Merge pull request #1031 from wadpac/combined_PR_for_issues_1026_1029…

…_1030

Fixes three smaller issues 1026 1029 1030

NEWS.md

@@ -1,3 +1,11 @@
+# CHANGES IN GGIR VERSION 3.0-5
+
+- Part 5: Fix bug in functionality for Sensewear data (externally derived epoch data) #1030
+
+- Part 1: For externally derived epoch data in dataFormat actiwatch_csv, actiwatch_awd, actigraph_csv, and sensewear_xls the non-wear detection is no longer done based on a hard-coded 60 minute rolling window but the window length is now modifiable with the third value of argument windowsizes (in seconds) as also used for raw data. #1026
+
+- Part 5: Fix issue with defining days specific to when timewindow is MM, recordings starts at midnight and both first and last days are incomplete #1029
+
 # CHANGES IN GGIR VERSION 3.0-4
 
 - Part 5: Improved handling of inconsistent number of columns in part 5 milestone data #1002

R/HASPT.R

@@ -63,6 +63,10 @@ HASPT = function(angle, perc = 10, spt_threshold = 15,
       ma <- function(x, n = 300 / ws3){stats::filter(x, rep(1 / n, n), sides = 2, circular = TRUE)}
       x = ma(x)
       activityThreshold = sd(x, na.rm = TRUE) * 0.2
+      # For sensewear external data this will not work as it mostly has values of 1 and up.
+      if (activityThreshold < min(activity)) {
+        activityThreshold = quantile(x, probs = 0.1)  
+      }
       if (HASPT.ignore.invalid == TRUE) {
         invalid = adjustlength(x, invalid)
         zeroMovement = which(x <= activityThreshold & invalid == 0)
@@ -121,6 +125,5 @@ HASPT = function(angle, perc = 10, spt_threshold = 15,
     }
     tib.threshold = pp
   }
-  invisible(list(SPTE_start=SPTE_start,
-                 SPTE_end=SPTE_end, tib.threshold=tib.threshold))
+  invisible(list(SPTE_start = SPTE_start, SPTE_end = SPTE_end, tib.threshold = tib.threshold))
 }

R/convertEpochData.R

@@ -9,6 +9,7 @@ convertEpochData = function(datadir = c(), metadatadir = c(),
   tz = params_general[["desiredtz"]]
   epSizeShort = params_general[["windowsizes"]][1]
   epSizeLong = params_general[["windowsizes"]][2]
+  epSizeNonWear = params_general[["windowsizes"]][3]
   myfun = NULL
   # Identify input data file extensions
   if (dir.exists(datadir) == FALSE) {
@@ -557,7 +558,7 @@ convertEpochData = function(datadir = c(), metadatadir = c(),
                  params_general[["dataFormat"]] == "actigraph_csv" |
                  params_general[["dataFormat"]] == "sensewear_xls") {
         # Using rolling 60 minute sum to indicate whether it is nonwear
-        imp2 = zoo::rollapply(imp, width = (1*3600) / epSizeShort, FUN = sum, fill = 0)
+        imp2 = zoo::rollapply(imp, width = ceiling(epSizeNonWear / epSizeShort), FUN = sum, fill = 0)
         imp4 = imp2
         imp4[which(imp2 > 0)] = 0
         imp4[which(imp2 == 0)] = 3 # If rolling average is zero then consider it nonwear

R/g.part4.R

@@ -292,14 +292,12 @@ g.part4 = function(datadir = c(), metadatadir = c(), f0 = f0, f1 = f1,
           } else if ((length(params_sleep[["def.noc.sleep"]]) == 1 ||
                      length(params_sleep[["loglocation"]]) != 0) &&
                      length(SPTE_start) != 0) {
+
             # use SPTE algorithm (inside the g.sib.det function) as backup for sleeplog OR if user
             # explicitely asks for it
             defaultSptOnset = SPTE_start[j]
             defaultSptWake = SPTE_end[j]
-            guider = "HDCZA"
-            if (params_sleep[["sleepwindowType"]] == "TimeInBed" & params_general[["sensor.location"]] == "hip") {
-              guider = "HorAngle"
-            }
+            guider = params_sleep[["HASPT.algo"]] # HDCZA, NotWorn, HorAngle
             if (is.na(defaultSptOnset) == TRUE) {
               # If SPTE was not derived for this night, use average estimate for other nights
               availableestimate = which(is.na(SPTE_start) == FALSE)

R/g.part5.definedays.R

@@ -46,28 +46,26 @@ g.part5.definedays = function(nightsi, wi, indjump, nightsi_bu,
         qqq_backup = qqq
       } else if (wi > length(nightsi)) {
         qqq[1] = qqq_backup[2] + 1
-        if (wi <= length(nightsi)) {
-          qqq[2] = nightsi[wi] - 1
-        } else {
-          if (wi - indjump <= length(nightsi)) {
-            tmp1 = which(nightsi_bu == nightsi[wi - indjump])
-            qqq[2] = nightsi_bu[tmp1 + indjump] - 1
-            indjump = indjump + 1 # in case there are multiple days beyond nightsi
-            if (is.na(qqq[2])) { # if that does not work use last midnight and add 24 hours
-              index_lastmidn = which(nightsi_bu == nightsi[wi - (indjump - 1)]) + (indjump - 1)
-              if (length(index_lastmidn) > 0) {
-                qqq[2] = nightsi_bu[index_lastmidn] + (24*(60/epochSize) * 60) - 1
-              } else {
-                qqq[2] = NA
-              }
-            }
-          } else {
-            qqq[2] = NA
-          }
+        if (wi - indjump <= length(nightsi)) {
+          tmp1 = which(nightsi_bu == nightsi[wi - indjump])
+          qqq[2] = nightsi_bu[tmp1 + indjump] - 1
+          indjump = indjump + 1 # in case there are multiple days beyond nightsi
           if (is.na(qqq[2])) { # if that does not work use last midnight and add 24 hours
-            qqq[2] = qqq_backup[2] + (24*(60/epochSize) * 60) - 1
+            index_lastmidn = which(nightsi_bu == nightsi[wi - (indjump - 1)]) + (indjump - 1)
+            if (length(index_lastmidn) > 0) {
+              qqq[2] = nightsi_bu[index_lastmidn] + (24*(60/epochSize) * 60) - 1
+            } else {
+              qqq[2] = NA
+            }
           }
-          if (qqq[1] == qqq[2])  qqq[2] = qqq[2] + (24*(60/epochSize) * 60) - 1
+        } else {
+          qqq[2] = NA
+        }
+        if (is.na(qqq[2])) { # if that does not work use last midnight and add 24 hours
+          qqq[2] = qqq_backup[2] + (24*(60/epochSize) * 60) - 1
+        }
+        if (qqq[1] == qqq[2]) {
+          qqq[2] = qqq[2] + (24*(60/epochSize) * 60) - 1
         }
         if (is.na(qqq[2]) == TRUE | Nts < qqq[2]) {
           qqq[2] = Nts
@@ -88,6 +86,7 @@ g.part5.definedays = function(nightsi, wi, indjump, nightsi_bu,
     }
     # in MM, also define segments of the day based on qwindow
     if (!is.na(qqq[1]) & !is.na(qqq[2])) {
+      if (qqq[2] > Nts) qqq[2] = Nts
       fullQqq = qqq[1]:qqq[2]
       firstepoch = format(ts$time[qqq[1]],  "%H:%M:%S")
       lastepoch = format(ts$time[qqq[2]],  "%H:%M:%S")

R/g.part5_initialise_ts.R

@@ -16,8 +16,10 @@ g.part5_initialise_ts = function(IMP, M, params_247, params_general, longitudina
     angleName = "angley"
   } else if (longitudinal_axis == 3 && "anglex" %in% names(IMP$metashort)) {
     angleName = "anglez"
+  } else {
+    angleName = NULL
   }
-  if (angleName %in% names(IMP$metashort)) {
+  if (!is.null(angleName) && angleName %in% names(IMP$metashort)) {
     ts = data.frame(time = IMP$metashort[,1], ACC = IMP$metashort[,params_general[["acc.metric"]]] * scale,
                     guider = rep("unknown", nrow(IMP$metashort)),
                     angle = as.numeric(as.matrix(IMP$metashort[,which(names(IMP$metashort) == angleName)])))

man/GGIR.Rd

@@ -245,17 +245,21 @@ GGIR(mode = 1:5,
       \item{dataFormat}{
         Character (default = "raw").
         To indicate what the format is of the data in datadir.
-        Current alternatives: ukbiobank_csv, actiwatch_csv, actiwatch_awd, 
+        Alternatives: ukbiobank_csv, actiwatch_csv, actiwatch_awd, 
         actigraph_csv, and sensewear_xls, which correspond to epoch level data 
-        files from, respecitively,  UK Biobank in csv format, Actiwatch in csv 
+        files from, respecitively, UK Biobank in csv format, Actiwatch in csv 
         format, Actiwatch in awd format, ActiGraph csv format, and Sensewear in
         xls format (also works with xlsx). Here, the assumed epoch size for
         UK Biobank csvdata is 5 seconds. 
         The epoch size for the other non-raw data formats is 
         flexible, but make sure that you set first value of argument 
         \code{windowsizes} accordingly. Also when working with 
         non-raw data formats specify argument \code{extEpochData_timeformat} as
-        documented below.}
+        documented below. For ukbiobank_csv nonwear is a column in the data itself,
+        for actiwatch_csv, actiwatch_awd, actigraph_csv, and sensewear_xls non-wear 
+        is detected as 60 minute rolling zeros. The length of this window can be 
+        modified with the third value of argument \code{windowsizes} expressed in
+        seconds.}
 
        \item{maxRecordingInterval}{
         Numeric (default = NULL).