As part of a larger and more complex body of code, I am running into a dplyr / local data frame challenge. As the simplified example per below shows, the code includes a basic type of subsetting that works in base R:
#creation of data frame
dat=data.frame(group=c(rep(c("a","b","c","d"),2)),value=(seq(1,8,1)))
othergroup=dat[dat[,"group"]==dat[2,"group"],]
othergroup
This gives the desired answer:
group value
2 b 2
6 b 6
#loading dplyr
require(dplyr)
othergroup=dat[dat[,"group"]==dat[2,"group"],]
othergroup #still works
After just loading dplyr, all still works. However, after I run a dplyr operation, then a local data frame is created that no longer allows similar subsetting.
#pro-forma dplyr operation
dat = dat %>%
group_by(group)
othergroup=dat[dat[,"group"]==dat[2,"group"],] #error message
Error in Ops.data.frame(dat[, "group"], dat[2, "group"]) :
‘==’ only defined for equally-sized data frames
I understand that one can use the select function in dplyr, but I would like to re-use some existing code. Is there a way to coerce a dplyr generated "local data frame" back into a regular data frame?
只要这样做:
othergroup = dat[dat$group == dat$group[2],]
Once you group the data frame it becomes a tibble
object. One of the features of a tibble
is that when you subset it (eg dat[2,"group"]
), it always returns a tibble
. So dat[,"group"]==dat[2,"group"]
is comparing the whole tibble/data.frame. Not what you want.
If you have lots of this kind of sub-setting in old code and you don't want to change your old code, convert the tibble back to a data frame: dat=as.data.frame(dat)
.
Otherwise, Tatiana's solution works well.
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