Map Over Results to Create New Jobs

This function allows you to create new computational jobs (just like batchMap based on the results of a Registry.

Usage

batchMapResults(
  fun,
  ids = NULL,
  ...,
  more.args = list(),
  target,
  source = getDefaultRegistry()
)

Arguments

fun

[function]
Function which takes the result as first (unnamed) argument.

ids

[data.frame or integer]
A data.frame (or data.table) with a column named “job.id”. Alternatively, you may also pass a vector of integerish job ids. If not set, defaults to the return value of findDone. Invalid ids are ignored.

...

[ANY]
Arguments to vectorize over (list or vector). Passed to batchMap.

more.args

[list]
A list of further arguments passed to fun. Default is an empty list.

target

[Registry]
Empty Registry where new jobs are created for.

source

[Registry]
Registry. If not explicitly passed, uses the default registry (see setDefaultRegistry).

Value

[data.table] with ids of jobs added to target.

Note

The URI to the result files in registry source is hard coded as parameter in the target registry. This means that target is currently not portable between systems for computation.

See also

Other Results: loadResult(), reduceResults(), reduceResultsList()

Examples

# Source registry: calculate square of some numbers
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
#> No readable configuration file found
#> Created registry in '/tmp/batchtools-example/reg1' using cluster functions 'Interactive'
batchMap(function(x) list(square = x^2), x = 1:10, reg = tmp)
#> Adding 10 jobs ...
submitJobs(reg = tmp)
#> Submitting 10 jobs in 10 chunks using cluster functions 'Interactive' ...
waitForJobs(reg = tmp)
#> [1] TRUE

# Target registry: calculate the square root on results of first registry
target = makeRegistry(file.dir = NA, make.default = FALSE)
#> No readable configuration file found
#> Created registry in '/tmp/batchtools-example/reg2' using cluster functions 'Interactive'
batchMapResults(fun = function(x, y) list(sqrt = sqrt(x$square)), ids = 4:8,
  target = target, source = tmp)
#> Adding 5 jobs ...
submitJobs(reg = target)
#> Submitting 5 jobs in 5 chunks using cluster functions 'Interactive' ...
waitForJobs(reg = target)
#> [1] TRUE

# Map old to new ids. First, get a table with results and parameters
results = unwrap(rjoin(getJobPars(reg = target), reduceResultsDataTable(reg = target)))
print(results)
#> Key: <job.id>
#>    job.id   .id  sqrt
#>     <int> <int> <num>
#> 1:      1     4     4
#> 2:      2     5     5
#> 3:      3     6     6
#> 4:      4     7     7
#> 5:      5     8     8

# Parameter '.id' points to job.id in 'source'. Use a inner join to combine:
ijoin(results, unwrap(reduceResultsDataTable(reg = tmp)), by = c(".id" = "job.id"))
#> Key: <.id>
#>    job.id   .id  sqrt square
#>     <int> <int> <num>  <num>
#> 1:      1     4     4     16
#> 2:      2     5     5     25
#> 3:      3     6     6     36
#> 4:      4     7     7     49
#> 5:      5     8     8     64