R Link Explorer May 2026

Use igraph to plot relationships between referring domains and your site.

library(igraph)
g <- graph_from_dataframe(links_data)
plot(g, vertex.size=3, edge.arrow.size=0.2, main="Your Website Link Graph")

If your Spam Score is high (over 15–20%), scroll to the bottom of the "Backlinks" tab. R Link Explorer allows you to select multiple domains and generate a Disavow File (a .txt file).

This is the "Explorer" aspect—connecting the car to the outside world. If your vehicle has an R-LINK 2 system with a SIM card slot (or the built-in connectivity), the navigation features include: r link explorer

This refers to using the R programming language (with packages like httr, rvest, and igraph) to crawl, extract, and explore backlink profiles without relying on expensive third-party dashboards.

For the purpose of this article, we will focus primarily on the programmatic R approach—because this is where true exploration happens. If you only need a GUI, Moz’s web tool is fine. But if you need scale, customization, and deep analytics, R Link Explorer is your superpower. Use igraph to plot relationships between referring domains

One of the most overlooked aspects of link building is velocity—the rate at which you gain or lose links. A sudden drop in links could indicate a disavow file gone wrong or a hacked site.

Using R, you can import monthly link export CSVs and run a simple anomaly detection: If your Spam Score is high (over 15–20%),

library(ggplot2)
ggplot(link_velocity, aes(x = date)) +
geom_line(aes(y = new_links, color = "New Links")) +
geom_line(aes(y = lost_links, color = "Lost Links")) +
labs(title = "Link Velocity Explorer in R",
y = "Number of Links",
x = "Date") +
theme_minimal()

Outliers? Run a Z-score test. If lost links exceed 2 standard deviations from the mean, the R Link Explorer will flag it for manual review.