Participant.IDs <-c("WRC5", "WOL2", "WNC1", "SIM1", "MIH2", "MES8", "LIR1","HNN1", "DWM1", "D7EB", "CET8", "BLD3", "AET3")
-#list all the files
-files <-list.files(path ="Data", pattern ="*.csv", full.names =TRUE)
-files_xlsx <-list.files(path ="Data", pattern ="*.xlsx", full.names =TRUE)
-
-#extract only files for the participants in the list, using stringr
-files <- files[str_detect(files, str_c(Participant.IDs, collapse ="|"))]
-files_xlsx <- files_xlsx[str_detect(files_xlsx, str_c(Participant.IDs, collapse ="|"))]
-
-#general import settings for the data, as it is a german dataset
-column_names <-c("Zeile", "Datum", "Zeit", "Status") #this is a sequence of column
-german_encoding <-c(2, 4, 6, 8, 11, 13, 14, 16, 22) #these are the files that use german encoding
-int_encoding <- base::setdiff(1:24, german_encoding) #these are the files that use standard encoding
-na_s <-c("", "NA", "kZ") #these are the NA strings in the files
-auto.id <-"^\\d{5}_(.{4})"#this is the regex to extract the participant id
-tz <-"Europe/Berlin"#this is the timezone of the data
-
-#read in the files with german encoding
-LLdata <- import$Actiwatch_Spectrum(files[german_encoding],
-locale =locale(encoding="latin1"),
-tz = tz, na = na_s, auto.id = auto.id,
-column_names = column_names)
+N_participants <-length(Participant.IDs)
+
+#list all the files
+files <-list.files(path ="Data", pattern ="*.csv", full.names =TRUE)
+files_xlsx <-list.files(path ="Data", pattern ="*.xlsx", full.names =TRUE)
+
+#extract only files for the participants in the list, using stringr
+files <- files[str_detect(files, str_c(Participant.IDs, collapse ="|"))]
+files_xlsx <- files_xlsx[str_detect(files_xlsx, str_c(Participant.IDs, collapse ="|"))]
+
+#general import settings for the data, as it is a german dataset
+column_names <-c("Zeile", "Datum", "Zeit", "Status") #this is a sequence of column
+german_encoding <-c(2, 4, 6, 8, 11, 13, 14, 16, 22) #these are the files that use german encoding
+int_encoding <- base::setdiff(1:24, german_encoding) #these are the files that use standard encoding
+na_s <-c("", "NA", "kZ") #these are the NA strings in the files
+auto.id <-"^\\d{5}_(.{4})"#this is the regex to extract the participant id
+tz <-"Europe/Berlin"#this is the timezone of the data
+
+#read in the files with german encoding
+LLdata <- import$Actiwatch_Spectrum(files[german_encoding],
+locale =locale(encoding="latin1"),
+tz = tz, na = na_s, auto.id = auto.id,
+column_names = column_names)
Successfully read in 218654 observations from Actiwatch_Spectrum-file
@@ -3377,7 +3379,7 @@
Import
Code
#create and save a figure with an overview of the illuminance values
-Figure.S2 <-
+Figure.S1 <-LLdata %>%gg_days(y.axis = Weißes.Licht, x.axis.breaks =waiver(), linewidth =0.1,x.axis.format =waiver(),
@@ -3388,7 +3390,7 @@
We can see some variation in the data, but nothing unexpected.
@@ -3518,7 +3520,7 @@
Overview of
Code
#create and save a figure overview of the remaining days
-Figure.S3 <-
+Figure.S2 <-LLdata %>%gg_days(group =day(Datetime),geom ="ribbon", alpha =0.25, fill ="#EFC000", col ="#EFC000",
@@ -3533,7 +3535,7 @@
Overview of
strip.text =element_text(size=6),panel.spacing =unit(0.1, "lines"))
-Figure.S3
#create and save a figure that shows the non-wear times
-Figure.S4 <-
+Figure.S3 <-LLdata %>%gg_days(group =interaction(day(Datetime), consecutive_id(Status)),geom ="ribbon", alpha =0.25, aes_fill = Status, aes_col = Status,
@@ -3599,7 +3601,7 @@
#filter the data to only include valid daysLLdata <- LLdata %>%
-filter(valid_Day =="valid Day") %>%
+filter(valid_Day =="Valid days") %>%select(-valid_Day, -NonWear)
@@ -4256,7 +4258,7 @@
Included days
Code
#create and save a figure about the illuminance between season
-Figure.S5 <-
+Figure.S4 <-LLdata %>%ggplot(aes(x = MEDI, fill = Season)) +geom_density_ridges(aes(y = Id, height = ..density..), stat ="density", alpha =0.5) +
@@ -4269,7 +4271,7 @@
Included days
labs(x ="melanopic EDI (lux)", y ="Participant ID")+theme(legend.position ="bottom")
-Figure.S5
tab_footnote(footnote =paste0(
-"The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for 11 participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of ",
- sign_level,
-". The fraction of significant differences was compared against the power level threshold of ",
- Power_level,
-". The required sample size is the minimum sample size that reaches this threshold, with ",
- n_samples,
-" resamples per sample size (sample sizes from ",
-min(sample_size),
-" to ",
-max(sample_size),
-" were tested). The total amount of resamples/tests is ",
-nrow(resampled_data),
-" across all metrics."),
-locations = gt::cells_column_labels(columns = sample_size)
- )
+"The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for ",
+ N_participants,
+" participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of ",
+ sign_level,
+". The fraction of significant differences was compared against the power level threshold of ",
+ Power_level,
+". The required sample size is the minimum sample size that reaches this threshold, with ",
+ n_samples,
+" resamples per sample size (sample sizes from ",
+min(sample_size),
+" to ",
+max(sample_size),
+" were tested). The total amount of resamples/tests is ",
+nrow(resampled_data),
+" across all metrics."),
+locations = gt::cells_column_labels(columns = sample_size)
+ )
@@ -5496,7 +5500,7 @@
Results
Metrics that did not reach the threshold: Geometric sd (lx), Midpoint of darkest 5 hours (L5, hh:mm)
-
1 The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for 11 participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of 0.05. The fraction of significant differences was compared against the power level threshold of 0.8. The required sample size is the minimum sample size that reaches this threshold, with 1000 resamples per sample size (sample sizes from 3 to 50 were tested). The total amount of resamples/tests is 576000 across all metrics.
+
1 The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for 13 participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of 0.05. The fraction of significant differences was compared against the power level threshold of 0.8. The required sample size is the minimum sample size that reaches this threshold, with 1000 resamples per sample size (sample sizes from 3 to 50 were tested). The total amount of resamples/tests is 576000 across all metrics.
