Reshaping data of different time lengths in R - r

Question

I want to perform several repeated measures on my data. I first need to reshape the dataframe from a wide to a long format to do that.
This is my dataframe:
ID Group x1 x2 x3 y1 y2 y3 z1 z2
144 1 566 613 597 563 549 562 599 469
167 2 697 638 756 682 695 693 718 439.5
247 4 643 698 730 669 656 669 698 514.5
317 4 633 646 641 520 543 586 559 405.5
344 3 651 678 708 589 608 615 667 514
352 2 578 702 671 536 594 579 591 467.5
382 1 678 690 693 555 565 534 521 457.5
447 3 668 672 718 663 689 751 784 506.5
464 2 760 704 763 514 554 520 564 486
628 1 762 789 783 618 610 645 625 536
As you might notice, I have measured variable x and y on three time points and variable z at two points. I was wondering if it makes sense at all to try and reshape the data into long format, given the fact that I have separate time lengths.
I have not been able to do so. So first of all, does it even make sense to do it this way? Or should I make two dataframes? Second, if it does make sense, how?
EDIT: I would expect something like:
ID Group Timex Timey Timez x y z
144 1 1 1 1 566 563 599
144 1 2 2 2 613 549 469
144 1 3 3 597 562
167 2 1 1 1 697 682 718
167 2 2 2 2 638 695 439.5
167 2 3 3 756 693
....
But I'm not even sure if that makes sense at all, to have these empty cells?

Answer 1

Here is one idea. dt_all is the final output. Notice that this example does not create Timex, Timey, and Timez, but I would argue that one column called Time is sufficient and individual Timex, Timey, and Timez are redundant.
# Load packages
library(dplyr)
library(tidyr)
# Process the data
dt_all <- dt %>%
gather(Var, Value, -ID, -Group) %>%
mutate(Time = sub("[a-z]", "", Var), Type = sub("[0-9]", "", Var)) %>%
select(-Var) %>%
spread(Type, Value)
Data Preparation
# Create example data frames
dt <- read.table(text = "ID Group x1 x2 x3 y1 y2 y3 z1 z2
144 1 566 613 597 563 549 562 599 469
167 2 697 638 756 682 695 693 718 439.5
247 4 643 698 730 669 656 669 698 514.5
317 4 633 646 641 520 543 586 559 405.5
344 3 651 678 708 589 608 615 667 514
352 2 578 702 671 536 594 579 591 467.5
382 1 678 690 693 555 565 534 521 457.5
447 3 668 672 718 663 689 751 784 506.5
464 2 760 704 763 514 554 520 564 486
628 1 762 789 783 618 610 645 625 536",
header = TRUE)