Merge pull request #52 from PhysicalActivityOpenTools/issue51_classesAsFactor

Issue51 classes as factor

Author: jhmigueles

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: actimetric
 Type: Package
 Title: Classifies Accelerometer Data Into Physical Activity Types
-Version: 0.1.3
+Version: 0.1.4
 Authors@R: c(person("Jairo H","Migueles", role = c("aut","cre"),
                     email = "[email protected]",
                     comment = c(ORCID = "0000-0003-0366-6935")),

NEWS.md

@@ -1,3 +1,8 @@
+# actimetric 0.1.4
+
+* Time series: stores classes as factor in time series to ease interpretation #51
+* Documentation: minor revision of documentation for functions check_classifier and getBout #41
+
 # actimetric 0.1.3
 
 * Calibration: fix minor bug by which calibration procedure was using time column instead of acceleration columns in data #49

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/check_classifier.R

@@ -9,8 +9,6 @@
 #'
 #' @return Character with the definitive classifier
 #' @export
-#'
-#' @examples
 #' @author Jairo H. Migueles <[email protected]>
 #' check_classifier("Adult wrist Trost")
 check_classifier = function(classifier) {

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/getBout.R

@@ -10,7 +10,7 @@
 #' @param epoch Integer (default = 5) indicating the epoch length in seconds.
 #' @param boutmaxgap Integer (default = 1) with maximum consecutive gap length allowed in bout calculation.
 #'
-#' @return
+#' @return Vector with 0's and 1's indicating bout occurrences.
 #' @export
 #' @references GGIR R Package. doi: 10.1123/jmpb.2018-0063
 getBout = function(x, boutduration = 10, boutcriter = 0.8, boutmaxgap = 1, epoch = 5) {

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