"LineWise"LineWise-class.RdS4 class container for storing R-based code for a workflow step.
LineWise class instances are constructed by the LineWise function,
based on the R-based code, step name, and dependency tree.
When the container is built from the R Markdown, using importWF
function, two other slots are populated: codeChunkStart and rmdPath.
codeChunkStart will store the first line of each R chunk, and rmdPath
will store the R Markdown file path.
R code separated either by a semi-colon (\;), or by a newline, and
enclosed by braces ({ }).
character. Step name needs to be unique and is required when appending this step to the workflow.
integer. R Markdown code chunk line start. This element will be populated when
the object is built by importWF.
character. Path of R Markdown file used by importWF.
character. Dependency tree, required when appending this step to
the workflow. Character name of a previous step in the workflow.
Default is empty string "".
character. If the step has "mandatory" or "optional" flag for the execution.
character. If the step has "management" or "compute" flag for the execution.
List for reserving for each cluster job sufficient computing resources
including memory (Megabyte), number of nodes, CPU cores, walltime (minutes),
etc. It is necessary two aditional files: conffile and template.
conffile is the path to conf file (default location
./.batchtools.conf.R). This file contains in its simplest form just one
command, such as this line for the Slurm scheduler:
cluster.functions <- makeClusterFunctionsSlurm(template="batchtools.slurm.tmpl").
For more detailed information visit this page: https://mllg.github.io/batchtools/index.html
template The template files for a specific queueing/scheduling systems
can be downloaded from here:
https://github.com/mllg/batchtools/tree/master/inst/templates. Slurm,
PBS/Torque, and Sun Grid Engine (SGE) templates are provided
Objects can be created by calls of the form new("LineWise", ...).
codeLine:Object of class "expression" storing R-based
code.
codeChunkStart:Object of class "interger" storing start
line from the rmdPath file, when the
"LineWise" is built from R Markdown.
stepName:Object of class "character" storing step name.
dependency:Object of class "list" storing dependency
tree.
status:Object of class "list" storing status steps.
files:Object of class "list" storing file for
R Markdown file and the file containing
stdout and stderr after running the R-based code.
runInfo:Object of class "list" storing all the
runInfo information of the workflow
See 'Usage' for details on invocation.
Constructor:
Returns a LineWise object.
Accessors:
Printing method for the CodeLine slot.
Extract start line of the R Markdown R chunk.
Extract Rmarkdown file path.
Extract the step name.
Extract the dependency tree.
Extract status of the step.
Extract log file path storing stdout and stderr after running step.
Replacement method for append a R code line.
Replacement method for replace a R code line.
Methods:
Return a new LineWise object made of the selected R code lines.
Extract the slot information from LineWine object.
Replacement method for LineWine slots.
Extract slots elements by name.
Extract number of R-based code lines.
Extract slot names.
Summary view of LineWise elements.
signature(from = "LineWise", to = "list")as(LineWise, "list")
signature(from = "list", to = "LineWise")as(list, "LineWise")
Coerce back to list as(LineWise, "list")
showClass("LineWise")
#> Class "LineWise" [package "systemPipeR"]
#>
#> Slots:
#>
#> Name: codeLine codeChunkStart stepName dependency
#> Class: expression integer character list
#>
#> Name: status files runInfo
#> Class: list list list
lw <- LineWise(code = {
log_out <- log(10)
},
step_name = "R_log")
codeLine(lw)
#> log_out <- log(10)
## ImportWF option
file_path <- system.file("extdata/spr_simple_lw.Rmd", package="systemPipeR")
sal <- SPRproject(overwrite = TRUE)
#> Creating directory: /home/runner/work/systemPipeR/systemPipeR/docs/reference/data
#> Creating directory: /home/runner/work/systemPipeR/systemPipeR/docs/reference/param
#> Creating directory: /home/runner/work/systemPipeR/systemPipeR/docs/reference/results
#> Creating directory '/home/runner/work/systemPipeR/systemPipeR/docs/reference/.SPRproject'
#> Creating file '/home/runner/work/systemPipeR/systemPipeR/docs/reference/.SPRproject/SYSargsList.yml'
# file_path <- "../inst/extdata/spr_simple_lw.Rmd"
sal <- importWF(sal, file_path)
#> Reading Rmd file
#>
#>
#> ---- Actions ----
#> Checking chunk eval values
#> Checking chunk SPR option
#> Ignore non-SPR chunks: 17
#> Parse chunk code
#> Checking preprocess code for each step
#> No preprocessing code for SPR steps found
#> Now importing step 'firstStep'
#> Now importing step 'secondStep'
#> Now back up current Rmd file as template for `renderReport`
#> Template for renderReport is stored at
#> /home/runner/work/systemPipeR/systemPipeR/docs/reference/.SPRproject/workflow_template.Rmd
#> Edit this file manually is not recommended
#> Now check if required tools are installed
#> There is no commandline (SYSargs) step in this workflow, skip.
