R/create_flextable_summary.R
In pydemull/activAnalyzer: A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data
Defines functions
create_flextable_summary
Documented in
create_flextable_summary
#' Create a formatted table of results
#'
#' The function generates a formatted table with both means and medians of the metrics obtained following the physical behavior measurement.
#'
#' @param results_summary_means A dataframe with mean results obtained using the \code{\link{prepare_dataset}}, \code{\link{mark_wear_time}}, \code{\link{mark_intensity}}, \code{\link{recap_by_day}}, and then the \code{\link{average_results}} functions.
#' @param results_summary_medians A dataframe with median results obtained using the \code{\link{prepare_dataset}}, \code{\link{mark_wear_time}}, \code{\link{mark_intensity}}, \code{\link{recap_by_day}}, and then the \code{\link{average_results}} functions.
#' @param language A character value for setting the language with which the figure should be created: `en` for english; `fr` for french.
#' @param metrics A character value for setting the metrics to be shown in the figure. "volume" refers to "activity volume" metrics, step_acc" refers
#' to "step accumulation" metrics, and "int_distri" refers to intensity distribution metrics. By default, the function provides all computed metrics.
#' @param epoch_label A character value to be pasted into the names of the variables to build the figure
#'
#' @return A flextable object
#' @export
#'
#' @examples
#' \donttest{
#' file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
#' mydata <- prepare_dataset(data = file)
#' mydata_with_wear_marks <- mark_wear_time(
#' dataset = mydata,
#' TS = "TimeStamp",
#' cts = "vm",
#' frame = 90,
#' allowanceFrame = 2,
#' streamFrame = 30
#' )
#' 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",
#' )
#' summary_by_day <- recap_by_day(
#' data = mydata_with_intensity_marks,
#' age = 32,
#' weight = 67,
#' sex = "male",
#' valid_wear_time_start = "07:00:00",
#' valid_wear_time_end = "22:00:00"
#' )$df_all_metrics
#' results_summary_means <- average_results(
#' data = summary_by_day,
#' minimum_wear_time = 10,
#' fun = "mean"
#' )
#' results_summary_medians <- average_results(
#' data = summary_by_day,
#' minimum_wear_time = 10,
#' fun = "median"
#' )
#' create_flextable_summary(
#' results_summary_means,
#' results_summary_medians,
#' language = "en"
#' )
#' }
#'
create_flextable_summary <- function(
results_summary_means,
results_summary_medians,
language = c("en", "fr"),
metrics = c("all", "volume", "step_acc", "int_distri"),
epoch_label = "60s"
) {
# Get arguments
metrics <- match.arg(metrics)
language <- match.arg(language)
epoch_label = as.name(epoch_label)
flextable::set_flextable_defaults(fonts_ignore = TRUE)
# Create table for english language ===============================================================================
if (language == "en") {
# Set list of metrics to be used for filerting table of results
if (metrics == "all") {
selected_metrics <- c(
"Wear time (min)",
"Axis 1 total counts",
"VM total counts",
"Axis 1 mean (counts/min)",
"VM mean (counts/min)",
"SED time (min)",
"LPA time (min)",
"MPA time (min)",
"VPA time (min)",
"MVPA time (min)",
"SED wear time proportion (%)",
"LPA wear time proportion (%)",
"MPA wear time proportion (%)",
"VPA wear time proportion (%)",
"MVPA wear time proportion (%)",
"Ratio MVPA / SED",
"Total MVPA MET-hr",
"Total kcal",
"PAL",
"Total steps",
"Max step acc. 60 min (steps/min)",
"Max step acc. 30 min (steps/min)",
"Max step acc. 20 min (steps/min)",
"Max step acc. 5 min (steps/min)",
"Max step acc. 1 min (steps/min)",
"Peak step acc. 60 min (steps/min)",
"Peak step acc. 30 min (steps/min)",
"Peak step acc. 20 min (steps/min)",
"Peak step acc. 5 min (steps/min)",
"Peak step acc. 1 min (steps/min)",
"Intensity gradient",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
if (metrics == "volume") {
selected_metrics <- c(
"Wear time (min)",
"Axis 1 total counts",
"VM total counts",
"Axis 1 mean (counts/min)",
"VM mean (counts/min)",
"SED time (min)",
"LPA time (min)",
"MPA time (min)",
"VPA time (min)",
"MVPA time (min)",
"SED wear time proportion (%)",
"LPA wear time proportion (%)",
"MPA wear time proportion (%)",
"VPA wear time proportion (%)",
"MVPA wear time proportion (%)",
"Ratio MVPA / SED",
"Total MVPA MET-hr",
"Total kcal",
"PAL",
"Total steps"
)
}
if (metrics == "step_acc") {
selected_metrics <- c(
"Max step acc. 60 min (steps/min)",
"Max step acc. 30 min (steps/min)",
"Max step acc. 20 min (steps/min)",
"Max step acc. 5 min (steps/min)",
"Max step acc. 1 min (steps/min)",
"Peak step acc. 60 min (steps/min)",
"Peak step acc. 30 min (steps/min)",
"Peak step acc. 20 min (steps/min)",
"Peak step acc. 5 min (steps/min)",
"Peak step acc. 1 min (steps/min)"
)
}
if (metrics == "int_distri") {
selected_metrics <- c(
"Intensity gradient",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
flextable_summary <-
flextable::flextable(
tibble::tibble(
Metric = c("Number of valid days",
"Wear time (min)",
"Axis 1 total counts",
"VM total counts",
"Axis 1 mean (counts/min)",
"VM mean (counts/min)",
"SED time (min)",
"LPA time (min)",
"MPA time (min)",
"VPA time (min)",
"MVPA time (min)",
"SED wear time proportion (%)",
"LPA wear time proportion (%)",
"MPA wear time proportion (%)",
"VPA wear time proportion (%)",
"MVPA wear time proportion (%)",
"Ratio MVPA / SED",
"Total MVPA MET-hr",
"Total kcal",
"PAL",
"Total steps",
"Max step acc. 60 min (steps/min)",
