Awesome
<!-- README.md is generated from README.Rmd. Please edit that file -->
Disclaimer: this package is still under active development. Read the NEWS.md to be informed of the last changes.
Read complementary documentation at https://neo4j-rstats.github.io/user-guide/
neo4r
The goal of {neo4r} is to provide a modern and flexible Neo4J driver for R.
It’s modern in the sense that the results are returned as tibbles
whenever possible, it relies on modern tools, and it is designed to work
with pipes. Our goal is to provide a driver that can be easily
integrated in a data analysis workflow, especially by providing an API
working smoothly with other data analysis ({dplyr} or {purrr}) and
graph packages ({igraph}, {ggraph}, {visNetwork}…).
It’s flexible in the sense that it is rather unopinionated regarding the way it returns the results, by trying to stay as close as possible to the way Neo4J returns data. That way, you have the control over the way you will compute the results. At the same time, the result is not too complex, so that the “heavy lifting” of data wrangling is not left to the user.
The connexion object is also an easy to control R6 method, allowing you to update and query information from the API.
Server Connection
Please note that for now, the connection is only possible through http / https.
Installation
You can install {neo4r} from GitHub with:
# install.packages("remotes")
remotes::install_github("neo4j-rstats/neo4r")
or from CRAN :
install.packages("neo4r")
Create a connexion object
Start by creating a new connexion object with neo4j_api$new
library(neo4r)
con <- neo4j_api$new(
url = "http://localhost:7474",
user = "neo4j",
password = "plop"
)
This connexion object is designed to interact with the Neo4J API.
It comes with some methods to retrieve information from it. ping(),
for example, tests if the endpoint is available.
# Test the endpoint, that will not work :
con$ping()
#> [1] 401
Being an R6 object, con is flexible in the sense that you can change
url, user and password at any time:
con$reset_user("neo4j")
con$reset_password("password")
con$ping()
#> [1] 200
Other methods:
# Get Neo4J Version
con$get_version()
#> [1] "3.5.5"
# List constaints (if any)
con$get_constraints()
#> Null data.table (0 rows and 0 cols)
# Get a vector of labels (if any)
con$get_labels()
#> # A tibble: 0 x 1
#> # … with 1 variable: labels <chr>
# Get a vector of relationships (if any)
con$get_relationships()
#> # A tibble: 0 x 1
#> # … with 1 variable: labels <chr>
# Get index
con$get_index()
#> Null data.table (0 rows and 0 cols)
Call the API
You can either create a separate query or insert it inside the
call_neo4j function.
The call_neo4j() function takes several arguments :
query: the cypher querycon: the connexion objecttype: “rows” or “graph”: whether to return the results as a list of results in tibble, or as a graph object (with$nodesand$relationships)output: the output format (R or json)include_stats: whether or not to include the stats about the callmeta: whether or not to include the meta arguments of the nodes when calling with “rows”
The movie graph
Starting at version 0.1.3, the play_movie() function returns the full
cypher query to create the movie graph example from the Neo4J examples.
play_movies() %>%
call_neo4j(con)
#> $a
#> # A tibble: 10 x 2
#> born name
#> <int> <chr>
#> 1 1956 Tom Hanks
#> 2 1956 Tom Hanks
#> 3 1956 Tom Hanks
#> 4 1956 Tom Hanks
#> 5 1956 Tom Hanks
#> 6 1956 Tom Hanks
#> 7 1956 Tom Hanks
#> 8 1956 Tom Hanks
#> 9 1956 Tom Hanks
#> 10 1956 Tom Hanks
#>
#> $m
#> # A tibble: 10 x 3
#> tagline title released
#> <chr> <chr> <int>
#> 1 In every life there comes a time when that thi… That Thing You… 1996
#> 2 Once in a lifetime you get a chance to do some… A League of Th… 1992
#> 3 What if someone you never met, someone you nev… Sleepless in S… 1993
#> 4 A stiff drink. A little mascara. A lot of nerv… Charlie Wilson… 2007
#> 5 At the edge of the world, his journey begins. Cast Away 2000
#> 6 Walk a mile youll never forget. The Green Mile 1999
#> 7 Break The Codes The Da Vinci C… 2006
#> 8 This Holiday Season… Believe The Polar Expr… 2004
#> 9 A story of love, lava and burning desire. Joe Versus the… 1990
#> 10 Everything is connected Cloud Atlas 2012
#>
#> $d
#> # A tibble: 10 x 2
#> born name
#> <int> <chr>
#> 1 1956 Tom Hanks
#> 2 1943 Penny Marshall
#> 3 1941 Nora Ephron
#> 4 1931 Mike Nichols
#> 5 1951 Robert Zemeckis
#> 6 1959 Frank Darabont
#> 7 1954 Ron Howard
#> 8 1951 Robert Zemeckis
#> 9 1950 John Patrick Stanley
#> 10 1965 Tom Tykwer
#>
#> attr(,"class")
#> [1] "neo" "list"
“rows” format
The user chooses whether or not to return a list of tibbles when calling the API. You get as many objects as specified in the RETURN cypher statement.
