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Transform Values in Time Series Lists

Source: R/tsl_transform.R
tsl_transform.Rd

Function for time series transformations without changes in data dimensions. Generally, functions introduced via the argument f should not change the dimensions of the output time series list. See tsl_resample() and tsl_aggregate() for transformations requiring changes in time series dimensions.

This function supports a parallelization setup via future::plan(), and progress bars provided by the package progressr.

Usage

tsl_transform(tsl = NULL, f = NULL, ...)

Arguments

tsl

(required, list) Time series list. Default: NULL

f

(required, transformation function) name of a function taking a matrix as input. Currently, the following options are implemented, but any other function taking a matrix as input (for example, scale()) should work as well:

  • f_proportion: proportion computed by row.

  • f_percent: percentage computed by row.

  • f_hellinger: Hellinger transformation computed by row

  • f_scale_local: Local centering and/or scaling based on the variable mean and standard deviation in each time series within tsl.

  • f_scale_global: Global centering and/or scaling based on the variable mean and standard deviation across all time series within tsl.

  • f_smooth: Time series smoothing with a user defined rolling window.

  • f_detrend_difference: Differencing detrending of time series via diff().

  • f_detrend_linear: Detrending of seasonal time series via linear modeling.

  • f_detrend_gam: Detrending of seasonal time series via Generalized Additive Models.

...

(optional, additional arguments of f) Optional arguments for the transformation function.

Value

time series list

See also

Other tsl_processing: tsl_aggregate(), tsl_resample(), tsl_smooth(), tsl_stats()

Examples


#two time series
tsl <- tsl_initialize(
  x = fagus_dynamics,
  name_column = "name",
  time_column = "time"
) |>
  tsl_subset(
    names = c("Spain", "Sweden"),
    colnames = c("rainfall", "temperature")
  )

if(interactive()){
  tsl_plot(
    tsl = tsl
  )
}

#centering and scaling
#-----------------------------------------
#same mean and standard deviation are used to scale each variable across all time series
tsl_scale <- tsl_transform(
  tsl = tsl,
  f = f_scale_local
)

if(interactive()){
  tsl_plot(
    tsl = tsl_scale,
    guide_columns = 3
  )
}


#rescaling to a new range
#-----------------------------------------

#rescale between -100 and 100
tsl_rescaled <- tsl_transform(
  tsl = tsl,
  f = f_rescale_local,
  new_min = -100,
  new_max = 100
)

#old range
sapply(X = tsl, FUN = range)
#>      Spain Sweden
#> [1,]   4.5   -4.7
#> [2,] 216.6  189.6

#new range
sapply(X = tsl_rescaled, FUN = range)
#>      Spain Sweden
#> [1,]  -100   -100
#> [2,]   100    100



#numeric transformations
#-----------------------------------------
#eemian pollen counts
tsl <- tsl_initialize(
  x = eemian_pollen,
  name_column = "name",
  time_column = "time"
)
#> Warning: distantia::utils_prepare_time():  duplicated time indices in 'Krumbach_I':
#> - value 6.8 replaced with 6.825

if(interactive()){
  tsl_plot(
    tsl = tsl
  )
}

#percentages
tsl_percentage <- tsl_transform(
  tsl = tsl,
  f = f_percent
)

if(interactive()){
  tsl_plot(
    tsl = tsl_percentage
  )
}

#hellinger transformation
tsl_hellinger <- tsl_transform(
  tsl = tsl,
  f = f_hellinger
)

if(interactive()){
  tsl_plot(
    tsl = tsl_hellinger
  )
}