Check whether a row with values belongs to a data frame in R [duplicate]

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Existing function for seeing if a row exists in a data frame?

Suppose I have the following data frame in R.

df = data.frame('a'=c(1:3), 'b'=c(4:6))

This data frame contains three rows: (1,4), (2,5) and (3,6). Suppose I did not know which rows df contains and wanted to check whether a row (1,4) belongs to it, how can I check that?

My actual case involves comparison of 27 parameter values. Is there a solution in which I can do this without typing each and every parameter name? Thanks!

The reason I want to do this is that I have an R dataset called masterdata which contains simulation data. I want to update this data set with new data that is obtained as I make additional simulation runs with different parameter combinations. It is possible, however, that I may forget that I have run the simulation for a certain parameter combination and may run it again, in which case, the masterdata will be expanded with duplicate values. I can later go and remove these duplicate values, but I would not want the whole process of updating the data set to go through if the values are duplicate. For this I need to check if the data from a simulation run is already present in the masterdata. I can do this if I know how to check whether a given row belongs to the masterdata.

Thanks.

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There may be more efficient ways, but I think

tail(duplicated(rbind(masterdata,newvals)),1)

will do it: in other words, attach the new row to the end of the data frame and see whether it is duplicated or not.

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If you want to compare only two columns in the data.frame, then this does a trick:

> which(df$a+df$b*1i == 1+4i)
[1] 1

This may or may not be faster than other vectorized solution.

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Quite a few ways to do this. You can use ifelse() which is a vectorized solution to return a boolean value for each row of your dataframe if it meets your conditions.

> with(df, ifelse(a == 1 & b == 4, 1, 0))
[1] 1 0 0

Since you are probably only interested in knowing whether your parameter combination has been run at all, you can wrap sum() around the previous command:

> sum(with(df, ifelse(a == 1 & b == 4, 1, 0)))
[1] 1

Another alternative is to use nrow() and subset(). We'll again use the & operator for our testing:

> nrow(subset(df, a == 1 & b == 4))
[1] 1

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You don't need any more than a single unique call:

Test<-data.frame(a=c(1,2,2,2,3),b=c(1,2,2,3,3),c=(1,2,2,2,3))
Test
unique(Test) #Same with duplicated rows removed