## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----eval=FALSE---------------------------------------------------------------
# # Install lbugr from GitHub
# remotes::install_github("your-github-repo/lbugr")
# 
# 
# # Install the ladybug Python package
# reticulate::py_install("ladybug", pip = TRUE)

## ----eval=FALSE---------------------------------------------------------------
# library(lbugr)
# 
# # Create an in-memory database connection
# con <- lb_connection(":memory:")

## ----eval=FALSE---------------------------------------------------------------
# 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)")

## ----eval=FALSE---------------------------------------------------------------
# # 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")

## ----eval=FALSE---------------------------------------------------------------
# # 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