I have data from an online survey where respondents go through a loop of questions 1-3 times. The survey software (Qualtrics) records this data in multiple columns—that is, Q3.2 in the survey will have columns `Q3.2.1.`, `Q3.2.2.`, and `Q3.2.3.`: 

    df &lt;- data.frame(
      id = 1:10,
      time = as.Date(&#39;2009-01-01&#39;) + 0:9,
      Q3.2.1. = rnorm(10, 0, 1),
      Q3.2.2. = rnorm(10, 0, 1),
      Q3.2.3. = rnorm(10, 0, 1),
      Q3.3.1. = rnorm(10, 0, 1),
      Q3.3.2. = rnorm(10, 0, 1),
      Q3.3.3. = rnorm(10, 0, 1)
    )

    # Sample data

       id       time    Q3.2.1.     Q3.2.2.    Q3.2.3.     Q3.3.1.    Q3.3.2.     Q3.3.3.
    1   1 2009-01-01 -0.2059165 -0.29177677 -0.7107192  1.52718069 -0.4484351 -1.21550600
    2   2 2009-01-02 -0.1981136 -1.19813815  1.1750200 -0.40380049 -1.8376094  1.03588482
    3   3 2009-01-03  0.3514795 -0.27425539  1.1171712 -1.02641801 -2.0646661 -0.35353058
    ...

I want to combine all the QN.N* columns into tidy individual QN.N columns, ultimately ending up with something like this:

       id       time loop_number        Q3.2        Q3.3
    1   1 2009-01-01           1 -0.20591649  1.52718069
    2   2 2009-01-02           1 -0.19811357 -0.40380049
    3   3 2009-01-03           1  0.35147949 -1.02641801
    ...
    11  1 2009-01-01           2 -0.29177677  -0.4484351
    12  2 2009-01-02           2 -1.19813815  -1.8376094
    13  3 2009-01-03           2 -0.27425539  -2.0646661
    ...
    21  1 2009-01-01           3 -0.71071921 -1.21550600
    22  2 2009-01-02           3  1.17501999  1.03588482
    23  3 2009-01-03           3  1.11717121 -0.35353058
    ...


The `tidyr` library has the `gather()` function, which works great for combining *one* set of columns:

    library(dplyr)
    library(tidyr)
    library(stringr)

    df %&gt;% gather(loop_number, Q3.2, starts_with(&quot;Q3.2&quot;)) %&gt;% 
      mutate(loop_number = str_sub(loop_number,-2,-2)) %&gt;%
      select(id, time, loop_number, Q3.2)


       id       time loop_number        Q3.2
    1   1 2009-01-01           1 -0.20591649
    2   2 2009-01-02           1 -0.19811357
    3   3 2009-01-03           1  0.35147949
    ...
    29  9 2009-01-09           3 -0.58581232
    30 10 2009-01-10           3 -2.33393981

The resultant data frame has 30 rows, as expected (10 individuals, 3 loops each). However, gathering a second set of columns does not work correctly—it successfully makes the two combined columns `Q3.2` and `Q3.3`, but ends up with 90 rows instead of 30 (all combinations of 10 individuals, 3 loops of Q3.2, and 3 loops of Q3.3; the combinations will increase substantially for each group of columns in the actual data):

    df %&gt;% gather(loop_number, Q3.2, starts_with(&quot;Q3.2&quot;)) %&gt;% 
      gather(loop_number, Q3.3, starts_with(&quot;Q3.3&quot;)) %&gt;%
      mutate(loop_number = str_sub(loop_number,-2,-2))


       id       time loop_number        Q3.2        Q3.3
    1   1 2009-01-01           1 -0.20591649  1.52718069
    2   2 2009-01-02           1 -0.19811357 -0.40380049
    3   3 2009-01-03           1  0.35147949 -1.02641801
    ...
    89  9 2009-01-09           3 -0.58581232 -0.13187024
    90 10 2009-01-10           3 -2.33393981 -0.48502131


Is there a way to use multiple calls to `gather()` like this, combining small subsets of columns like this while maintaining the correct number of rows?

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Gather multiple sets of columns