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.
First, ensure you have the lbugr package installed. You
will also need reticulate to manage the Python
environment.
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.
Next, define your graph schema using Cypher queries. Let’s create a
simple schema with Person nodes and Knows
relationships.
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")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 2015This concludes the “Getting Started” guide. For more advanced topics, please see the other articles and the function reference.