library(magrittr)
'MATCH (tom {name: "Tom Hanks"}) RETURN tom;' %>%
call_neo4j(con)
#> $tom
#> # A tibble: 1 x 2
#> born name
#> <int> <chr>
#> 1 1956 Tom Hanks
#>
#> attr(,"class")
#> [1] "neo" "list"
'MATCH (cloudAtlas {title: "Cloud Atlas"}) RETURN cloudAtlas;' %>%
call_neo4j(con)
#> $cloudAtlas
#> # A tibble: 1 x 3
#> tagline title released
#> <chr> <chr> <int>
#> 1 Everything is connected Cloud Atlas 2012
#>
#> attr(,"class")
#> [1] "neo" "list"
"MATCH (people:Person)-[relatedTo]-(:Movie {title: 'Cloud Atlas'}) RETURN people.name, Type(relatedTo), relatedTo" %>%
call_neo4j(con, type = 'row')
#> $people.name
#> # A tibble: 10 x 1
#> value
#> <chr>
#> 1 Tom Hanks
#> 2 Jim Broadbent
#> 3 David Mitchell
#> 4 Tom Tykwer
#> 5 Lana Wachowski
#> 6 Stefan Arndt
#> 7 Jessica Thompson
#> 8 Halle Berry
#> 9 Hugo Weaving
#> 10 Lilly Wachowski
#>
#> $`Type(relatedTo)`
#> # A tibble: 10 x 1
#> value
#> <chr>
#> 1 ACTED_IN
#> 2 ACTED_IN
#> 3 WROTE
#> 4 DIRECTED
#> 5 DIRECTED
#> 6 PRODUCED
#> 7 REVIEWED
#> 8 ACTED_IN
#> 9 ACTED_IN
#> 10 DIRECTED
#>
#> $relatedTo
#> # A tibble: 18 x 3
#> roles summary rating
#> <list> <chr> <int>
#> 1 <chr [1]> <NA> NA
#> 2 <chr [1]> <NA> NA
#> 3 <chr [1]> <NA> NA
#> 4 <chr [1]> <NA> NA
#> 5 <chr [1]> <NA> NA
#> 6 <chr [1]> <NA> NA
#> 7 <chr [1]> <NA> NA
#> 8 <NULL> An amazing journey 95
#> 9 <chr [1]> <NA> NA
#> 10 <chr [1]> <NA> NA
#> 11 <chr [1]> <NA> NA
#> 12 <chr [1]> <NA> NA
#> 13 <chr [1]> <NA> NA
#> 14 <chr [1]> <NA> NA
#> 15 <chr [1]> <NA> NA
#> 16 <chr [1]> <NA> NA
#> 17 <chr [1]> <NA> NA
#> 18 <chr [1]> <NA> NA
#>
#> attr(,"class")
#> [1] "neo" "list"
By default, results are returned as an R list of tibbles. For example
here, RETURN tom will return a one element list, with object named
tom. We think this is the more “truthful” way to implement the outputs
regarding Neo4J calls.
When you want to return two nodes types, you’ll get two results, in the form of two tibbles - the result is a two elements list with each element being labelled the way it has been specified in the Cypher query.
