Neural Network

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-02-03

Note

Hiplot website

This page is the tutorial for source code version of the Hiplot Neural Network plugin. You can also use the Hiplot website to achieve no code ploting. For more information please see the following link:

https://hiplot.cn/basic/neural-network?lang=en

Setup

  • System Requirements: Cross-platform (Linux/MacOS/Windows)

  • Programming language: R

  • Dependent packages: data.table; jsonlite; NeuralNetTools; nnet

# Install packages
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("NeuralNetTools", quietly = TRUE)) {
  install.packages("NeuralNetTools")
}
if (!requireNamespace("nnet", quietly = TRUE)) {
  install.packages("nnet")
}

# Load packages
library(data.table)
library(jsonlite)
library(NeuralNetTools)
library(nnet)
sessioninfo::session_info("attached")
─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.2 (2025-10-31)
 os       Ubuntu 24.04.3 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-02-03
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.8.27 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package        * version  date (UTC) lib source
 data.table     * 1.18.2.1 2026-01-27 [1] RSPM
 jsonlite       * 2.0.0    2025-03-27 [1] RSPM
 NeuralNetTools * 1.5.3    2022-01-06 [1] RSPM
 nnet           * 7.3-20   2025-01-01 [3] CRAN (R 4.5.2)

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.2/lib/R/site-library
 [3] /opt/R/4.5.2/lib/R/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

Data Preparation

# Load data
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/neural-network/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)

# View data
head(data)
         Y1        Y2          X1         X2         X3
1 0.7646258 0.5494452 -0.89691455 -1.8923489  0.6408445
2 0.2383994 0.4605024  0.18484918  1.2928042 -1.6013778
3 0.3800247 0.2527468  1.58784533 -0.6182543 -0.7778154
4 0.3545279 0.6319730 -1.13037567  1.0409383 -1.6473925
5 0.3667356 0.4684437 -0.08025176  1.1758795  0.1542662
6 0.5509560 0.4439474  0.13242028 -1.5018321 -1.1756313

Visualization

# Neural Network
mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 10,
            maxint = 100, decay = 0)
# weights:  51
initial  value 52.086992 
iter  10 value 0.485423
iter  20 value 0.193680
iter  30 value 0.123716
iter  40 value 0.031775
iter  50 value 0.017802
iter  60 value 0.009108
iter  70 value 0.006694
iter  80 value 0.005395
iter  90 value 0.004144
iter 100 value 0.002620
final  value 0.002620 
stopped after 100 iterations
# plot
par(mar = numeric(4))
plotnet(mod)
FigureΒ 1: Neural Network