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    <ol> <li>First switch to a <code>Date</code> representation: <code>as.Date(df.MHwind_load$Date)</code></li> <li>Then call <code>weekdays</code> on the date vector to get a new factor labelled with day of week</li> <li>Then call <code>months</code> on the date vector to get a new factor labelled with name of month</li> <li>Optionally create a <code>years</code> variable (see below).</li> </ol> <p>Now <code>subset</code> the data frame using the relevant combination of these. Step 2. gets an answer to your task 3. Steps 3. and 4. get you to task 1. Task 2 might require a line or two of R. Or just select rows corresponding to, say, all the Mondays in a month and call <code>unique</code>, or its alter-ego <code>duplicated</code> on the results.</p> <p>To get you going...</p> <pre><code>newdf &lt;- df.MHwind_load ## build an augmented data set newdf$d &lt;- as.Date(newdf$Date) newdf$month &lt;- months(newdf$d) newdf$day &lt;- weekdays(newdf$d) ## for some reason R has no years function. Here's one years &lt;- function(x){ format(as.Date(x), format = "%Y") } newdf$year &lt;- years(newdf$d) # get observations from January to March of every year subset(newdf, month %*% in c('January', 'February', 'March')) # get all Monday observations subset(newdf, day == 'Monday') # get all Mondays in 1999 subset(newdf, day == 'Monday' &amp; year == '1999') # slightly fancier: _first_ Monday of each month # get the first weeks first.week.of.month &lt;- !duplicated(cbind(newdf$month, newdf$day)) # now pull out the mondays subset(newdf, first.monday.of.month &amp; day=='Monday') </code></pre>
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    1. POAggregating, restructuring hourly time series data in R
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    1. COThanks for the leads. I was able to use these functions but was stuck on constructing code to parse my data. I am trying @Bryan Goodrich's solution..
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    2. COActually now I've written this, I think I like `lubridate` thing a bit better in the other answers. Think of the code here as an intro to the `subset` command :-)
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