Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>I don't know why you'd use lubridate for this. If you're just looking for something less awesome than xts you could try this</p> <pre><code>tapply(bikecounts$Northbound, as.Date(bikecounts$Date, format="%m/%d/%Y"), sum) </code></pre> <p>Basically, you just need to <code>split</code> by Date, then apply a function.</p> <hr> <p>lubridate could be used for creating a grouping factor for split-apply problems. So, for example, if you want the sum for each month (ignoring year)</p> <pre><code>tapply(bikecounts$Northbound, month(mdy_hms(bikecounts$Date)), sum) </code></pre> <p>But, it's just using wrappers for base R functions, and in the case of the OP, I think the base R function <code>as.Date</code> is the easiest (as evidenced by the fact that the other Answers also ignored your request to use lubridate ;-) ).</p> <hr> <p>Something that wasn't covered by the <a href="https://stackoverflow.com/a/15794871">Answer</a> to the other <a href="https://stackoverflow.com/questions/15794710/data-frames-in-r">Question</a> linked to in the OP is <code>split.xts</code>. <code>period.apply</code> splits an <code>xts</code> at <code>endpoints</code> and applies a function to each group. You can find endpoints that are useful for a given task with the <code>endpoints</code> function. For example, if you have an xts object, <code>x</code>, then <code>endpoints(x, "months")</code> would give you the row numbers that are the last row of each month. <code>split.xts</code> leverages that to split an xts object -- <code>split(x, "months")</code> would return a list of xts objects where each component was for a different month.</p> <p>Although, <code>split.xts()</code> and <code>endpoints()</code> are primarily intended for <code>xts</code> objects, they also work on some other objects as well, including plain time based vectors. Even if you don't want to use xts objects, you still may find uses for <code>endpoints()</code> because of its convenience or its speed (implemented in C)</p> <pre><code>&gt; split.xts(as.Date("1970-01-01") + 1:10, "weeks") [[1]] [1] "1970-01-02" "1970-01-03" "1970-01-04" [[2]] [1] "1970-01-05" "1970-01-06" "1970-01-07" "1970-01-08" "1970-01-09" [6] "1970-01-10" "1970-01-11" &gt; endpoints(as.Date("1970-01-01") + 1:10, "weeks") [1] 0 3 10 </code></pre> <p>I think lubridate's best use in this problem is for parsing the "Date" strings into POSIXct objects. i.e. the <code>mdy_hms</code> function in this case.</p> <p>Here's an <code>xts</code> solution that uses <code>lubridate</code> to parse the "Date" strings.</p> <pre><code>x &lt;- xts(bikecounts[, -1], mdy_hms(bikecounts$Date)) period.apply(x, endpoints(x, "days"), sum) apply.daily(x, sum) # identical to above </code></pre> <p>For this specific task, <code>xts</code> also has an optimized <code>period.sum</code> function (written in Fortran) that is very fast</p> <pre><code>period.sum(x, endpoints(x, "days")) </code></pre>
    singulars
    1. This table or related slice is empty.
    1. POAggregation by time period in lubridate
      singulars
      1. PTQuestion
    1. PTAnswer
    1. USCommunity
    1. USGSee
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. POAggregation by time period in lubridate
      singulars
      1. PTQuestion
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    3. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    1. COThis is useful as well. Happy to accept this answer if the consensus is "don't use lubridate; instead use XTS." That said, I hear lots of "use lubridate," so I kind of want to learn what the fuss is all about!
      singulars
      1. PO
      1. USPeter
    2. COI tend to agree with @GSee. I _think_ the easiest `lubridate` way to achieve a `ddply`-able grouping variable using `lubridate` functions only would be: `bikecounts$date2 <- mdy_hms(x = bikecounts$Date)`; `bikecounts$date3 <- round_date(x = bc$date2, unit = "day")`.
      singulars
      1. PO
      1. USHenrik
    3. COGreat set of answers from everybody. Props to this one for 1.) correcting my starting premise 2.) providing a base R example 3.) Showing what a lubridate version would look like, and 4.) explaining the benefits of xts.
      singulars
      1. PO
      1. USPeter
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload