The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

CircaCP: Getting Started

Shanshan Chen

2025-11-08

Read in the example actigraphy data, specify column names for Date, Time and Activity

csv <- system.file("extdata", "NHANES_11111.csv", package = "CircaCP")

df <- import_acti_file(csv, "Date", "Time", "MIMS")

head(df)
#> # A tibble: 6 × 4
#>   id           Date   Time     Activity
#>   <chr>        <chr>  <chr>       <dbl>
#> 1 NHANES_11111 9/1/13 21:30:00     6.08
#> 2 NHANES_11111 9/1/13 21:31:00    17.5 
#> 3 NHANES_11111 9/1/13 21:32:00     8.12
#> 4 NHANES_11111 9/1/13 21:33:00    11.4 
#> 5 NHANES_11111 9/1/13 21:34:00    12.1 
#> 6 NHANES_11111 9/1/13 21:35:00    27.8

screening actigraphy data to check minumum wearing days and continuous wearing

screen_fun <- if (exists("screen_wear", mode = "function")) {
  screen_wear
} else {
  stop("No screening function (screen_wear*) found in CircaCP.")
}

out <- screen_fun(
  df,
  min_days     = 5,
  max_zero_run = 200
)

out$status
#> [1] "ok"

apply consinor fitting

stopifnot(exists("sleep_cos", mode = "function"))
clean_df = out$clean_df
cos_df <- sleep_cos(clean_df, thr = 0.4)
cos_df$cos_para
#> [1]  14.47712 -12.47072 360.83514
cos_df$RMSE
#> [1] 15.25192

apply circacp algorithm

stopifnot(exists("sleep_detection", mode = "function"))
clean_df = out$clean_df
newdf <- sleep_detection(clean_df, thr = 0.4, dist = "ZAG")
head(newdf)
#>             id   Date     Time Activity label.cos label.sw   cosinor
#> 1 NHANES_11111 9/1/13 21:30:00    6.075         1        1 0.4996403
#> 2 NHANES_11111 9/1/13 21:31:00   17.457         1        1 0.4974587
#> 3 NHANES_11111 9/1/13 21:32:00    8.117         1        1 0.4952771
#> 4 NHANES_11111 9/1/13 21:33:00   11.413         1        1 0.4930956
#> 5 NHANES_11111 9/1/13 21:34:00   12.149         1        1 0.4909142
#> 6 NHANES_11111 9/1/13 21:35:00   27.817         1        1 0.4887330
#>   Activity_norm
#> 1    0.03745492
#> 2    0.10762970
#> 3    0.05004470
#> 4    0.07036592
#> 5    0.07490367
#> 6    0.17150344

plot estimated cosinor fitting and circadian cycles

ggplot2::ggplot(newdf, ggplot2::aes(x = seq_along(Activity))) +
    ggplot2::geom_line(ggplot2::aes(y = Activity_norm), alpha = 0.4) +
    ggplot2::geom_line(ggplot2::aes(y = cosinor/2), alpha = 0.4, color = "magenta") +
    ggplot2::geom_line(ggplot2::aes(y = label.cos), color = "blue") +
    ggplot2::labs(x = "Minute", y = "Activity", title = unique(newdf$id)) +
    ggplot2::scale_color_manual(
            name = NULL,
           values = c(
           "Activity (norm)" = "grey40",
           "Cosinor"     = "magenta",
            "Cosinor label"   = "blue"))+
  ggplot2::theme_bw(12)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.

plot estimated cosinor fitting and slee-wake cycles

ggplot2::ggplot(newdf, ggplot2::aes(x = seq_along(Activity))) +
    ggplot2::geom_line(ggplot2::aes(y = Activity_norm), alpha = 0.4) +
    ggplot2::geom_line(ggplot2::aes(y = cosinor/2), alpha = 0.4, color = "magenta") +
    ggplot2::geom_line(ggplot2::aes(y = label.sw), color = "forestgreen") +
    ggplot2::labs(x = "Minute", y = "Activity", title = unique(newdf$id)) +
    ggplot2::scale_color_manual(
            name = NULL,
           values = c(
           "Activity (norm)" = "grey40",
           "Cosinor / 2"     = "magenta",
            "Cosinor label"   = "blue",
            "Sleep/Wake"      = "forestgreen"))+
  ggplot2::theme_bw(12)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's colour values.

Non-parametric metrics for circadian rhythsm

np <- extract_nonparametric_metrics(newdf, L_window = 300, M_window=600)
head(np)
#> $L5_mean
#> [1] 0.9231071
#> 
#> $L5_start_min
#> [1] 79
#> 
#> $M10_mean
#> [1] 24.64013
#> 
#> $M10_start_min
#> [1] 664
#> 
#> $RA
#> [1] 0.9277785
#> 
#> $IS
#> [1] 0.30567

Extract all sleep and circadian rhythm related metrics

if (exists("extract_sleep_metrics_df", mode = "function")) {
  metrics_df <- extract_sleep_metrics_df(newdf)
} else if (exists("extract_sleep_metrics", mode = "function")) {
  metrics_df <- extract_sleep_metrics(newdf)  # may be a list
} else {
  stop("No extract_sleep_metrics* function found.")
}
head(metrics_df)
#>             id period_type       timestamp clock_min duration_hours       SRI
#> 1 NHANES_11111       sleep 9/1/13 22:14:00      1334       8.300000 0.8856151
#> 2 NHANES_11111       sleep 9/2/13 21:58:00      1318       8.333333 0.8856151
#> 3 NHANES_11111       sleep 9/3/13 23:25:00      1405       7.350000 0.8856151
#> 4 NHANES_11111       sleep 9/4/13 22:34:00      1354       8.583333 0.8856151
#> 5 NHANES_11111       sleep 9/5/13 23:28:00      1408       9.866667 0.8856151
#> 6 NHANES_11111       sleep  9/7/13 2:27:00       147       8.450000 0.8856151
#>      Mesor Amplitude Acrophase_min DN_Ratio        RA      IS        IV
#> 1 14.47712  12.47072      1080.835 185.4051 0.9277785 0.30567 0.4460329
#> 2 14.47712  12.47072      1080.835 185.4051 0.9277785 0.30567 0.4460329
#> 3 14.47712  12.47072      1080.835 185.4051 0.9277785 0.30567 0.4460329
#> 4 14.47712  12.47072      1080.835 185.4051 0.9277785 0.30567 0.4460329
#> 5 14.47712  12.47072      1080.835 185.4051 0.9277785 0.30567 0.4460329
#> 6 14.47712  12.47072      1080.835 185.4051 0.9277785 0.30567 0.4460329
#>     L5_mean L5_start_min M10_mean M10_start_min SleepTimeSD_hours
#> 1 0.9231071           79 24.64013           664          1.330825
#> 2 0.9231071           79 24.64013           664          1.330825
#> 3 0.9231071           79 24.64013           664          1.330825
#> 4 0.9231071           79 24.64013           664          1.330825
#> 5 0.9231071           79 24.64013           664          1.330825
#> 6 0.9231071           79 24.64013           664          1.330825
#>   WakeTimeSD_hours
#> 1         1.585032
#> 2         1.585032
#> 3         1.585032
#> 4         1.585032
#> 5         1.585032
#> 6         1.585032

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.