README

ggchangepoint provides a unified tidy interface to changepoint analysis in R. It wraps multiple detection engines (changepoint, changepoint.np, ecp, wbs, breakfast, not, mosum, fpop, IDetect, and gfpop) behind a consistent S3 result class (ggcpt) with broom-style methods (tidy(), glance(), augment()), ggplot2 integration via autoplot() and custom geoms, and a full method-comparison, evaluation, and simulation toolkit.

The engines beyond changepoint, changepoint.np, and ecp are optional (Suggests); install the ones you need. The original 0.1.0 functions (cpt_wrapper(), ecp_wrapper(), ggcptplot(), ggecpplot()) continue to work unchanged.

Installation

install.packages("ggchangepoint")

# install.packages("devtools")
devtools::install_github("PursuitOfDataScience/ggchangepoint")

Quick start

library(ggchangepoint)
library(ggplot2)

set.seed(2022)
x <- c(rnorm(100, 0, 1), rnorm(100, 10, 1))

res <- cpt_detect(x, method = "pelt", change_in = "mean")
res
#> ggcpt (changepoint detection result)
#>   Method:          pelt 
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         MBIC = NA 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467

The result is a ggcpt S3 object. Print it to see the changepoints, or use tidy(), glance(), and augment():

tidy(res)
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
glance(res)
#> # A tibble: 1 × 9
#>       n n_changepoints method change_in penalty_type penalty_value cp_convention
#>   <int>          <int> <chr>  <chr>     <chr>                <dbl> <chr>        
#> 1   200              1 pelt   mean      MBIC                    NA left         
#> # ℹ 2 more variables: total_cost <dbl>, runtime <dbl>

Unified detection across engines

cpt_detect(x, method = "binseg", change_in = "mean")
#> ggcpt (changepoint detection result)
#>   Method:          binseg 
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         MBIC = NA 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <dbl>    <dbl>
#> 1   100    0.467
cpt_detect(x, method = "wbs", change_in = "mean")
#> ggcpt (changepoint detection result)
#>   Method:          wbs 
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         sSIC = NA 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
cpt_detect(x, method = "fpop", change_in = "mean")
#> ggcpt (changepoint detection result)
#>   Method:          fpop 
#>   Change in:       mean 
#>   Changepoints found: 1 
#>   CP convention:   left 
#>   Penalty:         Manual = 10.5966347330961 
#>   Series length:   200 
#> 
#> Changepoints:
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467

Compare methods

ggcpt_compare(x, methods = c("pelt", "binseg", "fpop", "wbs"))

ggcpt_compare_table(x, methods = c("pelt", "binseg", "fpop", "wbs"))
#> # A tibble: 4 × 3
#>   method    cp cp_value
#>   <chr>  <dbl>    <dbl>
#> 1 pelt     100    0.467
#> 2 binseg   100    0.467
#> 3 fpop     100    0.467
#> 4 wbs      100    0.467

Evaluation

cpt_metrics(pred = c(100), truth = c(100), n = 200)
#> # A tibble: 1 × 12
#>       n n_pred n_truth precision recall    f1 covering hausdorff rand_index
#>   <int>  <int>   <int>     <dbl>  <dbl> <dbl>    <dbl>     <dbl>      <dbl>
#> 1   200      1       1         1      1     1        1         0          1
#> # ℹ 3 more variables: annotation_error <int>, mae_matched <dbl>,
#> #   rmse_matched <dbl>

Data simulation

dat <- cpt_simulate(200, changepoints = c(100), change_in = "mean",
                    params = c(0, 10), sd = 1)
attributes(dat)$true_changepoints
#> [1] 100

Built-in test signals include signal_blocks(), signal_fms(), signal_mix(), signal_teeth(), and signal_stairs().

Original wrappers (0.1.0 API)

The original cpt_wrapper(), ecp_wrapper(), ggcptplot(), and ggecpplot() continue to work unchanged for backward compatibility.

cpt_wrapper(x)
#> # A tibble: 1 × 2
#>      cp cp_value
#>   <int>    <dbl>
#> 1   100    0.467
ggcptplot(x)

ggchangepoint