R/create_fig_res_by_day.R
In pydemull/activAnalyzer: A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical Behavior Data
Defines functions
create_fig_res_by_day
Documented in
create_fig_res_by_day
#' Create a figure with metrics shown for each day
#'
#' The function generates a figure with several common metrics shown for each day of the physical behavior measurement.
#'
#' @param data A dataframe with results obtained using the \code{\link{prepare_dataset}}, \code{\link{mark_wear_time}}, \code{\link{mark_intensity}}, and then the \code{\link{recap_by_day}} functions.
#' @param minimum_wear_time_for_analysis A numeric value to indicate the minimum number of hours of wear time that was considered to validate a day.
#' @param start_day_analysis A character value to indicate the start of the period that was considered to validate a day based on wear time.
#' @param end_day_analysis A character value to indicate the end of the period that was considered to validate a day based on wear time.
#' @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 ggplot 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
#' create_fig_res_by_day(summary_by_day,
#' minimum_wear_time_for_analysis = 10,
#' start_day_analysis = "00:00:00",
#' end_day_analysis = "23:59:00",
#' language = "en")
#' }
#'
create_fig_res_by_day <- function(
data,
minimum_wear_time_for_analysis = 10,
start_day_analysis = "00:00:00",
end_day_analysis = "23:59:00",
language = c("en", "fr", "de"),
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)
# Set text sizes
strip_text = 10
title_text_size = 10
label_text_size = 3
# Create figure for english language ========================================================================
if (language == "en") {
# Set list of metrics and parameters to be used for filtering table of results and creating the figure
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, ")")
)
fig_title <- "All metrics"
n_col = 5
}
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"
)
fig_title <- "Activity volume metrics"
n_col = 5
}
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)"
)
fig_title <- "Step-accumulation metrics"
n_col = 5
}
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, ")")
)
fig_title <- "Intensity distribution metrics"
n_col = 3
}
# Make figure
plot <-
data %>%
dplyr::mutate(date = as.factor(date),
validity = ifelse(wear_time >= minimum_wear_time_for_analysis * 60, "Valid day", "Non valid day"),
validity = forcats::fct_relevel(validity, "Valid day", "Non valid day")) %>%
tidyr::pivot_longer(cols = c(
wear_time,
total_counts_axis1,
total_counts_vm,
axis1_per_min,
vm_per_min,
minutes_SED,
minutes_LPA,
minutes_MPA,
minutes_VPA,
minutes_MVPA,
percent_SED,
percent_LPA,
percent_MPA,
percent_VPA,
percent_MVPA,
ratio_mvpa_sed,
mets_hours_mvpa,
total_kcal,
pal,
total_steps,
max_steps_60min,
max_steps_30min,
max_steps_20min,
max_steps_5min,
max_steps_1min,
peak_steps_60min,
peak_steps_30min,
peak_steps_20min,
peak_steps_5min,
peak_steps_1min,
ig,
"M1/3",
M120,
M60,
M30,
M15,
M5
),
names_to = "variable", values_to = "value") %>%
dplyr::mutate(variable = forcats::fct_relevel(
variable,
"wear_time",
"total_counts_axis1",
"total_counts_vm",
"axis1_per_min",
"vm_per_min",
"minutes_SED",
"minutes_LPA",
"minutes_MPA",
"minutes_VPA",
"minutes_MVPA",
"percent_SED",
"percent_LPA",
"percent_MPA",
"percent_VPA",
"percent_MVPA",
"ratio_mvpa_sed",
"mets_hours_mvpa",
"total_kcal",
"pal",
"total_steps",
"max_steps_60min",
"max_steps_30min",
"max_steps_20min",
"max_steps_5min",
"max_steps_1min",
"peak_steps_60min",
"peak_steps_30min",
"peak_steps_20min",
"peak_steps_5min",
"peak_steps_1min",
"ig",
"M1/3",
"M120",
"M60",
"M30",
"M15",
"M5"
),
variable = forcats::fct_recode(
variable,
"Wear time (min)" = "wear_time",
"Axis 1 total counts"= "total_counts_axis1",
"VM total counts" = "total_counts_vm",
"Axis 1 mean (counts/min)" = "axis1_per_min",
"VM mean (counts/min)" = "vm_per_min",
"SED time (min)" = "minutes_SED",
"LPA time (min)" = "minutes_LPA",
"MPA time (min)" = "minutes_MPA",
"VPA time (min)" = "minutes_VPA",
"MVPA time (min)" = "minutes_MVPA",
"SED wear time proportion (%)" = "percent_SED",
"LPA wear time proportion (%)" = "percent_LPA",
"MPA wear time proportion (%)" = "percent_MPA",
"VPA wear time proportion (%)" = "percent_VPA",
"MVPA wear time proportion (%)" = "percent_MVPA",
"Ratio MVPA / SED" = "ratio_mvpa_sed",
"Total MVPA MET-hr" = "mets_hours_mvpa",
"Total kcal" = "total_kcal",
"PAL" = "pal",
"Total steps" = "total_steps",
"Max step acc. 60 min (steps/min)" = "max_steps_60min",
"Max step acc. 30 min (steps/min)" ="max_steps_30min",
"Max step acc. 20 min (steps/min)" ="max_steps_20min",
"Max step acc. 5 min (steps/min)" ="max_steps_5min",
"Max step acc. 1 min (steps/min)" ="max_steps_1min",
"Peak step acc. 60 min (steps/min)" ="peak_steps_60min",
"Peak step acc. 30 min (steps/min)" ="peak_steps_30min",
"Peak step acc. 20 min (steps/min)" ="peak_steps_20min",
"Peak step acc. 5 min (steps/min)" ="peak_steps_5min",
"Peak step acc. 1 min (steps/min)" ="peak_steps_1min",
"Intensity gradient" = "ig",
