Merge pull request #1054 from wadpac/issue1053_handle_dots_in_filenames

Minor issue fixes: handling multiple dots in filenames and clarify documentation for read.myacc.csv

NEWS.md

@@ -1,5 +1,7 @@
 # CHANGES IN GGIR VERSION 3.0-6
 
+- Part 1: Need to specify rmc.firstrow.acc when working with ad-hoc csv file format now better documented and read.myacc.csv now produces an error when used directly and user forgets to specify rmc.firstrow.acc #1034.
+
 - Part 1:
 
   - Improved readability and maintainability of the code #1027

R/g.inspectfile.R

@@ -28,8 +28,8 @@ g.inspectfile = function(datafile, desiredtz = "", params_rawdata = c(),
   # the output of this function for the original datafile is stored inside the RData file in the form of object I
   getbrand = function(filename = c(), datafile = c()) {
     sf = c(); isitageneactive = c(); mon = c(); dformat = c() #generating empty variables
-    extension = unlist(strsplit(filename,"[.]"))[2]
-
+    extension = unlist(strsplit(filename,"[.]"))
+    extension = extension[length(extension)]
     switch(extension,
             "bin" = { dformat = FORMAT$BIN },
             "cwa" = ,

R/read.myacc.csv.R

@@ -1,5 +1,5 @@
 read.myacc.csv = function(rmc.file=c(), rmc.nrow=Inf, rmc.skip=c(), rmc.dec=".",
-                          rmc.firstrow.acc = 1, rmc.firstrow.header=c(),
+                          rmc.firstrow.acc = c(), rmc.firstrow.header=c(),
                           rmc.header.length = c(),
                           rmc.col.acc = 1:3, rmc.col.temp = c(), rmc.col.time=c(),
                           rmc.unit.acc = "g", rmc.unit.temp = "C", 
@@ -63,7 +63,8 @@ read.myacc.csv = function(rmc.file=c(), rmc.nrow=Inf, rmc.skip=c(), rmc.dec=".",
   }
 
   if (is.null(rmc.firstrow.acc) || rmc.firstrow.acc < 1) {
-    rmc.firstrow.acc = 1
+    stop(paste0("\nParameter rmc.firstrow.acc always need to be specified ",
+                "when working with ad-hoc csv format data"))
   }
   skip = rmc.firstrow.acc - 1
   if (!is.null(rmc.skip) && length(rmc.skip) > 0) {

man/read.myacc.csv.Rd

@@ -11,7 +11,7 @@
 }
 \usage{
   read.myacc.csv(rmc.file=c(), rmc.nrow=Inf, rmc.skip = c(), rmc.dec=".",
-                          rmc.firstrow.acc = 1, rmc.firstrow.header=c(),
+                          rmc.firstrow.acc = c(), rmc.firstrow.header=c(),
                           rmc.header.length = c(),
                           rmc.col.acc = 1:3, rmc.col.temp = c(), 
                           rmc.col.time=c(),

vignettes/GGIR.Rmd

@@ -1660,7 +1660,7 @@ correspond to the date of analysis.
 | invalidepoch         | Is 1 if epoch was detect as invalid (e.g. non-wear), 0 if not.       |
 | guider   | Number to indicate what guider type was used, where 1=sleeplog, 2=HDCZA, 3=swetwindow, 4=L512, 5=HorAngle, 6=NotWorn |
 | window   | Numeric indicator of the analysis window in the recording. If timewindow = "MM" then these correspond to calendar days, if timewindow = "WW" then these correspond to which wakingup-wakingup window in the recording, if timewindow = "OO" then these correspond to which sleeponset-sleeponset window in the recording. So, in a recording of one week you may find window numbers 1, 2, 3, 4, 5 and 6.|
-| class_id | The behavioural class codes are documented in the exported csv file meta/ms5outraw/behaviouralcodes.csv. Class codes above class 8 will be analysis specific, because it depends on the number time variants of the bouts used. For example, if you look at MVPA lasting 1-10, 10-20, 30-40 then all of them will have their own class_id. |
+| class_id | The behavioural class codes are documented in the exported csv file meta/ms5outraw/behaviouralcodes.csv. Class codes above class 8 will be analysis specific, because it depends on the number time variants of the bouts used. For example, if you look at MVPA lasting 1-10, 10-20, 30-40 then all of them will have their own class_id. In behaviouralcodes.csv you will find a column with class_names which match the behavioural classes as reported in the part 5 report. |
 | invalid_fullwindow   | Fraction of the window (see above) that represents invalid data. I added this to make it easier to filter the timeseries based on whether days are valid or not.         |
 | invalid_sleepperiod  | Fraction of SPT within the current window that represents invalid data.          |
 | invalid_wakinghours  | Fraction of SPT within the current window that represents invalid data.          |

vignettes/readmyacccsv.Rmd

@@ -42,7 +42,7 @@ Internally GGIR loads csv files with accelerometer data and standardises the out
 
 # The read.myacc.csv function
 
-As for the rest of GGIR functions, `read.myacc.csv` is intended to be used within function `GGIR`. All the arguments of the `read.myacc.csv` can be easily recognized as they all start by "rmc". The GGIR function checks whether the argument rmc.firstrow.acc is provided by the user; in such case, GGIR will derive the reading of the data to the `read.myacc.csv`.
+As for the rest of GGIR functions, `read.myacc.csv` is intended to be used within function `GGIR`. All the arguments of the `read.myacc.csv` can be easily recognized as they all start by "rmc". The GGIR function checks whether the argument `rmc.firstrow.acc` is provided by the user; in such case, GGIR will attempt to read the data with `read.myacc.csv`. In other words you always need to specify `rmc.firstrow.acc` to use `read.myacc.csv`.
 
 ## Input arguments