2 Power at the required sample size
@@ -6071,728 +6075,732 @@
Results
Participant.IDs <- c("WRC5", "WOL2", "WNC1", "SIM1", "MIH2", "MES8", "LIR1", "HNN1", "DWM1", "D7EB", "CET8", "BLD3", "AET3")
-#list all the files
-files <- list.files(path = "Data", pattern = "*.csv", full.names = TRUE)
-files_xlsx <- list.files(path = "Data", pattern = "*.xlsx", full.names = TRUE)
-
-#extract only files for the participants in the list, using stringr
-files <- files[str_detect(files, str_c(Participant.IDs, collapse = "|"))]
-files_xlsx <- files_xlsx[str_detect(files_xlsx, str_c(Participant.IDs, collapse = "|"))]
-
-#general import settings for the data, as it is a german dataset
-column_names <- c("Zeile", "Datum", "Zeit", "Status") #this is a sequence of column
-german_encoding <- c(2, 4, 6, 8, 11, 13, 14, 16, 22) #these are the files that use german encoding
-int_encoding <- base::setdiff(1:24, german_encoding) #these are the files that use standard encoding
-na_s <- c("", "NA", "kZ") #these are the NA strings in the files
-auto.id <- "^\\d{5}_(.{4})" #this is the regex to extract the participant id
-tz <- "Europe/Berlin" #this is the timezone of the data
-
-#read in the files with german encoding
-LLdata <- import$Actiwatch_Spectrum(files[german_encoding],
- locale = locale(encoding="latin1"),
- tz = tz, na = na_s, auto.id = auto.id,
- column_names = column_names)
-
-#read in the files with english encoding
-LLdata2 <- import$Actiwatch_Spectrum(files[int_encoding],
- tz = tz, na = na_s, auto.id = auto.id,
- column_names = column_names
- )
-
-#read all excel files in and clean them up to be in comparable shape
-LLdata3 <-
- read_xlsx(
- files_xlsx[1], skip = 149, na = na_s, .name_repair = "universal",
- )[-1, ]
-LLdata4 <-
- read_xlsx(
- files_xlsx[2], skip = 164, na = na_s, .name_repair = "universal"
- )[-1, ]
-
-LLdata_xlsx <-
- list(LLdata3, LLdata4) %>%
- map2(files_xlsx,
- \(file, filenames) {
- file %>%
- mutate(file.name =
- filenames %>% basename() %>% tools::file_path_sans_ext(),
- Id = str_extract(file.name, auto.id, group = 1),
- Zeit = hms::as_hms(Zeit),
- Datetime = as.POSIXct(paste(Datum, Zeit),
- format = "%Y-%m-%d %H:%M:%S", tz = "UTC") %>%
- force_tz(tz),
- Zeile = as.integer(Zeile),
- across(
- c(Status..Nicht.am.Handgelenk., Markierung, Schlaf.Wach,
- Intervallstatus), as.factor))
- }) %>%
- list_rbind()
-
-#combine the two datasets
-LLdata <- join_datasets(LLdata, LLdata2, LLdata_xlsx)
-rm(LLdata2, LLdata_xlsx, LLdata3, LLdata4)
-
-#differentiate the datasets by Season
-LLdata <-
- LLdata %>%
- group_by(Id, Season = quarter(Datetime)) %>%
- mutate(
- Season = case_when(Season == 1 ~ "Winter", Season == 2 ~ "Summer") %>%
- factor(levels = c("Winter", "Summer")))
-
-#get a general overview
-Figure.1 <-
- LLdata %>% gg_overview() +
- labs(x = "Month", y = "Participant ID") +
- scale_x_datetime(date_breaks = "1 month", date_labels = "%b") +
- coord_cartesian(xlim = c(as.POSIXct("2015-01-09", tz = tz), NA)) +
- theme(axis.text=element_text(size=8),
- text = element_text(size = 10),
- axis.line = element_line(linewidth = 0.25),
- axis.ticks = element_line(linewidth = 0.25))
-Figure.1
-
-#save the figure
-ggsave("Figures/Figure1.png", Figure.1, height = 8.5, width = 8.5, units='cm')
-
-```
+N_participants <- length(Participant.IDs)
+
+#list all the files
+files <- list.files(path = "Data", pattern = "*.csv", full.names = TRUE)
+files_xlsx <- list.files(path = "Data", pattern = "*.xlsx", full.names = TRUE)
+
+#extract only files for the participants in the list, using stringr
+files <- files[str_detect(files, str_c(Participant.IDs, collapse = "|"))]
+files_xlsx <- files_xlsx[str_detect(files_xlsx, str_c(Participant.IDs, collapse = "|"))]
+
+#general import settings for the data, as it is a german dataset
+column_names <- c("Zeile", "Datum", "Zeit", "Status") #this is a sequence of column
+german_encoding <- c(2, 4, 6, 8, 11, 13, 14, 16, 22) #these are the files that use german encoding
+int_encoding <- base::setdiff(1:24, german_encoding) #these are the files that use standard encoding
+na_s <- c("", "NA", "kZ") #these are the NA strings in the files
+auto.id <- "^\\d{5}_(.{4})" #this is the regex to extract the participant id
+tz <- "Europe/Berlin" #this is the timezone of the data
+
+#read in the files with german encoding
+LLdata <- import$Actiwatch_Spectrum(files[german_encoding],
+ locale = locale(encoding="latin1"),
+ tz = tz, na = na_s, auto.id = auto.id,
+ column_names = column_names)
+
+#read in the files with english encoding
+LLdata2 <- import$Actiwatch_Spectrum(files[int_encoding],
+ tz = tz, na = na_s, auto.id = auto.id,
+ column_names = column_names
+ )
+
+#read all excel files in and clean them up to be in comparable shape
+LLdata3 <-
+ read_xlsx(
+ files_xlsx[1], skip = 149, na = na_s, .name_repair = "universal",
+ )[-1, ]
+LLdata4 <-
+ read_xlsx(
+ files_xlsx[2], skip = 164, na = na_s, .name_repair = "universal"
+ )[-1, ]
+
+LLdata_xlsx <-
+ list(LLdata3, LLdata4) %>%
+ map2(files_xlsx,
+ \(file, filenames) {
+ file %>%
+ mutate(file.name =
+ filenames %>% basename() %>% tools::file_path_sans_ext(),
+ Id = str_extract(file.name, auto.id, group = 1),
+ Zeit = hms::as_hms(Zeit),
+ Datetime = as.POSIXct(paste(Datum, Zeit),
+ format = "%Y-%m-%d %H:%M:%S", tz = "UTC") %>%
+ force_tz(tz),
+ Zeile = as.integer(Zeile),
+ across(
+ c(Status..Nicht.am.Handgelenk., Markierung, Schlaf.Wach,
+ Intervallstatus), as.factor))
+ }) %>%
+ list_rbind()
+
+#combine the two datasets
+LLdata <- join_datasets(LLdata, LLdata2, LLdata_xlsx)
+rm(LLdata2, LLdata_xlsx, LLdata3, LLdata4)
+
+#differentiate the datasets by Season
+LLdata <-
+ LLdata %>%
+ group_by(Id, Season = quarter(Datetime)) %>%
+ mutate(
+ Season = case_when(Season == 1 ~ "Winter", Season == 2 ~ "Summer") %>%
+ factor(levels = c("Winter", "Summer")))
+
+#get a general overview
+Figure.1 <-
+ LLdata %>% gg_overview() +
+ labs(x = "Month", y = "Participant ID") +
+ scale_x_datetime(date_breaks = "1 month", date_labels = "%b") +
+ coord_cartesian(xlim = c(as.POSIXct("2015-01-09", tz = tz), NA)) +
+ theme(axis.text=element_text(size=8),
+ text = element_text(size = 10),
+ axis.line = element_line(linewidth = 0.25),
+ axis.ticks = element_line(linewidth = 0.25))
+Figure.1
+
+#save the figure
+ggsave("Figures/Figure1.png", Figure.1, height = 8.5, width = 8.5, units='cm')
-The import doesn't suggest that there are any gaps in the dataset, but just to make sure, let us search for gaps in the data with the new grouping.
+```
-```{r gaps}
-#| code-fold: show
-#search for gaps (none are found)
-LLdata %>% gap_finder()
-```
-
-Now lets go into a more comprehensive overview.
+The import doesn't suggest that there are any gaps in the dataset, but just to make sure, let us search for gaps in the data with the new grouping.
+
+```{r gaps}
+#| code-fold: show
+#search for gaps (none are found)
+LLdata %>% gap_finder()
+```
-```{r overview}
-#| fig-height: 20
-#| fig-width: 13
-
-#create and save a figure with an overview of the illuminance values
-Figure.S2 <-
-LLdata %>%
- gg_days(y.axis = Weißes.Licht, x.axis.breaks = waiver(), linewidth = 0.1,
- x.axis.format = waiver(),
- y.axis.label = "Illuminance (lx)", x.axis.label = "Dates")+
- facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left") +
- theme(axis.text=element_text(size=6),
- text = element_text(size=8),
- strip.text = element_text(size=6),
- panel.spacing = unit(0.1, "lines"))
-
-Figure.S2
-ggsave("Figures/FigureS2.png", Figure.S2, height = 20, width = 17, units='cm')
-
-
-```
+Now lets go into a more comprehensive overview.
+
+```{r overview}
+#| fig-height: 20
+#| fig-width: 13
+
+#create and save a figure with an overview of the illuminance values
+Figure.S1 <-
+LLdata %>%
+ gg_days(y.axis = Weißes.Licht, x.axis.breaks = waiver(), linewidth = 0.1,
+ x.axis.format = waiver(),
+ y.axis.label = "Illuminance (lx)", x.axis.label = "Dates")+
+ facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left") +
+ theme(axis.text=element_text(size=6),
+ text = element_text(size=8),
+ strip.text = element_text(size=6),
+ panel.spacing = unit(0.1, "lines"))
+
+Figure.S1
+ggsave("Figures/FigureS1.png", Figure.S1, height = 20, width = 17, units='cm')
+
-We can see some variation in the data, but nothing unexpected.
+```
-### Preparing the Data
+We can see some variation in the data, but nothing unexpected.
-There are several steps required before we can go into metrics calculation:
+### Preparing the Data
-- We need to calculate the `melanopic EDI` (MEDI), as these are not part of the dataset. `BAuA` has provided a calibration function that is based on their measurements of the device.
-- We need to filter for dates that are relevant for the analysis, i.e., that are `day-shifts`.
