Removing duplicates conditionally and collapsing rows afterwards

[Dallak]

Dear all,

I have already read some similar posts in here but my case has not been addressed here, I guess.

I have this dataset which has many duplicates. This data comes from two subjects/Filenames each has 3 different word. I want to end up with two rows for each word within subjects/Filenames based on the unduplicated value in m1:vowel_dur.

For example, all words extends to 10 rows because of duplicates. The duplicated values occur in two columns m1 and intensity_onset.

I want to delete any duplicated value with no corresponding (numeric) values in the same row. That is, for the word "3aadil", there are two duplicates in m1, and I want to delete the second duplicate because it stands alone in the row (with no corresponding value as opposed to to the first duplicate).

The same is true for the intensity_onset, I want to delete the second duplicate because it stands alone in the row. For some words such as "3uud" and "faatiH", the duplicated values occur first.

       ID Filename word   position Label    m1 intensity_onset ZCrossing_f Standard.deviation..Hz._f fric_dur vowel_dur
   <int> <chr>    <chr>  <chr>    <chr> <dbl> <chr>                 <dbl>                     <dbl>    <dbl>     <dbl>
 1     1 didi     3aadil ini      3-    23.4  NA                     NA                         NA      40        NA  
 2     2 didi     3aadil ini      3-    23.4  NA                     NA                         NA      NA        NA  
 3     3 didi     3aadil ini      3-    23.9  NA                     NA                         NA      50        NA  
 4     4 didi     3aadil ini      3-    23.9  NA                     NA                         NA      NA        NA  
 5     5 didi     3aadil ini      a:    NA    67.12023408            NA                         NA      NA        70  
 6     6 didi     3aadil ini      a:    NA    67.12023408            NA                         NA      NA        NA  
 7     7 didi     3aadil ini      a:    NA    66.44877898            NA                         NA      NA        65.4
 8     8 didi     3aadil ini      a:    NA    66.44877898            NA                         NA      NA        NA  
 9     9 didi     3aadil ini      3-    NA    NA                     15.5                      392.     NA        NA  
10    10 didi     3aadil ini      3-    NA    NA                     15.6                      542.     NA        NA  
11     1 didi     3uud   ini      3-    18.1  NA                     NA                         NA      59.8      NA  
12     2 didi     3uud   ini      3-    18.1  NA                     NA                         NA      NA        NA  
13     3 didi     3uud   ini      3-    21.4  NA                     NA                         NA      55.0      NA  
14     4 didi     3uud   ini      3-    21.4  NA                     NA                         NA      NA        NA  
15     5 didi     3uud   ini      u:    NA    64.32472991            NA                         NA      NA        NA  
16     6 didi     3uud   ini      u:    NA    64.32472991            NA                         NA      NA       152. 
17     7 didi     3uud   ini      u:    NA    62.05730297            NA                         NA      NA        NA  
18     8 didi     3uud   ini      u:    NA    62.05730297            NA                         NA      NA       147. 
19     9 didi     3uud   ini      3-    NA    NA                     27.9                      901.     NA        NA  
20    10 didi     3uud   ini      3-    NA    NA                     26.2                      558.     NA        NA  
21     1 didi     faatiH ini      f      7.70 NA                     NA                         NA      NA        NA  
22     2 didi     faatiH ini      f      7.70 NA                     NA                         NA     100        NA  
23     3 didi     faatiH ini      f      7.40 NA                     NA                         NA      NA        NA  
24     4 didi     faatiH ini      f      7.40 NA                     NA                         NA      70        NA  
25     5 didi     faatiH ini      a:    NA    64.89497288            NA                         NA      NA       110  
26     6 didi     faatiH ini      a:    NA    64.89497288            NA                         NA      NA        NA  
27     7 didi     faatiH ini      a:    NA    64.45085401            NA                         NA      NA        90.0
28     8 didi     faatiH ini      a:    NA    64.45085401            NA                         NA      NA        NA  
29     9 didi     faatiH ini      f     NA    NA                    121.                      3417.     NA        NA  
30    10 didi     faatiH ini      f     NA    NA                    137.                      3269.     NA        NA  

After deleting the duplicates I assume the data (for one subject) would look something like the following:

