Visualizing with D3 JavaScript Network Graphs from R
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Hi all, I want to visualize some of my courses I’ve followed by year. I have found D3 JavaScript Network Graphs from R with some nice and cool visualizations.
In this post I will describe how I transform my data for creating the RadialNetwork and DiagonalNetwork visualizations.
Loading and have a look at the data
First off all I had a Excel file with some of my followed some courses since 2013. I’ve saved it to a csv file courses.csv. Let have a look of the file.
setwd("~/Documents")
courses <- read.csv("courses.csv", sep=";",header=TRUE, stringsAsFactors = FALSE)
head(courses)
## year month course type ## 1 2013 December Excel 2007-2010 Expert: Analyse en rapportage in-class ## 2 2014 Januari Business Analysis (Vijfhart) in-class ## 3 2014 November Business Intelligence & Analytics (Cibit) in-class ## 4 2014 December Big Data Fundamentals (IIR Trainingen) in-class ## 5 2015 Januari Datameer Big Data Certified Analyst (Datameer) in-class ## 6 2015 Februari The Python programming language (AT Computing) in-class
str(courses)
## 'data.frame': 25 obs. of 4 variables: ## $ year : int 2013 2014 2014 2014 2015 2015 2015 2015 2015 2015 ... ## $ month : chr "December" "Januari" "November" "December" ... ## $ course: chr "Excel 2007-2010 Expert: Analyse en rapportage" "Business Analysis (Vijfhart)" "Business Intelligence & Analytics (Cibit)" "Big Data Fundamentals (IIR Trainingen)" ... ## $ type : chr "in-class" "in-class" "in-class" "in-class" ...
Oke, we have a data.frame of 25 observation of 4 variables (year, month, course and type). Since both visualizations are based on tree diagrams we need to create a tree from my data.frame. After some searching on the internet I’ve found the package data.tree. In this package there is a paragraph called “Create a tree from a data.frame”.
Let’s start to create a tree from my courses
Load the library.
library(data.tree)
The data.frame is a table and each row is a leaf. We create a path from root to leaf and save that into the data.frame as new column pathString.
Define the hierarchy (Year|Month|Type|Course)
courses$pathString <- paste("courses",courses$year,courses$month,courses$type,courses$course, sep= "|")
head(courses)
## year month course type ## 1 2013 December Excel 2007-2010 Expert: Analyse en rapportage in-class ## 2 2014 Januari Business Analysis (Vijfhart) in-class ## 3 2014 November Business Intelligence & Analytics (Cibit) in-class ## 4 2014 December Big Data Fundamentals (IIR Trainingen) in-class ## 5 2015 Januari Datameer Big Data Certified Analyst (Datameer) in-class ## 6 2015 Februari The Python programming language (AT Computing) in-class ## pathString ## 1 courses|2013|December|in-class|Excel 2007-2010 Expert: Analyse en rapportage ## 2 courses|2014|Januari|in-class|Business Analysis (Vijfhart) ## 3 courses|2014|November|in-class|Business Intelligence & Analytics (Cibit) ## 4 courses|2014|December|in-class|Big Data Fundamentals (IIR Trainingen) ## 5 courses|2015|Januari|in-class|Datameer Big Data Certified Analyst (Datameer) ## 6 courses|2015|Februari|in-class|The Python programming language (AT Computing)
After we have created the pathString we need to convert it to a node, with the package data.tree is that very easy.
