stores classes as factor in time series

Author: jhmigueles

R/aggregate_per_date.R

@@ -42,7 +42,9 @@ aggregate_per_date = function(tsDir, epoch, classifier, classes,
     # format timestamp to ease calculations
     ts$timestamp = strptime(paste(ts$date, ts$time), format = "%Y-%m-%d %H:%M:%OS", tz = "")
     ts$time = format(ts$timestamp, "%H:%M:%S")
-    ts$activity = factor(ts$activity, levels = 1:length(classes), labels = classes)
+    if (is.numeric(ts$activity)) { # ensures compatibility with versions <0.1.4
+      ts$activity = factor(ts$activity, levels = 1:length(classes), labels = classes)
+    }
     # Day of the week, used for summarizing per day
     availableDates = unique(ts$date)
     # matrix to store the results

R/classifySleep.R

@@ -76,6 +76,7 @@ classifySleep = function(anglez, starttime, classifier, infoClassifier, ts, do.s
     }
   }
   # add nonwear to ts
+  ts$activity = as.numeric(ts$activity)
   nonwear_id = length(classes) + 1
   classes = c(classes, "nonwear")
   ts$nonwear_orig = ts$nonwear
@@ -128,6 +129,6 @@ classifySleep = function(anglez, starttime, classifier, infoClassifier, ts, do.s
       ts$activity[which(ts$sleep_periods == 1)] = sleep_id
     }
   }
-  activity = ts$activity
+  activity = factor(x = ts$activity, levels = 1:length(classes), labels = classes)
   return(activity)
 }

R/runActimetric.R

@@ -227,7 +227,8 @@ runActimetric = function(input_directory = NULL, output_directory = NULL, studyn
       S = matrix(0,0,4) #dummy variable needed to cope with head-tailing succeeding blocks of data
       # ---------------------------------------------------------------------
       # Run Pipeline...
-      nonwear = enmo = agcounts = LFEcounts = tilt = anglez = activity = NULL
+      nonwear = enmo = agcounts = LFEcounts = tilt = anglez = NULL
+      activity = factor()
       while (isLastBlock == FALSE) {
         # 1 - read and extract calibration coefficients
         accread = ReadAndCalibrate(file = file, sf = sf, blocksize = blocksize,
@@ -340,7 +341,7 @@ runActimetric = function(input_directory = NULL, output_directory = NULL, studyn
         if (!is.null(LFEcounts)) {
           if (nrow(LFEcounts) == nrow(ts)) ts = as.data.frame(cbind(ts, LFEcounts))
         }
-        numeric_columns = sapply(ts, mode) == 'numeric'
+        numeric_columns = sapply(ts, class) == 'numeric'
         ts[numeric_columns] =  round(ts[numeric_columns], 3)
         ts  = do.call(data.frame,lapply(ts, function(x) replace(x, is.infinite(x), NA)))
         ts[is.na(ts)] = 0