      ID Filename word   position Label    m1 intensity_onset ZCrossing_f Standard.deviation..Hz._f fric_dur vowel_dur
      
1     1 didi     3aadil ini      3-    23.4  NA                     NA                         NA      40        NA  
2     3 didi     3aadil ini      3-    23.9  NA                     NA                         NA      50        NA  
3     5 didi     3aadil ini      a:    NA    67.12023408            NA                         NA      NA        70  
4     7 didi     3aadil ini      a:    NA    66.44877898            NA                         NA      NA        65.4
5     9 didi     3aadil ini      3-    NA    NA                     15.5                      392.     NA        NA  
6    10 didi     3aadil ini      3-    NA    NA                     15.6                      542.     NA        NA  
7     1 didi     3uud   ini      3-    18.1  NA                     NA                         NA      59.8      NA  
8     3 didi     3uud   ini      3-    21.4  NA                     NA                         NA      55.0      NA  
9     6 didi     3uud   ini      u:    NA    64.32472991            NA                         NA      NA       152. 
10    8 didi     3uud   ini      u:    NA    62.05730297            NA                         NA      NA       147. 
11    9 didi     3uud   ini      3-    NA    NA                     27.9                      901.     NA        NA  
12   10 didi     3uud   ini      3-    NA    NA                     26.2                      558.     NA        NA  
13    2 didi     faatiH ini      f     7.70  NA                     NA                         NA     100        NA  
14    4 didi     faatiH ini      f     7.40  NA                     NA                         NA      70        NA  
15    5 didi     faatiH ini      a:    NA    64.89497288            NA                         NA      NA       110  
16    7 didi     faatiH ini      a:    NA    64.45085401            NA                         NA      NA        90.0
17    9 didi     faatiH ini      f     NA    NA                     121.                      3417.     NA        NA  
18   10 didi     faatiH ini      f     NA    NA                     137.                      3269.     NA        NA

However, I now want to group the data by Filename, position, word and combine the rows. The output would be similar to this. Label within the grouping variables is less relevant to me; it does not matter which one to include as long as I end up with similar output.

      ID Filename word position Label m1    intensity_onset  ZCrossing_f Standard.deviation..Hz._f fric_dur vowel_dur
   
     1 didi     3aadil ini      3-    23.4  67.12023408          15.5             392.                40        70  
     2 didi     3aadil ini      3-    23.9  66.44877898          15.6             542.                50        65.4  
     1 didi     3uud   ini      3-    18.1  64.32472991          27.9             901.                59.8      152.  
     2 didi     3uud   ini      3-    21.4  62.05730297          26.2             558.                55.0      147.  
     1 didi     faatiH ini      f      7.70 64.89497288          121.             3417.               100       110  
     2 didi     faatiH ini      f      7.40 64.45085401          137.             3269.               70        90.0  