"Max step acc. 30 min (steps/min)",
"Max step acc. 20 min (steps/min)",
"Max step acc. 5 min (steps/min)",
"Max step acc. 1 min (steps/min)",
"Peak step acc. 60 min (steps/min)",
"Peak step acc. 30 min (steps/min)",
"Peak step acc. 20 min (steps/min)",
"Peak step acc. 5 min (steps/min)",
"Peak step acc. 1 min (steps/min)",
"Intensity gradient",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
),
"Daily mean | median" = c(
# Number of valid days
paste0(results_summary_means[["valid_days"]]),
# Wear time
paste0(format(round(results_summary_means[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["wear_time"]]), ") | ",
format(round(results_summary_medians[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["wear_time"]]), ")"),
# Total counts Axis 1
paste0(format(round(results_summary_means[["total_counts_axis1"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_axis1"]], 1), nsmall = 1)),
# Total counts VM
paste0(format(round(results_summary_means[["total_counts_vm"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_vm"]], 1), nsmall = 1)),
# Axis 1 per min
paste0(format(round(results_summary_means[["axis1_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["axis1_per_min"]], 1), nsmall = 1)),
# VM per min
paste0(format(round(results_summary_means[["vm_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["vm_per_min"]], 1), nsmall = 1)),
# SED time
paste0(format(round(results_summary_means[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_SED"]]), ") | ",
format(round(results_summary_medians[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_SED"]]), ")"),
# LPA time
paste0(format(round(results_summary_means[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_LPA"]]), ") | ",
format(round(results_summary_medians[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_LPA"]]), ")"),
# MPA time
paste0(format(round(results_summary_means[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MPA"]]), ")"),
# VPA time
paste0(format(round(results_summary_means[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_VPA"]]), ") | ",
format(round(results_summary_medians[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_VPA"]]), ")"),
# MVPA time
paste0(format(round(results_summary_means[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MVPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MVPA"]]), ")"),
# Percent time SED
paste0(format(round(results_summary_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_SED"]], 1), nsmall = 1)),
# Percent time LPA
paste0(format(round(results_summary_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_LPA"]], 1), nsmall = 1)),
# Percent time MPA
paste0(format(round(results_summary_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MPA"]], 1), nsmall = 1)),
# Percent time VPA
paste0(format(round(results_summary_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_VPA"]], 1), nsmall = 1)),
# Percent time MVPA
paste0(format(round(results_summary_means[["percent_MVPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MVPA"]], 1), nsmall = 1)),
# Ratio MVPA/SED
paste0(format(round(results_summary_means[["ratio_mvpa_sed"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ratio_mvpa_sed"]], 2), nsmall = 2)),
# MET-hr MVPA
paste0(format(round(results_summary_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["mets_hours_mvpa"]], 2), nsmall = 2)),
# Total kilocalories
paste0(format(round(results_summary_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["total_kcal"]], 2), nsmall = 2)),
# Physical activity level (PAL)
paste0(format(round(results_summary_means[["pal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["pal"]], 2), nsmall = 2)),
# Total number of steps
paste0(round(results_summary_means[["total_steps"]], 0), " | ", round(results_summary_medians[["total_steps"]], 0)),
# Max step accum 60min
paste0(format(round(results_summary_means[["max_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_60min"]], 2), nsmall = 2)),
# Max step accum 30min
paste0(format(round(results_summary_means[["max_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_30min"]], 2), nsmall = 2)),
# Max step accum 20min
paste0(format(round(results_summary_means[["max_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_20min"]], 2), nsmall = 2)),
# Max step accum 5min
paste0(format(round(results_summary_means[["max_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_5min"]], 2), nsmall = 2)),
# Max step accum 1min
paste0(format(round(results_summary_means[["max_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_1min"]], 2), nsmall = 2)),
# Peak step accum 60min
paste0(format(round(results_summary_means[["peak_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_60min"]], 2), nsmall = 2)),
# Peak step accum 30min
paste0(format(round(results_summary_means[["peak_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_30min"]], 2), nsmall = 2)),
# Peak step accum 20min
paste0(format(round(results_summary_means[["peak_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_20min"]], 2), nsmall = 2)),
# Peak step accum 5min
paste0(format(round(results_summary_means[["peak_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_5min"]], 2), nsmall = 2)),
# Peak step accum 1min
paste0(format(round(results_summary_means[["peak_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_1min"]], 2), nsmall = 2)),
# Intensity gradient
paste0(format(round(results_summary_means[["ig"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ig"]], 2), nsmall = 2)),
