Compute trend statistics

Runs Mann-Kendall significance test and Theil-Sen slope estimator on time series data for each variable, period, and trend start year.

Usage

cd_trend(x, trend_start = c(1950, 1980))

Arguments

x

A tibble from cd_extract() or cd_anomaly() with columns variable, period, year, and either value or anomaly.

trend_start

Integer vector of start years for trend windows. Default c(1950, 1980).

Value

A tibble with columns variable, period, trend_start, slope, intercept, mk_pvalue, n_years, and trend_on ("value" or "anomaly", the column the trend was run on). When x carries them, anomaly_type, unit and long_name are passed through — see the input contract in cd_anomaly(). unit is the anomaly's unit, so on raw values it is kept only for absolute and pct_point_diff series, where it is also the unit of the values. cd_summary() reads them. A combination with fewer than 3 years in its window gives no row; when none has 3, the result is a zero-row tibble with the same columns.

Examples

catalog <- cd_catalog(
  system.file("extdata", "example_catalog.json", package = "cd")
)
aoi <- sf::st_read(
  system.file("extdata", "example_aoi.gpkg", package = "cd"),
  quiet = TRUE
)
ts <- cd_extract(catalog, aoi)

# Trend on raw values
cd_trend(ts, trend_start = 1951)
#> # A tibble: 1 × 8
#>   variable period trend_start slope intercept mk_pvalue n_years trend_on
#>   <chr>    <chr>        <dbl> <dbl>     <dbl>     <dbl>   <int> <chr>   
#> 1 tmean    annual        1951 0.128     -253.     0.592      10 value   

# Also works on anomalies — uses 'anomaly' column automatically
bl <- cd_baseline(ts, baseline_years = 1951:1955)
ano <- cd_anomaly(ts, bl)
cd_trend(ano, trend_start = 1951)
#> # A tibble: 1 × 10
#>   variable period trend_start slope intercept mk_pvalue n_years trend_on
#>   <chr>    <chr>        <dbl> <dbl>     <dbl>     <dbl>   <int> <chr>   
#> 1 tmean    annual        1951 0.128     -251.     0.592      10 anomaly 
#> # ℹ 2 more variables: anomaly_type <chr>, unit <chr>