Aggregate values row-wise

Applies one or more provided summary functions row-wise to selected columns of a DTSg object.

Usage

# S3 method for class 'DTSg'
rowaggregate(
  x,
  resultCols,
  fun,
  ...,
  cols = self$cols(class = "numeric"),
  clone = getOption("DTSgClone")
)

Arguments

x: A DTSg object (S3 method only).
resultCols: A character vector either of length one (names of fun are appended in the case one or more functions are provided) or the same length as fun specifying the column names for the return values of fun.
fun: A summary function, (named) list of summary functions or (named) character vector specifying summary functions applied row-wise to all the values of the specified cols. The return value(s) must be of length one. See corresponding section for further information.
...: Further arguments passed on to fun.
cols: A character vector specifying the columns to apply fun to. Another possibility is a character string containing either comma separated column names, for example, "x,y,z", or the start and end column separated by a colon, for example, "x:z".
clone: A logical specifying if the object shall be modified in place or if a deep clone (copy) shall be made beforehand.

Value

Returns a DTSg object.

R6 alias

The raggregate() alias for this method is exclusively available in the R6 interface.

Summary functions

Some examples for fun are as follows:

mean
list(min = min, max = max)
c(sd = "sd", var = "var")

Examples

# new DTSg object
DT <- data.table::data.table(
  date = flow$date,
  flow1 = flow$flow - abs(rnorm(nrow(flow))),
  flow2 = flow$flow,
  flow3 = flow$flow + abs(rnorm(nrow(flow)))
)
x <- DTSg$new(values = DT)

# mean and standard deviation of multiple measurements per timestamp
## R6 method
x$rowaggregate(
  resultCols = "flow",
  fun = list(mean = mean, sd = sd)
)$print()
#> Values:
#>        .dateTime     flow1  flow2     flow3 flow.mean   flow.sd
#>           <POSc>     <num>  <num>     <num>     <num>     <num>
#>    1: 2007-01-01  7.809470  9.540 10.073822  9.141097 1.1837084
#>    2: 2007-01-02  8.152575  9.285  9.492132  8.976569 0.7210759
#>    3: 2007-01-03  7.984601  8.940  8.975120  8.633240 0.5620122
#>    4: 2007-01-04  8.030992  8.745  9.865976  8.880656 0.9249831
#>    5: 2007-01-05  8.359606  8.490  8.821966  8.557191 0.2383906
#>   ---                                                          
#> 2188: 2012-12-27 24.629565 26.685 26.890944 26.068503 1.2504042
#> 2189: 2012-12-28 27.529591 28.050 28.682806 28.087466 0.5775194
#> 2190: 2012-12-29 23.327736 23.580 23.973729 23.627155 0.3255679
#> 2191: 2012-12-30 18.686844 18.840 19.694849 19.073897 0.5431846
#> 2192: 2012-12-31 16.757876 17.250 18.066902 17.358259 0.6611942
#> 
#> Aggregated:     FALSE
#> Regular:        TRUE
#> Periodicity:    Time difference of 1 days
#> Missing values: explicit
#> Time zone:      UTC
#> Timestamps:     2192

## 'raggregate()' is an R6 alias for 'rowaggregate()'
x$raggregate(
  resultCols = "flow",
  fun = list(mean = mean, sd = sd)
)$print()
#> Values:
#>        .dateTime     flow1  flow2     flow3 flow.mean   flow.sd
#>           <POSc>     <num>  <num>     <num>     <num>     <num>
#>    1: 2007-01-01  7.809470  9.540 10.073822  9.141097 1.1837084
#>    2: 2007-01-02  8.152575  9.285  9.492132  8.976569 0.7210759
#>    3: 2007-01-03  7.984601  8.940  8.975120  8.633240 0.5620122
#>    4: 2007-01-04  8.030992  8.745  9.865976  8.880656 0.9249831
#>    5: 2007-01-05  8.359606  8.490  8.821966  8.557191 0.2383906
#>   ---                                                          
#> 2188: 2012-12-27 24.629565 26.685 26.890944 26.068503 1.2504042
#> 2189: 2012-12-28 27.529591 28.050 28.682806 28.087466 0.5775194
#> 2190: 2012-12-29 23.327736 23.580 23.973729 23.627155 0.3255679
#> 2191: 2012-12-30 18.686844 18.840 19.694849 19.073897 0.5431846
#> 2192: 2012-12-31 16.757876 17.250 18.066902 17.358259 0.6611942
#> 
#> Aggregated:     FALSE
#> Regular:        TRUE
#> Periodicity:    Time difference of 1 days
#> Missing values: explicit
#> Time zone:      UTC
#> Timestamps:     2192

## S3 method
print(rowaggregate(
  x = x,
  resultCols = "flow",
  fun = list(mean = mean, sd = sd)
))
#> Values:
#>        .dateTime     flow1  flow2     flow3 flow.mean   flow.sd
#>           <POSc>     <num>  <num>     <num>     <num>     <num>
#>    1: 2007-01-01  7.809470  9.540 10.073822  9.141097 1.1837084
#>    2: 2007-01-02  8.152575  9.285  9.492132  8.976569 0.7210759
#>    3: 2007-01-03  7.984601  8.940  8.975120  8.633240 0.5620122
#>    4: 2007-01-04  8.030992  8.745  9.865976  8.880656 0.9249831
#>    5: 2007-01-05  8.359606  8.490  8.821966  8.557191 0.2383906
#>   ---                                                          
#> 2188: 2012-12-27 24.629565 26.685 26.890944 26.068503 1.2504042
#> 2189: 2012-12-28 27.529591 28.050 28.682806 28.087466 0.5775194
#> 2190: 2012-12-29 23.327736 23.580 23.973729 23.627155 0.3255679
#> 2191: 2012-12-30 18.686844 18.840 19.694849 19.073897 0.5431846
#> 2192: 2012-12-31 16.757876 17.250 18.066902 17.358259 0.6611942
#> 
#> Aggregated:     FALSE
#> Regular:        TRUE
#> Periodicity:    Time difference of 1 days
#> Missing values: explicit
#> Time zone:      UTC
#> Timestamps:     2192