Pipe-friendly function that adds trend columns to a tibble or data.frame. Designed for exploratory analysis of monthly and quarterly economic time series. Supports multiple trend extraction methods and handles grouped data.
Usage
augment_trends(
data,
date_col = "date",
value_col = "value",
group_vars = NULL,
methods = "stl",
frequency = NULL,
suffix = NULL,
window = NULL,
smoothing = NULL,
band = NULL,
align = NULL,
params = list(),
.quiet = FALSE
)Arguments
- data
A
data.frame,tibble, ordata.tablecontaining the time series data.- date_col
Name of the date column. Defaults to
"date". Must be of classDate.- value_col
Name of the value column. Defaults to
"value". Must benumeric.- group_vars
Optional grouping variables for multiple time series. Can be a character vector of column names.
- methods
Character vector of trend methods. Options:
"hp","bk","cf","ma","stl","loess","spline","poly","bn","ucm","hamilton","spencer","ewma","wma","triangular","kernel","kalman","median","gaussian". Default is"stl".- frequency
The frequency of the series. Supports 4 (quarterly) or 12 (monthly). Will be auto-detected if not specified.
- suffix
Optional suffix for trend column names. If NULL, uses method names.
- window
Unified window/period parameter for moving average methods (ma, wma, triangular, stl, ewma, median, gaussian). Must be positive. If NULL, uses frequency-appropriate defaults. For EWMA, specifies the window size when using TTR's optimized implementation. Cannot be used simultaneously with
smoothingfor EWMA method.- smoothing
Unified smoothing parameter for smoothing methods (hp, loess, spline, ewma, kernel, kalman). For hp: use large values (1600+) or small values (0-1) that get converted. For EWMA: specifies the alpha parameter (0-1) for traditional exponential smoothing. Cannot be used simultaneously with
windowfor EWMA method. For kernel: multiplier of optimal bandwidth (1.0 = optimal, <1 = less smooth, >1 = more smooth). For kalman: controls the ratio of measurement to process noise (higher = more smoothing). For others: typically 0-1 range.- band
Unified band parameter for bandpass filters (bk, cf). Both values must be positive. Provide as
c(low, high)where low/high are periods in quarters, e.g.,c(6, 32).- align
Unified alignment parameter for moving average methods (ma, wma, triangular, gaussian). Valid values:
"center"(default, uses surrounding values),"right"(causal, uses past values only),"left"(anti-causal, uses future values only). Note: triangular only supports"center"and"right". If NULL, uses"center"as default.- params
Optional list of method-specific parameters for fine control.
- .quiet
If
TRUE, suppress informational messages.
Value
A tibble with original data plus trend columns named trend_{method} or
trend_{method}_{suffix} if suffix is provided.
Details
This function is designed for monthly (frequency = 12) and quarterly (frequency = 4) economic data. It uses economic-appropriate defaults for all trend extraction methods.
For grouped data, the function applies trend extraction to each group separately, maintaining the original data structure while adding trend columns.
Examples
# Simple STL decomposition on quarterly GDP construction data
gdp_construction |> augment_trends(value_col = "index")
#> Auto-detected quarterly (4 obs/year)
#> Computing STL trend with s.window = periodic
#> # A tibble: 122 × 3
#> date index trend_stl
#> <date> <dbl> <dbl>
#> 1 1995-01-01 100 102.
#> 2 1995-04-01 100 101.
#> 3 1995-07-01 100 100.
#> 4 1995-10-01 100 99.4
#> 5 1996-01-01 97.8 101.
#> 6 1996-04-01 101. 102.
#> 7 1996-07-01 107. 103.
#> 8 1996-10-01 103. 104.
#> 9 1997-01-01 101. 106.
#> 10 1997-04-01 108. 109.
#> # ℹ 112 more rows
# Multiple smoothing methods with unified parameter
gdp_construction |>
augment_trends(
value_col = "index",
methods = c("hp", "loess", "ewma"),
smoothing = 0.3
)
#> Auto-detected quarterly (4 obs/year)
#> Computing HP filter (two-sided) with lambda = 480
#> Computing loess trend with span = 0.3
#> Computing EWMA with alpha = 0.3
#> # A tibble: 122 × 5
#> date index trend_hp trend_loess trend_ewma
#> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 1995-01-01 100 99.1 97.5 100
#> 2 1995-04-01 100 100. 98.9 100
#> 3 1995-07-01 100 101. 100. 100
#> 4 1995-10-01 100 102. 102. 100
#> 5 1996-01-01 97.8 103. 103. 99.3
#> 6 1996-04-01 101. 104. 104. 99.8
#> 7 1996-07-01 107. 104. 105. 102.