'MATCH (tom:Person {name: "Tom Hanks"})-[:ACTED_IN]->(tomHanksMovies) RETURN tom,tomHanksMovies' %>%
call_neo4j(con)
#> $tom
#> # A tibble: 12 x 2
#> born name
#> <int> <chr>
#> 1 1956 Tom Hanks
#> 2 1956 Tom Hanks
#> 3 1956 Tom Hanks
#> 4 1956 Tom Hanks
#> 5 1956 Tom Hanks
#> 6 1956 Tom Hanks
#> 7 1956 Tom Hanks
#> 8 1956 Tom Hanks
#> 9 1956 Tom Hanks
#> 10 1956 Tom Hanks
#> 11 1956 Tom Hanks
#> 12 1956 Tom Hanks
#>
#> $tomHanksMovies
#> # A tibble: 12 x 3
#> tagline title released
#> <chr> <chr> <int>
#> 1 Houston, we have a problem. Apollo 13 1995
#> 2 At odds in life... in love on-line. Youve Got Mail 1998
#> 3 Once in a lifetime you get a chance to do some… A League of Th… 1992
#> 4 A story of love, lava and burning desire. Joe Versus the… 1990
#> 5 In every life there comes a time when that thi… That Thing You… 1996
#> 6 Break The Codes The Da Vinci C… 2006
#> 7 Everything is connected Cloud Atlas 2012
#> 8 At the edge of the world, his journey begins. Cast Away 2000
#> 9 Walk a mile youll never forget. The Green Mile 1999
#> 10 What if someone you never met, someone you nev… Sleepless in S… 1993
#> 11 This Holiday Season… Believe The Polar Expr… 2004
#> 12 A stiff drink. A little mascara. A lot of nerv… Charlie Wilson… 2007
#>
#> attr(,"class")
#> [1] "neo" "list"
Results can also be returned in JSON, for example for writing to a file:
tmp <- tempfile(fileext = ".json")
'MATCH (people:Person) RETURN people.name LIMIT 1' %>%
call_neo4j(con, output = "json") %>%
write(tmp)
jsonlite::read_json(tmp)
#> [[1]]
#> [[1]][[1]]
#> [[1]][[1]]$row
#> [[1]][[1]]$row[[1]]
#> [[1]][[1]]$row[[1]][[1]]
#> [1] "Keanu Reeves"
#>
#>
#>
#> [[1]][[1]]$meta
#> [[1]][[1]]$meta[[1]]
#> named list()
If you turn the type argument to "graph", you’ll get a graph
result:
'MATCH (tom:Person {name: "Tom Hanks"})-[act:ACTED_IN]->(tomHanksMovies) RETURN act,tom,tomHanksMovies' %>%
call_neo4j(con, type = "graph")
#> $nodes
#> # A tibble: 13 x 3
#> id label properties
#> <chr> <list> <list>
#> 1 144 <chr [1]> <list [3]>
#> 2 71 <chr [1]> <list [2]>
#> 3 67 <chr [1]> <list [3]>
#> 4 162 <chr [1]> <list [3]>
#> 5 78 <chr [1]> <list [3]>
#> 6 85 <chr [1]> <list [3]>
#> 7 111 <chr [1]> <list [3]>
#> 8 105 <chr [1]> <list [3]>
#> 9 150 <chr [1]> <list [3]>
#> 10 130 <chr [1]> <list [3]>
#> 11 73 <chr [1]> <list [3]>
#> 12 161 <chr [1]> <list [3]>
#> 13 159 <chr [1]> <list [3]>
#>
#> $relationships
#> # A tibble: 12 x 5
#> id type startNode endNode properties
#> <chr> <chr> <chr> <chr> <list>
#> 1 202 ACTED_IN 71 144 <list [1]>
#> 2 84 ACTED_IN 71 67 <list [1]>
#> 3 234 ACTED_IN 71 162 <list [1]>
#> 4 98 ACTED_IN 71 78 <list [1]>
#> 5 110 ACTED_IN 71 85 <list [1]>
#> 6 146 ACTED_IN 71 111 <list [1]>
#> 7 137 ACTED_IN 71 105 <list [1]>
#> 8 213 ACTED_IN 71 150 <list [1]>
#> 9 182 ACTED_IN 71 130 <list [1]>
#> 10 91 ACTED_IN 71 73 <list [1]>
#> 11 232 ACTED_IN 71 161 <list [1]>
#> 12 228 ACTED_IN 71 159 <list [1]>
#>
#> attr(,"class")
#> [1] "neo" "list"
The result is returned as one node or relationship by row.
Due to the specific data format of Neo4J, there can be more than one label and property by node and relationship. That’s why the results is returned, by design, as a list-dataframe.