-
-#### Calculating melanopic EDI
+There are several steps required before we can go into metrics calculation:
+
+- We need to calculate the `melanopic EDI` (MEDI), as these are not part of the dataset. `BAuA` has provided a calibration function that is based on their measurements of the device.
+- We need to filter for dates that are relevant for the analysis, i.e., that are `day-shifts`.
-```{r MEDI}
-#calculate melanopic EDI based on the BAuA calibration
-LLdata <- LLdata %>% mutate(MEDI = (Grünes.Licht+ Blaues.Licht)*4.3/1.3262)
-```
-
-#### Filtering for Day-Shifts
+#### Calculating melanopic EDI
+
+```{r MEDI}
+#calculate melanopic EDI based on the BAuA calibration
+LLdata <- LLdata %>% mutate(MEDI = (Grünes.Licht+ Blaues.Licht)*4.3/1.3262)
+```
-Sadly, the days with day-shift is not part of the dataset, but there is a manually collected list with that information. The `filter_list` will collect these dates and we filter the data accordingly with the `filter_Datetime_multiple()` function.
+#### Filtering for Day-Shifts
-```{r filtering}
-#gathering the dates for the filter
-filter_list <-
- list(
- #Winter
- #Consecutive Days
- list(start = "2015-01-14", end = "2015-01-16",
- only_Id = quote(Id == "AET3" & Season == "Winter")),
- list(start = "2015-01-19", end = "2015-01-20",
- only_Id = quote(Id == "HNN1" & Season == "Winter")),
- list(start = "2015-01-16", end = "2015-01-17",
- only_Id = quote(Id == "LIR1" & Season == "Winter")),
- list(start = "2015-01-23", end = "2015-01-25",
- only_Id = quote(Id == "MIH2" & Season == "Winter")),
- list(start = "2015-01-26", end = "2015-01-27",
- only_Id = quote(Id == "WNC1" & Season == "Winter")),
- list(start = "2015-01-14", end = "2015-01-18",
- only_Id = quote(Id == "WRC5" & Season == "Winter")),
- list(start = "2015-01-28", end = "2015-01-29",
- only_Id = quote(Id == "SIM1" & Season == "Winter")),
- #Days with gaps
- list(start = "2015-01-14", end = "2015-01-20",
- filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
- only_Id = quote(Id == "CET8" & Season == "Winter")),
- list(start = "2015-01-14", end = "2015-01-20",
- filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
- only_Id = quote(Id == "DWM1" & Season == "Winter")),
- list(start = "2015-01-14", end = "2015-01-20",
- filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
- only_Id = quote(Id == "D7EB" & Season == "Winter")),
- list(start = "2015-01-14", end = "2015-01-20",
- filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
- only_Id = quote(Id == "MES8" & Season == "Winter")),
- list(start = "2015-01-23", end = "2015-01-29",
- filter.expr = quote(!(day(Datetime) %in% c(24, 25))),
- only_Id = quote(Id == "BLD3" & Season == "Winter")),
- list(start = "2015-01-14", end = "2015-01-20",
- filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
- only_Id = quote(Id == "WOL2" & Season == "Winter")),
- #Summer
- #Consecutive Days
- list(start = "2015-06-15", end = "2015-06-16",
- only_Id = quote(Id == "AET3" & Season == "Summer")),
- list(start = "2015-06-11", end = "2015-06-12",
- only_Id = quote(Id == "CET8" & Season == "Summer")),
- list(start = "2015-06-20", end = "2015-06-22",
- only_Id = quote(Id == "DWM1" & Season == "Summer")),
- list(start = "2015-06-23", end = "2015-06-25",
- only_Id = quote(Id == "D7EB" & Season == "Summer")),
- list(start = "2015-06-10", end = "2015-06-14",
- only_Id = quote(Id == "HNN1" & Season == "Summer")),
- list(start = "2015-06-15", end = "2015-06-16",
- only_Id = quote(Id == "LIR1" & Season == "Summer")),
- list(start = "2015-06-13", end = "2015-06-14",
- only_Id = quote(Id == "MES8" & Season == "Summer")),
- list(start = "2015-06-10", end = "2015-06-12",
- only_Id = quote(Id == "MIH2" & Season == "Summer")),
- #Days with gaps
- list(start = "2015-06-20", end = "2015-06-25",
- filter.expr = quote(!(day(Datetime) %in% c(21,22,23))),
- only_Id = quote(Id == "WNC1" & Season == "Summer")),
- list(start = "2015-06-10", end = "2015-06-16",
- filter.expr = quote(!(day(Datetime) %in% c(13,14))),
- only_Id = quote(Id == "WOL2" & Season == "Summer")),
- list(start = "2015-06-10", end = "2015-06-16",
- filter.expr = quote(!(day(Datetime) %in% c(12,13,14))),
- only_Id = quote(Id == "WRC5" & Season == "Summer")),
- list(start = "2015-06-19", end = "2015-06-24",
- filter.expr = quote(!(day(Datetime) %in% c(20,21,22))),
- only_Id = quote(Id == "BLD3" & Season == "Summer")),
- list(start = "2015-06-19", end = "2015-06-25",
- filter.expr = quote(!(day(Datetime) %in% c(24))),
- only_Id = quote(Id == "SIM1" & Season == "Summer"))
- )
-
-#keep the full dataset for visualization later
-LLdata_full <- LLdata
-
-#filter the dates that are relevant for the analysis
-LLdata <-
-LLdata %>% filter_Datetime_multiple(arguments = filter_list,
- filter_function = filter_Date,
- full.day = TRUE)
-
-```
+Sadly, the days with day-shift is not part of the dataset, but there is a manually collected list with that information. The `filter_list` will collect these dates and we filter the data accordingly with the `filter_Datetime_multiple()` function.
+
+```{r filtering}
+#gathering the dates for the filter
+filter_list <-
+ list(
+ #Winter
+ #Consecutive Days
+ list(start = "2015-01-14", end = "2015-01-16",
+ only_Id = quote(Id == "AET3" & Season == "Winter")),
+ list(start = "2015-01-19", end = "2015-01-20",
+ only_Id = quote(Id == "HNN1" & Season == "Winter")),
+ list(start = "2015-01-16", end = "2015-01-17",
+ only_Id = quote(Id == "LIR1" & Season == "Winter")),
+ list(start = "2015-01-23", end = "2015-01-25",
+ only_Id = quote(Id == "MIH2" & Season == "Winter")),
+ list(start = "2015-01-26", end = "2015-01-27",
+ only_Id = quote(Id == "WNC1" & Season == "Winter")),
+ list(start = "2015-01-14", end = "2015-01-18",
+ only_Id = quote(Id == "WRC5" & Season == "Winter")),
+ list(start = "2015-01-28", end = "2015-01-29",
+ only_Id = quote(Id == "SIM1" & Season == "Winter")),
+ #Days with gaps
+ list(start = "2015-01-14", end = "2015-01-20",
+ filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
+ only_Id = quote(Id == "CET8" & Season == "Winter")),
+ list(start = "2015-01-14", end = "2015-01-20",
+ filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
+ only_Id = quote(Id == "DWM1" & Season == "Winter")),
+ list(start = "2015-01-14", end = "2015-01-20",
+ filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
+ only_Id = quote(Id == "D7EB" & Season == "Winter")),
+ list(start = "2015-01-14", end = "2015-01-20",
+ filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
+ only_Id = quote(Id == "MES8" & Season == "Winter")),
+ list(start = "2015-01-23", end = "2015-01-29",
+ filter.expr = quote(!(day(Datetime) %in% c(24, 25))),
+ only_Id = quote(Id == "BLD3" & Season == "Winter")),
+ list(start = "2015-01-14", end = "2015-01-20",
+ filter.expr = quote(!(day(Datetime) %in% c(17, 18))),
+ only_Id = quote(Id == "WOL2" & Season == "Winter")),
+ #Summer
+ #Consecutive Days
+ list(start = "2015-06-15", end = "2015-06-16",
+ only_Id = quote(Id == "AET3" & Season == "Summer")),
+ list(start = "2015-06-11", end = "2015-06-12",
+ only_Id = quote(Id == "CET8" & Season == "Summer")),
+ list(start = "2015-06-20", end = "2015-06-22",
+ only_Id = quote(Id == "DWM1" & Season == "Summer")),
+ list(start = "2015-06-23", end = "2015-06-25",
+ only_Id = quote(Id == "D7EB" & Season == "Summer")),