Here is the data

structure(list(ID = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L), Filename = c("didi", "didi", "didi", "didi", 
"didi", "didi", "didi", "didi", "didi", "didi", "didi", "didi", 
"didi", "didi", "didi", "didi", "didi", "didi", "didi", "didi", 
"didi", "didi", "didi", "didi", "didi", "didi", "didi", "didi", 
"didi", "didi", "me", "me", "me", "me", "me", "me", "me", "me", 
"me", "me", "me", "me", "me", "me", "me", "me", "me", "me", "me", 
"me", "me", "me", "me", "me", "me", "me", "me", "me", "me", "me"
), word = c("3aadil", "3aadil", "3aadil", "3aadil", "3aadil", 
"3aadil", "3aadil", "3aadil", "3aadil", "3aadil", "3uud", "3uud", 
"3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", 
"faatiH", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", 
"faatiH", "faatiH", "faatiH", "3aadil", "3aadil", "3aadil", "3aadil", 
"3aadil", "3aadil", "3aadil", "3aadil", "3aadil", "3aadil", "3uud", 
"3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", 
"3uud", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", 
"faatiH", "faatiH", "faatiH", "faatiH"), position = c("ini", 
"ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
"ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
"ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
"ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
"ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
"ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
"ini", "ini", "ini", "ini", "ini"), Label = c("3-", "3-", "3-", 
"3-", "a:", "a:", "a:", "a:", "3-", "3-", "3-", "3-", "3-", "3-", 
"u:", "u:", "u:", "u:", "3-", "3-", "f", "f", "f", "f", "a:", 
"a:", "a:", "a:", "f", "f", "3-", "3-", "3-", "3-", "a:", "a:", 
"a:", "a:", "3-", "3-", "3-", "3-", "3-", "3-", "u:", "u:", "u:", 
"u:", "3-", "3-", "f", "f", "f", "f", "a:", "a:", "a:", "a:", 
"f", "f"), m1 = c(23.384, 23.384, 23.8514444444444, 23.8514444444444, 
NA, NA, NA, NA, NA, NA, 18.0954761904762, 18.0954761904762, 21.4317894736842, 
21.4317894736842, NA, NA, NA, NA, NA, NA, 7.70211428571429, 7.70211428571429, 
7.39548, 7.39548, NA, NA, NA, NA, NA, NA, 34.5868076923077, 34.5868076923077, 
30.7383076923077, 30.7383076923077, NA, NA, NA, NA, NA, NA, 15.5986666666667, 
15.5986666666667, 18.5721428571429, 18.5721428571429, NA, NA, 
NA, NA, NA, NA, 5.23757142857143, 5.23757142857143, 1.79862068965517, 
1.79862068965517, NA, NA, NA, NA, NA, NA), intensity_onset = c(NA, 
NA, NA, NA, "67.12023408", "67.12023408", "66.44877898", "66.44877898", 
NA, NA, NA, NA, NA, NA, "64.32472991", "64.32472991", "62.05730297", 
"62.05730297", NA, NA, NA, NA, NA, NA, "64.89497288", "64.89497288", 
"64.45085401", "64.45085401", NA, NA, NA, NA, NA, NA, "69.96847194", 
"69.96847194", "68.92231773", "68.92231773", NA, NA, NA, NA, 
NA, NA, "66.19878876", "66.19878876", "65.94411944", "65.94411944", 
NA, NA, NA, NA, NA, NA, "62.94039775", "62.94039775", "66.36752717", 
"66.36752717", NA, NA), ZCrossing_f = c(NA, NA, NA, NA, NA, NA, 
NA, NA, 15.5, 15.6, NA, NA, NA, NA, NA, NA, NA, NA, 27.94, 26.19, 
NA, NA, NA, NA, NA, NA, NA, NA, 120.6, 136.71, NA, NA, NA, NA, 
NA, NA, NA, NA, 26.36, 25.74, NA, NA, NA, NA, NA, NA, NA, NA, 
25.6, 28.5, NA, NA, NA, NA, NA, NA, NA, NA, 149, 167.07), Standard.deviation..Hz._f = c(NA, 
NA, NA, NA, NA, NA, NA, NA, 391.8329, 542.3985, NA, NA, NA, NA, 
NA, NA, NA, NA, 901.3963, 558.205, NA, NA, NA, NA, NA, NA, NA, 
NA, 3417.1561, 3269.0159, NA, NA, NA, NA, NA, NA, NA, NA, 513.2463, 
507.5912, NA, NA, NA, NA, NA, NA, NA, NA, 483.0832, 472.0597, 
NA, NA, NA, NA, NA, NA, NA, NA, 3149.5155, 3022.7919), fric_dur = c(40, 
NA, 50, NA, NA, NA, NA, NA, NA, NA, 59.7810000000172, NA, 54.9839999999967, 
NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 70, NA, NA, NA, NA, 
NA, NA, 75.122000000003, NA, 74.9919999999984, NA, NA, NA, NA, 
NA, NA, NA, 50, NA, 40, NA, NA, NA, NA, NA, NA, NA, NA, 80, NA, 
84.695000000007, NA, NA, NA, NA, NA, NA), vowel_dur = c(NA, NA, 
NA, NA, 70, NA, 65.36107601, NA, NA, NA, NA, NA, NA, NA, NA, 
152.2170884, NA, 147.4494709, NA, NA, NA, NA, NA, NA, 110, NA, 
89.98453674, NA, NA, NA, NA, NA, NA, NA, 94.87799913, NA, 85.00816518, 
NA, NA, NA, NA, NA, NA, NA, NA, 80, NA, 100, NA, NA, NA, NA, 
NA, NA, 102.0144636, NA, 97.79336994, NA, NA, NA)), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -60L), groups = structure(list(
    Filename = c("didi", "didi", "didi", "didi", "didi", "didi", 
    "didi", "didi", "didi", "me", "me", "me", "me", "me", "me", 
    "me", "me", "me"), word = c("3aadil", "3aadil", "3aadil", 
    "3uud", "3uud", "3uud", "faatiH", "faatiH", "faatiH", "3aadil", 
    "3aadil", "3aadil", "3uud", "3uud", "3uud", "faatiH", "faatiH", 
    "faatiH"), m1 = c(23.384, 23.8514444444444, NA, 18.0954761904762, 
    21.4317894736842, NA, 7.39548, 7.70211428571429, NA, 30.7383076923077, 
    34.5868076923077, NA, 15.5986666666667, 18.5721428571429, 
    NA, 1.79862068965517, 5.23757142857143, NA), .rows = structure(list(
        1:2, 3:4, 5:10, 11:12, 13:14, 15:20, 23:24, 21:22, 25:30, 
        33:34, 31:32, 35:40, 41:42, 43:44, 45:50, 53:54, 51:52, 
        55:60), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), row.names = c(NA, -18L), class = c("tbl_df", 
"tbl", "data.frame"), .drop = TRUE))

