Using neo2R with Local Neo4j Instances

Introduction

This vignette demonstrates how to connect to a local Neo4j instance and import data from data frames.

Connecting to Local Neo4j

After installing Neo4j, use startGraph() to initialize the connection from R. If authentication has been disabled in Neo4j by setting NEO4J_AUTH=none, neither username nor password are required.

If you’re connecting to a local instance of Neo4j and an import directory has been defined in the configuration, you can specify it in order to allow import from data frames.

library(neo2R)

graph <- startGraph(
  "https://localhost:7473",
  username = "neo4j",
  password = "donttrustusers",
  importPath = "~/neo4j_home/neo4jImport",
  .opts = list(ssl_verifypeer = 0)
)

Importing Data from Data Frames

If you’re connected to a local instance of Neo4j and an import directory has been defined (see above), you can import data from data frames. Use the row prefix to refer to the data frame column.

# Create an index to speed-up MERGE
if (as.integer(graph$version[[1]]) >= 5) {
   try(cypher(graph, 'CREATE INDEX FOR (n:TestNode) ON (n.name)'), silent = TRUE)
} else {
   try(cypher(graph, 'CREATE INDEX ON :TestNode(name)'), silent = TRUE)
}
#> Neo.ClientError.Schema.EquivalentSchemaRuleAlreadyExists
#> An equivalent index already exists, 'Index( id=3, name='index_e8759119', type='RANGE', schema=(:TestNode {name}), indexProvider='range-1.0' )'.

# Define node properties in a data frame
set.seed(1)
nn <- 100000
nodes <- data.frame(
   "name" = paste(
      sample(LETTERS, nn, replace = TRUE),
      sample.int(nn, nn, replace = FALSE)
   ),
   "value" = rnorm(nn, 10, 3),
   stringsAsFactors = FALSE
)

# Import nodes
import_from_df(
  graph = graph,
  cql = 'MERGE (n:TestNode {name: row.name, value: toFloat(row.value)})',
  toImport = nodes
)

# Define edge properties in a data frame
ne <- 100000
edges <- data.frame(
  "from" = sample(nodes$name, ne, replace = TRUE),
  "to" = sample(nodes$name, ne, replace = TRUE),
  "property" = round(runif(ne) * 10),
  stringsAsFactors = FALSE
)

# Import edges
import_from_df(
   graph = graph,
   cql = prepCql(
      'MATCH (f:TestNode {name: row.from})',
      'MATCH (t:TestNode {name: row.to})',
      'MERGE (f)-[r:TestEdge {property: toInteger(row.property)}]->(t)'
   ),
   toImport = edges
)

Querying the Neo4j Database

You can query the Neo4j graph database using the cypher() function. Depending on the query, the function can return data in a data frame (by setting result = "row") or in a list with nodes, relationships and paths returned by the query by setting result = "graph".

# Get TestNode with value smaller than 4
# According to the normal distribution we expect 2.5% of the total
# number of nodes ==> ~2500 nodes
df <- cypher(
   graph,
   prepCql(
      'MATCH (n:TestNode) WHERE n.value <= 4',
      'RETURN n.name as name, n.value as value'
   )
)
print(dim(df))
#> [1] 2253    2
print(head(df))
#>      name    value
#> 1  N 2585 1.965486
#> 2 L 72527 3.345461
#> 3 Y 54240 2.372623
#> 4  N 1436 1.500713
#> 5 T 21434 3.592195
#> 6 M 91787 2.504475

# Multiple queries can be sent at once
dfl <- multicypher(
   graph,
   sprintf(
      paste(
         'MATCH (n:TestNode) WHERE n.value <= %s',
         'RETURN n.name as name, n.value as value'
      ),
      2:4
   )
)
print(lapply(dfl, dim))
#> [[1]]
#> [1] 386   2
#> 
#> [[2]]
#> [1] 954   2
#> 
#> [[3]]
#> [1] 2253    2

# Get all paths of length 5 starting from a subset of nodes 
net <- cypher(
   graph,
   prepCql(
      'MATCH p=(f:TestNode)-[:TestEdge*5..5]->(t:TestNode) WHERE f.value < 3',
      'RETURN p'
   ),
   result = "graph"
)
print(lapply(net, head, 3))
#> $nodes
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7`
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7`$elementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7"
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7`$labels
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7`$labels[[1]]
#> [1] "TestNode"
#> 
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7`$properties
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7`$properties$name
#> [1] "N 2585"
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7`$properties$value
#> [1] 1.965486
#> 
#> 
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347`
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347`$elementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347"
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347`$labels
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347`$labels[[1]]
#> [1] "TestNode"
#> 
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347`$properties
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347`$properties$name
#> [1] "V 30913"
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347`$properties$value
#> [1] 10.389
#> 
#> 
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440`
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440`$elementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440"
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440`$labels
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440`$labels[[1]]
#> [1] "TestNode"
#> 
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440`$properties
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440`$properties$name
#> [1] "Z 33864"
#> 
#> $nodes$`4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440`$properties$value
#> [1] 5.65467
#> 
#> 
#> 
#> 
#> $relationships
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543`
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543`$elementId
#> [1] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543`$startNodeElementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543`$endNodeElementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543`$type
#> [1] "TestEdge"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543`$properties
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543`$properties$property
#> [1] 8
#> 
#> 
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423`
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423`$elementId
#> [1] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423`$startNodeElementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:97347"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423`$endNodeElementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423`$type
#> [1] "TestEdge"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423`$properties
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423`$properties$property
#> [1] 0
#> 
#> 
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470`
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470`$elementId
#> [1] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470`$startNodeElementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:13440"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470`$endNodeElementId
#> [1] "4:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:79444"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470`$type
#> [1] "TestEdge"
#> 
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470`$properties
#> $relationships$`5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470`$properties$property
#> [1] 10
#> 
#> 
#> 
#> 
#> $paths
#> $paths[[1]]
#> [1] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543"
#> [2] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423"
#> [3] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470"  
#> [4] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7553" 
#> [5] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:94678"
#> 
#> $paths[[2]]
#> [1] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543"
#> [2] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423"
#> [3] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:470"  
#> [4] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:7553" 
#> [5] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:98608"
#> 
#> $paths[[3]]
#> [1] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:93543"
#> [2] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:31423"
#> [3] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:69425"
#> [4] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:71263"
#> [5] "5:189c469e-6afa-4d4f-b15c-f46d0ff5d9b4:58428"
print(table(unlist(lapply(net$paths, length))))
#> 
#>   5 
#> 945

August 23 2026

Introduction

Connecting to Local Neo4j

Importing Data from Data Frames

Querying the Neo4j Database

Further Reading