+ list(start = "2015-06-10", end = "2015-06-14",
+ only_Id = quote(Id == "HNN1" & Season == "Summer")),
+ list(start = "2015-06-15", end = "2015-06-16",
+ only_Id = quote(Id == "LIR1" & Season == "Summer")),
+ list(start = "2015-06-13", end = "2015-06-14",
+ only_Id = quote(Id == "MES8" & Season == "Summer")),
+ list(start = "2015-06-10", end = "2015-06-12",
+ only_Id = quote(Id == "MIH2" & Season == "Summer")),
+ #Days with gaps
+ list(start = "2015-06-20", end = "2015-06-25",
+ filter.expr = quote(!(day(Datetime) %in% c(21,22,23))),
+ only_Id = quote(Id == "WNC1" & Season == "Summer")),
+ list(start = "2015-06-10", end = "2015-06-16",
+ filter.expr = quote(!(day(Datetime) %in% c(13,14))),
+ only_Id = quote(Id == "WOL2" & Season == "Summer")),
+ list(start = "2015-06-10", end = "2015-06-16",
+ filter.expr = quote(!(day(Datetime) %in% c(12,13,14))),
+ only_Id = quote(Id == "WRC5" & Season == "Summer")),
+ list(start = "2015-06-19", end = "2015-06-24",
+ filter.expr = quote(!(day(Datetime) %in% c(20,21,22))),
+ only_Id = quote(Id == "BLD3" & Season == "Summer")),
+ list(start = "2015-06-19", end = "2015-06-25",
+ filter.expr = quote(!(day(Datetime) %in% c(24))),
+ only_Id = quote(Id == "SIM1" & Season == "Summer"))
+ )
+
+#keep the full dataset for visualization later
+LLdata_full <- LLdata
+
+#filter the dates that are relevant for the analysis
+LLdata <-
+LLdata %>% filter_Datetime_multiple(arguments = filter_list,
+ filter_function = filter_Date,
+ full.day = TRUE)
-#### Overview of the remaining days
+```
-```{r remaining days}
-#| fig-height: 20
-#| fig-width: 13
-#| warning: FALSE
-
-#create and save a figure overview of the remaining days
-Figure.S3 <-
-LLdata %>%
- gg_days(group = day(Datetime),
- geom = "ribbon", alpha = 0.25, fill = "#EFC000", col = "#EFC000",
- linewidth = 0.25, x.axis.breaks = waiver(),
- x.axis.format = waiver(),
- y.axis.label = "melanopic EDI (lx)",
- x.axis.label = "Dates",
- x.axis.limits = \(x) Datetime_limits(x, length = ddays(7)))+
- facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left")+
- theme(axis.text=element_text(size=6),
- text = element_text(size=8),
- strip.text = element_text(size=6),
- panel.spacing = unit(0.1, "lines"))
-
-Figure.S3
-ggsave("Figures/FigureS3.png", Figure.S3, height = 20, width = 17, units='cm')
-
-
-```
+#### Overview of the remaining days
+
+```{r remaining days}
+#| fig-height: 20
+#| fig-width: 13
+#| warning: FALSE
+
+#create and save a figure overview of the remaining days
+Figure.S2 <-
+LLdata %>%
+ gg_days(group = day(Datetime),
+ geom = "ribbon", alpha = 0.25, fill = "#EFC000", col = "#EFC000",
+ linewidth = 0.25, x.axis.breaks = waiver(),
+ x.axis.format = waiver(),
+ y.axis.label = "melanopic EDI (lx)",
+ x.axis.label = "Dates",
+ x.axis.limits = \(x) Datetime_limits(x, length = ddays(7)))+
+ facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left")+
+ theme(axis.text=element_text(size=6),
+ text = element_text(size=8),
+ strip.text = element_text(size=6),
+ panel.spacing = unit(0.1, "lines"))
+
+Figure.S2
+ggsave("Figures/FigureS2.png", Figure.S2, height = 20, width = 17, units='cm')
+
-#### Valid wear times
+```
-In the following section we will filter the data for valid wear times according to the `Status` column in the dataset. Unfortunately, because the data was stored in two different locale-settings, the column exists two times with slightly different names. The next section does necessary pre-processing to clean the data up.
+#### Valid wear times
-```{r cleanup}
-#clean up the data so that info that is stored in two columns is merged
-LLdata <- LLdata %>%
- mutate(Status = case_when(
- !is.na(Status..Nicht.am.Handgelenk.) ~ Status..Nicht.am.Handgelenk.,
- .default = NA_character_),
- Sleep = case_when(
- !is.na(Schlaf.Wach) ~ Schlaf.Wach,
- !is.na(S.W.Status) ~ S.W.Status,
- )) %>%
- select(Id, Season, Datetime, MEDI, Status, Sleep, Aktivität)
-
-#recode the data for Status and Sleep
-LLdata <- LLdata %>%
- mutate(Status =
- case_when(Status == 0 ~ "non wear", Status == 1 ~ "wear") %>%
- factor(levels = c("non wear", "wear")),
- Sleep =
- case_when(Sleep == 0 ~ "sleep", Sleep == 1 ~ "wake") %>%
- factor(levels = c("sleep", "wake")))
-
-```
+In the following section we will filter the data for valid wear times according to the `Status` column in the dataset. Unfortunately, because the data was stored in two different locale-settings, the column exists two times with slightly different names. The next section does necessary pre-processing to clean the data up.
+
+```{r cleanup}
+#clean up the data so that info that is stored in two columns is merged
+LLdata <- LLdata %>%
+ mutate(Status = case_when(
+ !is.na(Status..Nicht.am.Handgelenk.) ~ Status..Nicht.am.Handgelenk.,
+ .default = NA_character_),
+ Sleep = case_when(
+ !is.na(Schlaf.Wach) ~ Schlaf.Wach,
+ !is.na(S.W.Status) ~ S.W.Status,
+ )) %>%
+ select(Id, Season, Datetime, MEDI, Status, Sleep, Aktivität)
+
+#recode the data for Status and Sleep
+LLdata <- LLdata %>%
+ mutate(Status =
+ case_when(Status == 0 ~ "non wear", Status == 1 ~ "wear") %>%
+ factor(levels = c("non wear", "wear")),
+ Sleep =
+ case_when(Sleep == 0 ~ "sleep", Sleep == 1 ~ "wake") %>%
+ factor(levels = c("sleep", "wake")))
-##### Non-Wear Times
+```
-```{r nonwear times}
-#| fig-height: 20
-#| fig-width: 13
-#| warning: FALSE
-
-#create and save a figure that shows the non-wear times
-Figure.S4 <-
-LLdata %>%
- gg_days(group = interaction(day(Datetime), consecutive_id(Status)),
- geom = "ribbon", alpha = 0.25, aes_fill = Status, aes_col = Status,
- linewidth = 0.25, x.axis.breaks = waiver(),
- x.axis.format = waiver(),
- y.axis.label = "melanopic EDI (lx)",
- x.axis.label = "Dates",
- x.axis.limits = \(x) Datetime_limits(x, length = ddays(7)))+
- facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left")+
- theme(axis.text=element_text(size=6),
- text = element_text(size=8),
- strip.text = element_text(size=6),
- panel.spacing = unit(0.1, "lines"),
- legend.position = "bottom",
- legend.margin=margin(0,0,0,0),
- legend.box.margin=margin(-10,-10,0,-10))
-
-Figure.S4
-ggsave("Figures/FigureS4.png", Figure.S4, height = 20, width = 17, units='cm')
-
-
-```
+##### Non-Wear Times
+
+```{r nonwear times}
+#| fig-height: 20
+#| fig-width: 13
+#| warning: FALSE
+
+#create and save a figure that shows the non-wear times
+Figure.S3 <-
+LLdata %>%
+ gg_days(group = interaction(day(Datetime), consecutive_id(Status)),
+ geom = "ribbon", alpha = 0.25, aes_fill = Status, aes_col = Status,
+ linewidth = 0.25, x.axis.breaks = waiver(),
+ x.axis.format = waiver(),
+ y.axis.label = "melanopic EDI (lx)",
+ x.axis.label = "Dates",
+ x.axis.limits = \(x) Datetime_limits(x, length = ddays(7)))+
+ facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left")+
+ theme(axis.text=element_text(size=6),
+ text = element_text(size=8),
+ strip.text = element_text(size=6),
+ panel.spacing = unit(0.1, "lines"),
+ legend.position = "bottom",
+ legend.margin=margin(0,0,0,0),
+ legend.box.margin=margin(-10,-10,0,-10))
+
+Figure.S3
+ggsave("Figures/FigureS3.png", Figure.S3, height = 20, width = 17, units='cm')
+
-#### Included days
+```
-In this section we summarize the non-wear times by day and participant to judge whether a given participant-day has do be excluded from the analysis.
