B. Advanced usage

This vignette illustrates some more advanced concepts of the DTSg package, namely reference semantics, chaining and piping as well as swallowing and dropping.

First, let’s load the package as well as some data and let’s create a DTSg object:

Reference semantics

By default, every method manipulating the values of a DTSg object creates a deep clone (copy) of it beforehand. This behaviour can be overridden by setting the clone argument of the respective method to FALSE. Globally, deep cloning can be controlled with the help of the DTSgClone option:

TS$alter("2007-01-01", "2008-12-31")
# `TS` was deep cloned before shortening it, hence its end date is still in the
# year 2012
TS
#> Values:
#>        .dateTime   flow
#>           <POSc>  <num>
#>    1: 2007-01-01  9.540
#>    2: 2007-01-02  9.285
#>    3: 2007-01-03  8.940
#>    4: 2007-01-04  8.745
#>    5: 2007-01-05  8.490
#>   ---                  
#> 2188: 2012-12-27 26.685
#> 2189: 2012-12-28 28.050
#> 2190: 2012-12-29 23.580
#> 2191: 2012-12-30 18.840
#> 2192: 2012-12-31 17.250
#> 
#> Aggregated:     FALSE
#> Regular:        TRUE
#> Periodicity:    Time difference of 1 days
#> Missing values: explicit
#> Time zone:      UTC
#> Timestamps:     2192

options(DTSgClone = FALSE)
getOption("DTSgClone")
#> [1] FALSE
# `TS` was modified in place this time, hence its end date is in the year 2008
# now
TS$alter("2007-01-01", "2008-12-31")
TS
#> Values:
#>       .dateTime   flow
#>          <POSc>  <num>
#>   1: 2007-01-01  9.540
#>   2: 2007-01-02  9.285
#>   3: 2007-01-03  8.940
#>   4: 2007-01-04  8.745
#>   5: 2007-01-05  8.490
#>  ---                  
#> 727: 2008-12-27 18.180
#> 728: 2008-12-28 16.575
#> 729: 2008-12-29 13.695
#> 730: 2008-12-30 12.540
#> 731: 2008-12-31 11.940
#> 
#> Aggregated:     FALSE
#> Regular:        TRUE
#> Periodicity:    Time difference of 1 days
#> Missing values: explicit
#> Time zone:      UTC
#> Timestamps:     731

As we can see, with cloning set to FALSE, the object was altered in place, i.e. no assignment to a new or reassignment to an existing variable was necessary in order to make the changes stick. This is due to the R6 nature of DTSg objects.

Note

Using reference semantics can result in undesired behaviour. Merely assigning a variable representing a DTSg object to a new variable does not result in a copy of the object. Instead, both variables will reference and access the same data under the hood, i.e. changing one will also affect the other. In case you want a “real” copy of a DTSg object, you will have to use the clone method with its deep argument set to TRUE (for consistency with the R6 package its default is FALSE):

TSc <- TS$clone(deep = TRUE)
# or 'clone(TS, deep = TRUE)'

Chaining and piping

Especially in combination with reference semantics, chaining and piping can be a fast and comfortable way to apply several object manipulations in a row. While chaining only works in combination with the R6 interface, piping is an exclusive feature of the S3 interface.

Let’s start with chaining:

TS <- DTSg$
  new(flow)$
  alter("2007-01-01", "2008-12-31")$
  colapply(interpolateLinear)$
  aggregate(byYm____, mean)
TS
#> Values:
#>      .dateTime      flow
#>         <POSc>     <num>
#>  1: 2007-01-01 25.281290
#>  2: 2007-02-01 14.496964
#>  3: 2007-03-01 12.889839
#>  4: 2007-04-01 12.470500
#>  5: 2007-05-01  9.233226
#> ---                     
#> 20: 2008-08-01 12.641129
#> 21: 2008-09-01 13.710500
#> 22: 2008-10-01 10.626774
#> 23: 2008-11-01  8.902000
#> 24: 2008-12-01 16.435645
#> 
#> Aggregated:     TRUE
#> Regular:        FALSE
#> Periodicity:    1 months
#> Min lag:        Time difference of 28 days
#> Max lag:        Time difference of 31 days
#> Missing values: explicit
#> Time zone:      UTC
#> Timestamps:     24

