I have a year's worth of hourly data in a data frame in R:

```
> str(df.MHwind_load)   # compactly displays structure of data frame
'data.frame':   8760 obs. of  6 variables:
 $ Date         : Factor w/ 365 levels "2010-04-01","2010-04-02",..: 1 1 1 1 1 1 1 1 1 1 ...
 $ Time..HRs.   : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Hour.of.Year : int  1 2 3 4 5 6 7 8 9 10 ...
 $ Wind.MW      : int  375 492 483 476 486 512 421 396 456 453 ...
 $ MSEDCL.Demand: int  13293 13140 12806 12891 13113 13802 14186 14104 14117 14462 ...
 $ Net.Load     : int  12918 12648 12323 12415 12627 13290 13765 13708 13661 14009 ...

```

While preserving the hourly structure, I would like to know how to extract 

1. a particular month/group of months
2. the first day/first week etc of each month
3. all mondays, all tuesdays etc of the year

I have tried using "cut" without result and after looking online think that "lubridate" might be able to do so but haven't found suitable examples. I'd greatly appreciate help on this issue.

Edit: a sample of data in the data frame is below:

```
  Date Hour.of.Year  Wind.MW  datetime
1  2010-04-01  1  375  2010-04-01  00:00:00
2  2010-04-01  2  492  2010-04-01  01:00:00
3  2010-04-01  3  483  2010-04-01  02:00:00
4  2010-04-01  4  476  2010-04-01  03:00:00
5  2010-04-01  5  486  2010-04-01  04:00:00
6  2010-04-01  6  512  2010-04-01  05:00:00
7  2010-04-01  7  421  2010-04-01  06:00:00
8  2010-04-01  8  396  2010-04-01  07:00:00
9  2010-04-01  9  456  2010-04-01  08:00:00
10  2010-04-01  10  453  2010-04-01  09:00:00
..  ..  ...  ..........  ........
8758  2011-03-31  8758  302  2011-03-31  21:00:00
8759  2011-03-31  8759  378  2011-03-31  22:00:00
8760  2011-03-31  8760  356  2011-03-31  23:00:00

```

EDIT: Additional time-based operations I would like to perform on the same dataset
1. Perform hour-by-hour averaging for all data points i.e average of all values in the first hour of each day in the year. The output will be an "hourly profile" of the entire year (24 time points)
2. Perform the same for each week and each month i.e obtain 52 and 12 hourly profiles respectively
3. Do seasonal averages, for example for June to September