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := "M120",
"M60 (counts/{{epoch_label}})" := "M60",
"M30 (counts/{{epoch_label}})" := "M30",
"M15 (counts/{{epoch_label}})" := "M15",
"M5 (counts/{{epoch_label}})" := "M5"
)
) %>%
dplyr::filter(variable %in% selected_metrics) %>%
dplyr::mutate(value_maj = ifelse(is.na(value), 0, value)) %>%
ggplot(aes(x = date, y = value_maj, fill = validity, color = validity)) +
ggtitle(fig_title) +
geom_bar(stat = "identity") +
geom_point(size = 2, color = "red") +
geom_line(aes(group = 1), linewidth = 0.7, color = "red") +
geom_text(aes(label = ifelse(is.na(value), "NA", round(value, 1))), size = label_text_size, vjust = -0.6, color = "black") +
labs(x = "Date", y = "", fill = "") +
scale_y_continuous(expand = expansion(mult = c(.05, .2))) +
scale_fill_manual(labels = c("Valid day",
paste0("Non-valid day (<",
minimum_wear_time_for_analysis,
" hours between ", start_day_analysis,
" and ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
scale_color_manual(labels = c("Valid day",
paste0("Non-valid day (<",
minimum_wear_time_for_analysis,
" hours between ", start_day_analysis,
" and ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
theme_bw() +
theme(legend.position = "bottom",
legend.text = element_text(size = 15),
legend.background = element_rect(fill = "beige", colour = "beige"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = title_text_size, angle = 90, hjust = 1, vjust = 1.1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "beige", color = "beige"),
plot.margin = margin(1, 1, 0.5, 1, "cm"),
strip.text.x = element_text(size = strip_text),
plot.title = element_text(size = 15, color = "grey30", face = "bold")) +
facet_wrap(. ~ variable, scales = "free_y", ncol = n_col) +
guides(color = "none")
# Return plot
return(plot)
}
# Create figure for french language ===============================================================================
if (language == "fr") {
# Set list of metrics and parameters to be used for filtering table of results and creating the figure
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, ")")
)
fig_title <- "Tous les indicateurs"
n_col = 5
}
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"
)
fig_title <- "Indicteurs de volume d'activit\xc3\xa9"
n_col = 5
}
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)"
)
fig_title <- "Indicateurs d'accumulation de pas"
n_col = 5
}
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, ")")
)
fig_title <- "Indicateurs de distribution de l'intensit\xc3\xa9"
n_col = 3
}
# Make figure
plot <-
data %>%
dplyr::mutate(date = as.factor(date),
validity = ifelse(wear_time >= minimum_wear_time_for_analysis * 60, "Valid day", "Non valid day"),
validity = forcats::fct_relevel(validity, "Valid day", "Non valid day")) %>%
tidyr::pivot_longer(cols = c(
wear_time,
total_counts_axis1,
total_counts_vm,
axis1_per_min,
vm_per_min,
minutes_SED,
minutes_LPA,
minutes_MPA,
minutes_VPA,
minutes_MVPA,
percent_SED,
percent_LPA,
percent_MPA,
percent_VPA,
percent_MVPA,
ratio_mvpa_sed,
mets_hours_mvpa,
total_kcal,
pal,
total_steps,
max_steps_60min,
max_steps_30min,
max_steps_20min,
max_steps_5min,
max_steps_1min,
peak_steps_60min,
peak_steps_30min,
peak_steps_20min,
peak_steps_5min,
peak_steps_1min,
ig,
"M1/3",
M120,
M60,
M30,
M15,
M5
),
names_to = "variable", values_to = "value") %>%
dplyr::mutate(variable = forcats::fct_relevel(
variable,
"wear_time",
"total_counts_axis1",
"total_counts_vm",
"axis1_per_min",
"vm_per_min",
"minutes_SED",
"minutes_LPA",
"minutes_MPA",
"minutes_VPA",
"minutes_MVPA",
"percent_SED",
"percent_LPA",
"percent_MPA",
"percent_VPA",
"percent_MVPA",
"ratio_mvpa_sed",
"mets_hours_mvpa",
"total_kcal",
"pal",
"total_steps",
"max_steps_60min",
"max_steps_30min",
"max_steps_20min",
"max_steps_5min",
"max_steps_1min",
"peak_steps_60min",
"peak_steps_30min",
"peak_steps_20min",
"peak_steps_5min",
"peak_steps_1min",
"ig",
"M1/3",
"M120",
"M60",
"M30",
"M15",
"M5"
),
variable = forcats::fct_recode(
variable,
"Temps de port (min)" = "wear_time",
"Total counts Axe 1 "= "total_counts_axis1",
"Total counts VM" = "total_counts_vm",
"Moyenne Axe 1 (counts/min)" = "axis1_per_min",
"Moyenne VM (counts/min)" = "vm_per_min",
"Temps SED (min)" = "minutes_SED",
"Temps LPA (min)" = "minutes_LPA",
"Temps MPA (min)" = "minutes_MPA",
"Temps VPA (min)" = "minutes_VPA",
"Temps MVPA (min)" = "minutes_MVPA",
"Proportion du temps de port SED (%)" = "percent_SED",
"Proportion du temps de port LPA (%)" = "percent_LPA",
"Proportion du temps de port MPA (%)" = "percent_MPA",
"Proportion du temps de port VPA (%)" = "percent_VPA",
"Proportion du temps de port MVPA (%)" = "percent_MVPA",
"Ratio MVPA / SED" = "ratio_mvpa_sed",
"Total MET-hr" = "mets_hours_mvpa",
"Total kcal" = "total_kcal",
"NAP" = "pal",
"Total pas" = "total_steps",
"Acc. pas max. 60 min (pas/min)" = "max_steps_60min",
"Acc. pas max. 30 min (pas/min)" ="max_steps_30min",
"Acc. pas max. 20 min (pas/min)" ="max_steps_20min",
"Acc. pas max. 5 min (pas/min)" ="max_steps_5min",
"Acc. pas max. 1 min (pas/min)" ="max_steps_1min",
