Reshaping grouped data via dcast to 'wide' format with rows for each unique combination of group variables. The response are arranged in separate columns for each datum in predictors. If a combination of groups identifies multiple rows, the number of rows in a group is returned to CS for the whole dataset instead of the response variable value.

reshapeWide(
  dataset = cs.in.dataset(),
  preds = cs.in.predictors(),
  resps = cs.in.responses(),
  groups = cs.in.groupvars(),
  auxs = cs.in.auxiliaries(),
  scriptvars = cs.in.scriptvars(),
  return.results = FALSE,
  ...
)

Arguments

dataset

[data.frame]
Dataset with named columns. The names correspond to predictors and responses.

preds

[character]
Character vector of predictor variables.

resps

[character]
Character vector of response variables.

groups

[character]
Character vector of group variables.

auxs

[character]
Character vector of auxiliary variables.

scriptvars

[list]
Named list of script variables set via the Cornerstone "Script Variables" menu. For details see below.

return.results

[logical(1)]
If FALSE the function returns TRUE invisibly. If TRUE, it returns a list of results. Default is FALSE.

...

[ANY]
Additional arguments to be passed to dcast . Please consider possible script variables (scriptvars) to prevent duplicates.

Value

Logical [TRUE] invisibly and outputs to Cornerstone or, if return.results = TRUE, list of resulting data.frame object:

reshapeWide

Dataset with reshaped data.

Details

One script variables is summarized in scriptvars list:

drop

[logical(1)]
Drop missing combinations (TRUE) or include all (FALSE). Default is TRUE.
For details see dcast.

Examples

# Reshape dataset to wide format:
reshapeWide(Indometh, "time", "conc", "Subject", character(0)
            , list(drop = TRUE, simpleName = FALSE, aggr.fun = "mean"), return.results = TRUE
            )
#> $reshapeWide
#> Key: <time>
#>      time conc_mean_1 conc_mean_4 conc_mean_2 conc_mean_5 conc_mean_6
#>     <num>       <num>       <num>       <num>       <num>       <num>
#>  1:  0.25        1.50        1.85        2.03        2.05        2.31
#>  2:  0.50        0.94        1.39        1.63        1.04        1.44
#>  3:  0.75        0.78        1.02        0.71        0.81        1.03
#>  4:  1.00        0.48        0.89        0.70        0.39        0.84
#>  5:  1.25        0.37        0.59        0.64        0.30        0.64
#>  6:  2.00        0.19        0.40        0.36        0.23        0.42
#>  7:  3.00        0.12        0.16        0.32        0.13        0.24
#>  8:  4.00        0.11        0.11        0.20        0.11        0.17
#>  9:  5.00        0.08        0.10        0.25        0.08        0.13
#> 10:  6.00        0.07        0.07        0.12        0.10        0.10
#> 11:  8.00        0.05        0.07        0.08        0.06        0.09
#>     conc_mean_3
#>           <num>
#>  1:        2.72
#>  2:        1.49
#>  3:        1.16
#>  4:        0.80
#>  5:        0.80
#>  6:        0.39
#>  7:        0.22
#>  8:        0.12
#>  9:        0.11
#> 10:        0.08
#> 11:        0.08
#>