Takes a numeric vector(s) or date and will return a tibble of one of the following:
"sin"
"cos"
"tan"
"sincos"
c("sin","cos","tan", "sincos")
Usage
hai_hyperbolic_augment(
.data,
.value,
.names = "auto",
.scale_type = c("sin", "cos", "tan", "sincos")
)
Arguments
- .data
The data being passed that will be augmented by the function.
- .value
This is passed
rlang::enquo()
to capture the vectors you want to augment.- .names
The default is "auto"
- .scale_type
A character of one of the following: "sin","cos","tan", "sincos" All can be passed by setting the param equal to c("sin","cos","tan","sincos")
Details
Takes a numeric vector or date and will return a vector of one of the following:
"sin"
"cos"
"tan"
"sincos"
c("sin","cos","tan", "sincos")
This function is intended to be used on its own in order to add columns to a tibble.
See also
Other Augment Function:
hai_fourier_augment()
,
hai_fourier_discrete_augment()
,
hai_polynomial_augment()
,
hai_scale_zero_one_augment()
,
hai_scale_zscore_augment()
,
hai_winsorized_move_augment()
,
hai_winsorized_truncate_augment()
Examples
suppressPackageStartupMessages(library(dplyr))
len_out <- 10
by_unit <- "month"
start_date <- as.Date("2021-01-01")
data_tbl <- tibble(
date_col = seq.Date(from = start_date, length.out = len_out, by = by_unit),
a = rnorm(len_out),
b = runif(len_out)
)
hai_hyperbolic_augment(data_tbl, b, .scale_type = "sin")
#> # A tibble: 10 × 4
#> date_col a b hyperbolic_b_sin
#> <date> <dbl> <dbl> <dbl>
#> 1 2021-01-01 0.853 0.450 0.435
#> 2 2021-02-01 -0.405 0.973 0.827
#> 3 2021-03-01 -0.420 0.128 0.127
#> 4 2021-04-01 0.327 0.758 0.687
#> 5 2021-05-01 0.975 0.996 0.839
#> 6 2021-06-01 -0.832 0.768 0.695
#> 7 2021-07-01 1.99 0.670 0.621
#> 8 2021-08-01 -0.384 0.162 0.161
#> 9 2021-09-01 -1.09 0.989 0.835
#> 10 2021-10-01 -0.726 0.327 0.321
hai_hyperbolic_augment(data_tbl, b, .scale_type = "tan")
#> # A tibble: 10 × 4
#> date_col a b hyperbolic_b_tan
#> <date> <dbl> <dbl> <dbl>
#> 1 2021-01-01 0.853 0.450 0.484
#> 2 2021-02-01 -0.405 0.973 1.47
#> 3 2021-03-01 -0.420 0.128 0.128
#> 4 2021-04-01 0.327 0.758 0.946
#> 5 2021-05-01 0.975 0.996 1.54
#> 6 2021-06-01 -0.832 0.768 0.965
#> 7 2021-07-01 1.99 0.670 0.792
#> 8 2021-08-01 -0.384 0.162 0.163
#> 9 2021-09-01 -1.09 0.989 1.52
#> 10 2021-10-01 -0.726 0.327 0.339