"Acc. pas pic 60 min (pas/min)" ="peak_steps_60min",
"Acc. pas pic 30 min (pas/min)" ="peak_steps_30min",
"Acc. pas pic 20 min (pas/min)" ="peak_steps_20min",
"Acc. pas pic 5 min (pas/min)" ="peak_steps_5min",
"Acc. pas pic 1 min (pas/min)" ="peak_steps_1min",
"Gradient d'intensit\xc3\xa9" = "ig",
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := "M120",
"M60 (counts/{{epoch_label}})" := "M60",
"M30 (counts/{{epoch_label}})" := "M30",
"M15 (counts/{{epoch_label}})" := "M15",
"M5 (counts/{{epoch_label}})" := "M5"
)
) %>%
dplyr::filter(variable %in% selected_metrics) %>%
dplyr::mutate(value_maj = ifelse(is.na(value), 0, value)) %>%
ggplot(aes(x = date, y = value_maj, fill = validity, color = validity)) +
ggtitle(fig_title) +
geom_bar(stat = "identity") +
geom_point(size = 2, color = "red") +
geom_line(aes(group = 1), linewidth = 0.7, color = "red") +
geom_text(aes(label = ifelse(is.na(value), "NA", round(value, 1))), size = label_text_size, vjust = -0.6, color = "black") +
labs(x = "Date", y = "", fill = "") +
scale_y_continuous(expand = expansion(mult = c(.05, .2))) +
scale_fill_manual(labels = c("Jour valide",
paste0("Jour non valide (<",
minimum_wear_time_for_analysis,
" heures entre ", start_day_analysis,
" et ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
scale_color_manual(labels = c("Jour valide",
paste0("Jour non valide(<",
minimum_wear_time_for_analysis,
" heures entre ", start_day_analysis,
" et ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
theme_bw() +
theme(legend.position = "bottom",
legend.text = element_text(size = 15),
legend.background = element_rect(fill = "beige", colour = "beige"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = title_text_size, angle = 90, hjust = 1, vjust = 1.1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "beige", color = "beige"),
plot.margin = margin(1, 1, 0.5, 1, "cm"),
strip.text.x = element_text(size = strip_text),
plot.title = element_text(size = 15, color = "grey30", face = "bold")) +
facet_wrap(. ~ variable, scales = "free_y", ncol = n_col) +
guides(color = "none")
# Return plot
return(plot)
}
# Create figure for german language ========================================================================
if (language == "de") {
# Set list of metrics and parameters to be used for filtering table of results and creating the figure
if (metrics == "all") {
selected_metrics <- c(
"Tragezeit (min)",
"Achse 1 Gesamtanzahl Messungen",
"VM Gesamtanzahl Messungens",
"Mittelwert Achse 1 (Messungen/min)",
"VM-Mittelwert (Messungen /min)",
"SED-Zeit (min)",
"LPA-Zeit (min)",
"MPA-Zeit (min)",
"VPA-Zeit (min)",
"MVPA-Zeit (min)",
"SED-Tragezeitanteil (%)",
"LPA-Tragezeitanteil (%)",
"MPA-Tragezeitanteil (%)",
"VPA-Tragezeitanteil (%)",
"MVPA-Tragezeitanteil (%)",
"Verh\xc3\xa4ltnis MVPA/SED",
"Gesamt-MVPA-MET-Stunden",
"Gesamt-kcal",
"PAL",
"Schritte insgesamt",
"Max. Schritte akk. 60 min (Schritte/min)",
"Max. Schritte akk. 30 min (Schritte/min)",
"Max. Schritte akk. 20 min (Schritte/min)",
"Max. Schritte akk. 5 min (Schritte/min)",
"Max. Schritte akk. 1 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 60 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 30 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 20 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 5 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 1 min (Schritte/min)",
"Intensit\xc3\xa4tsgradient",
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, ")")
)
fig_title <- "All metrics"
n_col = 5
}
if (metrics == "volume") {
selected_metrics <- c(
"Tragezeit (min)",
"Achse 1 Gesamtanzahl Messungen",
"VM Gesamtanzahl Messungens",
"Mittelwert Achse 1 (Messungen/min)",
"VM-Mittelwert (Messungen /min)",
"SED-Zeit (min)",
"LPA-Zeit (min)",
"MPA-Zeit (min)",
"VPA-Zeit (min)",
"MVPA-Zeit (min)",
"SED-Tragezeitanteil (%)",
"LPA-Tragezeitanteil (%)",
"MPA-Tragezeitanteil (%)",
"VPA-Tragezeitanteil (%)",
"MVPA-Tragezeitanteil (%)",
"Verh\xc3\xa4ltnis MVPA/SED",
"Gesamt-MVPA-MET-Stunden",
"Gesamt-kcal",
"PAL",
"Schritte insgesamt"
)
fig_title <- "Statistiken zum Aktivit\xc3\xa4tsvolumen"
n_col = 5
}
if (metrics == "step_acc") {
selected_metrics <- c(
"Max. Schritte akk. 60 min (Schritte/min)",
"Max. Schritte akk. 30 min (Schritte/min)",
"Max. Schritte akk. 20 min (Schritte/min)",
"Max. Schritte akk. 5 min (Schritte/min)",
"Max. Schritte akk. 1 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 60 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 30 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 20 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 5 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 1 min (Schritte/min)"
)
fig_title <- "Metriken zur Schrittzahl (akkumuliert)"
n_col = 5
}
if (metrics == "int_distri") {
selected_metrics <- c(
"Intensit\xc3\xa4tsgradient",
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, ")")
)
fig_title <- "Metriken der Intensit\xc3\xa4tsverteilung"
n_col = 3
}
# Make figure
plot <-
data %>%
dplyr::mutate(date = as.factor(date),
validity = ifelse(wear_time >= minimum_wear_time_for_analysis * 60, "Valid day", "Non valid day"),