For piping, we have to make sure the magrittr package is installed and have to load it for access to its forward-pipe operator first (starting with R 4.1.0, the same can be achieved with R’s native pipe operator |>):

if (requireNamespace("magrittr", quietly = TRUE)) {
  library(magrittr)

  TS <- new("DTSg", flow) %>%
    alter("2007-01-01", "2008-12-31") %>%
    colapply(interpolateLinear) %>%
    aggregate(byYm____, mean)
  TS
}
#> Values:
#>      .dateTime      flow
#>         <POSc>     <num>
#>  1: 2007-01-01 25.281290
#>  2: 2007-02-01 14.496964
#>  3: 2007-03-01 12.889839
#>  4: 2007-04-01 12.470500
#>  5: 2007-05-01  9.233226
#> ---                     
#> 20: 2008-08-01 12.641129
#> 21: 2008-09-01 13.710500
#> 22: 2008-10-01 10.626774
#> 23: 2008-11-01  8.902000
#> 24: 2008-12-01 16.435645
#> 
#> Aggregated:     TRUE
#> Regular:        FALSE
#> Periodicity:    1 months
#> Min lag:        Time difference of 28 days
#> Max lag:        Time difference of 31 days
#> Missing values: explicit
#> Time zone:      UTC
#> Timestamps:     24

Swallowing and dropping

An extension to reference semantics of existing DTSg objects are reference semantics during object creation. This behaviour can be triggered with the help of the swallow argument of the new method. When set to TRUE, a data.table provided through the values argument is “swallowed” by the DTSg object, i.e. no copy of it is made and all references to it are removed from the global (and only the global) environment upon successful object creation:

library(data.table)

DT <- copy(flow)
ls(pattern = "^DT$")
#> [1] "DT"
TS <- DTSg$new(DT, swallow = TRUE)
ls(pattern = "^DT$")
#> character(0)

The opposite of swallowing is called dropping. This term refers to querying the values of a DTSg object as a reference while removing all references to the original DTSg object from the global (and again only the global) environment at the same time:

TS <- DTSg$new(flow)
ls(pattern = "^TS$")
#> [1] "TS"
DT <- TS$values(drop = TRUE)
ls(pattern = "^TS$")
#> character(0)

Column access

Sometimes need may arise to access a column other than the one currently processed from a function within the colapply method. This can be accomplished in the following way:

# add a new column recording if a certain value is missing or not before
# carrying out a linear interpolation
TS <- DTSg$new(flow)
TS$summary()
#>       flow        
#>  Min.   :  4.995  
#>  1st Qu.:  8.085  
#>  Median : 11.325  
#>  Mean   : 16.197  
#>  3rd Qu.: 18.375  
#>  Max.   :290.715  
#>  NA's   :23
TS$
  colapply(
    function(x, ...) {ifelse(is.na(x), TRUE, FALSE)},
    resultCols = "missing"
  )$
  colapply(interpolateLinear)$
  summary()
#>       flow          missing       
#>  Min.   :  4.995   Mode :logical  
#>  1st Qu.:  8.126   FALSE:2169     
#>  Median : 11.408   TRUE :23       
#>  Mean   : 16.212                  
#>  3rd Qu.: 18.439                  
#>  Max.   :290.715

# undo the linear interpolation (requires additional access to the previously
# created column named "missing", which can be carried out with the help of the
# `getCol` method or its shortcut, the `[` operator, and the freely chosen `y`
# argument)
TS$
  colapply(
    function(x, y, ...) {ifelse(y, NA, x)},
    y = TS$getCol("missing") # or 'y = TS["missing"]'
  )$
  summary()
#>       flow          missing       
#>  Min.   :  4.995   Mode :logical  
#>  1st Qu.:  8.085   FALSE:2169     
#>  Median : 11.325   TRUE :23       
#>  Mean   : 16.197                  
#>  3rd Qu.: 18.375                  
#>  Max.   :290.715                  
#>  NA's   :23

Please refer to the help pages for further details.