Exploration of replacing part of a Time Series
install.packages('zoo')
library('zoo')
timeSeries <- ts(1:96, freq=12, start=2001); timeSeries

     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2001   1   2   3   4   5   6   7   8   9  10  11  12
2002  13  14  15  16  17  18  19  20  21  22  23  24
2003  25  26  27  28  29  30  31  32  33  34  35  36
2004  37  38  39  40  41  42  43  44  45  46  47  48
2005  49  50  51  52  53  54  55  56  57  58  59  60
2006  61  62  63  64  65  66  67  68  69  70  71  72
2007  73  74  75  76  77  78  79  80  81  82  83  84
2008  85  86  87  88  89  90  91  92  93  94  95  96

#
# If you already know the indexes of the elements to 
# replace, just do it:
#
timeSeries[13:36] <- NA
timeSeries

     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2001   1   2   3   4   5   6   7   8   9  10  11  12
2002  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
2003  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
2004  37  38  39  40  41  42  43  44  45  46  47  48
2005  49  50  51  52  53  54  55  56  57  58  59  60
2006  61  62  63  64  65  66  67  68  69  70  71  72
2007  73  74  75  76  77  78  79  80  81  82  83  84
2008  85  86  87  88  89  90  91  92  93  94  95  96

#
# However, sometimes you might want to refer to the
# Time Series part by date. Below is one way to do
# that.
#

#
# Reset the Time Series and then look at just two years, 2002 and 2003
#
timeSeries <- ts(1:96, freq=12, start=2001);
window(timeSeries, start=c(2002,1), end=c(2003,12))

     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2002  13  14  15  16  17  18  19  20  21  22  23  24
2003  25  26  27  28  29  30  31  32  33  34  35  36

#
# Copy this part of the timeSeries for safety.
#
original = window(timeSeries, start=c(2002,1), end=c(2003,12))

#
# Change 2002 and 2003 to NA because a lawsuit is pending.
# 
window(timeSeries, start=c(2002,1), end=c(2003,12)) <- NA
timeSeries

     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2001   1   2   3   4   5   6   7   8   9  10  11  12
2002  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
2003  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA  NA
2004  37  38  39  40  41  42  43  44  45  46  47  48
2005  49  50  51  52  53  54  55  56  57  58  59  60
2006  61  62  63  64  65  66  67  68  69  70  71  72
2007  73  74  75  76  77  78  79  80  81  82  83  84
2008  85  86  87  88  89  90  91  92  93  94  95  96

#
# The lawsuit is over and you won. Retrieve the data.
#

window(timeSeries, start=c(2002,1), end=c(2003,12)) <- original

> timeSeries
     Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2001   1   2   3   4   5   6   7   8   9  10  11  12
2002  13  14  15  16  17  18  19  20  21  22  23  24
2003  25  26  27  28  29  30  31  32  33  34  35  36
2004  37  38  39  40  41  42  43  44  45  46  47  48
2005  49  50  51  52  53  54  55  56  57  58  59  60
2006  61  62  63  64  65  66  67  68  69  70  71  72
2007  73  74  75  76  77  78  79  80  81  82  83  84
2008  85  86  87  88  89  90  91  92  93  94  95  96