An introduction to using R for SEO

Predictive analytics refers to using historical data and analyzing it using statistics to predict future events.

It is carried out in seven steps, namely: definition of the project, data collection, data analysis, statistics, modeling and model monitoring.

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

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

Predictive analytics can be used in almost any industry, including healthcare, telecom, oil and gas, insurance, travel, retail, financial services, and pharmaceuticals.

Several programming languages ​​can be used in predictive analysis, e.g. B. R, MATLAB, Python and Golang.

What is R and why is it used for SEO?

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

It is commonly used by statisticians, bioinformaticians, and data miners to develop statistical software 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 designed for statisticians but has grown into a powerhouse for data analysis, machine learning, and analytics. It is also used for predictive analytics due to its data processing capabilities.

R can handle different data structures like lists, vectors and arrays.

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

Also, it’s an open-source project, which means anyone can improve their code. This helps in fixing bugs and makes it easy for developers to build applications on top of its framework.

What are the advantages of R Vs. MATLAB, Python, Golang, SAS and Rust?


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

Because of this, they work in different ways to take advantage of predictive analytics.

As a high-level language, the most up-to-date MATLAB is faster than R.

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

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

The bottom line is that users who want to solve complex things with little programming can use MATLAB. On the other hand, users 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 several respects.

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

Second, they are easy to learn and implement and require no prior knowledge of other programming languages.

Overall, both languages ​​are good at handling data, be it automation, manipulation, big data, or analytics.

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

Python is more efficient in providing machine learning and deep learning.

Because of this, R is best suited for deep statistical analysis with nice data visualizations and few lines of code.

R vs Golang

Golang is an open source project that Google started in 2007. This project was developed to solve problems when building projects in other programming languages.

It is based on C/C++ to fill the gaps. Hence, it has the following advantages: memory safety, maintaining multithreading, automatic variable declaration and garbage collection.

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

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

R vs SAS

SAS is a set of statistical software tools developed and maintained by the SAS Institute.

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

SAS is similar to R in many ways, making it a great alternative.

For example, it was first introduced in 1976, making it a powerhouse for extensive information. It’s also easy to learn and debug, comes with a nice GUI, and produces nice output.

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

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

Therefore, R might be the best option if you are looking for a free predictive data analytics suite.

Finally, SAS lacks graphical representation, a major setback when it comes to visualizing predictive data analytics.

R vs Rust

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

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

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

It is compatible with other programming languages ​​like C and C++.

Unlike R, Rust is a universal programming language.

That means it specializes in something other than statistical analysis. Due to its complexity compared to R, Rust can take some time to learn.

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

Getting started with R

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


Coursera is an online education website that covers various courses. Universities and industry-leading companies 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 more than 21,000 reviews:


YouTube has an extensive library of R programming tutorials.

Video tutorials are easy to follow and give you the opportunity to learn directly from experienced developers.

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

YouTube also offers playlists that cover each topic in depth with examples.

A good YouTube resource for learning R comes from


Udemy offers paid courses created by professionals in different languages. It contains a combination of video and text tutorials.

At the end of each course, users receive certificates.

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

One of the top rated courses on Udemy is produced by Ligency.

Using R for data collection and modelling

Using R with the Google Analytics API for reporting

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

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

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

The API helps companies export data and merge it with other external business data for advanced processing. It also helps automate queries and reports.

Although you can use other languages ​​like Python with the GA API, R has an extended googleanalyticsR package.

It’s a simple package because you just need to install R on the computer and adapt queries already available online for different tasks. With minimal R programming experience, you can pull data from GA and send it to Google Sheets or save it locally in CSV format.

With this data, you can often overcome data cardinality issues when exporting data directly from the Google Analytics UI.

If you choose to go the Google Sheets route, you can use these sheets as a data source to create Looker Studio (formerly Data Studio) reports and speed up your client reports, reducing unnecessary workload.

Using R with Google Search Console

Google Search Console (GSC) is a free tool from 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 need to use the searchConsoleR library.

GSC data collection via R can be used to export and categorize search queries from GSC with GPT-3, extract GSC data at large scale with reduced filtering, and batch indexing requests to the Indexing API (for specific page types ) to send.

How to use the GSC API with R

See the following steps:

  1. Download and install R Studio (CRAN download link).
  2. Install the two R packages known as searchConsoleR with the following command install.packages(“searchConsoleR”)
  3. Load the package with the Library() command ie Library (“searchConsoleR”)
  4. Also load OAth 2.0g scr_auth() Command. This will automatically open the Google sign-in page. Sign in with your credentials to complete the Google Search Console connection to R.
  5. Use the commands from searchConsoleR’s official GitHub repositoryy to access data in your Search Console using R.

Fetching queries through the API in small batches also allows you to fetch a larger and more accurate dataset instead of filtering in the Google Search Console UI and exporting to Google Sheets.

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


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

If you’re using R to extract things like Google Auto Suggest, PAAs, or as an ad hoc ranking check, you might want to invest.

More resources:

Featured image: Billion Photos/Shutterstock

Leave a Comment