+#### Included days
-```{r nonwear summary}
-#| warning: FALSE
-
-#calculate the daily non-wear times as the percentage of non-wear times and
-#check whether it exceeds the set threshold
-Nonwear_summary <-
-LLdata %>% group_by(Id, Season, Day = date(Datetime)) %>%
- summarize(NonWear = sum(Status == "non wear")/sum(!is.na(Status)),
- .groups = "drop_last") %>%
- mutate(valid_Day =
- (NonWear < (1-valid_data_threshold)) %>%
- factor() %>%
- forcats::fct_recode("valid Day" = "TRUE", "invalid Day" = "FALSE"),
- Id = forcats::fct_drop(Id)
- )
-
-#create and save a figure that shows the non-wear times >0 by day and participant
-Figure.2 <-
-Nonwear_summary %>%
- filter(NonWear > 0) %>%
- ggplot(aes(x= fct_reorder(interaction(Id, Day, sep = " "), NonWear), y = NonWear)) +
- geom_col() +
- geom_text(aes(label = vec_fmt_percent(NonWear)),
- nudge_y = 0.02, size = 2.5, show.legend = FALSE) +
- labs(x = "Participant-Day", y = "Non-Wear Time", fill = "Participant") +
- coord_flip() +
- theme_minimal()+
- scale_y_continuous(labels = scales::label_percent(accuracy = 1))+
- guides(fill = "none")+
- gghighlight::gghighlight(
- NonWear > 1- valid_data_threshold, use_direct_label = FALSE)+
- geom_hline(yintercept = 1- valid_data_threshold, linetype = "dashed",
- color = "red", linewidth = 0.25) +
- annotate(geom = "text", label = "Threshold criterion\nfor valid days",
- x = 10, y = 1.005- valid_data_threshold, hjust = 0, vjust = 0,
- color = "red", size = 3)+
- theme(axis.text=element_text(size=8))
-
-Figure.2
-ggsave("Figures/Figure2.png", Figure.2, height = 8.5, width = 13, units='cm')
-
-#summarize the non-wear times as a table
-Nonwear_summary %>%
- gtsummary::tbl_summary(
- statistic = list(NonWear ~"{min} - {max}"),
- label = list(NonWear ~ "Non-Wear Time",
- valid_Day ~ "Valid Days"),
- by = valid_Day)
-
-```
+In this section we summarize the non-wear times by day and participant to judge whether a given participant-day has do be excluded from the analysis.
+
+```{r nonwear summary}
+#| warning: FALSE
+
+#calculate the daily non-wear times as the percentage of non-wear times and
+#check whether it exceeds the set threshold
+Nonwear_summary <-
+LLdata %>% group_by(Id, Season, Day = date(Datetime)) %>%
+ summarize(NonWear = sum(Status == "non wear")/sum(!is.na(Status)),
+ .groups = "drop_last") %>%
+ mutate(valid_Day =
+ (NonWear < (1-valid_data_threshold)) %>%
+ factor() %>%
+ forcats::fct_recode("Valid days" = "TRUE", "Invalid days" = "FALSE"),
+ Id = forcats::fct_drop(Id)
+ )
+
+#create and save a figure that shows the non-wear times >0 by day and participant
+Figure.2 <-
+Nonwear_summary %>%
+ filter(NonWear > 0) %>%
+ ggplot(aes(x= fct_reorder(interaction(Id, Day, sep = " "), NonWear), y = NonWear)) +
+ geom_col() +
+ geom_text(aes(label = vec_fmt_percent(NonWear)),
+ nudge_y = 0.02, size = 2.5, show.legend = FALSE) +
+ labs(x = "Participant-Day", y = "Non-Wear Time", fill = "Participant") +
+ coord_flip() +
+ theme_minimal()+
+ scale_y_continuous(labels = scales::label_percent(accuracy = 1))+
+ guides(fill = "none")+
+ gghighlight::gghighlight(
+ NonWear > 1- valid_data_threshold, use_direct_label = FALSE)+
+ geom_hline(yintercept = 1- valid_data_threshold, linetype = "dashed",
+ color = "red", linewidth = 0.25) +
+ annotate(geom = "text", label = "Threshold criterion\nfor valid days",
+ x = 10, y = 1.005- valid_data_threshold, hjust = 0, vjust = 0,
+ color = "red", size = 3)+
+ theme(axis.text=element_text(size=8))
+
+Figure.2
+ggsave("Figures/Figure2.png", Figure.2, height = 8.5, width = 13, units='cm')
+
+#summarize the non-wear times as a table
+Nonwear_summary %>%
+ gtsummary::tbl_summary(
+ statistic = list(NonWear ~"{min} - {max}"),
+ label = list(NonWear ~ "Non-Wear Time",
+ valid_Day ~ "Valid Days"),
+ by = valid_Day)
-```{r valid days}
-#| fig-height: 20
-#| fig-width: 13
-#| warning: FALSE
-
-#connect valid and non-valid days to the data
-LLdata <-
- LLdata %>% group_by(Id, Season, Day = date(Datetime)) %>% left_join(Nonwear_summary)
-
-LLdata_full <-
- LLdata_full %>% group_by(Id, Season, Day = date(Datetime)) %>%
- left_join(Nonwear_summary) %>%
- filter_Date(start = "2015-06-10", end = "2015-06-16",
- only_Id = Id == "WOL2" & Season == "Summer")
-
-#create and save a figure about the illuminance on valid, invalid and non-dayshift days
-Figure.3 <-
-LLdata_full %>%
- gg_days(group = day(Datetime),
- geom = "ribbon", alpha = 0.25, aes_fill = valid_Day, aes_col = valid_Day,
- linewidth = 0.1, x.axis.breaks = waiver(),
- x.axis.format = waiver(),
- y.axis.label = "melanopic EDI (lx)",
- x.axis.label = "Dates",
- x.axis.limits = \(x) Datetime_limits(x, length = ddays(7)))+
- facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left")+
- scale_fill_discrete(type = c("#EFC000", "red"),
- labels = c("Valid", "Invalid", "non Dayshift (excluded)"))+
- scale_color_discrete(type = c("#EFC000", "red"),
- labels = c("Valid", "Invalid", "non Dayshift (excluded)"))+
- theme(axis.text=element_text(size=5.5),
- text = element_text(size=8),
- strip.text = element_text(size=6),
- panel.spacing = unit(0.1, "lines"),
- legend.position = "bottom",
- legend.margin=margin(0,0,0,0),
- legend.box.margin=margin(-10,-10,0,-10),
- axis.line = element_line(linewidth = 0.25),
- axis.ticks = element_line(linewidth = 0.25),
- legend.text = element_text(size=6),
- legend.title = element_text(size=7))+
- labs(fill = "Inclusion", color = "Inclusion")
-
-Figure.3
-ggsave("Figures/Figure3.png", Figure.3, height = 21, width = 17, units='cm')
-
-#filter the data to only include valid days
-LLdata <- LLdata %>%
- filter(valid_Day == "valid Day") %>%
- select(-valid_Day, -NonWear)
-
-```
+```
+
+```{r valid days}
+#| fig-height: 20
+#| fig-width: 13
+#| warning: FALSE
+
+#connect valid and non-valid days to the data
+LLdata <-
+ LLdata %>% group_by(Id, Season, Day = date(Datetime)) %>% left_join(Nonwear_summary)
+
+LLdata_full <-
+ LLdata_full %>% group_by(Id, Season, Day = date(Datetime)) %>%
+ left_join(Nonwear_summary) %>%
+ filter_Date(start = "2015-06-10", end = "2015-06-16",
+ only_Id = Id == "WOL2" & Season == "Summer")
+
+#create and save a figure about the illuminance on valid, invalid and non-dayshift days
+Figure.3 <-
+LLdata_full %>%
+ gg_days(group = day(Datetime),
+ geom = "ribbon", alpha = 0.25, aes_fill = valid_Day, aes_col = valid_Day,
+ linewidth = 0.1, x.axis.breaks = waiver(),
+ x.axis.format = waiver(),
+ y.axis.label = "melanopic EDI (lx)",
+ x.axis.label = "Dates",
+ x.axis.limits = \(x) Datetime_limits(x, length = ddays(7)))+
+ facet_wrap(Id ~ Season, scales = "free_x", ncol = 2, strip.position = "left")+
+ scale_fill_discrete(type = c("#EFC000", "red"),