# M1/3
paste0(format(round(results_summary_means[["M1/3"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M1/3"]], 1), nsmall = 1)),
# M120
paste0(format(round(results_summary_means[["M120"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M120"]], 1), nsmall = 1)),
# M60
paste0(format(round(results_summary_means[["M60"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M60"]], 1), nsmall = 1)),
# M30
paste0(format(round(results_summary_means[["M30"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M30"]], 1), nsmall = 1)),
# M15
paste0(format(round(results_summary_means[["M15"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M15"]], 1), nsmall = 1)),
# M5
paste0(format(round(results_summary_means[["M5"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M5"]], 1), nsmall = 1))
)
) %>% dplyr::filter(Metric %in% c("Number of valid days", selected_metrics))
) %>%
flextable::theme_zebra() %>%
flextable::align(align = "left", part = "all" ) %>%
flextable::width(width = 3.25)
return(flextable_summary)
}
# Create table for french language ===============================================================================
if (language == "fr") {
# Set list of metrics to be used for filerting table of results
if (metrics == "all") {
selected_metrics <- c(
"Temps de port (min)",
"Total counts Axe 1 ",
"Total counts VM",
"Moyenne Axe 1 (counts/min)",
"Moyenne VM (counts/min)",
"Temps SED (min)",
"Temps LPA (min)",
"Temps MPA (min)",
"Temps VPA (min)",
"Temps MVPA (min)",
"Proportion du temps de port SED (%)",
"Proportion du temps de port LPA (%)",
"Proportion du temps de port MPA (%)",
"Proportion du temps de port VPA (%)",
"Proportion du temps de port MVPA (%)",
"Ratio MVPA / SED",
"Total MET-hr",
"Total kcal",
"NAP",
"Total pas",
"Acc. pas max. 60 min (pas/min)",
"Acc. pas max. 30 min (pas/min)",
"Acc. pas max. 20 min (pas/min)",
"Acc. pas max. 5 min (pas/min)",
"Acc. pas max. 1 min (pas/min)",
"Acc. pas pic 60 min (pas/min)",
"Acc. pas pic 30 min (pas/min)",
"Acc. pas pic 20 min (pas/min)",
"Acc. pas pic 5 min (pas/min)",
"Acc. pas pic 1 min (pas/min)",
"Gradient d'intensit\xc3\xa9",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
if (metrics == "volume") {
selected_metrics <- c(
"Temps de port (min)",
"Total counts Axe 1 ",
"Total counts VM",
"Moyenne Axe 1 (counts/min)",
"Moyenne VM (counts/min)",
"Temps SED (min)",
"Temps LPA (min)",
"Temps MPA (min)",
"Temps VPA (min)",
"Temps MVPA (min)",
"Proportion du temps de port SED (%)",
"Proportion du temps de port LPA (%)",
"Proportion du temps de port MPA (%)",
"Proportion du temps de port VPA (%)",
"Proportion du temps de port MVPA (%)",
"Ratio MVPA / SED",
"Total MET-hr",
"Total kcal",
"NAP",
"Total pas"
)
}
if (metrics == "step_acc") {
selected_metrics <- c(
"Acc. pas max. 60 min (pas/min)",
"Acc. pas max. 30 min (pas/min)",
"Acc. pas max. 20 min (pas/min)",
"Acc. pas max. 5 min (pas/min)",
"Acc. pas max. 1 min (pas/min)",
"Acc. pas pic 60 min (pas/min)",
"Acc. pas pic 30 min (pas/min)",
"Acc. pas pic 20 min (pas/min)",
"Acc. pas pic 5 min (pas/min)",
"Acc. pas pic 1 min (pas/min)"
)
}
if (metrics == "int_distri") {
selected_metrics <- c(
"Gradient d'intensit\xc3\xa9",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
flextable_summary <-
flextable::flextable(
tibble::tibble(
Indicateur = c(
"Nombre de jours valides",
"Temps de port (min)",
"Total counts Axe 1 ",
"Total counts VM",
"Moyenne Axe 1 (counts/min)",
"Moyenne VM (counts/min)",
"Temps SED (min)",
"Temps LPA (min)",
"Temps MPA (min)",
"Temps VPA (min)",
"Temps MVPA (min)",
"Proportion du temps de port SED (%)",
"Proportion du temps de port LPA (%)",
"Proportion du temps de port MPA (%)",
"Proportion du temps de port VPA (%)",
"Proportion du temps de port MVPA (%)",
"Ratio MVPA / SED",
"Total MET-hr",
"Total kcal",
"NAP",
"Total pas",
"Acc. pas max. 60 min (pas/min)",
"Acc. pas max. 30 min (pas/min)",
"Acc. pas max. 20 min (pas/min)",
"Acc. pas max. 5 min (pas/min)",
"Acc. pas max. 1 min (pas/min)",
"Acc. pas pic 60 min (pas/min)",
"Acc. pas pic 30 min (pas/min)",
"Acc. pas pic 20 min (pas/min)",
"Acc. pas pic 5 min (pas/min)",
"Acc. pas pic 1 min (pas/min)",
"Gradient d'intensit\xc3\xa9",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
),
"Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c(
# Nombre de jours valides
paste0(results_summary_means[["valid_days"]]),
# Temps de port
paste0(format(round(results_summary_means[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["wear_time"]]), ") | ",
format(round(results_summary_medians[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["wear_time"]]), ")"),
# Total counts Axis 1
paste0(format(round(results_summary_means[["total_counts_axis1"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_axis1"]], 1), nsmall = 1)),
# Total counts VM
paste0(format(round(results_summary_means[["total_counts_vm"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_vm"]], 1), nsmall = 1)),
# Axis 1 par min
paste0(format(round(results_summary_means[["axis1_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["axis1_per_min"]], 1), nsmall = 1)),
# VM par min
paste0(format(round(results_summary_means[["vm_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["vm_per_min"]], 1), nsmall = 1)),
# Minutes SED
paste0(format(round(results_summary_means[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_SED"]]), ") | ",
format(round(results_summary_medians[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_SED"]]), ")"),