Thank you for your time, patience, and help in advance!

[tbendsen]

I think the answer to your question would be somewhat complicated, as the structure of your data is (to say it mildly) not very pretty. Is there any chance that your original data can be read into R, in a more standard format, ie one variable pr. column (as you already have), and one observation pr. row instead of one observation spread over several rows. Alternatively with one observation split over several tables, with a unique id telling how rows from different tables are to be combined.

/Thomas

[xvalda]

Hi @Dallak,

In the code below

• df_1 is for the first output you mentioned, I assumed that a duplicate is when all columns from Filename to Standard.deviation... are identifical.
• df_2 is for the last output you mentioned, I removed the Label and ID variables

You'll notice the output shows twice the amount of rows that you have in your examples, it's because your data has 2 values for Filenames (didi & me) whereas the example outputs you submitted just had 1 (didi).

Hope it helps and let me know if you need some comments for the code.

library(tidyverse)

#your original dataframe
df <- structure(list(ID = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
                            1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
                            6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
                            1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 
                            6L, 7L, 8L, 9L, 10L), 
                     Filename = c("didi", "didi", "didi", "didi",
                                  "didi", "didi", "didi", "didi", "didi", "didi", "didi", "didi",
                                  "didi", "didi", "didi", "didi", "didi", "didi", "didi", "didi",
                                  "didi", "didi", "didi", "didi", "didi", "didi", "didi", "didi",
                                  "didi", "didi", "me", "me", "me", "me", "me", "me", "me", "me",
                                  "me", "me", "me", "me", "me", "me", "me", "me", "me", "me", "me",
                                  "me", "me", "me", "me", "me", "me", "me", "me", "me", "me", "me"), 
                     word = c("3aadil", "3aadil", "3aadil", "3aadil", "3aadil", 
                              "3aadil", "3aadil", "3aadil", "3aadil", "3aadil", "3uud", "3uud", 
                              "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", 
                              "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", 
                              "faatiH", "faatiH", "faatiH", "3aadil", "3aadil", "3aadil", "3aadil",
                              "3aadil", "3aadil", "3aadil", "3aadil", "3aadil", "3aadil", "3uud", 
                              "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", "3uud", 
                              "3uud", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", "faatiH", 
                              "faatiH", "faatiH", "faatiH", "faatiH"), 
                     position = c("ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
                                  "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
                                  "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
                                  "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
                                  "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
                                  "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", "ini", 
                                  "ini", "ini", "ini", "ini", "ini"), 
                     Label = c("3-", "3-", "3-", 
                               "3-", "a:", "a:", "a:", "a:", "3-", "3-", "3-", "3-", "3-", "3-", 
                               "u:", "u:", "u:", "u:", "3-", "3-", "f", "f", "f", "f", "a:", 
                               "a:", "a:", "a:", "f", "f", "3-", "3-", "3-", "3-", "a:", "a:", 
                               "a:", "a:", "3-", "3-", "3-", "3-", "3-", "3-", "u:", "u:", "u:", 
                               "u:", "3-", "3-", "f", "f", "f", "f", "a:", "a:", "a:", "a:", 
                               "f", "f"), 
                     m1 = c(23.384, 23.384, 23.8514444444444, 23.8514444444444, 
                            NA, NA, NA, NA, NA, NA, 18.0954761904762, 18.0954761904762, 21.4317894736842, 
                            21.4317894736842, NA, NA, NA, NA, NA, NA, 7.70211428571429, 7.70211428571429, 
                            7.39548, 7.39548, NA, NA, NA, NA, NA, NA, 34.5868076923077, 34.5868076923077, 
                            30.7383076923077, 30.7383076923077, NA, NA, NA, NA, NA, NA, 15.5986666666667, 