+ labels = c("Valid", "Invalid", "non Dayshift (excluded)"))+
+ scale_color_discrete(type = c("#EFC000", "red"),
+ labels = c("Valid", "Invalid", "non Dayshift (excluded)"))+
+ theme(axis.text=element_text(size=5.5),
+ text = element_text(size=8),
+ strip.text = element_text(size=6),
+ panel.spacing = unit(0.1, "lines"),
+ legend.position = "bottom",
+ legend.margin=margin(0,0,0,0),
+ legend.box.margin=margin(-10,-10,0,-10),
+ axis.line = element_line(linewidth = 0.25),
+ axis.ticks = element_line(linewidth = 0.25),
+ legend.text = element_text(size=6),
+ legend.title = element_text(size=7))+
+ labs(fill = "Inclusion", color = "Inclusion")
+
+Figure.3
+ggsave("Figures/Figure3.png", Figure.3, height = 21, width = 17, units='cm')
+
+#filter the data to only include valid days
+LLdata <- LLdata %>%
+ filter(valid_Day == "Valid days") %>%
+ select(-valid_Day, -NonWear)
-```{r illuminance season}
-#| fig-height: 13
-#| fig-width: 13
-#| warning: FALSE
-
-#create and save a figure about the illuminance between season
-Figure.S5 <-
-LLdata %>%
- ggplot(aes(x = MEDI, fill = Season)) +
- geom_density_ridges(aes(y = Id, height = ..density..), stat = "density", alpha = 0.5) +
- geom_vline(xintercept = 250, col = "red", linetype = "dashed") +
- scale_x_continuous(
- breaks = c(0, 1, 10, 100, 250, 1000, 10000),
- trans = "symlog",
- labels = \(x) format(x, scientific = FALSE, big.mark = " ")) +
- theme_cowplot()+
- labs(x = "melanopic EDI (lux)", y = "Participant ID")+
- theme(legend.position = "bottom")
-
-Figure.S5
-ggsave("Figures/FigureS5.png", Figure.S5, height = 17, width = 17, units='cm')
-
-```
+```
+
+```{r illuminance season}
+#| fig-height: 13
+#| fig-width: 13
+#| warning: FALSE
+
+#create and save a figure about the illuminance between season
+Figure.S4 <-
+LLdata %>%
+ ggplot(aes(x = MEDI, fill = Season)) +
+ geom_density_ridges(aes(y = Id, height = ..density..), stat = "density", alpha = 0.5) +
+ geom_vline(xintercept = 250, col = "red", linetype = "dashed") +
+ scale_x_continuous(
+ breaks = c(0, 1, 10, 100, 250, 1000, 10000),
+ trans = "symlog",
+ labels = \(x) format(x, scientific = FALSE, big.mark = " ")) +
+ theme_cowplot()+
+ labs(x = "melanopic EDI (lux)", y = "Participant ID")+
+ theme(legend.position = "bottom")
+
+Figure.S4
+ggsave("Figures/FigureS4.png", Figure.S4, height = 17, width = 17, units='cm')
-### Metrics
+```
-In this section we calculate the various metrics for the analysis. The following metrics[^2] have been selected, all based on melanopic EDI:
+### Metrics
-[^2]: More Information about those metrics can be found under: Hartmeyer S, Andersen M. Towards a framework for light-dosimetry studies: Quantification metrics. Lighting Research & Technology. 2023;0(0). doi:10.1177/14771535231170500
+In this section we calculate the various metrics for the analysis. The following metrics[^2] have been selected, all based on melanopic EDI:
-- Geometric mean and standard deviation
-- Luminous exposure (lx\*h)
-- Time above 250 lux (h)
-- Time above 1000 lux (h)
-- Mean timing of light above 250 lux (h)
-- Mean timing of light below 10 lux (h)
-- Intradaily variability
-- Mean across the darkest (L5) and brightest (M10) hours
-- Midpoint of the darkest (L5) and brightest (M10) hours
-
-```{r metrics}
-#| warning: FALSE
-
-#calculate the metrics
-Metrics_dataset <-
- LLdata %>%
- summarize(
- across(
- .cols = MEDI,
- .fns = list(
- `Geometric mean (lx)` = \(x) geomean(x, na.rm = TRUE),
- `Geometric sd (lx)` = \(x) geosd(x, na.rm = TRUE),
- `Luminous exposure (lx*h)` = \(x) sum(x, na.rm = TRUE)*30/60/60,
- `Time above 250 lx (h)` = \(x) tat(x, 250, 30, "hours", as_df = FALSE),
- `Time above 1000 lx (h)` =
- \(x) tat(x, 1000, 30, "hours", as_df = FALSE),
- `Mean timing of light above 250 lx (hh:mm)` =
- \(x) {mlit(x, Datetime, 250, as_df = FALSE) %>%
- as_datetime() %>% hms::as_hms()},
- `Mean timing of light below 10 lx (hh:mm)` =
- \(x) {mlit(x, Datetime, 10, as_df = FALSE) %>%
- as_datetime() %>% hms::as_hms()},
- `Intradaily variability` =
- \(x) intradaily_variability(x, Datetime, as_df = FALSE),
- `Mean of darkest 5 hours (L5, lux)` =
- \(x) bright_dark_period(
- x, Datetime, "dark", "5 hours", 30, loop = TRUE, as_df = FALSE
- ) %>% .[1],
- `Midpoint of darkest 5 hours (L5, hh:mm)` =
- \(x) bright_dark_period(
- x, Datetime, "dark", "5 hours", 30, loop = TRUE, as_df = FALSE
- ) %>% .[2] %>%
- as_datetime() %>% hms::as_hms(),
- `Mean of brightest 10 hours (M10, lux)` =
- \(x) bright_dark_period(
- x, Datetime, "bright", "10 hours", 30, loop = TRUE, as_df = FALSE
- ) %>% .[1],
- `Midpoint of brightest 10 hours (M10, hh:mm)` =
- \(x) bright_dark_period(
- x, Datetime, "bright", "10 hours", 30, loop = TRUE, as_df = FALSE
- ) %>% .[2] %>%
- as_datetime() %>% hms::as_hms()
- ),
- .names = "{.fn}"),
- .groups = "drop_last"
- )
-
-#styling formula for time
-style_time <- function(x, format = "%H:%M"){
- x %>% hms::as_hms() %>% as.POSIXlt() %>% format(format)
-}
-
-#create a table-summary of the metrics
-Metrics_dataset %>%
- ungroup() %>%
- select(-Day, -Id) %>%
- tbl_summary(by = Season, missing_text = "Days without Metric",
- digits = list(
- `Midpoint of darkest 5 hours (L5, hh:mm)` ~ style_time,
- `Midpoint of brightest 10 hours (M10, hh:mm)` ~ style_time,
- `Mean timing of light above 250 lx (hh:mm)` ~ style_time,
- `Mean timing of light below 10 lx (hh:mm)` ~ style_time
- )
- )
-
-```
+[^2]: More Information about those metrics can be found under: Hartmeyer S, Andersen M. Towards a framework for light-dosimetry studies: Quantification metrics. Lighting Research & Technology. 2023;0(0). doi:10.1177/14771535231170500
+
+- Geometric mean and standard deviation
+- Luminous exposure (lx\*h)
+- Time above 250 lux (h)
+- Time above 1000 lux (h)
+- Mean timing of light above 250 lux (h)
+- Mean timing of light below 10 lux (h)
+- Intradaily variability
+- Mean across the darkest (L5) and brightest (M10) hours
+- Midpoint of the darkest (L5) and brightest (M10) hours
+
+```{r metrics}
+#| warning: FALSE
+
+#calculate the metrics
+Metrics_dataset <-
+ LLdata %>%
+ summarize(
+ across(
+ .cols = MEDI,
+ .fns = list(
+ `Geometric mean (lx)` = \(x) geomean(x, na.rm = TRUE),
+ `Geometric sd (lx)` = \(x) geosd(x, na.rm = TRUE),
+ `Luminous exposure (lx*h)` = \(x) sum(x, na.rm = TRUE)*30/60/60,
+ `Time above 250 lx (h)` = \(x) tat(x, 250, 30, "hours", as_df = FALSE),
+ `Time above 1000 lx (h)` =
+ \(x) tat(x, 1000, 30, "hours", as_df = FALSE),
+ `Mean timing of light above 250 lx (hh:mm)` =
+ \(x) {mlit(x, Datetime, 250, as_df = FALSE) %>%
+ as_datetime() %>% hms::as_hms()},
+ `Mean timing of light below 10 lx (hh:mm)` =
+ \(x) {mlit(x, Datetime, 10, as_df = FALSE) %>%