# Minutes LPA
paste0(format(round(results_summary_means[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_LPA"]]), ") | ",
format(round(results_summary_medians[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_LPA"]]), ")"),
# Minutes MPA
paste0(format(round(results_summary_means[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MPA"]]), ")"),
# Minutes VPA
paste0(format(round(results_summary_means[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_VPA"]]), ") | ",
format(round(results_summary_medians[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_VPA"]]), ")"),
# Minutes MVPA
paste0(format(round(results_summary_means[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MVPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MVPA"]]), ")"),
# % SED
paste0(format(round(results_summary_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_SED"]], 1), nsmall = 1)),
# % LPA
paste0(format(round(results_summary_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_LPA"]], 1), nsmall = 1)),
# % MPA
paste0(format(round(results_summary_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MPA"]], 1), nsmall = 1)),
# % VPA
paste0(format(round(results_summary_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_VPA"]], 1), nsmall = 1)),
# % MVPA
paste0(format(round(results_summary_means[["percent_MVPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MVPA"]], 1), nsmall = 1)),
# Ratio MVPA/SED
paste0(format(round(results_summary_means[["ratio_mvpa_sed"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ratio_mvpa_sed"]], 2), nsmall = 2)),
# Total METs-hr
paste0(format(round(results_summary_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["mets_hours_mvpa"]], 2), nsmall = 2)),
# Total kcal
paste0(format(round(results_summary_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["total_kcal"]], 2), nsmall = 2)),
# NAP
paste0(format(round(results_summary_means[["pal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["pal"]], 2), nsmall = 2)),
# Total pas
paste0(round(results_summary_means[["total_steps"]], 0), " | ", round(results_summary_medians[["total_steps"]], 0)),
# Max step accum 60min
paste0(format(round(results_summary_means[["max_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_60min"]], 2), nsmall = 2)),
# Max step accum 30min
paste0(format(round(results_summary_means[["max_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_30min"]], 2), nsmall = 2)),
# Max step accum 20min
paste0(format(round(results_summary_means[["max_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_20min"]], 2), nsmall = 2)),
# Max step accum 5min
paste0(format(round(results_summary_means[["max_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_5min"]], 2), nsmall = 2)),
# Max step accum 1min
paste0(format(round(results_summary_means[["max_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_1min"]], 2), nsmall = 2)),
# Peak step accum 60min
paste0(format(round(results_summary_means[["peak_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_60min"]], 2), nsmall = 2)),
# Peak step accum 30min
paste0(format(round(results_summary_means[["peak_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_30min"]], 2), nsmall = 2)),
# Peak step accum 20min
paste0(format(round(results_summary_means[["peak_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_20min"]], 2), nsmall = 2)),
# Peak step accum 5min
paste0(format(round(results_summary_means[["peak_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_5min"]], 2), nsmall = 2)),
# Peak step accum 1min
paste0(format(round(results_summary_means[["peak_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_1min"]], 2), nsmall = 2)),
# Gradient d'intensit\xc3\xa9
paste0(format(round(results_summary_means[["ig"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ig"]], 2), nsmall = 2)),
# M1/3
paste0(format(round(results_summary_means[["M1/3"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M1/3"]], 1), nsmall = 1)),
# M120
paste0(format(round(results_summary_means[["M120"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M120"]], 1), nsmall = 1)),
# M60
paste0(format(round(results_summary_means[["M60"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M60"]], 1), nsmall = 1)),
# M30
paste0(format(round(results_summary_means[["M30"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M30"]], 1), nsmall = 1)),
# M15
paste0(format(round(results_summary_means[["M15"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M15"]], 1), nsmall = 1)),
# M5
paste0(format(round(results_summary_means[["M5"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M5"]], 1), nsmall = 1))
)
) %>% dplyr::filter(Indicateur %in% c("Nombre de jours valides", selected_metrics))
) %>%
flextable::theme_zebra() %>%
flextable::align(align = "left", part = "all" ) %>%
flextable::width(width = 3.25)
return(flextable_summary)
}
}
pydemull/activAnalyzer documentation built on June 1, 2025, 1:09 p.m.
R Package Documentation
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Defines functions create_flextable_summary
Documented in create_flextable_summary
#' Create a formatted table of results
#'
#' The function generates a formatted table with both means and medians of the metrics obtained following the physical behavior measurement.
#'
#' @param results_summary_means A dataframe with mean results obtained using the \code{\link{prepare_dataset}}, \code{\link{mark_wear_time}}, \code{\link{mark_intensity}}, \code{\link{recap_by_day}}, and then the \code{\link{average_results}} functions.