#> Import done
#>
sal <- runWF(sal)
#> Running Step: firstStep
#> Running Session: Management
#>
|
| | 0%
|
|======================================================================| 100%
#> Step Status: Success
#> Running Step: secondStep
#> Running Session: Management
#>
|
| | 0%
|
|======================================================================| 100%
#> Step Status: Success
#> Done with workflow running, now consider rendering logs & reports
#> To render logs, run: sal <- renderLogs(sal)
#> From command-line: Rscript -e "sal = systemPipeR::SPRproject(resume = TRUE); sal = systemPipeR::renderLogs(sal)"
#> To render reports, run: sal <- renderReport(sal)
#> From command-line: Rscript -e "sal= s ystemPipeR::SPRproject(resume = TRUE); sal = systemPipeR::renderReport(sal)"
#> This message is displayed once per R session
#>
lw2 <- sal$stepsWF[[2]]
lw2
#> Instance of 'LineWise'
#> Code Chunk length: 3
names(lw2)
#> [1] "codeLine" "codeChunkStart" "stepName" "dependency"
#> [5] "status" "files" "runInfo"
length(lw2)
#> [1] 3
## Accessors
codeLine(lw2)
#> setosa <- read.delim("results/setosa.csv", sep = ",")
#> versicolor <- read.delim("results/versicolor.csv", sep = ",")
#> virginica <- read.delim("results/virginica.csv", sep = ",")
codeChunkStart(lw2)
#> integer(0)
rmdPath(lw2)
#> NULL
stepName(lw2)
#> [1] "secondStep"
dependency(lw2)
#> $secondStep
#> [1] "firstStep"
#>
status(lw2)
#> $status.summary
#> [1] "Success"
#>
#> $status.completed
#> Step Status
#> 1 secondStep Success
#>
#> $status.time
#> Step time_start time_end
#> 1 secondStep 2023-05-11 01:52:42 2023-05-11 01:52:42
#>
#> $total.time
#> $total.time$time_start
#> [1] "2023-05-11 01:52:42 UTC"
#>
#> $total.time$time_end
#> [1] "2023-05-11 01:52:42 UTC"
#>
#>
files(lw2)
#> $log
#> [1] ".SPRproject/Rsteps/_logRstep_secondStep_May112023_015242"
#>
## Replacement
appendCodeLine(lw2, after = 0) <- "log <- log(10)"
codeLine(lw2)
#> log <- log(10)
#> setosa <- read.delim("results/setosa.csv", sep = ",")
#> versicolor <- read.delim("results/versicolor.csv", sep = ",")
#> virginica <- read.delim("results/virginica.csv", sep = ",")
replaceCodeLine(lw2, 1) <- "plot(iris)"
codeLine(lw2)
#> plot(iris)
#> setosa <- read.delim("results/setosa.csv", sep = ",")
#> versicolor <- read.delim("results/versicolor.csv", sep = ",")
#> virginica <- read.delim("results/virginica.csv", sep = ",")
## Coerce
lw2 <- linewise(lw2) ## OR lw2 <- as(lw2, "list")
lw2 <- as(lw2, "LineWise")