Tag Archives | code

Strange behavior from the cut function with dates in R

Update: @hadlywickham tweeted to me to let me know that  daylight savings time was the culprit. Though this explains the behavior I document in the first part of this post, the behavior of the cut function using truncated dates (discussed further down the post) is still unexplained.

I recently encountered some strange behavior from R when using the cut.POSIXt method with “day” as the interval specification. This function isn’t working as I intended and I doubt that it is working properly. I’ll show you the behavior I’m seeing (and what I was expecting) then I’ll show you my current base R workaround. To generate a reproducible example, I’ll use this latemail function I gleaned from this stack overflow post.

latemail <- function(N, st="2013/01/01", et="2013/12/31") {
 st <- as.POSIXct(as.Date(st))
 et <- as.POSIXct(as.Date(et))
 dt <- as.numeric(difftime(et,st,unit="sec"))
 ev <- sort(runif(N, 0, dt))
 rt <- st + ev

And generate some data…

#generate 1000 random POSIXlt dates and times
bar<-data.frame("date"=latemail(1000, st="2013/03/02", et="2013/03/30"))
# assign factors based on the day portion of the POSIXlt object
bar$dateCut <- cut(bar$date, "day", labels = FALSE)

I expected that all rows with the date 2013-03-01 would receive factor 1, all rows with the date 2013-03-02 would receive factor 2, and so on. At first glance this seems to be what is happening.

head(bar, 10)
     date                 dateCut
1    2013-03-01 19:10:31  1
2    2013-03-01 19:31:31  1
3    2013-03-01 19:55:02  1
4    2013-03-01 20:09:36  1
5    2013-03-01 20:13:32  1
6    2013-03-01 22:15:42  1
7    2013-03-01 22:16:06  1
8    2013-03-01 23:41:50  1
9    2013-03-02 00:30:53  2
10   2013-03-02 01:08:52  2

Note that at row 9 the date changes from March 1 to March 2 and the factor (dateCut) changes from 1 to 2. So far so good. But we shall see some strange things in the midnight hour.  
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Insert random NAs in a vector in R

I was recently writing a function which was going to need to deal with NAs in some kind of semi-intelligent way. I wanted to test it with some fake data, meaning that I was going to need a vector with some random NAs sprinkled in. After a few disappointing google searches and a stack overflow post or two that left something to be desired, I sat down, thought for a few minutes, and came up with this.

#create a vector of random values
 foo <- rnorm(n=100, mean=20, sd=5)
#randomly choose 15 indices to replace
#this is the step in which I thought I was clever
#because I use which() and %in% in the same line
 ind <- which(foo %in% sample(foo, 15))
#now replace those indices in foo with NA
#here is our vector with 15 random NAs 

Not especially game changing but more elegant than any of the solutions I found on the interwebs, so there it is FTW.


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