index.Rmd

---
title: "Neo4j gRaphs"
author: 'Bea Hernández'
date: '`r Sys.Date()`'
output: 
  revealjs::revealjs_presentation:
    css: rladies.css
    highlight: pygments
    transition: slide
    fig_width: 6
    fig_height: 4
---

## About

![](images/bea.JPG)

Data Scientist focused on finding patterns and relations between online consumers. She also co-organizes [R-Ladies Madrid](rladies.org) and is a member of the [NASADatanauts](https://open.nasa.gov/explore/datanauts/2017/fall/#datanaut-beatriz-hernandez).

Twitter / Github : @Chucheria

## About

1. About Graphs
2. About Neo4j data in R
3. About analyzing and visualizing Neo4j data in R

# Graphs + Neo4j

## Graphs + Neo4j {.title-h2}

**A graph G is a collection of entities (nodes) and the relationships (edges) that connect those entities**

**_G = {N, E}_**

```{r, include=FALSE}
# Connector
library(RNeo4j)
# Manipulate data
library(dplyr)
# Work with graphs
library(igraph)
# Visualize
library(ggplot2)
library(ggthemes)
library(visNetwork)

graph <- startGraph("http://localhost:7474/db/data/", username="neo4j", password="root")
```

```{r kevin, echo=FALSE}
q <- ("MATCH (a:Actor { name: 'Kevin Bacon' })-[r]-(m:Movie) 
      RETURN a.name AS from, m.title AS to, m.releaseDate AS epoch")
bacon <- cypher(graph, q) %>%
  mutate(epoch = as.numeric(epoch),
         year = format(as.POSIXct(epoch/1000, origin="1970-01-01"), format="%Y"),
         recent = ifelse(year >= 2000, TRUE, FALSE)) 

ig <- graph_from_data_frame(bacon)
data <- toVisNetworkData(ig)
visNetwork(nodes = data$nodes, edges = data$edges, height = "500px")
```

## Graphs + Neo4j {.title-h2}

A graph database stores your data in a graph.

Tables and foreign keys are nodes and relationships.

- _What does math say?_

Graphs can be undirected 

- _What does Neo4j say?_

You have to make directed relationships.
Forget about them when you make your queries.

## Graphs + Neo4j {.title-h2}

Nodes can have properties.

Relationships can have properties.

Node labels are the best. Gives us set advantages.

```{r properties}
q <- ("MATCH (n:Movie { title: 'Footloose' }) 
      RETURN properties(n) AS properties")
properties <- cypherToList(graph, q)

purrr::map(properties, ~names(.x$properties))
```

# Neo4j + R

## Neo4j + R {.title-h2}

### Set your environment
```{r environment, eval=FALSE}
# Connector
library(RNeo4j)
# Manipulate data
library(dplyr)
# Work with graphs
library(igraph)
# Visualize
library(ggplot2)
library(ggthemes)
library(visNetwork)
## If you want to use the Grammar of graphics
# library(ggraph)

graph <- startGraph("http://localhost:7474/db/data/", 
                    username="", 
                    password="")
```

## Neo4j + R {.title-h2}


```{r most_movies}
q <- ("MATCH (a:Actor)-[:ACTS_IN]->(m:Movie) 
      RETURN m.genre AS genre, COUNT(m) AS movies 
      ORDER BY movies DESC
      LIMIT 20")
most_movies <- cypher(graph, q)
```

## Neo4j + R {.title-h2}

```{r}
ggplot(most_movies, aes(reorder(genre, -movies), movies)) + 
  geom_bar(stat="identity") +
  theme_tufte() + 
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  ggtitle("Most played genres")
```

## Neo4j + R {.title-h2}

```{r most_genres}
q <- ("MATCH (a:Actor)-[:ACTS_IN]->(m:Movie) 
      WHERE m.genre IN ['Comedy', 'Drama', 'Action'] 
      RETURN a.name AS name, m.genre AS genre, COUNT(m) AS count 
      ORDER BY count DESC 
      LIMIT 30")
most_genres <- cypher(graph, q)
```

## Neo4j + R {.title-h2}

### Visualize
```{r}
ggplot(most_genres, aes(name, genre)) +
  geom_tile(aes(fill = genre, alpha = count)) + 
  theme_tufte() + 
  theme(axis.title.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) + 
  ggtitle("Actors with most movies by genre ")
```

# RStats

## RStats {.title-h2}

### Betweenness centrality

The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex. It represents the degree of which nodes stand between each other.

$$betweenness(v) = \sum_{x \neq y \in V} \frac{\sigma_{xy}(v)}{\sigma_{xy}}$$


```{r betweenness}
q <- ("MATCH (a1:Actor)-[:ACTS_IN]->(m:Movie),
      (a2:Actor)-[:ACTS_IN]->(m)
      RETURN a1.name AS from, a2.name AS to, COUNT(*) AS weight 
      ORDER BY weight DESC
      LIMIT 10000")
betweenness <- cypher(graph, q)
ig <- graph_from_data_frame(betweenness)
```
## RStats {.title-h2}

### Betweenness centrality
```{r}
b <- betweenness(ig)

b[1:5]
```
## RStats {.title-h2}

### Betweenness centrality
```{r fig.height=5}
V(ig)$label <- NA
V(ig)$size = 4
V(ig)$color = "cyan"

plot(ig, edge.arrow.size = 0.1)
```



# Let's Kahoot!

## Come and play

[Kahoot](https://play.kahoot.it/#/k/cc1a145d-977c-4654-9e73-339018c13870)


![](images/kahoot.png)