar in many ways.

First, they are both open source languages. This means that they are free to download and use.

Secondly, it is easy to learn and implement, and does not require prior experience with other programming languages.

In general, both languages ​​are good at processing data, whether it is automation, processing, big data, or analysis.

R has the upper hand when it comes to predictive analysis. This is because it has its roots in statistical analysis, while Python is a general purpose programming language.

Python is more efficient when deploying machine learning and deep learning.

For this reason, R is best for deep statistical analysis with beautiful data visualizations and a few lines of code.

R vs. golang

Golang is an open source project launched by Google in 2007. This project was developed to solve problems when creating projects in other programming languages.

Based on C/C++ to fill in the gaps. Thus, it has the following advantages: memory safety, multi-threading preservation, variable automatic declaration, and garbage collection.

Golang is compatible with other programming languages, such as C and C++. In addition, it uses classic C syntax, but with enhanced features.

The main disadvantage compared to R is that it is new to the market – thus, it has fewer libraries and very little information available online.

R vs. Sass

SAS is a suite of statistical software tools created and maintained by the SAS Institute.

This software suite is ideal for predictive data analysis, business intelligence, multivariate analysis, forensic investigation, advanced analytics, and data management.

It is similar to the SAS R in different ways, which makes it a great alternative.

For example, it was first launched in 1976, making it a powerhouse of massive information. It’s also easy to learn and debug, comes with a nice GUI, and provides great output.

SAS is more difficult than R because it is a procedural language that requires more lines of code.

The main drawback is that SAS is a paid software suite.

So, R might be your best choice if you are looking for a free predictive data analytics suite.

Finally, SAS lacks graphical display, which is a major hurdle when visualizing predictive data analysis.

R vs. Rust

Rust is an open source multi-paradigm programming language that was launched in 2012.

Its compiler is one of the most used by developers to create efficient and powerful software.

In addition, Rust provides stable performance and is very useful, especially when creating large programs, thanks to the guaranteed memory security.

It is compatible with other programming languages, such as C and C++.

Unlike R, Rust is a general purpose programming language.

This means that he specializes in something other than statistical analysis. It may take time to learn Rust due to its complexities compared to R.

Therefore, R is the ideal language for predictive data analysis.

Getting started with R.

If you are interested in learning the R language, here are some great resources that you can use, both free and paid.


Coursera is an online education website that covers different courses. Higher education institutions and leading companies in the industry develop most of the courses.

It’s a good place to start with R, as most of the courses are free and of high quality.

For example, this R programming course was developed by Johns Hopkins University and has over 21,000 reviews:


YouTube has an extensive library of R programming tutorials.

The video tutorials are easy to follow, and they give you the chance to learn directly from experienced developers.

Another advantage of YouTube tutorials is that you can do them at your own pace.

YouTube also provides playlists that cover each topic extensively with examples.

A good YouTube resource for learning R comes from


Udemy offers paid courses created by professionals in different languages. It includes a collection of videos and text tutorials.

At the end of each course, certificates are awarded to users.

One of the main advantages of Udemy is the flexibility of its courses.

One of the highest rated courses on Udemy has been produced by Ligency.

Use R for data collection and modeling

Using R with Google Analytics API for reporting

Google Analytics (GA) is a free tool that webmasters use to gather useful information from websites and apps.

However, pulling information from the platform for further data analysis and processing is a hurdle.

You can use the Google Analytics API to export data to CSV format or connect it to big data systems.

The API helps companies export data and combine it with other external business data for advanced processing. It also helps in automating queries and reporting.

Although you can use other languages ​​such as Python with the GA API, R has an advanced language googleanalyticsR package.

It’s an easy package since you just need to install R on your computer and customize the already available online queries for various tasks. With minimal R programming experience, you can pull data from GA and send it to Google Sheets, or store it locally in CSV format.

With this data, you can often get around basic data issues when exporting data directly from the Google Analytics user interface.

If you choose the Google Sheets route, you can use these Sheets as a data source to generate Looker Studio (formerly Data Studio) reports, speeding up customer reporting, reducing unnecessary busy work.

Using R with Google Search Console

Google Search Console (GSC) is a free tool provided by Google that shows how a website is performing in search.

You can use it to check the number of impressions, clicks, and page ranking position.

Advanced statisticians can connect Google Search Console to R for in-depth data processing or integration with other platforms such as CRM and big data.

To connect the search console to R, you must use the searchConsoleR library.

GSC data collection through R can be used to export search queries and categorize from GSC using GPT-3, extract GSC data at scale with low filtering, and submit batch indexing requests through the Indexing API (for certain page types).

How to use the GSC API with R.

See the steps below:

  1. Download and install R studio (CRAN download link).
  2. Install the two R packages known as searchConsoleR using the following command install.packages (“searchConsoleR”)
  3. Download the package using the library() command any Library (“searchConsoleR”)
  4. Download the OAth 2.0 using scr_auth() Command. This will automatically open the Google login page. Sign in with your credentials to finish connecting Google Search Console to R.
  5. Use commands from the searchConsoleR is an official github repository to access data on Search Console using R.

Pulling queries through the API will allow you, in small batches, to pull a larger, more granular dataset against filtering in the Google Search Console UI, and export to Google Sheets.

As with Google Analytics, you can then use Google Sheet as a data source for Looker Studio, automating weekly or monthly impression, click, and indexing status reports.


While a lot of focus in the SEO industry is on Python, and how it can be used in a variety of use cases from data mining to SERP scraping, I believe R is a powerful language to learn and use for data analysis and modeling.

When using R to extract things like Google Auto Suggests, PAAs, or as a custom rating check, you might want to invest in it.

More resources:

  • Introduction to Python and machine learning for technical SEO
  • Coding for SEO: 10 Ways Your Coding Skills Can Improve Your SEO Efforts
  • Advanced Technical SEO: A Complete Guide

Featured image: Billion Images / Shutterstock

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