In tgirke/systemPipeR: systemPipeR: Workflow Environment for Data Analysis and Report Generation

knitr::opts_chunk$set(echo = TRUE)

Build the Workflow

• Load systemPipeR library:

appendStep(sal) <- LineWise(code={
 library(systemPipeR)
  }, 
  step_name = "load_library", 
  dependency = NA)

Export dataset to file

• Add first step as LineWise

appendStep(sal) <- LineWise(code={
 mapply(
   function(x, y) write.csv(x, y),
   split(iris, factor(iris$Species)),
   file.path("results", paste0(names(split(iris, factor(iris$Species))), ".csv")))
  }, 
  step_name = "export_iris", 
  dependency = "load_library")

Compress data

• Adding the second step, as SYSargs2

targetspath <- system.file("extdata/cwl/gunzip", "targets_gunzip.txt", package = "systemPipeR")
appendStep(sal) <- SYSargsList(step_name = "gzip", 
                      targets = targetspath, dir = TRUE,
                      wf_file = "gunzip/workflow_gzip.cwl", input_file = "gunzip/gzip.yml",
                      dir_path = system.file("extdata/cwl", package = "systemPipeR"),
                      inputvars = c(FileName = "_FILE_PATH_", SampleName = "_SampleName_"), 
                      dependency = "export_iris")

Decompress data

appendStep(sal) <- SYSargsList(step_name = "gunzip", 
                      targets = "gzip", dir = TRUE,
                      wf_file = "gunzip/workflow_gunzip.cwl", input_file = "gunzip/gunzip.yml",
                      dir_path = system.file("extdata/cwl", package = "systemPipeR"),
                      inputvars = c(gzip_file = "_FILE_PATH_", SampleName = "_SampleName_"), 
                      rm_targets_col = "FileName", 
                      dependency = "gzip")

Import data to R and perform statistical analysis and visualization

appendStep(sal) <- LineWise(code={
  df <- lapply(getColumn(sal, step="gunzip", 'outfiles'), function(x) read.delim(x, sep=",")[-1])
  df <- do.call(rbind, df)
  stats <- data.frame(cbind(mean=apply(df[,1:4], 2, mean),
                            sd=apply(df[,1:4], 2, sd)))
  stats$species <- rownames(stats)

  plot <- ggplot2::ggplot(stats, ggplot2::aes(x=species, y=mean, fill=species)) + 
    ggplot2::geom_bar(stat = "identity", color="black", position=ggplot2::position_dodge()) +
    ggplot2::geom_errorbar(ggplot2::aes(ymin=mean-sd, ymax=mean+sd), width=.2,
                           position=ggplot2::position_dodge(.9)) 
  }, 
  step_name = "stats", 
  dependency = "gunzip", 
  run_step = "optional")

tgirke/systemPipeR documentation built on Sept. 24, 2024, 9:48 a.m.