data.imputation: Data imputation for the cleaned data with annotation
In postGGIR: Data Processing after Running 'GGIR' for Accelerometer Data

Data imputation for the merged ENMO data with annotation. The missing values were imputated by the average ENMO over all the valid days for each subject.

1	data.imputation(workdir, csvInput)

`workdir`	`character` Directory where the output needs to be stored. Note that this directory must exist.
`csvInput`	`character` File name with or without directory for sample information in CSV format. The ENMO data will be read through read.csv(csvInput,header=1) command, and the missing values were imputated by the average ENMO over all the valid days for each subject at each time point. In this package, csvInput = flag_All_studyname_ENMO.data.Xs.csv.

Files were written to the specified sub-directory, named as impu.flag_All_studyname_ENMO.data.Xs.csv, which Xs is the epoch size to which acceleration was averaged (seconds) in GGIR output. This excel file includs the following columns,

`filename`	accelerometer file name
`Date`	date recored from the GGIR part2.summary file
`id`	IDs recored from the GGIR part2.summary file
`calender_date`	date in the format of yyyy-mm-dd
`N.valid.hours`	number of hours with valid data recored from the part2_daysummary.csv file in the GGIR output
`N.hours`	number of hours of measurement recored from the part2_daysummary.csv file in the GGIR output
`weekday`	day of the week-Day of the week
`measurementday`	day of measurement-Day number relative to start of the measurement
`newID`	new IDs defined as the user-defined function of filename2id(), e.g. substrings of the filename
`Nmiss_c9_c31`	number of NAs from the 9th to 31th column in the part2_daysummary.csv file in the GGIR output
`missing`	"M" indicates missing for an invalid day, and "C" indicates completeness for a valid day
`Ndays`	number of days of measurement
`ith_day`	rank of the measurementday, for example, the value is 1,2,3,4,-3,-2,-1 for measurementday = 1,...,7
`Nmiss`	number of missing (invalid) days
`Nnonmiss`	number of non-missing (valid) days
`misspattern`	indicators of missing/nonmissing for all measurement days at the subject level
`RowNonWear`	number of columnns in the non-wearing matrix
`NonWearMin`	number of minutes of non-wearing
`daysleeper`	If 0 then the person is a nightsleeper (sleep period did not overlap with noon) if value=1 then the person is a daysleeper (sleep period did overlap with noon).
`remove16h7day`	indicator of a key qulity control output. If remove16h7day=1, the day need to be removed. If remove16h7day=0, the day need to be kept.
`duplicate`	If duplicate="remove", the accelerometer files will not be used in the data analysis of part5.
`ImpuMiss.b`	number of missing values on the ENMO data before imputation
`ImpuMiss.a`	number of missing values on the ENMO data after imputation
`KEEP`	The value is "keep"/"remove", e.g. KEEP="remove" if remove16h7day=1 or duplicate="remove" or ImpuMiss.a>0