                            15.5986666666667, 18.5721428571429, 18.5721428571429, NA, NA, 
                            NA, NA, NA, NA, 5.23757142857143, 5.23757142857143, 1.79862068965517, 
                            1.79862068965517, NA, NA, NA, NA, NA, NA), 
                     intensity_onset = c(NA, 
                                         NA, NA, NA, "67.12023408", "67.12023408", "66.44877898", "66.44877898", 
                                         NA, NA, NA, NA, NA, NA, "64.32472991", "64.32472991", "62.05730297", 
                                         "62.05730297", NA, NA, NA, NA, NA, NA, "64.89497288", "64.89497288", 
                                         "64.45085401", "64.45085401", NA, NA, NA, NA, NA, NA, "69.96847194", 
                                         "69.96847194", "68.92231773", "68.92231773", NA, NA, NA, NA, 
                                         NA, NA, "66.19878876", "66.19878876", "65.94411944", "65.94411944", 
                                         NA, NA, NA, NA, NA, NA, "62.94039775", "62.94039775", "66.36752717", 
                                         "66.36752717", NA, NA), 
                     ZCrossing_f = c(NA, NA, NA, NA, NA, NA, 
                                     NA, NA, 15.5, 15.6, NA, NA, NA, NA, NA, NA, NA, NA, 27.94, 26.19, 
                                     NA, NA, NA, NA, NA, NA, NA, NA, 120.6, 136.71, NA, NA, NA, NA, 
                                     NA, NA, NA, NA, 26.36, 25.74, NA, NA, NA, NA, NA, NA, NA, NA, 
                                     25.6, 28.5, NA, NA, NA, NA, NA, NA, NA, NA, 149, 167.07), 
                     Standard.deviation..Hz._f = c(NA, 
                                                   NA, NA, NA, NA, NA, NA, NA, 391.8329, 542.3985, NA, NA, NA, NA, 
                                                   NA, NA, NA, NA, 901.3963, 558.205, NA, NA, NA, NA, NA, NA, NA, 
                                                   NA, 3417.1561, 3269.0159, NA, NA, NA, NA, NA, NA, NA, NA, 513.2463, 
                                                   507.5912, NA, NA, NA, NA, NA, NA, NA, NA, 483.0832, 472.0597, 
                                                   NA, NA, NA, NA, NA, NA, NA, NA, 3149.5155, 3022.7919), 
                     fric_dur = c(40, 
                                  NA, 50, NA, NA, NA, NA, NA, NA, NA, 59.7810000000172, NA, 54.9839999999967, 
                                  NA, NA, NA, NA, NA, NA, NA, NA, 100, NA, 70, NA, NA, NA, NA, 
                                  NA, NA, 75.122000000003, NA, 74.9919999999984, NA, NA, NA, NA, 
                                  NA, NA, NA, 50, NA, 40, NA, NA, NA, NA, NA, NA, NA, NA, 80, NA, 
                                  84.695000000007, NA, NA, NA, NA, NA, NA), 
                     vowel_dur = c(NA, NA, 
                                   NA, NA, 70, NA, 65.36107601, NA, NA, NA, NA, NA, NA, NA, NA, 
                                   152.2170884, NA, 147.4494709, NA, NA, NA, NA, NA, NA, 110, NA, 
                                   89.98453674, NA, NA, NA, NA, NA, NA, NA, 94.87799913, NA, 85.00816518, 
                                   NA, NA, NA, NA, NA, NA, NA, NA, 80, NA, 100, NA, NA, NA, NA, 
                                   NA, NA, 102.0144636, NA, 97.79336994, NA, NA, NA)), 
                class = c("grouped_df", 
                          "tbl_df", "tbl", "data.frame"), 
                row.names = c(NA, -60L), 
                groups = structure(list(
                  Filename = c("didi", "didi", "didi", "didi", "didi", "didi", 
                               "didi", "didi", "didi", "me", "me", "me", "me", "me", "me", 
                               "me", "me", "me"), 
                  word = c("3aadil", "3aadil", "3aadil", 
                           "3uud", "3uud", "3uud", "faatiH", "faatiH", "faatiH", "3aadil", 
                           "3aadil", "3aadil", "3uud", "3uud", "3uud", "faatiH", "faatiH", 
                           "faatiH"), 
                  m1 = c(23.384, 23.8514444444444, NA, 18.0954761904762, 
                         21.4317894736842, NA, 7.39548, 7.70211428571429, NA, 30.7383076923077, 
                         34.5868076923077, NA, 15.5986666666667, 18.5721428571429, 
                         NA, 1.79862068965517, 5.23757142857143, NA), 
                  .rows = structure(list(
                    1:2, 3:4, 5:10, 11:12, 13:14, 15:20, 23:24, 21:22, 25:30, 
                    33:34, 31:32, 35:40, 41:42, 43:44, 45:50, 53:54, 51:52, 
                    55:60), 
                    ptype = integer(0), 
                    class = c("vctrs_list_of", 
                              "vctrs_vctr", "list"))), 
                  row.names = c(NA, -18L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE))

