Hi Thomas,
Jeff is correct that this can be handled via merge, e.g.
df3 <- merge( df2, df1, by="Serial", all=FALSE )
This operation is called an "inner join", and you could use other tools,
such as the dplyr package to accomplish the same thing
df3 <- dplyr::inner_join( df2, df1, by="Serial" )
H
> On Jan 8, 2020, at 6:52 AM, Thomas Subia wrote:
>
> Colleagues,
>
> I have two data frames which look like this.
>
> Data frame 1
>
> Serial Pre.HolePre.flowPre.Date
> 1 30361 0.2419-Nov-19
> 2 30362
"merge" is generally the base R answer to this question, and there are
equivalent functions in various contributed packages.
However, it is necessary to identify which columns in each table uniquely
identify each row ("primary key"). If your Serial 3036 shows up 10 times in the
first table and
Colleagues,
I have two data frames which look like this.
Data frame 1
Serial Pre.HolePre.flowPre.Date
1 30361 0.2419-Nov-19
2 30362 0.212 19-Nov-19
3 30363
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