#' @param results_summary_medians A dataframe with median results obtained using the \code{\link{prepare_dataset}}, \code{\link{mark_wear_time}}, \code{\link{mark_intensity}}, \code{\link{recap_by_day}}, and then the \code{\link{average_results}} functions.
#' @param language A character value for setting the language with which the figure should be created: `en` for english; `fr` for french.
#' @param metrics A character value for setting the metrics to be shown in the figure. "volume" refers to "activity volume" metrics, step_acc" refers
#' to "step accumulation" metrics, and "int_distri" refers to intensity distribution metrics. By default, the function provides all computed metrics.
#' @param epoch_label A character value to be pasted into the names of the variables to build the figure
#'
#' @return A flextable object
#' @export
#'
#' @examples
#' \donttest{
#' file <- system.file("extdata", "acc.agd", package = "activAnalyzer")
#' mydata <- prepare_dataset(data = file)
#' mydata_with_wear_marks <- mark_wear_time(
#' dataset = mydata,
#' TS = "TimeStamp",
#' cts = "vm",
#' frame = 90,
#' allowanceFrame = 2,
#' streamFrame = 30
#' )
#' 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",
#' )
#' summary_by_day <- recap_by_day(
#' data = mydata_with_intensity_marks,
#' age = 32,
#' weight = 67,
#' sex = "male",
#' valid_wear_time_start = "07:00:00",
#' valid_wear_time_end = "22:00:00"
#' )$df_all_metrics
#' results_summary_means <- average_results(
#' data = summary_by_day,
#' minimum_wear_time = 10,
#' fun = "mean"
#' )
#' results_summary_medians <- average_results(
#' data = summary_by_day,
#' minimum_wear_time = 10,
#' fun = "median"
#' )
#' create_flextable_summary(
#' results_summary_means,
#' results_summary_medians,
#' language = "en"
#' )
#' }
#'
create_flextable_summary <- function(
results_summary_means,
results_summary_medians,
language = c("en", "fr"),
metrics = c("all", "volume", "step_acc", "int_distri"),
epoch_label = "60s"
) {
# Get arguments
metrics <- match.arg(metrics)
language <- match.arg(language)
epoch_label = as.name(epoch_label)
flextable::set_flextable_defaults(fonts_ignore = TRUE)
# Create table for english language ===============================================================================
if (language == "en") {
# Set list of metrics to be used for filerting table of results
if (metrics == "all") {
selected_metrics <- c(
"Wear time (min)",
"Axis 1 total counts",
"VM total counts",
"Axis 1 mean (counts/min)",
"VM mean (counts/min)",
"SED time (min)",
"LPA time (min)",
"MPA time (min)",
"VPA time (min)",
"MVPA time (min)",
"SED wear time proportion (%)",
"LPA wear time proportion (%)",
"MPA wear time proportion (%)",
"VPA wear time proportion (%)",
"MVPA wear time proportion (%)",
"Ratio MVPA / SED",
"Total MVPA MET-hr",
"Total kcal",
"PAL",
"Total steps",
"Max step acc. 60 min (steps/min)",
"Max step acc. 30 min (steps/min)",
"Max step acc. 20 min (steps/min)",
"Max step acc. 5 min (steps/min)",
"Max step acc. 1 min (steps/min)",
"Peak step acc. 60 min (steps/min)",
"Peak step acc. 30 min (steps/min)",
"Peak step acc. 20 min (steps/min)",
"Peak step acc. 5 min (steps/min)",
"Peak step acc. 1 min (steps/min)",
"Intensity gradient",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
if (metrics == "volume") {
selected_metrics <- c(
"Wear time (min)",
"Axis 1 total counts",
"VM total counts",
"Axis 1 mean (counts/min)",
"VM mean (counts/min)",
"SED time (min)",
"LPA time (min)",
"MPA time (min)",
"VPA time (min)",
"MVPA time (min)",
"SED wear time proportion (%)",
"LPA wear time proportion (%)",
"MPA wear time proportion (%)",
"VPA wear time proportion (%)",
"MVPA wear time proportion (%)",
"Ratio MVPA / SED",
"Total MVPA MET-hr",
"Total kcal",
"PAL",
"Total steps"
)
}
if (metrics == "step_acc") {
selected_metrics <- c(
"Max step acc. 60 min (steps/min)",
"Max step acc. 30 min (steps/min)",
"Max step acc. 20 min (steps/min)",
"Max step acc. 5 min (steps/min)",
"Max step acc. 1 min (steps/min)",
"Peak step acc. 60 min (steps/min)",
"Peak step acc. 30 min (steps/min)",
"Peak step acc. 20 min (steps/min)",
"Peak step acc. 5 min (steps/min)",
"Peak step acc. 1 min (steps/min)"
)
}
if (metrics == "int_distri") {
selected_metrics <- c(
"Intensity gradient",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
flextable_summary <-
flextable::flextable(
tibble::tibble(
Metric = c("Number of valid days",
"Wear time (min)",
"Axis 1 total counts",
"VM total counts",
"Axis 1 mean (counts/min)",
"VM mean (counts/min)",
"SED time (min)",
"LPA time (min)",
"MPA time (min)",
"VPA time (min)",
"MVPA time (min)",
"SED wear time proportion (%)",
"LPA wear time proportion (%)",
"MPA wear time proportion (%)",
"VPA wear time proportion (%)",
"MVPA wear time proportion (%)",
"Ratio MVPA / SED",
"Total MVPA MET-hr",
"Total kcal",
"PAL",
"Total steps",
"Max step acc. 60 min (steps/min)",
"Max step acc. 30 min (steps/min)",
"Max step acc. 20 min (steps/min)",
"Max step acc. 5 min (steps/min)",
"Max step acc. 1 min (steps/min)",
"Peak step acc. 60 min (steps/min)",
"Peak step acc. 30 min (steps/min)",
"Peak step acc. 20 min (steps/min)",
"Peak step acc. 5 min (steps/min)",
"Peak step acc. 1 min (steps/min)",
"Intensity gradient",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
),
"Daily mean | median" = c(
# Number of valid days
paste0(results_summary_means[["valid_days"]]),