We have designed several functions to unnest the output :
+unnest_nodes(), that can unnest a node dataframe
:
res <- 'MATCH (tom:Person {name:"Tom Hanks"})-[a:ACTED_IN]->(m)<-[:ACTED_IN]-(coActors) RETURN m AS acted,coActors.name' %>%
call_neo4j(con, type = "graph")
unnest_nodes(res$nodes)
#> # A tibble: 11 x 5
#> id value tagline title released
#> <chr> <chr> <chr> <chr> <int>
#> 1 144 Movie Houston, we have a problem. Apollo 13 1995
#> 2 67 Movie At odds in life... in love on-line. Youve Got M… 1998
#> 3 162 Movie Once in a lifetime you get a chance t… A League of… 1992
#> 4 78 Movie A story of love, lava and burning des… Joe Versus … 1990
#> 5 85 Movie In every life there comes a time when… That Thing … 1996
#> 6 111 Movie Break The Codes The Da Vinc… 2006
#> 7 105 Movie Everything is connected Cloud Atlas 2012
#> 8 150 Movie At the edge of the world, his journey… Cast Away 2000
#> 9 130 Movie Walk a mile youll never forget. The Green M… 1999
#> 10 73 Movie What if someone you never met, someon… Sleepless i… 1993
#> 11 159 Movie A stiff drink. A little mascara. A lo… Charlie Wil… 2007
Please, note that this function will return NA for the properties that
aren’t in a node.
Also, it is possible to unnest either the properties or the labels :
res %>%
extract_nodes() %>%
unnest_nodes(what = "properties")
#> # A tibble: 11 x 5
#> id label tagline title released
#> <chr> <list> <chr> <chr> <int>
#> 1 144 <chr [… Houston, we have a problem. Apollo 13 1995
#> 2 67 <chr [… At odds in life... in love on-line. Youve Got M… 1998
#> 3 162 <chr [… Once in a lifetime you get a chance… A League of… 1992
#> 4 78 <chr [… A story of love, lava and burning d… Joe Versus … 1990
#> 5 85 <chr [… In every life there comes a time wh… That Thing … 1996
#> 6 111 <chr [… Break The Codes The Da Vinc… 2006
#> 7 105 <chr [… Everything is connected Cloud Atlas 2012
#> 8 150 <chr [… At the edge of the world, his journ… Cast Away 2000
#> 9 130 <chr [… Walk a mile youll never forget. The Green M… 1999
#> 10 73 <chr [… What if someone you never met, some… Sleepless i… 1993
#> 11 159 <chr [… A stiff drink. A little mascara. A … Charlie Wil… 2007
res %>%
extract_nodes() %>%
unnest_nodes(what = "label")
#> # A tibble: 11 x 3
#> id properties value
#> <chr> <list> <chr>
#> 1 144 <list [3]> Movie
#> 2 67 <list [3]> Movie
#> 3 162 <list [3]> Movie
#> 4 78 <list [3]> Movie
#> 5 85 <list [3]> Movie
#> 6 111 <list [3]> Movie
#> 7 105 <list [3]> Movie
#> 8 150 <list [3]> Movie
#> 9 130 <list [3]> Movie
#> 10 73 <list [3]> Movie
#> 11 159 <list [3]> Movie
unnest_relationships()
There is only one nested column in the relationship table, thus the function is quite straightforward :
'MATCH (people:Person)-[relatedTo]-(:Movie {title: "Cloud Atlas"}) RETURN people.name, Type(relatedTo), relatedTo' %>%
call_neo4j(con, type = "graph") %>%
extract_relationships() %>%
unnest_relationships()
#> # A tibble: 23 x 8
#> id type startNode endNode roles value summary rating
#> <chr> <chr> <chr> <chr> <list> <lgl> <chr> <int>
#> 1 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 2 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 3 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 4 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 5 140 ACTED_IN 107 105 <chr [1]> NA <NA> NA
#> 6 140 ACTED_IN 107 105 <chr [1]> NA <NA> NA
#> 7 140 ACTED_IN 107 105 <chr [1]> NA <NA> NA
#> 8 144 WROTE 109 105 <NULL> NA <NA> NA
#> 9 141 DIRECTED 108 105 <NULL> NA <NA> NA
#> 10 143 DIRECTED 6 105 <NULL> NA <NA> NA
#> # … with 13 more rows
Note that unnest_relationships() only does one level of unnesting.
unnest_graph
This function takes a graph results, and does unnest_nodes and
unnest_relationships.