validity = forcats::fct_relevel(validity, "Valid day", "Non valid day")) %>%
tidyr::pivot_longer(cols = c(
wear_time,
total_counts_axis1,
total_counts_vm,
axis1_per_min,
vm_per_min,
minutes_SED,
minutes_LPA,
minutes_MPA,
minutes_VPA,
minutes_MVPA,
percent_SED,
percent_LPA,
percent_MPA,
percent_VPA,
percent_MVPA,
ratio_mvpa_sed,
mets_hours_mvpa,
total_kcal,
pal,
total_steps,
max_steps_60min,
max_steps_30min,
max_steps_20min,
max_steps_5min,
max_steps_1min,
peak_steps_60min,
peak_steps_30min,
peak_steps_20min,
peak_steps_5min,
peak_steps_1min,
ig,
"M1/3",
M120,
M60,
M30,
M15,
M5
),
names_to = "variable", values_to = "value") %>%
dplyr::mutate(variable = forcats::fct_relevel(
variable,
"wear_time",
"total_counts_axis1",
"total_counts_vm",
"axis1_per_min",
"vm_per_min",
"minutes_SED",
"minutes_LPA",
"minutes_MPA",
"minutes_VPA",
"minutes_MVPA",
"percent_SED",
"percent_LPA",
"percent_MPA",
"percent_VPA",
"percent_MVPA",
"ratio_mvpa_sed",
"mets_hours_mvpa",
"total_kcal",
"pal",
"total_steps",
"max_steps_60min",
"max_steps_30min",
"max_steps_20min",
"max_steps_5min",
"max_steps_1min",
"peak_steps_60min",
"peak_steps_30min",
"peak_steps_20min",
"peak_steps_5min",
"peak_steps_1min",
"ig",
"M1/3",
"M120",
"M60",
"M30",
"M15",
"M5"
),
variable = forcats::fct_recode(
variable,
"Tragezeit (min)" = "wear_time",
"Achse 1 Gesamtanzahl Messungen" = "total_counts_axis1",
"VM Gesamtanzahl Messungens" = "total_counts_vm",
"Mittelwert Achse 1 (Messungen/min)" = "axis1_per_min",
"VM-Mittelwert (Messungen /min)" = "vm_per_min",
"SED-Zeit (min)" = "minutes_SED",
"LPA-Zeit (min)" = "minutes_LPA",
"MPA-Zeit (min)" = "minutes_MPA",
"VPA-Zeit (min)" = "minutes_VPA",
"MVPA-Zeit (min)" = "minutes_MVPA",
"SED-Tragezeitanteil (%)" = "percent_SED",
"LPA-Tragezeitanteil (%)" = "percent_LPA",
"MPA-Tragezeitanteil (%)" = "percent_MPA",
"VPA-Tragezeitanteil (%)" = "percent_VPA",
"MVPA-Tragezeitanteil (%)" = "percent_MVPA",
"Verh\xc3\xa4ltnis MVPA/SED" = "ratio_mvpa_sed",
"Gesamt-MVPA-MET-Stunden" = "mets_hours_mvpa",
"Gesamt-kcal" = "total_kcal",
"PAL" = "pal",
"Schritte insgesamt" = "total_steps",
"Max. Schritte akk. 60 min (Schritte/min)" = "max_steps_60min",
"Max. Schritte akk. 30 min (Schritte/min)" ="max_steps_30min",
"Max. Schritte akk. 20 min (Schritte/min)" ="max_steps_20min",
"Max. Schritte akk. 5 min (Schritte/min)" ="max_steps_5min",
"Max. Schritte akk. 1 min (Schritte/min)" ="max_steps_1min",
"H\xc3\xb6chstanzahl Schritte akk. 60 min (Schritte/min)" ="peak_steps_60min",
"H\xc3\xb6chstanzahl Schritte akk. 30 min (Schritte/min)" ="peak_steps_30min",
"H\xc3\xb6chstanzahl Schritte akk. 20 min (Schritte/min)" ="peak_steps_20min",
"H\xc3\xb6chstanzahl Schritte akk. 5 min (Schritte/min)" ="peak_steps_5min",
"H\xc3\xb6chstanzahl Schritte akk. 1 min (Schritte/min)" ="peak_steps_1min",
"Intensit\xc3\xa4tsgradient" = "ig",
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := "M120",
"M60 (counts/{{epoch_label}})" := "M60",
"M30 (counts/{{epoch_label}})" := "M30",
"M15 (counts/{{epoch_label}})" := "M15",
"M5 (counts/{{epoch_label}})" := "M5"
)
) %>%
dplyr::filter(variable %in% selected_metrics) %>%
dplyr::mutate(value_maj = ifelse(is.na(value), 0, value)) %>%
ggplot(aes(x = date, y = value_maj, fill = validity, color = validity)) +
ggtitle(fig_title) +
geom_bar(stat = "identity") +
geom_point(size = 2, color = "red") +
geom_line(aes(group = 1), linewidth = 0.7, color = "red") +
geom_text(aes(label = ifelse(is.na(value), "NA", round(value, 1))), size = label_text_size, vjust = -0.6, color = "black") +
labs(x = "Date", y = "", fill = "") +
scale_y_continuous(expand = expansion(mult = c(.05, .2))) +
scale_fill_manual(labels = c("G\xc3\xbcltiger Tag",
paste0("Nicht-g\xc3\xbcltiger Tag (<",
minimum_wear_time_for_analysis,
" Stunden zwischen ", start_day_analysis,
" und ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
scale_color_manual(labels = c("G\xc3\xbcltiger Tag",
paste0("Nicht-g\xc3\xbcltiger Tag (<",
minimum_wear_time_for_analysis,
" Stunden zwischen ", start_day_analysis,
" und ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
theme_bw() +
theme(legend.position = "bottom",
legend.text = element_text(size = 15),
legend.background = element_rect(fill = "beige", colour = "beige"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = title_text_size, angle = 90, hjust = 1, vjust = 1.1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "beige", color = "beige"),
plot.margin = margin(1, 1, 0.5, 1, "cm"),
strip.text.x = element_text(size = strip_text),
plot.title = element_text(size = 15, color = "grey30", face = "bold")) +
facet_wrap(. ~ variable, scales = "free_y", ncol = n_col) +
guides(color = "none")
# Return plot
return(plot)
}
}
pydemull/activAnalyzer documentation built on June 1, 2025, 1:09 p.m.
R Package Documentation
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Defines functions create_fig_res_by_day
Documented in create_fig_res_by_day
#' Create a figure with metrics shown for each day
#'
#' The function generates a figure with several common metrics shown for each day of the physical behavior measurement.