#the first output you mentioned
df_1 <- df %>% 
  unite(concat, Filename:ZCrossing_f, remove = FALSE) %>% 
  group_by(concat) %>% 
  mutate(n = n()) %>% 
  rowwise() %>% 
  mutate(all_rows_na = ifelse(sum(fric_dur, vowel_dur, na.rm = TRUE) == 0, TRUE, FALSE), 
         delete = ifelse(n > 1 & all_rows_na == TRUE, "delete", "keep")) %>% 
  filter(delete == "keep") %>% 
  ungroup() %>% 
  select(-c(concat, n:delete))
df_1
#> # A tibble: 36 × 11
#>       ID Filename word   position Label    m1 intensity_onset ZCrossing_f
#>    <int> <chr>    <chr>  <chr>    <chr> <dbl> <chr>                 <dbl>
#>  1     1 didi     3aadil ini      3-     23.4 <NA>                   NA  
#>  2     3 didi     3aadil ini      3-     23.9 <NA>                   NA  
#>  3     5 didi     3aadil ini      a:     NA   67.12023408            NA  
#>  4     7 didi     3aadil ini      a:     NA   66.44877898            NA  
#>  5     9 didi     3aadil ini      3-     NA   <NA>                   15.5
#>  6    10 didi     3aadil ini      3-     NA   <NA>                   15.6
#>  7     1 didi     3uud   ini      3-     18.1 <NA>                   NA  
#>  8     3 didi     3uud   ini      3-     21.4 <NA>                   NA  
#>  9     6 didi     3uud   ini      u:     NA   64.32472991            NA  
#> 10     8 didi     3uud   ini      u:     NA   62.05730297            NA  
#> # … with 26 more rows, and 3 more variables: Standard.deviation..Hz._f <dbl>,
#> #   fric_dur <dbl>, vowel_dur <dbl>

