How to create “NA” for missing data in a time series

Question

I have several files of data that look like this:

X code year month day pp  
1 4515 1953     6   1  0  
2 4515 1953     6   2  0  
3 4515 1953     6   3  0  
4         


        

Answer 1

I had to deal with the similar problem with a monthly time series. I did it with directly joining two data.table/data.frame by the time variable. My point is that time series is also a kind of datasets. So you can also manipulate any time series as regular dataset in a regular way. Here is my solution:

library(zoo)    
(full <- data.table(yrAndMo = as.yearmon(seq(as.Date('2008-01-01'), by = '1 month', length = someLength)))) 
# the full time horizon that you want to have
#  yrAndMo
#  1: Jan 2008
#  2: Feb 2008
#  3: Mar 2008
#  4: Apr 2008
#  5: May 2008
# ---         
# 98: Feb 2016
# 99: Mar 2016
# 100: Apr 2016
# 101: May 2016
# 102: Jun 2016

exampleDat # the actually data you want to append to the full time horizon
# yrAndMo someValue
# 1 Mar 2010      7500
# 2 Jun 2010      1115
# 3 Mar 2011      2726
# 4 Apr 2011      1865
# 5 Nov 2011      1695
# 6 Dec 2012     10000
# 7 Mar 2016      1000

library(plyr)
join(full, exampleDat, by = 'yrAndMo', type = "left")
#   yrAndMo someValue
#   1: Jan 2008        NA
#   2: Feb 2008        NA
#   3: Mar 2008        NA
#   4: Apr 2008        NA
#   5: May 2008        NA
#  ---                   
#  98: Feb 2016        NA
#  99: Mar 2016      1000
# 100: Apr 2016        NA
# 101: May 2016        NA
# 102: Jun 2016        NA

after this you can easily change the class of the dataset back to any type of time series that you want to have. I preferred read.zoo.