#'
#' @param data A dataframe with results obtained using the \code{\link{prepare_dataset}}, \code{\link{mark_wear_time}}, \code{\link{mark_intensity}}, and then the \code{\link{recap_by_day}} functions.
#' @param minimum_wear_time_for_analysis A numeric value to indicate the minimum number of hours of wear time that was considered to validate a day.
#' @param start_day_analysis A character value to indicate the start of the period that was considered to validate a day based on wear time.
#' @param end_day_analysis A character value to indicate the end of the period that was considered to validate a day based on wear time.
#' @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 ggplot 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
#' create_fig_res_by_day(summary_by_day,
#' minimum_wear_time_for_analysis = 10,
#' start_day_analysis = "00:00:00",
#' end_day_analysis = "23:59:00",
#' language = "en")
#' }
#'
create_fig_res_by_day <- function(
data,
minimum_wear_time_for_analysis = 10,
start_day_analysis = "00:00:00",
end_day_analysis = "23:59:00",
language = c("en", "fr", "de"),
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)
# Set text sizes
strip_text = 10
title_text_size = 10
label_text_size = 3
# Create figure for english language ========================================================================
if (language == "en") {
# Set list of metrics and parameters to be used for filtering table of results and creating the figure
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, ")")
)
fig_title <- "All metrics"
n_col = 5
}
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"
)
fig_title <- "Activity volume metrics"
n_col = 5
}
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)"
)
fig_title <- "Step-accumulation metrics"
n_col = 5
}
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, ")")
)
fig_title <- "Intensity distribution metrics"
n_col = 3
}
# Make figure
plot <-
data %>%
dplyr::mutate(date = as.factor(date),
validity = ifelse(wear_time >= minimum_wear_time_for_analysis * 60, "Valid day", "Non valid day"),
validity = forcats::fct_relevel(validity, "Valid day", "Non valid day")) %>%
tidyr::pivot_longer(cols = c(
wear_time,
total_counts_axis1,
total_counts_vm,
axis1_per_min,
vm_per_min,
minutes_SED,
minutes_LPA,
minutes_MPA,
minutes_VPA,
minutes_MVPA,
percent_SED,
percent_LPA,
percent_MPA,
percent_VPA,
percent_MVPA,
ratio_mvpa_sed,
mets_hours_mvpa,
total_kcal,
pal,
total_steps,
max_steps_60min,
max_steps_30min,
max_steps_20min,
max_steps_5min,
max_steps_1min,
peak_steps_60min,
peak_steps_30min,
peak_steps_20min,
peak_steps_5min,
peak_steps_1min,
ig,
"M1/3",
M120,
M60,
M30,
M15,
M5
),
names_to = "variable", values_to = "value") %>%
dplyr::mutate(variable = forcats::fct_relevel(
variable,
"wear_time",
"total_counts_axis1",
"total_counts_vm",
"axis1_per_min",
"vm_per_min",
"minutes_SED",
"minutes_LPA",
"minutes_MPA",
"minutes_VPA",
"minutes_MVPA",
"percent_SED",
"percent_LPA",
"percent_MPA",
"percent_VPA",
"percent_MVPA",
"ratio_mvpa_sed",
"mets_hours_mvpa",
"total_kcal",
"pal",
"total_steps",
"max_steps_60min",
"max_steps_30min",
"max_steps_20min",
"max_steps_5min",
"max_steps_1min",
"peak_steps_60min",
"peak_steps_30min",
"peak_steps_20min",
"peak_steps_5min",
"peak_steps_1min",
"ig",
"M1/3",
"M120",
"M60",
"M30",
"M15",
"M5"
),
variable = forcats::fct_recode(
variable,
"Wear time (min)" = "wear_time",
"Axis 1 total counts"= "total_counts_axis1",
"VM total counts" = "total_counts_vm",
"Axis 1 mean (counts/min)" = "axis1_per_min",
"VM mean (counts/min)" = "vm_per_min",
"SED time (min)" = "minutes_SED",
"LPA time (min)" = "minutes_LPA",
"MPA time (min)" = "minutes_MPA",
"VPA time (min)" = "minutes_VPA",
"MVPA time (min)" = "minutes_MVPA",
"SED wear time proportion (%)" = "percent_SED",
"LPA wear time proportion (%)" = "percent_LPA",
"MPA wear time proportion (%)" = "percent_MPA",
"VPA wear time proportion (%)" = "percent_VPA",
"MVPA wear time proportion (%)" = "percent_MVPA",
"Ratio MVPA / SED" = "ratio_mvpa_sed",
"Total MVPA MET-hr" = "mets_hours_mvpa",
"Total kcal" = "total_kcal",
"PAL" = "pal",
"Total steps" = "total_steps",
"Max step acc. 60 min (steps/min)" = "max_steps_60min",
"Max step acc. 30 min (steps/min)" ="max_steps_30min",
"Max step acc. 20 min (steps/min)" ="max_steps_20min",
"Max step acc. 5 min (steps/min)" ="max_steps_5min",
"Max step acc. 1 min (steps/min)" ="max_steps_1min",
"Peak step acc. 60 min (steps/min)" ="peak_steps_60min",
"Peak step acc. 30 min (steps/min)" ="peak_steps_30min",
"Peak step acc. 20 min (steps/min)" ="peak_steps_20min",
"Peak step acc. 5 min (steps/min)" ="peak_steps_5min",
"Peak step acc. 1 min (steps/min)" ="peak_steps_1min",
"Intensity gradient" = "ig",
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := "M120",
"M60 (counts/{{epoch_label}})" := "M60",
"M30 (counts/{{epoch_label}})" := "M30",
"M15 (counts/{{epoch_label}})" := "M15",
"M5 (counts/{{epoch_label}})" := "M5"
