Getting Started with lbugr

The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.

Introduction

Welcome to lbugr! This guide will walk you through the basic steps to get started with lbugr, from installation to running your first query. lbugr is the R interface to the Ladybug Graph Database, a fork of the Kuzu graph database.

Installation

First, ensure you have the lbugr package installed. You will also need reticulate to manage the Python environment.

# Install lbugr from GitHub
remotes::install_github("your-github-repo/lbugr")


# Install the ladybug Python package
reticulate::py_install("ladybug", pip = TRUE)

Basic Usage

1. Create a Connection

The first step is to create a connection to a Ladybug database. You can create an in-memory database or connect to a database on disk.

library(lbugr)

# Create an in-memory database connection
con <- lb_connection(":memory:")

2. Create a Schema

Next, define your graph schema using Cypher queries. Let’s create a simple schema with Person nodes and Knows relationships.

lb_execute(con, paste("CREATE NODE TABLE Person(name STRING, age INT64,",
                        "PRIMARY KEY (name))"))
lb_execute(con, "CREATE REL TABLE Knows(FROM Person TO Person, since INT64)")

3. Load Data

You can load data from R data frames directly into your Ladybug database.

# Create a data frame of persons
persons_df <- data.frame(
  name = c("Alice", "Bob", "Carol"),
  age = c(35, 45, 25)
)

# Create a data frame of relationships
knows_df <- data.frame(
  from_person = c("Alice", "Bob"),
  to_person = c("Bob", "Carol"),
  since = c(2010, 2015)
)

# Load data into Ladybug
lb_copy_from_df(con, persons_df, "Person")
lb_copy_from_df(con, knows_df, "Knows")

4. Query Data

Finally, you can query your graph using Cypher and retrieve the results as an R data frame.

# Execute a query
result <- lb_execute(con, paste("MATCH (a:Person)-[k:Knows]->(b:Person)",
                                  "RETURN a.name, b.name, k.since"))

# Convert the result to a data frame
df <- as.data.frame(result)
print(df)
#>    a.name b.name since
#> 1   Alice    Bob  2010
#> 2     Bob  Carol  2015

This concludes the “Getting Started” guide. For more advanced topics, please see the other articles and the function reference.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.