Convert a stacked (tall) multivariate time series data frame (benchmarking() and stock_benchmarking()
data format) into a non-stacked (wide) multivariate time series data frame.
This function, combined with tsDF_to_ts(), is useful to convert the benchmarked data frame returned by a call to
benchmarking() or stock_benchmarking() back into a "mts" object, where multiple series were benchmarked in
BY-group processing mode.
Usage
unstack_tsDF(
ts_df,
ser_cName = "series",
yr_cName = "year",
per_cName = "period",
val_cName = "value"
)Arguments
- ts_df
(mandatory)
Data frame (object of class "data.frame") that contains the multivariate time series data to be unstacked.
- ser_cName
(optional)
String specifying the name of the character variable (column) in the input time series data frame that contains the series identifier (the time series variable names in the output data frame).
Default value is
ser_cName = "series".- yr_cName, per_cName
(optional)
Strings specifying the name of the numeric variables (columns) in the input time series data frame that contain the data point year and period identifiers. These variables are transferred to the output data frame with the same names.
Default values are
yr_cName = "year"andper_cName = "period".- val_cName
(optional)
String specifying the name of the numeric variable (column) in the input time series data frame that contains the data point values.
Default value is
val_cName = "value".
Value
The function returns a data frame with three or more variables:
Data point year, type numeric (see argument
yr_cName)Data point period, type numeric (see argument
per_cName)One time series data variable for each distinct value of the input data frame variable specified with argument
ser_cName, type numeric (see argumentsser_cNameandval_cName)
Note: the function returns a "data.frame" object than can be explicitly coerced to another type of object
with the appropriate as*() function (e.g., tibble::as_tibble() would coerce it to a tibble).
Examples
# Proportional benchmarking for multiple (3) quarterly series processed with
# argument `by` (in BY-group mode)
ind_vec <- c(1.9, 2.4, 3.1, 2.2, 2.0, 2.6, 3.4, 2.4, 2.3)
ind_df <- ts_to_tsDF(ts(data.frame(ser1 = ind_vec,
ser2 = ind_vec * 100,
ser3 = ind_vec * 10),
start = c(2015, 1), frequency = 4))
bmk_vec <- c(10.3, 10.2)
bmk_df <- ts_to_bmkDF(ts(data.frame(ser1 = bmk_vec,
ser2 = bmk_vec * 100,
ser3 = bmk_vec * 10),
start = 2015, frequency = 1),
ind_frequency = 4)
out_bench <- benchmarking(stack_tsDF(ind_df),
stack_bmkDF(bmk_df),
rho = 0.729, lambda = 1, biasOption = 3,
by = "series",
quiet = TRUE)
#>
#> Benchmarking by-group 1 (series=ser1)
#> =====================================
#>
#> Benchmarking by-group 2 (series=ser2)
#> =====================================
#>
#> Benchmarking by-group 3 (series=ser3)
#> =====================================
# Initial and final (benchmarked) quarterly time series data frames
ind_df
#> year period ser1 ser2 ser3
#> 1 2015 1 1.9 190 19
#> 2 2015 2 2.4 240 24
#> 3 2015 3 3.1 310 31
#> 4 2015 4 2.2 220 22
#> 5 2016 1 2.0 200 20
#> 6 2016 2 2.6 260 26
#> 7 2016 3 3.4 340 34
#> 8 2016 4 2.4 240 24
#> 9 2017 1 2.3 230 23
unstack_tsDF(out_bench$series)
#> year period ser1 ser2 ser3
#> 1 2015 1 2.049326 204.9326 20.49326
#> 2 2015 2 2.601344 260.1344 26.01344
#> 3 2015 3 3.337638 333.7638 33.37638
#> 4 2015 4 2.311691 231.1691 23.11691
#> 5 2016 1 2.021090 202.1090 20.21090
#> 6 2016 2 2.554801 255.4801 25.54801
#> 7 2016 3 3.292193 329.2193 32.92193
#> 8 2016 4 2.331915 233.1915 23.31915
#> 9 2017 1 2.268017 226.8017 22.68017