)
) %>%
dplyr::filter(variable %in% selected_metrics) %>%
dplyr::mutate(value_maj = ifelse(is.na(value), 0, value)) %>%
ggplot(aes(x = date, y = value_maj, fill = validity, color = validity)) +
ggtitle(fig_title) +
geom_bar(stat = "identity") +
geom_point(size = 2, color = "red") +
geom_line(aes(group = 1), linewidth = 0.7, color = "red") +
geom_text(aes(label = ifelse(is.na(value), "NA", round(value, 1))), size = label_text_size, vjust = -0.6, color = "black") +
labs(x = "Date", y = "", fill = "") +
scale_y_continuous(expand = expansion(mult = c(.05, .2))) +
scale_fill_manual(labels = c("Valid day",
paste0("Non-valid day (<",
minimum_wear_time_for_analysis,
" hours between ", start_day_analysis,
" and ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
scale_color_manual(labels = c("Valid day",
paste0("Non-valid day (<",
minimum_wear_time_for_analysis,
" hours between ", start_day_analysis,
" and ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
theme_bw() +
theme(legend.position = "bottom",
legend.text = element_text(size = 15),
legend.background = element_rect(fill = "beige", colour = "beige"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = title_text_size, angle = 90, hjust = 1, vjust = 1.1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "beige", color = "beige"),
plot.margin = margin(1, 1, 0.5, 1, "cm"),
strip.text.x = element_text(size = strip_text),
plot.title = element_text(size = 15, color = "grey30", face = "bold")) +
facet_wrap(. ~ variable, scales = "free_y", ncol = n_col) +
guides(color = "none")
# Return plot
return(plot)
}
# Create figure for french language ===============================================================================
if (language == "fr") {
# Set list of metrics and parameters to be used for filtering table of results and creating the figure
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, ")")
)
fig_title <- "Tous les indicateurs"
n_col = 5
}
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"
)
fig_title <- "Indicteurs de volume d'activit\xc3\xa9"
n_col = 5
}
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)"
)
fig_title <- "Indicateurs d'accumulation de pas"
n_col = 5
}
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, ")")
)
fig_title <- "Indicateurs de distribution de l'intensit\xc3\xa9"
n_col = 3
}
# Make figure
plot <-
data %>%
dplyr::mutate(date = as.factor(date),
validity = ifelse(wear_time >= minimum_wear_time_for_analysis * 60, "Valid day", "Non valid day"),
validity = forcats::fct_relevel(validity, "Valid day", "Non valid day")) %>%
tidyr::pivot_longer(cols = c(
wear_time,
total_counts_axis1,
total_counts_vm,
axis1_per_min,
vm_per_min,
minutes_SED,
minutes_LPA,
minutes_MPA,
minutes_VPA,
minutes_MVPA,
percent_SED,
percent_LPA,
percent_MPA,
percent_VPA,
percent_MVPA,
ratio_mvpa_sed,
mets_hours_mvpa,
total_kcal,
pal,
total_steps,
max_steps_60min,
max_steps_30min,
max_steps_20min,
max_steps_5min,
max_steps_1min,
peak_steps_60min,
peak_steps_30min,
peak_steps_20min,
peak_steps_5min,
peak_steps_1min,
ig,
"M1/3",
M120,
M60,
M30,
M15,
M5
),
names_to = "variable", values_to = "value") %>%
dplyr::mutate(variable = forcats::fct_relevel(
variable,
"wear_time",
"total_counts_axis1",
"total_counts_vm",
"axis1_per_min",
"vm_per_min",
"minutes_SED",
"minutes_LPA",
"minutes_MPA",
"minutes_VPA",
"minutes_MVPA",
"percent_SED",
"percent_LPA",
"percent_MPA",
"percent_VPA",
"percent_MVPA",
"ratio_mvpa_sed",
"mets_hours_mvpa",
"total_kcal",
"pal",
"total_steps",
"max_steps_60min",
"max_steps_30min",
"max_steps_20min",
"max_steps_5min",
"max_steps_1min",
"peak_steps_60min",
"peak_steps_30min",
"peak_steps_20min",
"peak_steps_5min",
"peak_steps_1min",
"ig",
"M1/3",
"M120",
"M60",
"M30",
"M15",
"M5"
),
variable = forcats::fct_recode(
variable,
"Temps de port (min)" = "wear_time",
"Total counts Axe 1 "= "total_counts_axis1",
"Total counts VM" = "total_counts_vm",
"Moyenne Axe 1 (counts/min)" = "axis1_per_min",
"Moyenne VM (counts/min)" = "vm_per_min",
"Temps SED (min)" = "minutes_SED",
"Temps LPA (min)" = "minutes_LPA",
"Temps MPA (min)" = "minutes_MPA",
"Temps VPA (min)" = "minutes_VPA",
"Temps MVPA (min)" = "minutes_MVPA",
"Proportion du temps de port SED (%)" = "percent_SED",
"Proportion du temps de port LPA (%)" = "percent_LPA",
"Proportion du temps de port MPA (%)" = "percent_MPA",
"Proportion du temps de port VPA (%)" = "percent_VPA",
"Proportion du temps de port MVPA (%)" = "percent_MVPA",
"Ratio MVPA / SED" = "ratio_mvpa_sed",
"Total MET-hr" = "mets_hours_mvpa",
"Total kcal" = "total_kcal",
"NAP" = "pal",
"Total pas" = "total_steps",
"Acc. pas max. 60 min (pas/min)" = "max_steps_60min",
"Acc. pas max. 30 min (pas/min)" ="max_steps_30min",
"Acc. pas max. 20 min (pas/min)" ="max_steps_20min",
"Acc. pas max. 5 min (pas/min)" ="max_steps_5min",
"Acc. pas max. 1 min (pas/min)" ="max_steps_1min",
"Acc. pas pic 60 min (pas/min)" ="peak_steps_60min",
"Acc. pas pic 30 min (pas/min)" ="peak_steps_30min",