coursesTree <- as.Node(courses, pathDelimiter = "|") coursesTree
## levelName ## 1 courses ## 2 ¦--2013 ## 3 ¦ °--December ## 4 ¦ °--in-class ## 5 ¦ °--Excel 2007-2010 Expert: Analyse en rapportage ## 6 ¦--2014 ## 7 ¦ ¦--Januari ## 8 ¦ ¦ °--in-class ## 9 ¦ ¦ °--Business Analysis (Vijfhart) ## 10 ¦ ¦--November ## 11 ¦ ¦ °--in-class ## 12 ¦ ¦ °--Business Intelligence & Analytics (Cibit) ## 13 ¦ °--December ## 14 ¦ °--in-class ## 15 ¦ °--Big Data Fundamentals (IIR Trainingen) ## 16 ¦--2015 ## 17 ¦ ¦--Januari ## 18 ¦ ¦ °--in-class ## 19 ¦ ¦ °--Datameer Big Data Certified Analyst (Datameer) ## 20 ¦ ¦--Februari ## 21 ¦ ¦ °--in-class ## 22 ¦ ¦ °--The Python programming language (AT Computing) ## 23 ¦ ¦--May ## 24 ¦ ¦ °--online ## 25 ¦ ¦ ¦--The Data Scientist's Toolbox (Coursera) ## 26 ¦ ¦ °--Quick start to JavaScript Volume 1,2 & 3 (Pluralsight) ## 27 ¦ ¦--June ## 28 ¦ ¦ °--online ## 29 ¦ ¦ ¦--R Programming (Cousera) ## 30 ¦ ¦ ¦--Big Data Analytics with Tableau (Pluralsight) ## 31 ¦ ¦ ¦--Data Analysis Fundamentals with Tableau (Pluralsight) ## 32 ¦ ¦ °--Getting and Cleaning Data (Coursera) ## 33 ¦ ¦--November ## 34 ¦ ¦ ¦--online ## 35 ¦ ¦ ¦ °--Exploratory Data Analysis (Coursera) ## 36 ¦ ¦ °--in-class ## 37 ¦ ¦ °--Applied Statistics and data analysis (Tridata) ## 38 ¦ °--December ## 39 ¦ ¦--online ## 40 ¦ ¦ ¦--Reproducible Research (Coursera) ## 41 ¦ ¦ °--R Programming (Data-Mania) ## 42 ¦ °--in-class ## 43 ¦ °--Scrum Master ## 44 °--2016 ## 45 ¦--Januari ## 46 ¦ ¦--online ## 47 ¦ ¦ °--Statistical Inference (Coursera) ## 48 ¦ °--in-class ## 49 ¦ ¦--Using Splunk 6 (Splunk) ## 50 ¦ ¦--Searching and Reporting with Splunk 6 (Splunk) ## 51 ¦ ¦--Creating Splunk 6.3 Knowledge Objects (Splunk) ## 52 ¦ °--Splunk Tutorial (Splunk) ## 53 ¦--April ## 54 ¦ °--in-class ## 55 ¦ °--JavaScript (Vijfhart) ## 56 °--August ## 57 °--online ## 58 ¦--JavaScript (Codecademy) ## 59 °--Intro to Descriptive Statistics (Udemy)
The last thing we need is a list-to-list structure before we can plot the two networkD3 visulizatios
coursesTreeList <- ToListExplicit(coursesTree, unname =TRUE)
Plot the two visulizatios
library(networkD3)
diagonalNetwork(List=coursesTreeList,
fontSize = 13,
fontFamily = "OpenSans-Light",
nodeStroke = "orange",
linkColour = "#AAA",
opacity = 0.9)
radialNetwork(List=coursesTreeList,
fontSize = 13,
fontFamily = "OpenSans-Light",
nodeStroke = "orange",
opacity = 0.9)
Well that look nice!! This package is great!!
This code can also be find on my GitHub
SessionInfo
sessionInfo() ## R version 3.3.2 (2016-10-31) ## Platform: x86_64-apple-darwin13.4.0 (64-bit) ## Running under: macOS Sierra 10.12.1 ## ## locale: ## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8 ## ## attached base packages: ## [1] stats graphics grDevices utils datasets methods base ## ## other attached packages: ## [1] magrittr_1.5 RWordPress_0.2-3 networkD3_0.2.13 data.tree_0.4.0 ## [5] knitr_1.15 ## ## loaded via a namespace (and not attached): ## [1] igraph_1.0.1 Rcpp_0.12.8 rstudioapi_0.6 devtools_1.12.0 ## [5] munsell_0.4.3 colorspace_1.3-0 R6_2.2.0 httr_1.2.1 ## [9] stringr_1.1.0 plyr_1.8.4 tools_3.3.2 visNetwork_1.0.2 ## [13] webshot_0.3.2 git2r_0.15.0 withr_1.0.2 influenceR_0.1.0 ## [17] DiagrammeR_0.8.4 htmltools_0.3.5 yaml_2.1.14 digest_0.6.10 ## [21] assertthat_0.1 tibble_1.2 bitops_1.0-6 htmlwidgets_0.8 ## [25] RCurl_1.95-4.8 curl_2.2 memoise_1.0.0 evaluate_0.10 ## [29] rmarkdown_1.1 stringi_1.1.2 XMLRPC_0.3-0 scales_0.4.1 ## [33] XML_3.98-1.5 jsonlite_1.1 markdown_0.7.7
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