#second output
df_2 <- df_1 %>% 
  select(-c(Label, ID)) %>% 
  mutate(intensity_onset = as.double(intensity_onset)) %>% 
  pivot_longer(m1:vowel_dur, names_to = "names", values_to = "value") %>% 
  drop_na(value) %>% 
  pivot_wider(names_from = names, values_from = value) %>% 
  relocate(setdiff(names(df_1), c("ID", "Label"))) %>% 
  unnest_longer(m1:vowel_dur) %>% 
  suppressWarnings()
df_2
#> # A tibble: 12 × 9
#>    Filename word   position    m1 intensity_onset ZCrossing_f Standard.deviatio…
#>    <chr>    <chr>  <chr>    <dbl>           <dbl>       <dbl>              <dbl>
#>  1 didi     3aadil ini      23.4             67.1        15.5               392.
#>  2 didi     3aadil ini      23.9             66.4        15.6               542.
#>  3 didi     3uud   ini      18.1             64.3        27.9               901.
#>  4 didi     3uud   ini      21.4             62.1        26.2               558.
#>  5 didi     faatiH ini       7.70            64.9       121.               3417.
#>  6 didi     faatiH ini       7.40            64.5       137.               3269.
#>  7 me       3aadil ini      34.6             70.0        26.4               513.
#>  8 me       3aadil ini      30.7             68.9        25.7               508.
#>  9 me       3uud   ini      15.6             66.2        25.6               483.
#> 10 me       3uud   ini      18.6             65.9        28.5               472.
#> 11 me       faatiH ini       5.24            62.9       149                3150.
#> 12 me       faatiH ini       1.80            66.4       167.               3023.
#> # … with 2 more variables: fric_dur <dbl>, vowel_dur <dbl>

[Dallak]

This is absolutely incredible, @xvalda !
I deeply appreciate your prompt help!
It is working on my actual data.
Thank you so much!

Yes, I would love to get some comments for the code in order to better understand your clever approach.

Thank you again!

[xvalda]

Thanks for the appreciation @Dallak
I made a few comments below, I hope they make sense.
You can progressively run different chunks of the pipe to see new data representation after each stage.
Cheers,

df_1 <- df %>% 
  #pasting all columns that define what makes a row unique, from Filename until Standard.deviation..Hz._f
  #only two columns not pasted are fric_dur & vowel_dur since they're the "value" variables
  #new column is "concat" 
  unite(concat, Filename:Standard.deviation..Hz._f, remove = FALSE) %>% 
  #grouping by pasted column and counting occurences to see how many of each unique combinations we have
  group_by(concat) %>% 
  mutate(n = n()) %>% 
  #rowwise perform subsequent operations one row at a time
  rowwise() %>% 
  #all_rows_na tells you if any of the two columns holds a value (FALSE) or not (TRUE)
  mutate(all_rows_na = ifelse(sum(fric_dur, vowel_dur, na.rm = TRUE) == 0, TRUE, FALSE), 
         #if specific row is repeated more than once and has a duplicate, then mark as "delete", otherwise "keep"
         delete = ifelse(n > 1 & all_rows_na == TRUE, "delete", "keep")) %>% 
  #filter only for rows you want to keep
  filter(delete == "keep") %>% 
  #good practice to ungroup after grouping, mostly when you assign to a new object (df_1) that will be reused later
  #ungroup is necessary here to remove the "concat" column (since we grouped with it)
  ungroup() %>% 
  select(-c(concat, n:delete))
df_1

df_2 <- df_1 %>% 
  #remove Label column as per your specification and the ID column that doesn't hold any viable information with our transformations
  select(-c(Label, ID)) %>% 
  #intensity_onset was parsed as "character", needs to be a double for the pivot operation (value column below can only contain one data type)
  mutate(intensity_onset = as.double(intensity_onset)) %>% 
  #long format on all double types columns, then we remove all rows that are NA
  pivot_longer(m1:vowel_dur, names_to = "names", values_to = "value") %>% 
  drop_na(value) %>% 
  #back to original wide format but this time it will ignore all rows with NAs
  pivot_wider(names_from = names, values_from = value) %>% 
  #double pivoting changed column orders, we revert to original column orders
  #for that we use column names from df_1 except "ID" and "Label" (setdiff)
  relocate(setdiff(names(df_1), c("ID", "Label"))) %>% 
  #pivot_wider had two values for each combination of Filename/word/position, giving results as embedded lists
  #hence the need to unnest in a long format
  unnest_longer(m1:vowel_dur) %>% 
  #I suppressed the warning from the pivot_wider operation "Values from `value` are not uniquely identified"
  #although sweeping under the carpet is not always the best solution
  suppressWarnings()

[Dallak]

@xvalda

You can progressively run different chunks of the pipe to see new data representation after each stage.

Thank you so much for these comments!
I will follow your suggestion. This is a very elegant approach.