[R] how to handle irregularly spaced data as timeseries

[Kirk Wythers]

I have a set of modeled climate data recorded at irregular intervals.  
The format of the data is such that there are monthly measurements for  
the years 2000, 2020, 2050, 2080, etc. Therefore I have 12 regular  
records, a skip of some number of years, then 12 more monthly records,  
another skip, and so on.... I created a dataframe from .txt with the  
read.table() command. For starters I need to simply plot the data as a  
timeseries with DATE on the x axis. I am just getting my feet wet with  
R, so I'm struggling a bit to follow the help() pages. Can anyone  
point me in the proper direction? Thank you in advance.

Here is a piece of the dataframe:

 > cloq.worldclim.HADCM3.A2a
           DATE YEAR MONTH DAY DOY TMAX  TMIN    PAR PRECIP
1  15-Jan-2000 2000     1  15  16 -7.2 -19.7 334.99    2.5
2  15-Feb-2000 2000     2  15  46 -3.5 -16.8 471.48    1.7
3  15-Mar-2000 2000     3  15  75  2.7  -9.4 636.96    4.1
4  15-Apr-2000 2000     4  15 106 11.3  -2.1 726.60    5.6
5  15-May-2000 2000     5  15 136 18.9   3.3 767.50    8.3
6  15-Jun-2000 2000     6  15 167 23.8   8.3 783.51   10.3
7  15-Jul-2000 2000     7  15 197 26.9  12.1 827.87    9.9
8  15-Aug-2000 2000     8  15 228 25.3  11.2 775.03   10.1
9  15-Sep-2000 2000     9  15 259 19.7   6.7 649.83    9.2
10 15-Oct-2000 2000    10  15 289 13.3   1.1 500.14    6.2
11 15-Nov-2000 2000    11  15 320  3.1  -6.3 349.14    4.4
12 15-Dec-2000 2000    12  15 350 -4.8 -15.4 293.07    2.9
13 15-Jan-2020 2020     1  15  16 -7.4 -19.6 334.99    2.5
14 15-Feb-2020 2020     2  15  46 -3.1 -16.2 471.48    1.7
15 15-Mar-2020 2020     3  15  75  3.0  -8.7 636.96    4.9
16 15-Apr-2020 2020     4  15 106 12.9  -5.0 726.60    7.4
17 15-May-2020 2020     5  15 136 20.1   4.4 767.50    8.8
18 15-Jun-2020 2020     6  15 167 25.2   9.6 783.51   10.4
19 15-Jul-2020 2020     7  15 197 28.9  13.6 827.87    9.4
20 15-Aug-2020 2020     8  15 228 27.3  13.1 775.03   11.6
21 15-Sep-2020 2020     9  15 259 22.0   8.8 649.83    9.6
22 15-Oct-2020 2020    10  15 289 15.4   2.6 500.14    6.8
23 15-Nov-2020 2020    11  15 320  4.3  -4.8 349.14    4.8
24 15-Dec-2020 2020    12  15 350 -4.1 -14.3 293.07    2.5
25 15-Jan-2050 2050     1  15  16 -5.5 -17.3 334.99    3.2
26 15-Feb-2050 2050     2  15  46 -1.7 -14.2 471.48    1.8
27 15-Mar-2050 2050     3  15  75  4.0  -7.1 636.96    5.1
28 15-Apr-2050 2050     4  15 106 13.4  -3.0 726.60    7.1
29 15-May-1950 2050     5  15 136 21.7   5.8 767.50    8.4
30 15-Jun-2050 2050     6  15 167 27.2  11.2 783.51    9.5
31 15-Jul-2050 2050     7  15 197 31.2  15.3 827.87    9.4
32 15-Aug-2050 2050     8  15 228 30.4  15.0 775.03    9.6
33 15-Sep-2050 2050     9  15 259 23.8  10.1 649.83   10.3
34 15-Oct-2050 2050    10  15 289 16.6   3.9 500.14    7.7
35 15-Nov-2050 2050    11  15 320  5.5  -3.6 349.14    5.3
36 15-Dec-2050 2050    12  15 350 -3.3 -13.2 293.07    2.5