Dear Thomas,
As others have noted, it's not obvious why one needs to use dplyr.
Here's a solution in base R (with your original data in D):
> D1 <- cbind(Location="P1A0B0D", D[, 1:2])
> D2 <- cbind(Location="P190.90D", D[, c(1, 3)])
> colnames(D1)[3] <- colnames(D2)[3] <- "Measurement"
> rbind(D1[, c(2, 1, 3)], D2[, c(2, 1, 3)])
Time_stamp Location Measurement
1 Jun-10 10:34 P1A0B0D -0.000208
2 Jun-10 10:51 P1A0B0D -0.000228
3 Jun-10 11:02 P1A0B0D -0.000234
4 Jun-10 11:17 P1A0B0D -0.000220
5 Jun-10 11:25 P1A0B0D -0.000238
6 Jun-10 10:34 P190.90D -0.000195
7 Jun-10 10:51 P190.90D -0.000188
8 Jun-10 11:02 P190.90D -0.000204
9 Jun-10 11:17 P190.90D -0.000205
10 Jun-10 11:25 P190.90D -0.000195
It would be simple to generalize this as a function.
I hope this helps,
John
On 2022-06-21 12:22 p.m., Thomas Subia wrote:> Colleagues:
>
> The header of my data set is:
> Time_stamp P1A0B0D P190-90D
> Jun-10 10:34 -0.000208 -0.000195
> Jun-10 10:51 -0.000228 -0.000188
> Jun-10 11:02 -0.000234 -0.000204
> Jun-10 11:17 -0.00022 -0.000205
> Jun-10 11:25 -0.000238 -0.000195
>
> I want my data set to resemble:
>
> Time_stamp Location Measurement
> Jun-10 10:34 P1A0B0D -0.000208
> Jun-10 10:51 P1A0B0D -0.000228
> Jun-10 11:02 P1A0B0D -0.000234
> Jun-10 11:17 P1A0B0D -0.00022
> Jun-10 11:25 P1A0B0D -0.000238
> Jun-10 10:34 P190-90D -0.000195
> Jun-10 10:51 P190-90D -0.000188
> Jun-10 11:02 P190-90D -0.000204
> Jun-10 11:17 P190-90D -0.000205
> Jun-10 11:25 P190-90D -0.000195
>
> I need some advice on how to do this using dplyr.
>
> V/R
> Thomas Subia
>
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