"Acc. pas pic 20 min (pas/min)" ="peak_steps_20min",
"Acc. pas pic 5 min (pas/min)" ="peak_steps_5min",
"Acc. pas pic 1 min (pas/min)" ="peak_steps_1min",
"Gradient d'intensit\xc3\xa9" = "ig",
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := "M120",
"M60 (counts/{{epoch_label}})" := "M60",
"M30 (counts/{{epoch_label}})" := "M30",
"M15 (counts/{{epoch_label}})" := "M15",
"M5 (counts/{{epoch_label}})" := "M5"
)
) %>%
dplyr::filter(variable %in% selected_metrics) %>%
dplyr::mutate(value_maj = ifelse(is.na(value), 0, value)) %>%
ggplot(aes(x = date, y = value_maj, fill = validity, color = validity)) +
ggtitle(fig_title) +
geom_bar(stat = "identity") +
geom_point(size = 2, color = "red") +
geom_line(aes(group = 1), linewidth = 0.7, color = "red") +
geom_text(aes(label = ifelse(is.na(value), "NA", round(value, 1))), size = label_text_size, vjust = -0.6, color = "black") +
labs(x = "Date", y = "", fill = "") +
scale_y_continuous(expand = expansion(mult = c(.05, .2))) +
scale_fill_manual(labels = c("Jour valide",
paste0("Jour non valide (<",
minimum_wear_time_for_analysis,
" heures entre ", start_day_analysis,
" et ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
scale_color_manual(labels = c("Jour valide",
paste0("Jour non valide(<",
minimum_wear_time_for_analysis,
" heures entre ", start_day_analysis,
" et ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
theme_bw() +
theme(legend.position = "bottom",
legend.text = element_text(size = 15),
legend.background = element_rect(fill = "beige", colour = "beige"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = title_text_size, angle = 90, hjust = 1, vjust = 1.1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "beige", color = "beige"),
plot.margin = margin(1, 1, 0.5, 1, "cm"),
strip.text.x = element_text(size = strip_text),
plot.title = element_text(size = 15, color = "grey30", face = "bold")) +
facet_wrap(. ~ variable, scales = "free_y", ncol = n_col) +
guides(color = "none")
# Return plot
return(plot)
}
# Create figure for german language ========================================================================
if (language == "de") {
# Set list of metrics and parameters to be used for filtering table of results and creating the figure
if (metrics == "all") {
selected_metrics <- c(
"Tragezeit (min)",
"Achse 1 Gesamtanzahl Messungen",
"VM Gesamtanzahl Messungens",
"Mittelwert Achse 1 (Messungen/min)",
"VM-Mittelwert (Messungen /min)",
"SED-Zeit (min)",
"LPA-Zeit (min)",
"MPA-Zeit (min)",
"VPA-Zeit (min)",
"MVPA-Zeit (min)",
"SED-Tragezeitanteil (%)",
"LPA-Tragezeitanteil (%)",
"MPA-Tragezeitanteil (%)",
"VPA-Tragezeitanteil (%)",
"MVPA-Tragezeitanteil (%)",
"Verh\xc3\xa4ltnis MVPA/SED",
"Gesamt-MVPA-MET-Stunden",
"Gesamt-kcal",
"PAL",
"Schritte insgesamt",
"Max. Schritte akk. 60 min (Schritte/min)",
"Max. Schritte akk. 30 min (Schritte/min)",
"Max. Schritte akk. 20 min (Schritte/min)",
"Max. Schritte akk. 5 min (Schritte/min)",
"Max. Schritte akk. 1 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 60 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 30 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 20 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 5 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 1 min (Schritte/min)",
"Intensit\xc3\xa4tsgradient",
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, ")")
)
fig_title <- "All metrics"
n_col = 5
}
if (metrics == "volume") {
selected_metrics <- c(
"Tragezeit (min)",
"Achse 1 Gesamtanzahl Messungen",
"VM Gesamtanzahl Messungens",
"Mittelwert Achse 1 (Messungen/min)",
"VM-Mittelwert (Messungen /min)",
"SED-Zeit (min)",
"LPA-Zeit (min)",
"MPA-Zeit (min)",
"VPA-Zeit (min)",
"MVPA-Zeit (min)",
"SED-Tragezeitanteil (%)",
"LPA-Tragezeitanteil (%)",
"MPA-Tragezeitanteil (%)",
"VPA-Tragezeitanteil (%)",
"MVPA-Tragezeitanteil (%)",
"Verh\xc3\xa4ltnis MVPA/SED",
"Gesamt-MVPA-MET-Stunden",
"Gesamt-kcal",
"PAL",
"Schritte insgesamt"
)
fig_title <- "Statistiken zum Aktivit\xc3\xa4tsvolumen"
n_col = 5
}
if (metrics == "step_acc") {
selected_metrics <- c(
"Max. Schritte akk. 60 min (Schritte/min)",
"Max. Schritte akk. 30 min (Schritte/min)",
"Max. Schritte akk. 20 min (Schritte/min)",
"Max. Schritte akk. 5 min (Schritte/min)",
"Max. Schritte akk. 1 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 60 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 30 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 20 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 5 min (Schritte/min)",
"H\xc3\xb6chstanzahl Schritte akk. 1 min (Schritte/min)"
)
fig_title <- "Metriken zur Schrittzahl (akkumuliert)"
n_col = 5
}
if (metrics == "int_distri") {
selected_metrics <- c(
"Intensit\xc3\xa4tsgradient",
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, ")")
)
fig_title <- "Metriken der Intensit\xc3\xa4tsverteilung"
n_col = 3
}
# Make figure
plot <-
data %>%
dplyr::mutate(date = as.factor(date),
