# activAnalyzer

The activAnalyzer package was primarily built for working through a Shiny app. The procedure for using the app is explained in the related user’s guide. The functions used in this app can also be used to analyze data outside the app, as shown below.

## Loading packages

    library(activAnalyzer)
    library(magrittr)
    library(ggplot2)
    library(patchwork)
    library(dplyr)

## Getting file

    file <- system.file("extdata", "acc.agd", package = "activAnalyzer")

## Preparing dataset

    mydata <- prepare_dataset(data = file)

## Getting nonwear/wear time marks

    mydata_with_wear_marks <-
      mydata %>%
      mark_wear_time(
        to_epoch = 60,
        cts = "vm",
        frame = 90,
        allowanceFrame = 2,
        streamFrame = 30
        )
    #> frame is 90
    #> streamFrame is 30
    #> allowanceFrame is 2

## Viewing data with nonwear/wear time marks

    plot_data(data = mydata_with_wear_marks, metric = "vm")

## Viewing data with nonwear/wear time marks and with a zoom in on the figure

    plot_data(
      data = mydata_with_wear_marks,
      metric = "vm",
      zoom_from = "16:00:00",
      zoom_to = "18:00:00"
      )

## Getting activity intensity marks

    mydata_with_intensity_marks <-
      mark_intensity(
         data = mydata_with_wear_marks,
         col_axis = "vm",
         equation = "Sasaki et al. (2011) [Adults]",
         sed_cutpoint = 200,
         mpa_cutpoint = 2690,
         vpa_cutpoint = 6167,
         age = 32,
         weight = 67,
         sex = "male"
        )

## Viewing data with activity intensity marks

    plot_data_with_intensity(
      mydata_with_intensity_marks,
      metric = "vm",
      valid_wear_time_start = "00:00:00",
      valid_wear_time_end = "23:59:59"
      )

## Viewing data with activity intensity marks and grey bands to highlight the daily period considered for analysis

    plot_data_with_intensity(
      mydata_with_intensity_marks,
      metric = "vm",
      valid_wear_time_start = "07:00:00",
      valid_wear_time_end = "22:00:00"
      )

## Viewing data with activity intensity marks and a zoom in on the figure

    plot_data_with_intensity(
      mydata_with_intensity_marks,
      metric = "vm",
      zoom_from = "13:00:00",
      zoom_to = "16:30:00"
      )

## Getting activity metrics by day

    results_by_day <-
      mydata_with_intensity_marks %>%
      recap_by_day(
        age = 32,
        weight = 67,
        sex = "male",
        valid_wear_time_start = "07:00:00",
        valid_wear_time_end = "22:00:00",
        start_first_bin = 0,
        start_last_bin = 10000,
        bin_width = 500
        )

## Computing means of the metrics using the valid days

    mean_results <-
      results_by_day$df_all_metrics  %>%
      average_results(minimum_wear_time = 10, fun = "mean")

## Computing medians of the metrics using the valid days

    median_results <-
      results_by_day$df_all_metrics  %>%
      average_results(minimum_wear_time = 10, fun = "median")

## Showing activity volume metrics

### Results by day: Tabular view

    results_by_day$df_all_metrics %>%
      dplyr::select(date:total_steps) %>%
      reactable::reactable(striped = TRUE, defaultColDef = reactable::colDef(align = "center", minWidth = 180))

### Results by day: Graphical view

    create_fig_res_by_day(
      results_by_day$df_all_metrics,
      minimum_wear_time_for_analysis = 10,
      start_day_analysis = "00:00:00",
      end_day_analysis = "23:59:00",
      metrics = "volume",
      epoch_label = "60s"
      ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))

### Means | Medians computed using valid days

    create_flextable_summary(
      results_summary_means = mean_results,
      results_summary_medians = median_results,
      metrics = "volume",
      epoch_label = "60s"
      )

### Comparisons with norms and recommendations

    # PAL
    g_pal <- create_fig_pal(score = mean_results[["pal"]], "en") + theme(plot.margin = margin(2, 1, 0.5, 1, "cm"))

    # Steps
    g_steps <- create_fig_steps(score = mean_results[["total_steps"]], "en") + theme(plot.margin = margin(0, 1, 0.5, 1, "cm"))

    # MVPA
    g_mvpa <- create_fig_mvpa(score = mean_results[["minutes_MVPA"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))