'MATCH (people:Person)-[relatedTo]-(:Movie {title: "Cloud Atlas"}) RETURN people.name, Type(relatedTo), relatedTo' %>%
call_neo4j(con, type = "graph") %>%
unnest_graph()
#> $nodes
#> # A tibble: 11 x 7
#> id value born name tagline title released
#> <chr> <chr> <int> <chr> <chr> <chr> <int>
#> 1 71 Person 1956 Tom Hanks <NA> <NA> NA
#> 2 105 Movie NA <NA> Everything is conn… Cloud At… 2012
#> 3 107 Person 1949 Jim Broadbent <NA> <NA> NA
#> 4 109 Person 1969 David Mitchell <NA> <NA> NA
#> 5 108 Person 1965 Tom Tykwer <NA> <NA> NA
#> 6 6 Person 1965 Lana Wachowski <NA> <NA> NA
#> 7 110 Person 1961 Stefan Arndt <NA> <NA> NA
#> 8 169 Person NA Jessica Thomp… <NA> <NA> NA
#> 9 106 Person 1966 Halle Berry <NA> <NA> NA
#> 10 4 Person 1960 Hugo Weaving <NA> <NA> NA
#> 11 5 Person 1967 Lilly Wachows… <NA> <NA> NA
#>
#> $relationships
#> # A tibble: 23 x 8
#> id type startNode endNode roles value summary rating
#> <chr> <chr> <chr> <chr> <list> <lgl> <chr> <int>
#> 1 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 2 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 3 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 4 137 ACTED_IN 71 105 <chr [1]> NA <NA> NA
#> 5 140 ACTED_IN 107 105 <chr [1]> NA <NA> NA
#> 6 140 ACTED_IN 107 105 <chr [1]> NA <NA> NA
#> 7 140 ACTED_IN 107 105 <chr [1]> NA <NA> NA
#> 8 144 WROTE 109 105 <NULL> NA <NA> NA
#> 9 141 DIRECTED 108 105 <NULL> NA <NA> NA
#> 10 143 DIRECTED 6 105 <NULL> NA <NA> NA
#> # … with 13 more rows
#>
#> attr(,"class")
#> [1] "neo" "list"
Extraction
There are two convenient functions to extract nodes and relationships:
'MATCH (bacon:Person {name:"Kevin Bacon"})-[*1..4]-(hollywood) RETURN DISTINCT hollywood' %>%
call_neo4j(con, type = "graph") %>%
extract_nodes()
#> # A tibble: 135 x 3
#> id label properties
#> <chr> <list> <list>
#> 1 72 <chr [1]> <list [2]>
#> 2 68 <chr [1]> <list [2]>
#> 3 54 <chr [1]> <list [2]>
#> 4 34 <chr [1]> <list [2]>
#> 5 70 <chr [1]> <list [2]>
#> 6 69 <chr [1]> <list [2]>
#> 7 67 <chr [1]> <list [3]>
#> 8 163 <chr [1]> <list [2]>
#> 9 166 <chr [1]> <list [2]>
#> 10 77 <chr [1]> <list [2]>
#> # … with 125 more rows
'MATCH p=shortestPath(
(bacon:Person {name:"Kevin Bacon"})-[*]-(meg:Person {name:"Meg Ryan"})
)
RETURN p' %>%
call_neo4j(con, type = "graph") %>%
extract_relationships()
#> # A tibble: 4 x 5
#> id type startNode endNode properties
#> <chr> <chr> <chr> <chr> <list>
#> 1 202 ACTED_IN 71 144 <list [1]>
#> 2 203 ACTED_IN 19 144 <list [1]>
#> 3 91 ACTED_IN 71 73 <list [1]>
#> 4 92 ACTED_IN 34 73 <list [1]>
Convert for common graph packages
{igraph}
In order to be converted into a graph object:
-
The nodes should be a dataframe with the first column being a series of unique ID, understood as “names” by igraph - these are the ID columns from Neo4J. Other columns are considered attributes.
-
relationships need a start and an end, i.e. startNode and endNode in the Neo4J results.
