Add template command, close #4

README.md

@@ -20,13 +20,23 @@ git clone https://github.com/hiplot/hiplot-plugin-generator
 cd hiplot-plugin-generator
 ```
 
-Then you can prepare a R script descibe below.
+Then you can prepare a R script named `source.R` with:
 
-Let's get started with the Hello World plugin for Hiplot, which shows the core concept of **Hisub** and most common setting.
+```sh
+./hisub.R template
+```
+
+Next modify the `source.R` as your wish and convert it into your Hiplot plugin.
+
+```sh
+./hisub.R source.R <output-directory>
+```
 
 
 ### 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`:
 
 ```R

examples/ezcox.R

@@ -78,16 +78,17 @@ call_ezcox <- function(data,
  vars_to_show,
  drop_controls,
  add_caption) {
-
  if (ncol(data) < 3) {
  stop("Input data should have at least 3 columns!")
  }
 
  if (!all(c("time", "status") %in% colnames(data))) {
  cat("WARN: 'time' and 'status' colnames not exist in input data.",
- sep = "\n")
+ sep = "\n"
+ )
  cat("WARN: rename the first and the second column as 'time' and 'status'.",
- sep = "\n")
+ sep = "\n"
+ )
  colnames(data)[1:2] <- c("time", "status")
  }
 
@@ -119,5 +120,3 @@ call_ezcox <- function(data,
  add_caption = add_caption
  )
 }
-
-

examples/pcatools.R

@@ -81,71 +81,82 @@ call_pcatools <- function(datTable, sampleInfo,
  plotloadingsHighCol,
  eigencorplotMetavars,
  eigencorplotComponents) {
- row.names(datTable) <- datTable[,1]
- datTable <- datTable[,-1]
- row.names(sampleInfo) <- sampleInfo[,1]
- sampleInfo <- sampleInfo[,-1]
+ row.names(datTable) <- datTable[, 1]
+ datTable <- datTable[, -1]
+ row.names(sampleInfo) <- sampleInfo[, 1]
+ sampleInfo <- sampleInfo[, -1]
  data3 <- pca(datTable, metadata = sampleInfo, removeVar = (100 - top_var) / 100)
 
  p1 <- PCAtools::screeplot(
- data3, components = getComponents(data3, 1:screeplotComponents),
+ data3,
+ components = getComponents(data3, 1:screeplotComponents),
  axisLabSize = 14, titleLabSize = 20,
  colBar = screeplotColBar,
  gridlines.major = FALSE, gridlines.minor = FALSE,
- returnPlot = TRUE)
+ returnPlot = TRUE
+ )
 
  p2 <- PCAtools::pairsplot(
- data3, components = getComponents(data3, c(1:pairsplotComponents)),
+ data3,
+ components = getComponents(data3, c(1:pairsplotComponents)),
  triangle = TRUE, trianglelabSize = 12,
  hline = 0, vline = 0,
  pointSize = 0.8, gridlines.major = FALSE, gridlines.minor = FALSE,
- colby = 'Grade',
- title = '', plotaxes = FALSE,
- margingaps = unit(c(0.01, 0.01, 0.01, 0.01), 'cm'),
+ colby = "Grade",
+ title = "", plotaxes = FALSE,
+ margingaps = unit(c(0.01, 0.01, 0.01, 0.01), "cm"),
  returnPlot = TRUE,
- colkey = get_hiplot_color(conf$general$palette, -1,
- conf$general$palette_custom)) #!!
+ colkey = get_hiplot_color(
+ conf$general$palette, -1,
+ conf$general$palette_custom
+ )
+ ) # !!
 
  params_biplot <- list(data3,
- showLoadings = TRUE,
- lengthLoadingsArrowsFactor = 1.5,
- sizeLoadingsNames = 4,
- colLoadingsNames = 'red4',
- # other parameters
- lab = NULL,
- hline = 0, vline = c(-25, 0, 25),
- vlineType = c('dotdash', 'solid', 'dashed'),
- gridlines.major = FALSE, gridlines.minor = FALSE,
- pointSize = 5,
- legendPosition = 'none', legendLabSize = 16, legendIconSize = 8.0,
- drawConnectors = FALSE,
- title = 'PCA bi-plot',
- subtitle = 'PC1 versus PC2',
- caption = '27 PCs ≈ 80%',
- returnPlot = TRUE)
- if (!is.null(biplotShapeBy) && biplotShapeBy != "") {
+ showLoadings = TRUE,
+ lengthLoadingsArrowsFactor = 1.5,
+ sizeLoadingsNames = 4,
+ colLoadingsNames = "red4",
+ # other parameters
+ lab = NULL,
+ hline = 0, vline = c(-25, 0, 25),
+ vlineType = c("dotdash", "solid", "dashed"),
+ gridlines.major = FALSE, gridlines.minor = FALSE,
+ pointSize = 5,
+ legendPosition = "none", legendLabSize = 16, legendIconSize = 8.0,
+ drawConnectors = FALSE,
+ title = "PCA bi-plot",
+ subtitle = "PC1 versus PC2",
+ caption = "27 PCs ≈ 80%",
+ returnPlot = TRUE
+ )
+ if (!is.null(biplotShapeBy) && biplotShapeBy != "") {
  params_biplot$shape <- biplotShapeBy
  }
  if (!is.null(biplotColBy) && biplotColBy != "") {
  params_biplot$colby <- biplotColBy
- params_biplot$colkey <- get_hiplot_color(conf$general$palette, -1,
- conf$general$palette_custom) #!!
+ params_biplot$colkey <- get_hiplot_color(
+ conf$general$palette, -1,
+ conf$general$palette_custom
+ ) # !!
  }
 
