[This article was first published on R-SquareD, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Converting data.frames to different periods –

When I’m working with financial time series data, I usually use xts objects. I convert the data to a data.frame when I’m ready to plot. Sometimes, I find that I want to adjust the periodicity after I’ve converted the data to a data.frame and dplyr doesn’t have a built in ToPeriod function to handle this.

There are many ways to do this, but here is a simple method I created using lubridate.

dt x y z
2016-09-30 0.5929255 0.9471731 0.3849695
2016-10-31 0.4608359 0.4818123 0.3505995
2016-11-30 0.0489204 0.1026600 0.5048000
2016-12-31 0.4354312 0.7474725 0.1319946
2017-01-31 0.1969235 0.9362995 0.0475031
2017-02-28 0.2869447 0.3290996 0.9325527
2017-03-31 0.7202285 0.1793812 0.4118351
2017-04-30 0.8789776 0.4084741 0.2664088
2017-05-31 0.7721041 0.0749422 0.2096817
2017-06-30 0.4888230 0.8979686 0.0206246
2017-07-26 0.9185885 0.4004092 0.5670242

This is a very simple method to create periodicity transformation using dplyr and lubridate. The first function creates a new column that distinctly identifies each month/year that each record belongs to using ceiling_date. This could be any interval of time from second to hours to quarters, to years. I can also prefix the unit with an integer to create custom intervals (“5 days”).

Next, I group_by the new column so that it can perform a subset function upon each month. The top_n function will return the largest value within a grouping for the field passed (date). If I wanted to return the first observation within each month, simply change the n parameter to -1 (top_n(n=-1, date)).

The final two functions are just to clean up the data by ungrouping it and removing the Monthly column.

That is all nice, but if I’m going to be doing this more than once, I would rather have a function to handle it more generically.

dt x y z
2016-09-30 0.5929255 0.9471731 0.3849695
2016-10-01 0.5546463 0.5019259 0.2752113
2017-01-01 0.8042691 0.8392850 0.9429900
2017-04-01 0.8989569 0.0962568 0.2398289
2017-07-01 0.1345319 0.9558426 0.6833553

This function is obviously more complicated than the script above, but the field parameter adds some challenges. I want my data wrangling functions to follow the dplyr convention of passing expressions as field names. This keeps everything consistent. I use the deparse function to convert the expression to a string so that I can access the data. I could use lazyeval for this (which is how dplyr works) but it creates many more complications and unatractive code. There are limitations to deparse but they do not impact this function (see https://cran.r-project.org/web/packages/lazyeval/vignettes/lazyeval.html).

It would be useful to pass a aggregation function to this function to aggregate by the period (mean, median, max, etc.). I will create this function in the future, for now I use xts to handle this.

To leave a comment for the author, please follow the link and comment on their blog: R-SquareD.

R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

# Never miss an update! Subscribe to R-bloggers to receive e-mails with the latest R posts.(You will not see this message again.)

Click here to close (This popup will not appear again)