    # SED
    g_sed <- create_fig_sed(score = mean_results[["minutes_SED"]], "en") + theme(plot.margin = margin(0, 1, 0, 1, "cm"))

    # MVPA/SED ratio
    g_ratio <- create_fig_ratio_mvpa_sed(score = mean_results[["ratio_mvpa_sed"]], "en") + theme(plot.margin = margin(0, 1, 1, 1, "cm"))

    # Whole figure
    g_pal / g_steps / (g_mvpa | g_sed | g_ratio) +
        plot_layout(heights = c(0.8, 0.7, 1.5)) & theme(legend.justification = "center")

## Showing step accumulation metrics

### Results by day: Tabular view

    results_by_day$df_all_metrics %>%
      dplyr::select(date, max_steps_60min:peak_steps_1min) %>%
      reactable::reactable(striped = TRUE, defaultColDef = reactable::colDef(align = "center", minWidth = 180))

### Results by day: Graphical view

    create_fig_res_by_day(
      results_by_day$df_all_metrics,
      minimum_wear_time_for_analysis = 10,
      start_day_analysis = "00:00:00",
      end_day_analysis = "23:59:00",
      metrics = "step_acc",
      epoch_label = "60s"
      ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))

### Means | Medians computed using valid days

    create_flextable_summary(
      results_summary_means = mean_results,
      results_summary_medians = median_results,
      metrics = "step_acc",
      epoch_label = "60s"
      )

## Showing intensity gradient and MX metrics

### Distribution of time spent in intensity bins

      results_by_day$p_band

      results_by_day$p_log

### Results by day: Tabular view

    results_by_day$df_all_metrics %>%
      dplyr::select(date, ig:M5) %>%
      reactable::reactable(striped = TRUE, defaultColDef = reactable::colDef(align = "center", minWidth = 180))

### Results by day: Graphical view

    create_fig_res_by_day(
      results_by_day$df_all_metrics,
      minimum_wear_time_for_analysis = 10,
      start_day_analysis = "00:00:00",
      end_day_analysis = "23:59:00",
      metrics = "step_acc",
      epoch_label = "60s"
      ) + theme(plot.margin = margin(1, 1, 1, 1, "cm"))

### Means | Medians computed using valid days

    create_flextable_summary(
      results_summary_means = mean_results,
      results_summary_medians = median_results,
      metrics = "int_distri",
      epoch_label = "60s"
      )

### Radar view based on the means of valid days

    create_fig_mx_summary(
    data = mean_results,
    labels = NULL,
    mpa_cutpoint = 2690,
    vpa_cutpoint = 6167
    )

## Showing sedentary behaviour accumulation metrics based on valid days

### Getting results

    accum_metrics_sed <-
      compute_accumulation_metrics(
        data = mydata_with_intensity_marks,
        behaviour = "sed",
        dates = c("2021-04-07", "2021-04-08", "2021-04-09", "2021-04-10", "2021-04-11")
        )

### Temporal distribution of sedentary bouts

    accum_metrics_sed$p_breaks

### Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index

    p1 <- accum_metrics_sed$p_alpha  + guides(color = "none", fill = "none")
    p2 <- accum_metrics_sed$p_MBD    + guides(color = "none", fill = "none")
    p3 <- accum_metrics_sed$p_UBD
    p4 <- accum_metrics_sed$p_gini

    (p1 | p2) / (p3 | p4) + plot_layout(guides = "collect") & theme(legend.position = 'bottom')

## Showing physical activity accumulation metrics based on valid days

### Getting results

    accum_metrics_pa <-
      compute_accumulation_metrics(
        mydata_with_intensity_marks,
        behaviour = "pa",
        dates = c("2021-04-07", "2021-04-08", "2021-04-09", "2021-04-10", "2021-04-11")
        )

### Temporal distribution of physical activity bouts

    accum_metrics_pa$p_breaks

### Alpha coefficient, median bout duration (MBD), usual bout duration (UBD), and Gini index

    p1 <- accum_metrics_pa$p_alpha  + guides(color = "none", fill = "none")
    p2 <- accum_metrics_pa$p_MBD    + guides(color = "none", fill = "none")
    p3 <- accum_metrics_pa$p_UBD
    p4 <- accum_metrics_pa$p_gini

    (p1 | p2) / (p3 | p4) + plot_layout(guides = "collect") & theme(legend.position = 'bottom')