+ as_datetime() %>% hms::as_hms()},
+ `Intradaily variability` =
+ \(x) intradaily_variability(x, Datetime, as_df = FALSE),
+ `Mean of darkest 5 hours (L5, lux)` =
+ \(x) bright_dark_period(
+ x, Datetime, "dark", "5 hours", 30, loop = TRUE, as_df = FALSE
+ ) %>% .[1],
+ `Midpoint of darkest 5 hours (L5, hh:mm)` =
+ \(x) bright_dark_period(
+ x, Datetime, "dark", "5 hours", 30, loop = TRUE, as_df = FALSE
+ ) %>% .[2] %>%
+ as_datetime() %>% hms::as_hms(),
+ `Mean of brightest 10 hours (M10, lux)` =
+ \(x) bright_dark_period(
+ x, Datetime, "bright", "10 hours", 30, loop = TRUE, as_df = FALSE
+ ) %>% .[1],
+ `Midpoint of brightest 10 hours (M10, hh:mm)` =
+ \(x) bright_dark_period(
+ x, Datetime, "bright", "10 hours", 30, loop = TRUE, as_df = FALSE
+ ) %>% .[2] %>%
+ as_datetime() %>% hms::as_hms()
+ ),
+ .names = "{.fn}"),
+ .groups = "drop_last"
+ )
+
+#styling formula for time
+style_time <- function(x, format = "%H:%M"){
+ x %>% hms::as_hms() %>% as.POSIXlt() %>% format(format)
+}
+
+#create a table-summary of the metrics
+Metrics_dataset %>%
+ ungroup() %>%
+ select(-Day, -Id) %>%
+ tbl_summary(by = Season, missing_text = "Days without Metric",
+ digits = list(
+ `Midpoint of darkest 5 hours (L5, hh:mm)` ~ style_time,
+ `Midpoint of brightest 10 hours (M10, hh:mm)` ~ style_time,
+ `Mean timing of light above 250 lx (hh:mm)` ~ style_time,
+ `Mean timing of light below 10 lx (hh:mm)` ~ style_time
+ )
+ )
-### Bootstrapping
+```
-In this section we implement and perform the bootstrapping procedure to obtain the power estimates depending on sample size. We implement the following mixed-effect formula for our model:
+### Bootstrapping
-`Metric ~ Season + (1|Id)`
+In this section we implement and perform the bootstrapping procedure to obtain the power estimates depending on sample size. We implement the following mixed-effect formula for our model:
-```{r bootstrapping}
-#change the hms columns to numeric
-Metrics_dataset <-
- Metrics_dataset %>% mutate(across(where(hms::is_hms), as.numeric))
-
-#pivot data so that we have one row per metric
-Metrics_dataset2 <-
-Metrics_dataset %>%
- pivot_longer(c(-Id, -Season, -Day),
- names_to = "Name", values_to = "Metric")
-
-#function definition to perform the bootstrapping
-resample_within_id <-
- function(data,
- n_participants,
- n_replicates = 2,
- seed = NULL){
- #set a seed
- if(!is.null(seed)) set.seed(seed)
-
- #prepare the data
- data <- data %>% ungroup() %>% nest(data = -Id)
-
- #create a dataframe with the resamples
- participants <-
- 1:n_replicates %>%
- map(\(x)
- data %>%
- sample_n(n_participants, replace = TRUE)
- ) %>%
- list_rbind(names_to = "resamples") %>%
- mutate(Id = paste0(Id, "_", 1:n_participants) %>%
- factor(levels = unique(.))
- )
-
- #un- and re-nest the data, with resampling of days on a season/participant/metric level
- participants %>% unnest(data) %>%
- group_by(resamples, Season, Id, Name) %>%
- sample_frac(replace = TRUE) %>%
- ungroup() %>% nest(data = c(-resamples, -Name)) %>%
- mutate(sample_size = n_participants, .before = 1)
- }
-
-#perfom the bootstrapping
-resampled_data <-
- sample_size %>%
- map(\(x)
- {resample_within_id(Metrics_dataset2, n_participants = x, n_samples)}
- ) %>% list_rbind()
-```
-
-```{r statistics}
-#| warning: FALSE
-
-#create the formula
-Formula <- Metric ~ Season + (1|Id)
-
-#perform the statistical analysis
-boostrappedModels <-
-resampled_data %>%
- rowwise() %>%
- mutate(model = list(lmerTest::lmer(Formula, data = data)),
- pvalue = model %>% lmerTest::difflsmeans() %>% .$`Pr(>|t|)`) %>%
- select(sample_size, Name, pvalue)
-
-#compare the significance level of the test with the threshold significance
-#calculate power based on the fraction of significant results (mean of trues)
-#nest the data for plotting
-Power_data <-
-boostrappedModels %>%
- mutate(sign = pvalue <= sign_level) %>%
- group_by(Name, sample_size) %>%
- summarize(
- power = mean(sign),
- .groups = "keep"
- ) %>%
- nest(data = -Name)
-
-#save the power data
-write_csv(Power_data %>% unnest(cols = data), "Results/Power_data.csv")
-
-#if needed, load a previously calculated power data
-# Power_data <- read_csv("Results/Power_data.csv") %>% nest(data = -Name)
-
-```
+`Metric ~ Season + (1|Id)`
+
+```{r bootstrapping}
+#change the hms columns to numeric
+Metrics_dataset <-
+ Metrics_dataset %>% mutate(across(where(hms::is_hms), as.numeric))
+
+#pivot data so that we have one row per metric
+Metrics_dataset2 <-
+Metrics_dataset %>%
+ pivot_longer(c(-Id, -Season, -Day),
+ names_to = "Name", values_to = "Metric")
+
+#function definition to perform the bootstrapping
+resample_within_id <-
+ function(data,
+ n_participants,
+ n_replicates = 2,
+ seed = NULL){
+ #set a seed
+ if(!is.null(seed)) set.seed(seed)
+
+ #prepare the data
+ data <- data %>% ungroup() %>% nest(data = -Id)
+
+ #create a dataframe with the resamples
+ participants <-
+ 1:n_replicates %>%
+ map(\(x)
+ data %>%
+ sample_n(n_participants, replace = TRUE)
+ ) %>%
+ list_rbind(names_to = "resamples") %>%
+ mutate(Id = paste0(Id, "_", 1:n_participants) %>%
+ factor(levels = unique(.))
+ )
+
+ #un- and re-nest the data, with resampling of days on a season/participant/metric level
+ participants %>% unnest(data) %>%
+ group_by(resamples, Season, Id, Name) %>%
+ sample_frac(replace = TRUE) %>%
+ ungroup() %>% nest(data = c(-resamples, -Name)) %>%
+ mutate(sample_size = n_participants, .before = 1)
+ }
+
+#perfom the bootstrapping
+resampled_data <-
+ sample_size %>%
+ map(\(x)
+ {resample_within_id(Metrics_dataset2, n_participants = x, n_samples)}
+ ) %>% list_rbind()
+```
+
+```{r statistics}
+#| warning: FALSE
+
+#create the formula
+Formula <- Metric ~ Season + (1|Id)
+
+#perform the statistical analysis
+boostrappedModels <-
+resampled_data %>%
+ rowwise() %>%
+ mutate(model = list(lmerTest::lmer(Formula, data = data)),
+ pvalue = model %>% lmerTest::difflsmeans() %>% .$`Pr(>|t|)`) %>%
+ select(sample_size, Name, pvalue)
+
+#compare the significance level of the test with the threshold significance
+#calculate power based on the fraction of significant results (mean of trues)
+#nest the data for plotting
+Power_data <-
+boostrappedModels %>%
+ mutate(sign = pvalue <= sign_level) %>%
+ group_by(Name, sample_size) %>%
+ summarize(
+ power = mean(sign),
+ .groups = "keep"
+ ) %>%
+ nest(data = -Name)
+
+#save the power data
+write_csv(Power_data %>% unnest(cols = data), "Results/Power_data.csv")
+
+#if needed, load a previously calculated power data
+# Power_data <- read_csv("Results/Power_data.csv") %>% nest(data = -Name)
-### Results
+```
-In this section we summarize the results of the bootstrapping procedure.
