README.md

HiSub: generate Hiplot native plugin from R script

Hiplot plugin (self-contained Apps generated by Shiny or other techniques are not included) uses JSON to communicate with the front end, it is not easy to code and maintain both R script and JSON files at the same time. In current stage, a formal Hiplot plugin has the following structure in a directory:

• plot.R : where the core computation implemented.
• ui.json: UI controls for the plugin.
• data.json: set the default arguments to UI widgets and the backend plot.R.
• meta.json: set plugin metadata like author, emails, etc.
• README.md: detail usage of the plugin in English.
• README.zh_cn.md: detail usage of the plugin Chinese.

To happier make Hiplot plugin, we develop HiSub and let it help us convert a R script with special comments to a Hiplot plugin (excepts the two README.* files).

Usage

Firstly clone the tool to local machine.

git clone https://github.com/hiplot/hiplot-plugin-generator
cd hiplot-plugin-generator

Then you can prepare a R script named source.R with:

./hisub.R template

Next modify the source.R as your wish and convert it into your Hiplot plugin.

./hisub.R source.R <output-directory>

For better use this tools, I recommend you to generate a soft link to the hisub.R with command similar to

ln -s ~/Documents/GitHub/hiplot-plugin-generator/hisub.R ~/.local/bin/hisub

Change the first path to your cloned hisub.R path and change the second path to a path storing the executable file. NOTE, the second path should be in system PATH variable so you can call HiSub with hisub.

Hello World

For preparing the core R script, let's get started with the Hello World plugin for Hiplot, which shows the core concept of Hisub and most common setting.

The contents of helloworld.R:

# @hiplot start
# @appname helloworld
# @apptitle
# Hiplot Hello World
# Hiplot 示例插件
# @target basic
# @tag test dotplot
# @author your name
# @url your project link
# @citation any reference you should link to
# @version 0.1.0
# @release 2021-01-01
# @description
# en: One sentence to describe this plugin.
# zh: 插件一段话简单介绍。
# @main helloworld
# @library ggplot2 readr
# @param data export::data::hiplot-textarea::{"default": "data.txt", "required": true}
# en: Data Table
# zh: 数据表
# @param x export::dataArg::data::{"index":1, "default": "mpg", "required": true}
# en: X Axis Variable
# zh: X 轴变量
# @param y export::dataArg::data::{"index":2, "default": "vs", "blackItems": "carb", "required": false}
# en: X Axis Variable
# zh: Y 轴变量
# @param size export::extra::slider::{"default":2, "min":0.5, "max":5, "step":0.5, "class":"col-12"}
# en: Dot Size
# zh: 点大小
# @param add_line export::extra::switch::{"default": true, "class":"col-12"}
# en: Add Line
# zh: 添加线图
# @return ggplot::["pdf", "png"]::{"cliMode": true, "title": "A test plot", "width":4, "height": 4, "theme": "theme_bw"}
# @data
# # You can write the code to generate the example data
# # 'data.txt' described in parameter data, or you can
# # omit this tag and submit prepared data files.
# # File size <100Kb is recommended.
# # 此处可以编写生成示例数据（建议小于 100Kb）的代码
# # 示例数据文件需要跟数据表格参数对应起来
# # 或者忽略该标签，提交已经准备好的示例数据
# library(readr)
# data("mtcars")
# write_tsv(mtcars, "data.txt")
# @hiplot end

library(ggplot2)
helloworld <- function(data, x, y, size = 2, add_line = TRUE) {
  if (y == "") stop("y must be provided!")
  p <- ggplot(data, aes_string(x = x, y = y))
  p <- p + geom_point(size = size)
  if (add_line) {
    p <- p + geom_line()
  }
  # Here export a ggplot object
  # Or the whole main function generate a basic R plot
  return(p)
}

We can divide the contents into 2 parts:

1. The comment PART describe the metadata of this plugin and how the parameters map to a web widget (e.g., select) and their default values.
2. The code PART describe how to generate the result plot and we should include the core plotting code in a function.

NOTE: we use tag@xxx to recognize the useful information. HiSub will only parse the comments between @hiplot start and @hiplot end. And only the parameters with export:: will be parsed.

With the R script, we can generate the Hiplot plugin with command:

./hisub.R examples/helloworld.R test_hello

HiSub accept as many as parameters you can put, but only the first will be treated as the R script to parse

Others:

• The last argument should be a directory path for the plugin, typically it has the same name as your plugin name.
• Other middle arguments will be treated as necessary files (like README.md, currently it cannot be scripts called by the 1st R script ) and copied to the destination directory (specified by the last argument).

Then you can submit the generated data.json and ui.json to https://hiplot.com.cn/developer/plugin-preview (You can also read exmaples provided by this link and see how to set the parameter settings) to see how it works. For our "Hello World", it looks like:

Tag List

This section I will briefly describe supported tags, for more information, please read the Docs.

• @hiplot, 1 line, seperates the useful comments parsed by HiSub, only start and end are valid.
• @appname, 1 line, set your plugin name (cannot have space, it will be used to set the plugin URL on hiplot).
• @apptitle, 3 lines, set your plugin title.
  • The 2nd line set English title.
  • The 3rd line set Chinese title.
• @target: 1 line, where the target url should be deployed to, includes basic, advance, clinical-tools, etc.
• @tag: 1 line, tags for your plugin, seperated by space.
• @author: 1 line, your name.
• @url: 1 line, your project link, like GitHub for your plugin (Optional).
• @citation: many lines, your reference or how to cite you (Optional).
• @version: 1 line, the version of this plugin.
• @release: 1 line, the release date of this version.
• @description: many lines, briefly describe the plugin.
  • For English, you should start the line with en:.
  • For Chinese, you should start the line with zh:.
• @main: 1 line, the main function to call for plotting.
• @library: 1 line, required packages for your plugin, so Hiplot team can check if they are installed.
• @param: many lines. From the 2nd line, it is same as @description. For the 1st line, it has the rules:
  • <param_name> export::<param-type>::<widget-type>::<param_setting>
  • All param_names are used to describe the parameters in the main function, here is hello world().
  • param-type can be data (for data table), dataArg (for selecting column names in a data table), extra (custom parameters in your main function).
  • widget-type can be slider, switch, select, autocomplete, color-picker, text-field.
  • param_setting (JSON format) depends on the widget-type. In most common cases, a default item is supported to set default value for the parameter, others items will be passed to front end for setting widget UI and behavior.
• @return: 1line, it has the rules:
  • <output-type>::<output-format>::<output-setting>
  • output-type can be ggplot, plot (for plots not generated by ggplot) and directory (outputing a whole directory, you need read docs for more).
  • output-format (JSON format) sets the figure output format like PDF, PNG, etc.
  • output-setting (JSON format) corresponds to default general parameters in a plot which provided in Hiplot UI but cannot be mapped to parameters of the main functions, like title, palette, theme, width and height of the plot, etc. (cliMode is suggested enable, it works faster for simple plugin).
• @data: many line, it contains code to generate example data described in data table parameters (Optional).

More Examples

• ezcox - https://hiplot.com.cn/basic/ezcox
• pcatools - https://hiplot.com.cn/basic/pcatools

Docs

See doc.md

© 2020 Hiplot team