  p3 <- do.call(PCAtools::biplot, params_biplot)
 
  p4 <- PCAtools::plotloadings(
- data3, rangeRetain = 0.01, labSize = 4,
+ data3,
+ rangeRetain = 0.01, labSize = 4,
  components = getComponents(data3, c(1:plotloadingsComponents)),
- title = 'Loadings plot', axisLabSize = 12,
- subtitle = 'PC1, PC2, PC3, PC4, PC5',
- caption = 'Top 1% variables',
+ title = "Loadings plot", axisLabSize = 12,
+ subtitle = "PC1, PC2, PC3, PC4, PC5",
+ caption = "Top 1% variables",
  gridlines.major = FALSE, gridlines.minor = FALSE,
  shape = 24, shapeSizeRange = c(4, 8),
  col = c(plotloadingsLowCol, plotloadingsMidCol, plotloadingsHighCol),
- legendPosition = 'none',
+ legendPosition = "none",
  drawConnectors = FALSE,
- returnPlot = TRUE)
+ returnPlot = TRUE
+ )
 
  eigencorplotMetavars <- unlist(eigencorplotMetavars)
  if (length(eigencorplotMetavars) > 0) {
@@ -159,43 +170,47 @@ call_pcatools <- function(datTable, sampleInfo,
  metavars = metavars,
  cexCorval = 1.0,
  fontCorval = 2,
- posLab = 'all',
+ posLab = "all",
  rotLabX = 45,
  scale = TRUE,
  main = "PC clinical correlates",
  cexMain = 1.5,
  plotRsquared = FALSE,
- corFUN = 'pearson',
- corUSE = 'pairwise.complete.obs',
- signifSymbols = c('****', '***', '**', '*', ''),
+ corFUN = "pearson",
+ corUSE = "pairwise.complete.obs",
+ signifSymbols = c("****", "***", "**", "*", ""),
  signifCutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1),
- returnPlot = TRUE)
+ returnPlot = TRUE
+ )
 
  p6 <- plot_grid(
  p1, p2, p3,
  ncol = 3,
- labels = c('A', 'B Pairs plot', 'C'),
- label_fontfamily = 'serif',
- label_fontface = 'bold',
+ labels = c("A", "B Pairs plot", "C"),
+ label_fontfamily = "serif",
+ label_fontface = "bold",
  label_size = 22,
- align = 'h',
- rel_widths = c(1.10, 0.80, 1.10))
+ align = "h",
+ rel_widths = c(1.10, 0.80, 1.10)
+ )
 
  p7 <- plot_grid(
  p4,
  as.grob(p5),
  ncol = 2,
- labels = c('D', 'E'),
- label_fontfamily = 'serif',
- label_fontface = 'bold',
+ labels = c("D", "E"),
+ label_fontfamily = "serif",
+ label_fontface = "bold",
  label_size = 22,
- align = 'h',
- rel_widths = c(0.8, 1.2))
+ align = "h",
+ rel_widths = c(0.8, 1.2)
+ )
 
  p <- plot_grid(
  p6, p7,
  ncol = 1,
- rel_heights = c(1.1, 0.9))
+ rel_heights = c(1.1, 0.9)
+ )
 
  out_xlsx <- paste(opt$outputFilePrefix, ".xlsx", sep = "")
  write.xlsx(as.data.frame(data3$rotated), out_xlsx, row.names = TRUE)

hisub.R

@@ -10,7 +10,67 @@
 # 6. 基于配置和输入文件生成 plot.R
 #
 # ./hisub.R examples/helloworld.R test_hello
-VERSION = 0.1
+VERSION <- 0.2
+
+TEMPLATE <- '# @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)
+}
+'
 
 message("HiSub version ", VERSION)
 message("Copyright (c) 2021 Hiplot (https://hiplot.com.cn/)")
@@ -25,6 +85,15 @@ message("Checking input...")
 Args <- commandArgs(trailingOnly = TRUE)
 # Args <- c("test.R", "test-plugin2")
 
+if (length(Args) == 1) {
+ if (Args[1] == "template") {
+ message("'template' command detected. Generating template 'source.R'.")
+ write_lines(TEMPLATE, file = "source.R")
+ message("Done")
+ quit("no")
+ }
+}
+
 # 如果传入的不是 2 个参数，中间的文件原样拷贝到插件目录以支持
 # 已准备好的数据文件或其他所需脚本
 fc <- file_content <- read_lines(Args[1])