README.Rmd

---
title: "R interface for GraphFrames"
output:
  github_document:
    fig_width: 9
    fig_height: 5
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(eval = TRUE)
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_chunk$set(fig.path = "tools/readme/", dev = "png")
```

[![Build Status](https://travis-ci.org/rstudio/graphframes.svg?branch=master)](https://travis-ci.org/rstudio/graphframes) [![Coverage status](https://codecov.io/gh/rstudio/graphframes/branch/master/graph/badge.svg)](https://codecov.io/github/rstudio/graphframes?branch=master) [![CRAN status](https://www.r-pkg.org/badges/version/graphframes)](https://cran.r-project.org/package=graphframes)

- Support for [GraphFrames](https://graphframes.github.io/) which aims to provide the functionality of [GraphX](http://spark.apache.org/graphx/).
- Perform graph algorithms like: [PageRank](https://graphframes.github.io/api/scala/index.html#org.graphframes.lib.PageRank), [ShortestPaths](https://graphframes.github.io/api/scala/index.html#org.graphframes.lib.ShortestPaths) and many [others](https://graphframes.github.io/api/scala/#package).
- Designed to work with [sparklyr](https://spark.rstudio.com) and the [sparklyr extensions](http://spark.rstudio.com/extensions.html).

## Installation

For those already using `sparklyr` simply run:

```{r eval=FALSE}
install.packages("graphframes")
# or, for the development version,
# devtools::install_github("rstudio/graphframes")
```

Otherwise, install first `sparklyr` from CRAN using:

```{r eval=FALSE}
install.packages("sparklyr")
```

The examples make use of the `highschool` dataset from the `ggplot` package.

## Getting Started

We will calculate [PageRank](https://en.wikipedia.org/wiki/PageRank) over the built-in "friends" dataset as follows.

```{r message=FALSE}
library(graphframes)
library(sparklyr)
library(dplyr)

# connect to spark using sparklyr
sc <- spark_connect(master = "local", version = "2.3.0")

# obtain the example graph
g <- gf_friends(sc)

# compute PageRank
results <- gf_pagerank(g, tol = 0.01, reset_probability = 0.15)
results
```

We can then visualize the results by collecting the results to R:

```{r, message = FALSE}
library(tidygraph)
library(ggraph)

vertices <- results %>%
  gf_vertices() %>%
  collect()

edges <- results %>%
  gf_edges() %>%
  collect()

edges %>%
  as_tbl_graph() %>%
  activate(nodes) %>%
  left_join(vertices, by = c(name = "id")) %>%
  ggraph(layout = "nicely") +
  geom_node_label(aes(label = name.y, color = pagerank)) +
  geom_edge_link(
    aes(
      alpha = weight,
      start_cap = label_rect(node1.name.y),
      end_cap = label_rect(node2.name.y)
    ),
    arrow = arrow(length = unit(4, "mm"))
  ) +
  theme_graph(fg_text_colour = 'white')
```

## Further Reading

Appart from calculating `PageRank` using `gf_pagerank`, many other functions are available, including: 

- `gf_bfs()`: Breadth-first search (BFS).
- `gf_connected_components()`: Connected components.
- `gf_shortest_paths()`: Shortest paths algorithm.
- `gf_scc()`: Strongly connected components.
- `gf_triangle_count()`: Computes the number of triangles passing through each vertex and others.
- `gf_degrees()`: Degrees of vertices

For instance, one can calculate the degrees of vertices using `gf_degrees` as follows:

```{r message=FALSE}
gf_friends(sc) %>% gf_degrees()
```

Finally, we disconnect from Spark:

```{r}
spark_disconnect(sc)
```