# Wear time
paste0(format(round(results_summary_means[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["wear_time"]]), ") | ",
format(round(results_summary_medians[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["wear_time"]]), ")"),
# Total counts Axis 1
paste0(format(round(results_summary_means[["total_counts_axis1"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_axis1"]], 1), nsmall = 1)),
# Total counts VM
paste0(format(round(results_summary_means[["total_counts_vm"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_vm"]], 1), nsmall = 1)),
# Axis 1 per min
paste0(format(round(results_summary_means[["axis1_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["axis1_per_min"]], 1), nsmall = 1)),
# VM per min
paste0(format(round(results_summary_means[["vm_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["vm_per_min"]], 1), nsmall = 1)),
# SED time
paste0(format(round(results_summary_means[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_SED"]]), ") | ",
format(round(results_summary_medians[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_SED"]]), ")"),
# LPA time
paste0(format(round(results_summary_means[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_LPA"]]), ") | ",
format(round(results_summary_medians[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_LPA"]]), ")"),
# MPA time
paste0(format(round(results_summary_means[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MPA"]]), ")"),
# VPA time
paste0(format(round(results_summary_means[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_VPA"]]), ") | ",
format(round(results_summary_medians[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_VPA"]]), ")"),
# MVPA time
paste0(format(round(results_summary_means[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MVPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MVPA"]]), ")"),
# Percent time SED
paste0(format(round(results_summary_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_SED"]], 1), nsmall = 1)),
# Percent time LPA
paste0(format(round(results_summary_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_LPA"]], 1), nsmall = 1)),
# Percent time MPA
paste0(format(round(results_summary_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MPA"]], 1), nsmall = 1)),
# Percent time VPA
paste0(format(round(results_summary_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_VPA"]], 1), nsmall = 1)),
# Percent time MVPA
paste0(format(round(results_summary_means[["percent_MVPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MVPA"]], 1), nsmall = 1)),
# Ratio MVPA/SED
paste0(format(round(results_summary_means[["ratio_mvpa_sed"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ratio_mvpa_sed"]], 2), nsmall = 2)),
# MET-hr MVPA
paste0(format(round(results_summary_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["mets_hours_mvpa"]], 2), nsmall = 2)),
# Total kilocalories
paste0(format(round(results_summary_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["total_kcal"]], 2), nsmall = 2)),
# Physical activity level (PAL)
paste0(format(round(results_summary_means[["pal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["pal"]], 2), nsmall = 2)),
# Total number of steps
paste0(round(results_summary_means[["total_steps"]], 0), " | ", round(results_summary_medians[["total_steps"]], 0)),
# Max step accum 60min
paste0(format(round(results_summary_means[["max_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_60min"]], 2), nsmall = 2)),
# Max step accum 30min
paste0(format(round(results_summary_means[["max_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_30min"]], 2), nsmall = 2)),
# Max step accum 20min
paste0(format(round(results_summary_means[["max_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_20min"]], 2), nsmall = 2)),
# Max step accum 5min
paste0(format(round(results_summary_means[["max_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_5min"]], 2), nsmall = 2)),
# Max step accum 1min
paste0(format(round(results_summary_means[["max_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_1min"]], 2), nsmall = 2)),
# Peak step accum 60min
paste0(format(round(results_summary_means[["peak_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_60min"]], 2), nsmall = 2)),
# Peak step accum 30min
paste0(format(round(results_summary_means[["peak_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_30min"]], 2), nsmall = 2)),
# Peak step accum 20min
paste0(format(round(results_summary_means[["peak_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_20min"]], 2), nsmall = 2)),
# Peak step accum 5min
paste0(format(round(results_summary_means[["peak_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_5min"]], 2), nsmall = 2)),
# Peak step accum 1min
paste0(format(round(results_summary_means[["peak_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_1min"]], 2), nsmall = 2)),
# Intensity gradient
paste0(format(round(results_summary_means[["ig"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ig"]], 2), nsmall = 2)),
# M1/3
paste0(format(round(results_summary_means[["M1/3"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M1/3"]], 1), nsmall = 1)),
# M120
paste0(format(round(results_summary_means[["M120"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M120"]], 1), nsmall = 1)),
# M60
paste0(format(round(results_summary_means[["M60"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M60"]], 1), nsmall = 1)),