#> 8 1996-10-01 103. 105. 106. 102.
#> 9 1997-01-01 101. 106. 106. 102.
#> 10 1997-04-01 108. 107. 107. 104.
#> # ℹ 112 more rows
# Moving averages with unified window on monthly data
vehicles |>
tail(60) |>
augment_trends(
value_col = "production",
methods = c("ma", "wma", "triangular"),
window = 8
)
#> Auto-detected monthly (12 obs/year)
#> Computing 2x8-period MA (auto-adjusted for even-window centering)
#> Computing 8-period weighted MA with linear weights, center alignment
#> Computing 8-period triangular MA with center alignment
#> # A tibble: 60 × 5
#> date production trend_ma trend_wma trend_triangular
#> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 2020-08-01 193421 NA NA NA
#> 2 2020-09-01 219033 NA NA NA
#> 3 2020-10-01 230927 NA NA NA
#> 4 2020-11-01 249104 216181. 213615. 224546.
#> 5 2020-12-01 261321 215282. 208187. 218335.
#> 6 2021-01-01 180904 212021. 205996. 209996.
#> 7 2021-02-01 186718 204914. 200905. 203805.
#> 8 2021-03-01 208801 195115. 193166. 198122.
#> 9 2021-04-01 191853 188457. 188418. 193806.
#> 10 2021-05-01 206221 185917 181049. 190493.
#> # ℹ 50 more rows
# Economic indicators with different methods
ibcbr |>
tail(48) |>
augment_trends(
value_col = "index",
methods = c("median", "kalman", "kernel"),
window = 9,
smoothing = 0.15
)
#> Auto-detected monthly (12 obs/year)
#> Computing 9-period median filter with endrule = median
#> Computing Kalman smoother with measurement noise = auto
#> Computing kernel smoother with bandwidth = {bandwidth}, kernel = normal
#> # A tibble: 48 × 5
#> date index trend_median trend_kalman trend_kernel
#> <date> <dbl> <dbl> <dbl> <dbl>
#> 1 2021-08-01 99.3 97.1 98.0 99.3
#> 2 2021-09-01 97.1 97.1 97.9 97.1
#> 3 2021-10-01 96.5 97.8 97.8 96.5
#> 4 2021-11-01 97.8 97.8 97.9 97.8
#> 5 2021-12-01 99.2 97.8 98.0 99.1
#> 6 2022-01-01 91.9 97.8 98.0 92.0
#> 7 2022-02-01 95.7 99.2 98.6 95.7
#> 8 2022-03-01 105. 99.3 99.5 105.
#> 9 2022-04-01 100. 99.8 99.8 100.
#> 10 2022-05-01 99.8 100. 100. 99.8
#> # ℹ 38 more rows
# Moving average with right alignment (causal filter)
vehicles |>
tail(60) |>
augment_trends(
value_col = "production",
methods = "ma",
window = 12,
align = "right"
)
#> Auto-detected monthly (12 obs/year)
#> Computing 12-period MA with right alignment
#> # A tibble: 60 × 3
#> date production trend_ma
#> <date> <dbl> <dbl>
#> 1 2020-08-01 193421 NA
#> 2 2020-09-01 219033 NA
#> 3 2020-10-01 230927 NA
#> 4 2020-11-01 249104 NA
#> 5 2020-12-01 261321 NA
#> 6 2021-01-01 180904 NA
#> 7 2021-02-01 186718 NA
#> 8 2021-03-01 208801 NA
#> 9 2021-04-01 191853 NA
#> 10 2021-05-01 206221 NA
#> # ℹ 50 more rows
# Advanced: fine-tune specific methods
electric |>
tail(72) |>
augment_trends(
value_col = "consumption",
methods = "median",
window = 7
)
#> Auto-detected monthly (12 obs/year)
#> Computing 7-period median filter with endrule = median
#> # A tibble: 72 × 3
#> date consumption trend_median
#> <date> <dbl> <dbl>
#> 1 2019-08-01 10987 10987
#> 2 2019-09-01 11379 11379
#> 3 2019-10-01 11973 11973
#> 4 2019-11-01 12424 12201
#> 5 2019-12-01 12201 12383
#> 6 2020-01-01 12909 12383
#> 7 2020-02-01 12383 12383
#> 8 2020-03-01 12432 12318
#> 9 2020-04-01 12318 12318
#> 10 2020-05-01 11756 11852
#> # ℹ 62 more rows