validity = ifelse(wear_time >= minimum_wear_time_for_analysis * 60, "Valid day", "Non valid day"),
validity = forcats::fct_relevel(validity, "Valid day", "Non valid day")) %>%
tidyr::pivot_longer(cols = c(
wear_time,
total_counts_axis1,
total_counts_vm,
axis1_per_min,
vm_per_min,
minutes_SED,
minutes_LPA,
minutes_MPA,
minutes_VPA,
minutes_MVPA,
percent_SED,
percent_LPA,
percent_MPA,
percent_VPA,
percent_MVPA,
ratio_mvpa_sed,
mets_hours_mvpa,
total_kcal,
pal,
total_steps,
max_steps_60min,
max_steps_30min,
max_steps_20min,
max_steps_5min,
max_steps_1min,
peak_steps_60min,
peak_steps_30min,
peak_steps_20min,
peak_steps_5min,
peak_steps_1min,
ig,
"M1/3",
M120,
M60,
M30,
M15,
M5
),
names_to = "variable", values_to = "value") %>%
dplyr::mutate(variable = forcats::fct_relevel(
variable,
"wear_time",
"total_counts_axis1",
"total_counts_vm",
"axis1_per_min",
"vm_per_min",
"minutes_SED",
"minutes_LPA",
"minutes_MPA",
"minutes_VPA",
"minutes_MVPA",
"percent_SED",
"percent_LPA",
"percent_MPA",
"percent_VPA",
"percent_MVPA",
"ratio_mvpa_sed",
"mets_hours_mvpa",
"total_kcal",
"pal",
"total_steps",
"max_steps_60min",
"max_steps_30min",
"max_steps_20min",
"max_steps_5min",
"max_steps_1min",
"peak_steps_60min",
"peak_steps_30min",
"peak_steps_20min",
"peak_steps_5min",
"peak_steps_1min",
"ig",
"M1/3",
"M120",
"M60",
"M30",
"M15",
"M5"
),
variable = forcats::fct_recode(
variable,
"Tragezeit (min)" = "wear_time",
"Achse 1 Gesamtanzahl Messungen" = "total_counts_axis1",
"VM Gesamtanzahl Messungens" = "total_counts_vm",
"Mittelwert Achse 1 (Messungen/min)" = "axis1_per_min",
"VM-Mittelwert (Messungen /min)" = "vm_per_min",
"SED-Zeit (min)" = "minutes_SED",
"LPA-Zeit (min)" = "minutes_LPA",
"MPA-Zeit (min)" = "minutes_MPA",
"VPA-Zeit (min)" = "minutes_VPA",
"MVPA-Zeit (min)" = "minutes_MVPA",
"SED-Tragezeitanteil (%)" = "percent_SED",
"LPA-Tragezeitanteil (%)" = "percent_LPA",
"MPA-Tragezeitanteil (%)" = "percent_MPA",
"VPA-Tragezeitanteil (%)" = "percent_VPA",
"MVPA-Tragezeitanteil (%)" = "percent_MVPA",
"Verh\xc3\xa4ltnis MVPA/SED" = "ratio_mvpa_sed",
"Gesamt-MVPA-MET-Stunden" = "mets_hours_mvpa",
"Gesamt-kcal" = "total_kcal",
"PAL" = "pal",
"Schritte insgesamt" = "total_steps",
"Max. Schritte akk. 60 min (Schritte/min)" = "max_steps_60min",
"Max. Schritte akk. 30 min (Schritte/min)" ="max_steps_30min",
"Max. Schritte akk. 20 min (Schritte/min)" ="max_steps_20min",
"Max. Schritte akk. 5 min (Schritte/min)" ="max_steps_5min",
"Max. Schritte akk. 1 min (Schritte/min)" ="max_steps_1min",
"H\xc3\xb6chstanzahl Schritte akk. 60 min (Schritte/min)" ="peak_steps_60min",
"H\xc3\xb6chstanzahl Schritte akk. 30 min (Schritte/min)" ="peak_steps_30min",
"H\xc3\xb6chstanzahl Schritte akk. 20 min (Schritte/min)" ="peak_steps_20min",
"H\xc3\xb6chstanzahl Schritte akk. 5 min (Schritte/min)" ="peak_steps_5min",
"H\xc3\xb6chstanzahl Schritte akk. 1 min (Schritte/min)" ="peak_steps_1min",
"Intensit\xc3\xa4tsgradient" = "ig",
"M1/3 (counts/{{epoch_label}})" := "M1/3",
"M120 (counts/{{epoch_label}})" := "M120",
"M60 (counts/{{epoch_label}})" := "M60",
"M30 (counts/{{epoch_label}})" := "M30",
"M15 (counts/{{epoch_label}})" := "M15",
"M5 (counts/{{epoch_label}})" := "M5"
)
) %>%
dplyr::filter(variable %in% selected_metrics) %>%
dplyr::mutate(value_maj = ifelse(is.na(value), 0, value)) %>%
ggplot(aes(x = date, y = value_maj, fill = validity, color = validity)) +
ggtitle(fig_title) +
geom_bar(stat = "identity") +
geom_point(size = 2, color = "red") +
geom_line(aes(group = 1), linewidth = 0.7, color = "red") +
geom_text(aes(label = ifelse(is.na(value), "NA", round(value, 1))), size = label_text_size, vjust = -0.6, color = "black") +
labs(x = "Date", y = "", fill = "") +
scale_y_continuous(expand = expansion(mult = c(.05, .2))) +
scale_fill_manual(labels = c("G\xc3\xbcltiger Tag",
paste0("Nicht-g\xc3\xbcltiger Tag (<",
minimum_wear_time_for_analysis,
" Stunden zwischen ", start_day_analysis,
" und ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
scale_color_manual(labels = c("G\xc3\xbcltiger Tag",
paste0("Nicht-g\xc3\xbcltiger Tag (<",
minimum_wear_time_for_analysis,
" Stunden zwischen ", start_day_analysis,
" und ", end_day_analysis, ")")),
values = c("#00BE6C", "#FC717F")) +
theme_bw() +
theme(legend.position = "bottom",
legend.text = element_text(size = 15),
legend.background = element_rect(fill = "beige", colour = "beige"),
axis.title.x = element_blank(),
axis.text.x = element_text(size = title_text_size, angle = 90, hjust = 1, vjust = 1.1),
axis.text.y = element_blank(),
axis.ticks = element_blank(),
plot.background = element_rect(fill = "beige", color = "beige"),
plot.margin = margin(1, 1, 0.5, 1, "cm"),
strip.text.x = element_text(size = strip_text),
plot.title = element_text(size = 15, color = "grey30", face = "bold")) +
facet_wrap(. ~ variable, scales = "free_y", ncol = n_col) +
guides(color = "none")
# Return plot
return(plot)
}
}
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