Here how to create a graph object from a {neo4r}
result:
G <- "MATCH a=(p:Person {name: 'Tom Hanks'})-[r:ACTED_IN]->(m:Movie) RETURN a;" %>%
call_neo4j(con, type = "graph")
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(purrr)
#>
#> Attaching package: 'purrr'
#> The following object is masked from 'package:magrittr':
#>
#> set_names
# Create a dataframe with col 1 being the ID,
# And columns 2 being the names
G$nodes <- G$nodes %>%
unnest_nodes(what = "properties") %>%
# We're extracting the first label of each node, but
# this column can also be removed if not needed
mutate(label = map_chr(label, 1))
head(G$nodes)
#> # A tibble: 6 x 7
#> id label tagline title released born name
#> <chr> <chr> <chr> <chr> <int> <int> <chr>
#> 1 144 Movie Houston, we have a problem. Apollo 13 1995 NA <NA>
#> 2 71 Person <NA> <NA> NA 1956 Tom H…
#> 3 67 Movie At odds in life... in love… Youve Got… 1998 NA <NA>
#> 4 162 Movie Once in a lifetime you get… A League … 1992 NA <NA>
#> 5 78 Movie A story of love, lava and … Joe Versu… 1990 NA <NA>
#> 6 85 Movie In every life there comes … That Thin… 1996 NA <NA>
We then reorder the relationnship table:
G$relationships <- G$relationships %>%
unnest_relationships() %>%
select(startNode, endNode, type, everything()) %>%
mutate(roles = unlist(roles))
head(G$relationships)
#> # A tibble: 6 x 5
#> startNode endNode type id roles
#> <chr> <chr> <chr> <chr> <chr>
#> 1 71 144 ACTED_IN 202 Jim Lovell
#> 2 71 67 ACTED_IN 84 Joe Fox
#> 3 71 162 ACTED_IN 234 Jimmy Dugan
#> 4 71 78 ACTED_IN 98 Joe Banks
#> 5 71 85 ACTED_IN 110 Mr. White
#> 6 71 111 ACTED_IN 146 Dr. Robert Langdon
graph_object <- igraph::graph_from_data_frame(
d = G$relationships,
directed = TRUE,
vertices = G$nodes
)
plot(graph_object)
This can also be used with {ggraph} :
library(ggraph)
#> Loading required package: ggplot2
graph_object %>%
ggraph() +
geom_node_label(aes(label = label)) +
geom_edge_link() +
theme_graph()
#> Using `nicely` as default layout
{visNetwork}
{visNetwork} expects the following format :
nodes
- “id” : id of the node, needed in edges information
- “label” : label of the node
- “group” : group of the node. Groups can be configure with visGroups
- “value” : size of the node
- “title” : tooltip of the node
edges
- “from” : node id of begin of the edge
- “to” : node id of end of the edge
- “label” : label of the edge
- “value” : size of the node
- “title” : tooltip of the node
(from ?visNetwork::visNetwork).
visNetwork is smart enough to transform a list column into several
label, so we don’t have to worry too much about this one.
Here’s how to convert our {neo4r}
result:
G <-"MATCH a=(p:Person {name: 'Tom Hanks'})-[r:ACTED_IN]->(m:Movie) RETURN a;" %>%
call_neo4j(con, type = "graph")
# We'll just unnest the properties
G$nodes <- G$nodes %>%
unnest_nodes(what = "properties")
head(G$nodes)
# Turn the relationships :
G$relationships <- G$relationships %>%
unnest_relationships() %>%
select(from = startNode, to = endNode, label = type)
head(G$relationships)
visNetwork::visNetwork(G$nodes, G$relationships)
Sending data to the API
You can simply send queries has we have just seen, by writing the cypher query and call the api.
Transform elements to cypher queries
vec_to_cypher()creates a list :
vec_to_cypher(iris[1, 1:3], "Species")
#> [1] "(:`Species` {`Sepal.Length`: '5.1', `Sepal.Width`: '3.5', `Petal.Length`: '1.4'})"
- and
vec_to_cypher_with_var()creates a cypher call starting with a variable :
vec_to_cypher_with_var(iris[1, 1:3], "Species", a)
#> [1] "(a:`Species` {`Sepal.Length`: '5.1', `Sepal.Width`: '3.5', `Petal.Length`: '1.4'})"
This can be combined inside a cypher call:
paste("MERGE", vec_to_cypher(iris[1, 1:3], "Species"))
#> [1] "MERGE (:`Species` {`Sepal.Length`: '5.1', `Sepal.Width`: '3.5', `Petal.Length`: '1.4'})"
Reading and sending a cypher file :
read_cypherreads a cypher file and returns a tibble of all the calls:
read_cypher("data-raw/create.cypher")
#> # A tibble: 4 x 1
#> cypher
#> <chr>
#> 1 CREATE CONSTRAINT ON (b:Band) ASSERT b.name IS UNIQUE;
#> 2 CREATE CONSTRAINT ON (c:City) ASSERT c.name IS UNIQUE;
#> 3 CREATE CONSTRAINT ON (r:record) ASSERT r.name IS UNIQUE;
#> 4 CREATE (ancient:Band {name: 'Ancient', formed: 1992}), (acturus:Band {na…
send_cypherreads a cypher file, and send it the the API. By default, the stats are returned.