# M30
paste0(format(round(results_summary_means[["M30"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M30"]], 1), nsmall = 1)),
# M15
paste0(format(round(results_summary_means[["M15"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M15"]], 1), nsmall = 1)),
# M5
paste0(format(round(results_summary_means[["M5"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M5"]], 1), nsmall = 1))
)
) %>% dplyr::filter(Metric %in% c("Number of valid days", selected_metrics))
) %>%
flextable::theme_zebra() %>%
flextable::align(align = "left", part = "all" ) %>%
flextable::width(width = 3.25)
return(flextable_summary)
}
# Create table for french language ===============================================================================
if (language == "fr") {
# Set list of metrics to be used for filerting table of results
if (metrics == "all") {
selected_metrics <- c(
"Temps de port (min)",
"Total counts Axe 1 ",
"Total counts VM",
"Moyenne Axe 1 (counts/min)",
"Moyenne VM (counts/min)",
"Temps SED (min)",
"Temps LPA (min)",
"Temps MPA (min)",
"Temps VPA (min)",
"Temps MVPA (min)",
"Proportion du temps de port SED (%)",
"Proportion du temps de port LPA (%)",
"Proportion du temps de port MPA (%)",
"Proportion du temps de port VPA (%)",
"Proportion du temps de port MVPA (%)",
"Ratio MVPA / SED",
"Total MET-hr",
"Total kcal",
"NAP",
"Total pas",
"Acc. pas max. 60 min (pas/min)",
"Acc. pas max. 30 min (pas/min)",
"Acc. pas max. 20 min (pas/min)",
"Acc. pas max. 5 min (pas/min)",
"Acc. pas max. 1 min (pas/min)",
"Acc. pas pic 60 min (pas/min)",
"Acc. pas pic 30 min (pas/min)",
"Acc. pas pic 20 min (pas/min)",
"Acc. pas pic 5 min (pas/min)",
"Acc. pas pic 1 min (pas/min)",
"Gradient d'intensit\xc3\xa9",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
if (metrics == "volume") {
selected_metrics <- c(
"Temps de port (min)",
"Total counts Axe 1 ",
"Total counts VM",
"Moyenne Axe 1 (counts/min)",
"Moyenne VM (counts/min)",
"Temps SED (min)",
"Temps LPA (min)",
"Temps MPA (min)",
"Temps VPA (min)",
"Temps MVPA (min)",
"Proportion du temps de port SED (%)",
"Proportion du temps de port LPA (%)",
"Proportion du temps de port MPA (%)",
"Proportion du temps de port VPA (%)",
"Proportion du temps de port MVPA (%)",
"Ratio MVPA / SED",
"Total MET-hr",
"Total kcal",
"NAP",
"Total pas"
)
}
if (metrics == "step_acc") {
selected_metrics <- c(
"Acc. pas max. 60 min (pas/min)",
"Acc. pas max. 30 min (pas/min)",
"Acc. pas max. 20 min (pas/min)",
"Acc. pas max. 5 min (pas/min)",
"Acc. pas max. 1 min (pas/min)",
"Acc. pas pic 60 min (pas/min)",
"Acc. pas pic 30 min (pas/min)",
"Acc. pas pic 20 min (pas/min)",
"Acc. pas pic 5 min (pas/min)",
"Acc. pas pic 1 min (pas/min)"
)
}
if (metrics == "int_distri") {
selected_metrics <- c(
"Gradient d'intensit\xc3\xa9",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
)
}
flextable_summary <-
flextable::flextable(
tibble::tibble(
Indicateur = c(
"Nombre de jours valides",
"Temps de port (min)",
"Total counts Axe 1 ",
"Total counts VM",
"Moyenne Axe 1 (counts/min)",
"Moyenne VM (counts/min)",
"Temps SED (min)",
"Temps LPA (min)",
"Temps MPA (min)",
"Temps VPA (min)",
"Temps MVPA (min)",
"Proportion du temps de port SED (%)",
"Proportion du temps de port LPA (%)",
"Proportion du temps de port MPA (%)",
"Proportion du temps de port VPA (%)",
"Proportion du temps de port MVPA (%)",
"Ratio MVPA / SED",
"Total MET-hr",
"Total kcal",
"NAP",
"Total pas",
"Acc. pas max. 60 min (pas/min)",
"Acc. pas max. 30 min (pas/min)",
"Acc. pas max. 20 min (pas/min)",
"Acc. pas max. 5 min (pas/min)",
"Acc. pas max. 1 min (pas/min)",
"Acc. pas pic 60 min (pas/min)",
"Acc. pas pic 30 min (pas/min)",
"Acc. pas pic 20 min (pas/min)",
"Acc. pas pic 5 min (pas/min)",
"Acc. pas pic 1 min (pas/min)",
"Gradient d'intensit\xc3\xa9",
paste0("M1/3", " (counts/", epoch_label, ")"),
paste0("M120", " (counts/", epoch_label, ")"),
paste0("M60", " (counts/", epoch_label, ")"),
paste0("M30", " (counts/", epoch_label, ")"),
paste0("M15", " (counts/", epoch_label, ")"),
paste0("M5", " (counts/", epoch_label, ")")
),
"Moyenne | m\xc3\xa9diane journali\xc3\xa8re obtenue \xc3\xa0 partir des jours valides" = c(
# Nombre de jours valides
paste0(results_summary_means[["valid_days"]]),
# Temps de port
paste0(format(round(results_summary_means[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["wear_time"]]), ") | ",
format(round(results_summary_medians[["wear_time"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["wear_time"]]), ")"),
# Total counts Axis 1
paste0(format(round(results_summary_means[["total_counts_axis1"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_axis1"]], 1), nsmall = 1)),
# Total counts VM
paste0(format(round(results_summary_means[["total_counts_vm"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["total_counts_vm"]], 1), nsmall = 1)),
# Axis 1 par min
paste0(format(round(results_summary_means[["axis1_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["axis1_per_min"]], 1), nsmall = 1)),
# VM par min
paste0(format(round(results_summary_means[["vm_per_min"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["vm_per_min"]], 1), nsmall = 1)),
# Minutes SED
paste0(format(round(results_summary_means[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_SED"]]), ") | ",
