Generate network for your data
Jayachandra N 2025-02-12
Are you looking to explore the complex relationships within your CSV data? The netknitr open-source framework, developed using R, RShiny, and visNetwork, makes this task simple. All you need to do is install netknitr on your computer, and you can easily generate network maps to visualize the connections within your data.
# install.packages('netknitr')
# netknitr::knitNet()Curious about how it works? Check out the step-by-step guide provided by the netknitr framework to see how it generates network maps.
Load
# install.packages('netknitr')
library(netknitr)
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(visNetwork)Data : It works for any data
set.seed(0009)
mtcars$name <- row.names(mtcars)
cols <- c('gear', 'name', "cyl")
my_data <- mtcars[,cols]
my_data## gear name cyl
## Mazda RX4 4 Mazda RX4 6
## Mazda RX4 Wag 4 Mazda RX4 Wag 6
## Datsun 710 4 Datsun 710 4
## Hornet 4 Drive 3 Hornet 4 Drive 6
## Hornet Sportabout 3 Hornet Sportabout 8
## Valiant 3 Valiant 6
## Duster 360 3 Duster 360 8
## Merc 240D 4 Merc 240D 4
## Merc 230 4 Merc 230 4
## Merc 280 4 Merc 280 6
## Merc 280C 4 Merc 280C 6
## Merc 450SE 3 Merc 450SE 8
## Merc 450SL 3 Merc 450SL 8
## Merc 450SLC 3 Merc 450SLC 8
## Cadillac Fleetwood 3 Cadillac Fleetwood 8
## Lincoln Continental 3 Lincoln Continental 8
## Chrysler Imperial 3 Chrysler Imperial 8
## Fiat 128 4 Fiat 128 4
## Honda Civic 4 Honda Civic 4
## Toyota Corolla 4 Toyota Corolla 4
## Toyota Corona 3 Toyota Corona 4
## Dodge Challenger 3 Dodge Challenger 8
## AMC Javelin 3 AMC Javelin 8
## Camaro Z28 3 Camaro Z28 8
## Pontiac Firebird 3 Pontiac Firebird 8
## Fiat X1-9 4 Fiat X1-9 4
## Porsche 914-2 5 Porsche 914-2 4
## Lotus Europa 5 Lotus Europa 4
## Ford Pantera L 5 Ford Pantera L 8
## Ferrari Dino 5 Ferrari Dino 6
## Maserati Bora 5 Maserati Bora 8
## Volvo 142E 4 Volvo 142E 4res <- lapply(names(my_data), FUN = function(i) {
paste0(i, " : ", my_data[[i]])
}) %>% do.call('cbind', .) %>% as.data.frame()
names(res) <- names(my_data)
res %>% head()## gear name cyl
## 1 gear : 4 name : Mazda RX4 cyl : 6
## 2 gear : 4 name : Mazda RX4 Wag cyl : 6
## 3 gear : 4 name : Datsun 710 cyl : 4
## 4 gear : 3 name : Hornet 4 Drive cyl : 6
## 5 gear : 3 name : Hornet Sportabout cyl : 8
## 6 gear : 3 name : Valiant cyl : 6my_data <- res
head(my_data)## gear name cyl
## 1 gear : 4 name : Mazda RX4 cyl : 6
## 2 gear : 4 name : Mazda RX4 Wag cyl : 6
## 3 gear : 4 name : Datsun 710 cyl : 4
## 4 gear : 3 name : Hornet 4 Drive cyl : 6
## 5 gear : 3 name : Hornet Sportabout cyl : 8
## 6 gear : 3 name : Valiant cyl : 6Determine Nodes
## [1] "----Columns considerd for nodes----->"
## [1] "gear"
## [1] "name"
## [1] "cyl"
## id label group
## 1 1 gear : 4 gear
## 2 2 gear : 3 gear
## 3 3 gear : 5 gear
## 4 4 name : Mazda RX4 name
## 5 5 name : Mazda RX4 Wag name
## 6 6 name : Datsun 710 name
## 7 7 name : Hornet 4 Drive name
## 8 8 name : Hornet Sportabout name
## 9 9 name : Valiant name
## 10 10 name : Duster 360 name
## 11 11 name : Merc 240D name
## 12 12 name : Merc 230 name
## 13 13 name : Merc 280 name
## 14 14 name : Merc 280C name
## 15 15 name : Merc 450SE name
## 16 16 name : Merc 450SL name
## 17 17 name : Merc 450SLC name
## 18 18 name : Cadillac Fleetwood name
## 19 19 name : Lincoln Continental name
## 20 20 name : Chrysler Imperial name
## 21 21 name : Fiat 128 name
## 22 22 name : Honda Civic name
## 23 23 name : Toyota Corolla name
## 24 24 name : Toyota Corona name
## 25 25 name : Dodge Challenger name
## 26 26 name : AMC Javelin name
## 27 27 name : Camaro Z28 name
## 28 28 name : Pontiac Firebird name
## 29 29 name : Fiat X1-9 name
## 30 30 name : Porsche 914-2 name
## 31 31 name : Lotus Europa name
## 32 32 name : Ford Pantera L name
## 33 33 name : Ferrari Dino name
## 34 34 name : Maserati Bora name
## 35 35 name : Volvo 142E name
## 36 36 cyl : 6 cyl
## 37 37 cyl : 4 cyl
## 38 38 cyl : 8 cylCan include shapes and Colors
nodes$shape <- getShapes(nodes)
nodes$colors <- sample(c("darkred", "grey", "orange", "darkblue", "purple", 'green'), nrow(nodes), replace = T)
head(nodes)## id label group shape colors
## 1 1 gear : 4 gear dot orange
## 2 2 gear : 3 gear dot orange
## 3 3 gear : 5 gear dot darkblue
## 4 4 name : Mazda RX4 name star orange
## 5 5 name : Mazda RX4 Wag name star green
## 6 6 name : Datsun 710 name star darkblueDetermine Edges
associations <- getAssociation(my_data[,cols])
edges <- getEdges(associations,nodes)
head(edges)## from to
## 1 1 4
## 2 1 5
## 3 1 6
## 4 1 11
## 5 1 12
## 6 1 13Network
visNetwork(nodes, edges) %>%
visNodes(color = list(background = "lightblue",
border = "darkblue",
highlight = "yellow"),
shadow = list(enabled = TRUE, size = 10))