send_cypher("data-raw/constraints.cypher", con)
#> No data returned.
#> No data returned.
#> No data returned.
#> [[1]]
#> # A tibble: 12 x 2
#> type value
#> <chr> <dbl>
#> 1 contains_updates 1
#> 2 nodes_created 0
#> 3 nodes_deleted 0
#> 4 properties_set 0
#> 5 relationships_created 0
#> 6 relationship_deleted 0
#> 7 labels_added 0
#> 8 labels_removed 0
#> 9 indexes_added 0
#> 10 indexes_removed 0
#> 11 constraints_added 1
#> 12 constraints_removed 0
#>
#> [[2]]
#> # A tibble: 12 x 2
#> type value
#> <chr> <dbl>
#> 1 contains_updates 1
#> 2 nodes_created 0
#> 3 nodes_deleted 0
#> 4 properties_set 0
#> 5 relationships_created 0
#> 6 relationship_deleted 0
#> 7 labels_added 0
#> 8 labels_removed 0
#> 9 indexes_added 0
#> 10 indexes_removed 0
#> 11 constraints_added 1
#> 12 constraints_removed 0
#>
#> [[3]]
#> # A tibble: 12 x 2
#> type value
#> <chr> <dbl>
#> 1 contains_updates 1
#> 2 nodes_created 0
#> 3 nodes_deleted 0
#> 4 properties_set 0
#> 5 relationships_created 0
#> 6 relationship_deleted 0
#> 7 labels_added 0
#> 8 labels_removed 0
#> 9 indexes_added 0
#> 10 indexes_removed 0
#> 11 constraints_added 1
#> 12 constraints_removed 0
Sending csv dataframe to Neo4J
The load_csv sends an csv from an url to the Neo4J browser.
The args are :
on_load: the code to execute on loadcon: the connexion objecturl: the url of the csv to sendheader: whether or not the csv has a headerperiodic_commit: the volume for PERIODIC COMMITas: the AS argument for LOAD CSVformat: the format of the resultinclude_stats: whether or not to include the statsmeta: whether or not to return the meta information
Let’s use Neo4J northwind-graph example for that.
# Create the query that will create the nodes and relationships
on_load_query <- 'CREATE (n:Product)
SET n = row,
n.unitPrice = toFloat(row.unitPrice),
n.unitsInStock = toInteger(row.unitsInStock), n.unitsOnOrder = toInteger(row.unitsOnOrder),
n.reorderLevel = toInteger(row.reorderLevel), n.discontinued = (row.discontinued <> "0");'
# Send the csv
load_csv(url = "http://data.neo4j.com/northwind/products.csv",
con = con, header = TRUE, periodic_commit = 50,
as = "row", on_load = on_load_query)
#> No data returned.
#> # A tibble: 12 x 2
#> type value
#> <chr> <dbl>
#> 1 contains_updates 1
#> 2 nodes_created 77
#> 3 nodes_deleted 0
#> 4 properties_set 1155
#> 5 relationships_created 0
#> 6 relationship_deleted 0
#> 7 labels_added 77
#> 8 labels_removed 0
#> 9 indexes_added 0
#> 10 indexes_removed 0
#> 11 constraints_added 0
#> 12 constraints_removed 0
Using the Connection Pane
{neo4r} comes with a Connection Pane interface for RStudio.
Once installed, you can go to the “Connections”, and use the widget to connect to the Neo4J server:
Sandboxing in Docker
You can get an RStudio / Neo4J sandbox with Docker :
docker pull colinfay/neo4r-docker
docker run -e PASSWORD=plop -e ROOT=TRUE -d -p 8787:8787 neo4r
CoC
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.