format(round(results_summary_medians[["minutes_SED"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_SED"]]), ")"),
# Minutes LPA
paste0(format(round(results_summary_means[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_LPA"]]), ") | ",
format(round(results_summary_medians[["minutes_LPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_LPA"]]), ")"),
# Minutes MPA
paste0(format(round(results_summary_means[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MPA"]]), ")"),
# Minutes VPA
paste0(format(round(results_summary_means[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_VPA"]]), ") | ",
format(round(results_summary_medians[["minutes_VPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_VPA"]]), ")"),
# Minutes MVPA
paste0(format(round(results_summary_means[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_means[["minutes_MVPA"]]), ") | ",
format(round(results_summary_medians[["minutes_MVPA"]], 1), nsmall = 1), " (", hms::hms(minutes = results_summary_medians[["minutes_MVPA"]]), ")"),
# % SED
paste0(format(round(results_summary_means[["percent_SED"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_SED"]], 1), nsmall = 1)),
# % LPA
paste0(format(round(results_summary_means[["percent_LPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_LPA"]], 1), nsmall = 1)),
# % MPA
paste0(format(round(results_summary_means[["percent_MPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MPA"]], 1), nsmall = 1)),
# % VPA
paste0(format(round(results_summary_means[["percent_VPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_VPA"]], 1), nsmall = 1)),
# % MVPA
paste0(format(round(results_summary_means[["percent_MVPA"]], 1), nsmall = 1), " | ", format(round(results_summary_medians[["percent_MVPA"]], 1), nsmall = 1)),
# Ratio MVPA/SED
paste0(format(round(results_summary_means[["ratio_mvpa_sed"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ratio_mvpa_sed"]], 2), nsmall = 2)),
# Total METs-hr
paste0(format(round(results_summary_means[["mets_hours_mvpa"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["mets_hours_mvpa"]], 2), nsmall = 2)),
# Total kcal
paste0(format(round(results_summary_means[["total_kcal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["total_kcal"]], 2), nsmall = 2)),
# NAP
paste0(format(round(results_summary_means[["pal"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["pal"]], 2), nsmall = 2)),
# Total pas
paste0(round(results_summary_means[["total_steps"]], 0), " | ", round(results_summary_medians[["total_steps"]], 0)),
# Max step accum 60min
paste0(format(round(results_summary_means[["max_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_60min"]], 2), nsmall = 2)),
# Max step accum 30min
paste0(format(round(results_summary_means[["max_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_30min"]], 2), nsmall = 2)),
# Max step accum 20min
paste0(format(round(results_summary_means[["max_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_20min"]], 2), nsmall = 2)),
# Max step accum 5min
paste0(format(round(results_summary_means[["max_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_5min"]], 2), nsmall = 2)),
# Max step accum 1min
paste0(format(round(results_summary_means[["max_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["max_steps_1min"]], 2), nsmall = 2)),
# Peak step accum 60min
paste0(format(round(results_summary_means[["peak_steps_60min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_60min"]], 2), nsmall = 2)),
# Peak step accum 30min
paste0(format(round(results_summary_means[["peak_steps_30min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_30min"]], 2), nsmall = 2)),
# Peak step accum 20min
paste0(format(round(results_summary_means[["peak_steps_20min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_20min"]], 2), nsmall = 2)),
# Peak step accum 5min
paste0(format(round(results_summary_means[["peak_steps_5min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_5min"]], 2), nsmall = 2)),
# Peak step accum 1min
paste0(format(round(results_summary_means[["peak_steps_1min"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["peak_steps_1min"]], 2), nsmall = 2)),
# Gradient d'intensit\xc3\xa9
paste0(format(round(results_summary_means[["ig"]], 2), nsmall = 2), " | ", format(round(results_summary_medians[["ig"]], 2), nsmall = 2)),
# M1/3
paste0(format(round(results_summary_means[["M1/3"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M1/3"]], 1), nsmall = 1)),
# M120
paste0(format(round(results_summary_means[["M120"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M120"]], 1), nsmall = 1)),
# M60
paste0(format(round(results_summary_means[["M60"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M60"]], 1), nsmall = 1)),
# M30
paste0(format(round(results_summary_means[["M30"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M30"]], 1), nsmall = 1)),
# M15
paste0(format(round(results_summary_means[["M15"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M15"]], 1), nsmall = 1)),
# M5
paste0(format(round(results_summary_means[["M5"]], 1), nsmall = 1), " | ",
format(round(results_summary_medians[["M5"]], 1), nsmall = 1))
)
) %>% dplyr::filter(Indicateur %in% c("Nombre de jours valides", selected_metrics))
) %>%
flextable::theme_zebra() %>%
flextable::align(align = "left", part = "all" ) %>%
flextable::width(width = 3.25)
return(flextable_summary)
}
}
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