diff_vec() applies a Differencing Transformation.
diff_inv_vec() inverts the differencing transformation.
Usage
diff_vec(
x,
lag = 1,
difference = 1,
log = FALSE,
initial_values = NULL,
silent = FALSE
)
diff_inv_vec(x, lag = 1, difference = 1, log = FALSE, initial_values = NULL)Arguments
- x
A numeric vector to be differenced or inverted.
- lag
Which lag (how far back) to be included in the differencing calculation.
- difference
The number of differences to perform.
1 Difference is equivalent to measuring period change.
2 Differences is equivalent to measuring period acceleration.
- log
If log differences should be calculated. Note that difference inversion of a log-difference is approximate.
- initial_values
Only used in the
diff_vec_inv()operation. A numeric vector of the initial values, which are used to invert differences. This vector is the original values that are the length of theNAmissing differences.- silent
Whether or not to report the initial values used to invert the difference as a message.
Details
Benefits:
This function is NA padded by default so it works well with dplyr::mutate() operations.
Difference Calculation
Single differencing, diff_vec(x_t) is equivalent to: x_t - x_t1,
where the subscript _t1 indicates the first lag.
This transformation can be interpereted as change.
Double Differencing Calculation
Double differencing, diff_vec(x_t, difference = 2) is equivalent to:
(x_t - x_t1) - (x_t - x_t1)_t1, where the subscript _t1 indicates the first lag.
This transformation can be interpereted as acceleration.
Log Difference Calculation
Log differencing, diff_vec(x_t, log = TRUE) is equivalent to:
log(x_t) - log(x_t1) = log(x_t / x_t1), where x_t is the series and x_t1 is the first lag.
The 1st difference diff_vec(difference = 1, log = TRUE) has an interesting property
where diff_vec(difference = 1, log = TRUE) %>% exp() is approximately 1 + rate of change.
See also
Advanced Differencing and Modeling:
step_diff()- Recipe fortidymodelsworkflowtk_augment_differences()- Adds many differences to adata.frame(tibble)
Additional Vector Functions:
Box Cox Transformation:
box_cox_vec()Lag Transformation:
lag_vec()Differencing Transformation:
diff_vec()Rolling Window Transformation:
slidify_vec()Loess Smoothing Transformation:
smooth_vec()Fourier Series:
fourier_vec()Missing Value Imputation for Time Series:
ts_impute_vec(),ts_clean_vec()
Examples
library(dplyr)
# --- USAGE ----
diff_vec(1:10, lag = 2, difference = 2) %>%
diff_inv_vec(lag = 2, difference = 2, initial_values = 1:4)
#> diff_vec(): Initial values: 1, 2, 3, 4
#> [1] 1 2 3 4 5 6 7 8 9 10
# --- VECTOR ----
# Get Change
1:10 %>% diff_vec()
#> diff_vec(): Initial values: 1
#> [1] NA 1 1 1 1 1 1 1 1 1
# Get Acceleration
1:10 %>% diff_vec(difference = 2)
#> diff_vec(): Initial values: 1, 2
#> [1] NA NA 0 0 0 0 0 0 0 0
# Get approximate rate of change
1:10 %>% diff_vec(log = TRUE) %>% exp() - 1
#> diff_vec(): Initial values: 1
#> [1] NA 1.0000000 0.5000000 0.3333333 0.2500000 0.2000000 0.1666667
#> [8] 0.1428571 0.1250000 0.1111111
# --- MUTATE ----
m4_daily %>%
group_by(id) %>%
mutate(difference = diff_vec(value, lag = 1)) %>%
mutate(
difference_inv = diff_inv_vec(
difference,
lag = 1,
# Add initial value to calculate the inverse difference
initial_values = value[1]
)
)
#> diff_vec(): Initial values: 2076.2
#> diff_vec(): Initial values: 1821.9
#> diff_vec(): Initial values: 9109.38
#> diff_vec(): Initial values: 5647.3
#> # A tibble: 9,743 × 5
#> # Groups: id [4]
#> id date value difference difference_inv
#> <fct> <date> <dbl> <dbl> <dbl>
#> 1 D10 2014-07-03 2076. NA 2076.
#> 2 D10 2014-07-04 2073. -2.80 2073.
#> 3 D10 2014-07-05 2049. -24.7 2049.
#> 4 D10 2014-07-06 2049. 0.200 2049.
#> 5 D10 2014-07-07 2006. -42.5 2006.
#> 6 D10 2014-07-08 2018. 11.2 2018.
#> 7 D10 2014-07-09 2019. 1.5 2019.
#> 8 D10 2014-07-10 2007. -11.7 2007.
#> 9 D10 2014-07-11 2010 2.60 2010
#> 10 D10 2014-07-12 2002. -8.5 2002.
#> # ℹ 9,733 more rows