neo4r

Server Connection

Installation

# install.packages("remotes")remotes::install_github("neo4j-rstats/neo4r")

install.packages("neo4r")

Create a connexion object

library(neo4r)con<-neo4j_api$new(url="http://localhost:7474",user="neo4j",password="plop")

# Test the endpoint, that will not work :con$ping()#> [1] 401

con$reset_user("neo4j")con$reset_password("password")con$ping()#> [1] 200

# Get Neo4J Versioncon$get_version()#> [1] "3.5.5"# List constaints (if any)con$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 indexcon$get_index()#> Null data.table (0 rows and 0 cols)

Call the API

The movie graph

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

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"

'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"

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()

'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"

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

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

'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

'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

'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}

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, unionlibrary(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 namesG$nodes<-G$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>

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)

library(ggraph)#> Loading required package: ggplot2graph_object %>%  ggraph()+   geom_node_label(aes(label=label))+  geom_edge_link()+   theme_graph()#> Using `nicely` as default layout

{visNetwork}

nodes

edges

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 propertiesG$nodes<-G$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

Transform elements to cypher queries

vec_to_cypher(iris[1,1:3],"Species")#> [1] "(:`Species` {`Sepal.Length`: '5.1', `Sepal.Width`: '3.5', `Petal.Length`: '1.4'})"

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'})"

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_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_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

# Create the query that will create the nodes and relationshipson_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 csvload_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

Sandboxing in Docker

docker pull colinfay/neo4r-dockerdocker run -e PASSWORD=plop -e ROOT=TRUE -d -p 8787:8787 neo4r

CoC