+### Results
-```{r results plot}
-#| fig-height: 13
-#| fig-width: 15
-
-#create a plot function that takes the Power_data for one metric and plots it
-Plot_function <- function(data, name) {
- data %>%
- ggplot(aes(x = sample_size, y = power)) + geom_col() +
- gghighlight(power >= Power_level, use_direct_label = FALSE) +
- geom_hline(yintercept = Power_level, col = "red", linetype = "dashed",
- linewidth = 0.25) +
- theme_cowplot() +
- annotate("label", x = mean(sample_size), y = Power_level+0.1, alpha = 0.75,
- label = "Required Power level", col = "red", size = 1.5,
- label.size = 0.2) +
- labs(x = "Sample size", y = "Power") +
- scale_x_continuous(breaks = c(3,5, seq(10, max(sample_size), by = 10)))+
- scale_y_continuous(labels = scales::percent_format(accuracy = 1))+
- coord_cartesian(expand = FALSE, ylim = c(0,1))+
- ggtitle(name)+
- theme(plot.title = element_text(size = 5.5),
- axis.text= element_text(size=6),
- axis.title= element_text(size=6),
- axis.line = element_line(linewidth = 0.25),
- axis.ticks = element_line(linewidth = 0.25)
- )
-}
-
-#apply all metrics to the plot function
-Power_data <-
- Power_data %>%
- mutate(plot = map2(data, Name, Plot_function))
-
-#create all plots in a grid and save it
-Figure.4 <-
- Power_data$plot %>%
- reduce(\(x, y) x + y) +
- plot_annotation(tag_levels = 'A') &
- theme(plot.tag = element_text(size = 8))
-Figure.4
-
-ggsave("Figures/Figure4.png", Figure.4, height = 15, width = 22.5, units='cm')
+In this section we summarize the results of the bootstrapping procedure.
+
+```{r results plot}
+#| fig-height: 13
+#| fig-width: 15
+
+#create a plot function that takes the Power_data for one metric and plots it
+Plot_function <- function(data, name) {
+ data %>%
+ ggplot(aes(x = sample_size, y = power)) + geom_col() +
+ gghighlight(power >= Power_level, use_direct_label = FALSE) +
+ geom_hline(yintercept = Power_level, col = "red", linetype = "dashed",
+ linewidth = 0.25) +
+ theme_cowplot() +
+ annotate("label", x = mean(sample_size), y = Power_level+0.1, alpha = 0.75,
+ label = "Required Power level", col = "red", size = 1.5,
+ label.size = 0.2) +
+ labs(x = "Sample size", y = "Power") +
+ scale_x_continuous(breaks = c(3,5, seq(10, max(sample_size), by = 10)))+
+ scale_y_continuous(labels = scales::percent_format(accuracy = 1))+
+ coord_cartesian(expand = FALSE, ylim = c(0,1))+
+ ggtitle(name)+
+ theme(plot.title = element_text(size = 5.5),
+ axis.text= element_text(size=6),
+ axis.title= element_text(size=6),
+ axis.line = element_line(linewidth = 0.25),
+ axis.ticks = element_line(linewidth = 0.25)
+ )
+}
+
+#apply all metrics to the plot function
+Power_data <-
+ Power_data %>%
+ mutate(plot = map2(data, Name, Plot_function))
+
+#create all plots in a grid and save it
+Figure.4 <-
+ Power_data$plot %>%
+ reduce(\(x, y) x + y) +
+ plot_annotation(tag_levels = 'A') &
+ theme(plot.tag = element_text(size = 8))
+Figure.4
-```
+ggsave("Figures/Figure4.png", Figure.4, height = 15, width = 22.5, units='cm')
-```{r results table}
+```
-#get the required sample size for each metric
-Power_summary <-
-Power_data %>% select(-plot) %>% unnest(data) %>%
- mutate(Power_reached = power >= Power_level) %>%
- filter(Power_reached, .preserve = TRUE) %>%
- slice_min(sample_size) %>%
- select(-Power_reached) %>%
- ungroup()
-
-#check which metrics did not reach the threshold
-set1 <- Power_summary$Name
-set2 <- Power_data$Name
-missing <- base::setdiff(set2, set1)
-
-
-Power_summary %>%
- arrange(sample_size, power) %>%
- gt() %>%
- gt::cols_label(
- sample_size = "required Sample Size",
- power = "Power",
- Name = "Metric"
- ) %>%
- fmt_percent(columns = power, decimals = 0) %>%
- tab_footnote(footnote = "Power at the required sample size",
- locations = gt::cells_column_labels(columns = power),
- ) %>%
- tab_footnote(
- footnote = md(paste0("Metrics that did not reach the threshold: **",
- missing %>% paste0(collapse = ", "), "**")),
- ) %>%
- tab_footnote(
- footnote =
- paste0(
- "The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for 11 participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of ",
- sign_level,
- ". The fraction of significant differences was compared against the power level threshold of ",
- Power_level,
- ". The required sample size is the minimum sample size that reaches this threshold, with ",
- n_samples,
- " resamples per sample size (sample sizes from ",
- min(sample_size),
- " to ",
- max(sample_size),
- " were tested). The total amount of resamples/tests is ",
- nrow(resampled_data),
- " across all metrics."),
- locations = gt::cells_column_labels(columns = sample_size)
- )
-
-
-```
+```{r results table}
+
+#get the required sample size for each metric
+Power_summary <-
+Power_data %>% select(-plot) %>% unnest(data) %>%
+ mutate(Power_reached = power >= Power_level) %>%
+ filter(Power_reached, .preserve = TRUE) %>%
+ slice_min(sample_size) %>%
+ select(-Power_reached) %>%
+ ungroup()
+
+#check which metrics did not reach the threshold
+set1 <- Power_summary$Name
+set2 <- Power_data$Name
+missing <- base::setdiff(set2, set1)
+
+
+Power_summary %>%
+ arrange(sample_size, power) %>%
+ gt() %>%
+ gt::cols_label(
+ sample_size = "required Sample Size",
+ power = "Power",
+ Name = "Metric"
+ ) %>%
+ fmt_percent(columns = power, decimals = 0) %>%
+ tab_footnote(footnote = "Power at the required sample size",
+ locations = gt::cells_column_labels(columns = power),
+ ) %>%
+ tab_footnote(
+ footnote = md(paste0("Metrics that did not reach the threshold: **",
+ missing %>% paste0(collapse = ", "), "**")),
+ ) %>%
+ tab_footnote(
+ footnote =
+ paste0(
+ "The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for ",
+ N_participants,
+ " participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of ",
+ sign_level,
+ ". The fraction of significant differences was compared against the power level threshold of ",
+ Power_level,
+ ". The required sample size is the minimum sample size that reaches this threshold, with ",
+ n_samples,
+ " resamples per sample size (sample sizes from ",
+ min(sample_size),
+ " to ",
+ max(sample_size),
+ " were tested). The total amount of resamples/tests is ",
+ nrow(resampled_data),
+ " across all metrics."),
+ locations = gt::cells_column_labels(columns = sample_size)
+ )
+
+
+```
diff --git a/PowerCalc.qmd b/PowerCalc.qmd
index d361388..330af16 100644
--- a/PowerCalc.qmd
+++ b/PowerCalc.qmd
@@ -99,6 +99,8 @@ A total of 26 files are in the folder (13 participants, 2 seasons each). They ar
Participant.IDs <- c("WRC5", "WOL2", "WNC1", "SIM1", "MIH2", "MES8", "LIR1",
"HNN1", "DWM1", "D7EB", "CET8", "BLD3", "AET3")
+N_participants <- length(Participant.IDs)
+
#list all the files
files <- list.files(path = "Data", pattern = "*.csv", full.names = TRUE)
files_xlsx <- list.files(path = "Data", pattern = "*.xlsx", full.names = TRUE)
@@ -427,7 +429,7 @@ LLdata %>% group_by(Id, Season, Day = date(Datetime)) %>%
mutate(valid_Day =
(NonWear < (1-valid_data_threshold)) %>%
factor() %>%
- forcats::fct_recode("valid Day" = "TRUE", "invalid Day" = "FALSE"),
+ forcats::fct_recode("Valid days" = "TRUE", "Invalid days" = "FALSE"),
Id = forcats::fct_drop(Id)
)
@@ -514,7 +516,7 @@ ggsave("Figures/Figure3.png", Figure.3, height = 21, width = 17, units='cm')
#filter the data to only include valid days
LLdata <- LLdata %>%
- filter(valid_Day == "valid Day") %>%
+ filter(valid_Day == "Valid days") %>%
select(-valid_Day, -NonWear)
```
@@ -803,7 +805,9 @@ Power_summary %>%
tab_footnote(
footnote =
paste0(
- "The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for 11 participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of ",
+ "The sample size calculation is based on a bootstrap resampling of daily metrics between winter and summer seasons for ",
+ N_participants,
+ " participants. For each resampled dataset, significance was tested in a mixed-effect model with a significance level of ",
sign_level,
". The fraction of significant differences was compared against the power level threshold of ",
Power_level,