Giter VIP home page Giter VIP logo

gephi's Introduction

Project Status: Active – The project has reached a stable, usable state and is being actively developed. license Lifecycle: maturing codecov Build Status

gephi

This is a simple package to export files into a csv format that gephi can understand. This package does not interface with the open source network vizualisation software gephi but it writes and reads in the same csv format as gephi.

I’ve found the need to convert tidygraph/igraph objects into a node and edge csv, to visualize in gephi quite often. This task should be trivial, but gephi is a bit particular and wants specific column names.

What does the package do?

Writes igraph files to csv format that gephi likes. Really? Gephi reads csv files just fine! Sure but it wants the columns in a particular order and named Source, Target etc. Let’s not do this by hand everytime: AUTOMATE THE BORING STUFF! HACK THE PLANET!

Installation

Install this developmental version with:

# install.packages("devtools")
devtools::install_github("RMHogervorst/gephi")

Example

  • igraph to csv
  • tidygraph to csv
  • dataframe to csv

Writing out an igraph file to csv:

library(gephi) # includes the graphexample file
library(igraph)
#> 
#> Attaching package: 'igraph'
#> The following objects are masked from 'package:stats':
#> 
#>     decompose, spectrum
#> The following object is masked from 'package:base':
#> 
#>     union
V(graphexample)
#> + 5/5 vertices, named, from b833ca0:
#> [1] a d b c f
E(graphexample)
#> + 5/5 edges from b833ca0 (vertex names):
#> [1] a->b a->c a->d d->b d->f
gephi_write_edges(graphexample, "edges.csv")
#> writing edgesgraphexample
#> to edgefile: edges.csv/n

Technically an tidygraph object is also an igraph object so the writing will work the same.

library(gephi)
library(tidygraph)
#> 
#> Attaching package: 'tidygraph'
#> The following object is masked from 'package:igraph':
#> 
#>     groups
#> The following object is masked from 'package:stats':
#> 
#>     filter

(tidy_graphexample <- tidygraph::as_tbl_graph(graphexample)) # Just to show where this function comes from
#> # A tbl_graph: 5 nodes and 5 edges
#> #
#> # A directed acyclic simple graph with 1 component
#> #
#> # Node Data: 5 x 2 (active)
#>   name  color 
#>   <chr> <chr> 
#> 1 a     blue  
#> 2 d     red   
#> 3 b     blue  
#> 4 c     blue  
#> 5 f     yellow
#> #
#> # Edge Data: 5 x 3
#>    from    to weight
#>   <int> <int>  <dbl>
#> 1     1     3      1
#> 2     1     4      1
#> 3     1     2      1
#> # … with 2 more rows

More specifically if you want to modify your graph and visualize a subset in gephi, here is a tidygraph worked example where I select only the edges that are blue, add a new edge property and write the resulting graph to csv:

tidy_graphexample %>%  # but the igraph object works just as well
    activate(nodes) %>% 
    filter(color == "blue") %>% 
    activate(edges) %>% 
    mutate(dongle = "dingle") %>% 
    gephi_write_edges("edges_subset.csv") %>% 
    print()
#> writing edges.
#> to edgefile: edges_subset.csv/n
#> # A tbl_graph: 3 nodes and 2 edges
#> #
#> # A rooted tree
#> #
#> # Edge Data: 2 x 4 (active)
#>    from    to weight dongle
#>   <int> <int>  <dbl> <chr> 
#> 1     1     2      1 dingle
#> 2     1     3      1 dingle
#> #
#> # Node Data: 3 x 2
#>   name  color
#>   <chr> <chr>
#> 1 a     blue 
#> 2 b     blue 
#> 3 c     blue

But is is also possible to write a set of edges when there is no graph object, just a dataframe.

a_nice_df <- data.frame(
    start = c(1,2,3,4,5,6,7),
    finish = c(2,4,4,7,2,1,3),
    weight = c(1,1,1,2,6,1,1)
)
print(a_nice_df) 
#>   start finish weight
#> 1     1      2      1
#> 2     2      4      1
#> 3     3      4      1
#> 4     4      7      2
#> 5     5      2      6
#> 6     6      1      1
#> 7     7      3      1
gephi_write_edges_from_df(a_nice_df, path = "edges2.csv")
#> writing edges from dataframe to edgefile: edges2.csv
Test coverage statistics
covr::package_coverage(type = "tests")
#> 
#> files differ in number of lines:
#> gephi Coverage: 32.00%
#> R/writing_tools.R: 18.75%
#> R/utils.R: 42.86%
#> R/read_tools.R: 100.00%
cleaning up after ourselves for this demo
file.remove("edges.csv")
#> [1] TRUE
file.remove("edges2.csv")
#> [1] TRUE
file.remove("edges_subset.csv")
#> [1] TRUE

Links

gephi's People

Contributors

rmhogervorst avatar

Watchers

James Cloos avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.