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Ratings

Source: vignettes/Ratings.Rmd
Ratings.Rmd

Introduction

This article outlines the process by which song ratings were calculated using the Fugazi Live Series metadata.

Song counts

Performance counts were calculated for all the released Fugazi songs that were performed live, using data from … how many shows?

one_row_per_show <- Repeatr1 %>% group_by(gid) %>% slice(1) %>% ungroup()
nrow(one_row_per_show)
#> [1] 902

These frequency counts do not necessarily measure the band’s preferences for the songs, as more recently released songs were available for fewer shows than older songs.

The results of this analysis, in descending order of performance count, are as follows:

fugazi_song_counts <- fugazi_song_counts %>%
  arrange(desc(count))
knitr::kable(fugazi_song_counts, "pipe")
songid song launchdate count
92 waiting room 1987-09-03 633
69 reclamation 1990-05-05 594
9 blueprint 1989-11-25 584
53 long division 1989-04-09 498
55 merchandise 1987-09-03 477
54 margin walker 1988-08-01 430
76 sieve-fisted find 1989-03-24 418
71 repeater 1989-07-20 410
89 turnover 1989-04-09 395
65 promises 1988-10-15 380
2 and the same 1987-09-26 376
90 two beats off 1989-05-03 371
37 give me the cure 1988-03-30 369
70 rend it 1991-12-08 346
83 suggestion 1987-12-03 340
75 shut the door 1989-03-24 323
78 song #1 1987-09-03 322
7 bed for the scraping 1994-11-20 299
28 facet squared 1991-08-12 299
82 styrofoam 1990-05-17 287
6 bad mouth 1987-10-16 280
72 reprovisional 1988-12-29 276
27 exit only 1990-07-06 275
23 do you like me 1994-11-20 272
44 instrument 1992-01-25 272
39 great cop 1991-12-08 268
74 runaway return 1990-02-11 265
67 public witness program 1993-02-05 264
85 target 1994-08-15 260
77 smallpox champion 1992-10-23 253
84 sweet and low 1992-05-15 249
16 by you 1993-04-24 233
13 bulldog front 1988-06-15 226
15 burning too 1988-08-01 215
14 burning 1988-02-06 214
34 forensic scene 1994-08-19 201
8 birthday pony 1994-08-15 199
40 greed 1989-03-24 188
49 latin roots 1990-10-01 182
46 kyeo 1987-10-07 173
10 break 1996-08-15 172
18 cassavetes 1991-07-28 171
51 lockdown 1987-12-03 165
20 closed captioned 1997-06-18 159
12 brendan #1 1989-03-24 158
4 arpeggiator 1997-05-02 154
79 stacks 1991-02-15 153
22 dear justice letter 1991-01-02 148
68 recap modotti 1997-05-03 141
93 walken’s syndrome 1992-10-23 141
30 fell, destroyed 1993-08-16 137
5 back to base 1994-11-20 136
47 last chance for a slow dance 1991-07-28 136
11 break-in 1987-10-16 134
73 returning the screw 1992-10-23 134
38 glueman 1988-05-12 133
62 place position 1996-08-15 133
45 joe #1 1987-09-03 132
29 fd 1997-05-02 115
56 nice new outfit 1991-02-20 115
52 long distance runner 1994-11-27 113
24 downed city 1994-11-20 111
59 number 5 1998-11-21 110
31 five corporations 1996-08-15 109
32 floating boy 1996-10-16 106
36 furniture 1987-09-03 94
58 no surprise 1996-09-29 89
60 oh 1998-11-29 80
61 pink frosty 1996-03-20 67
3 argument 1999-08-26 66
17 cashout 2000-09-30 62
19 caustic acrostic 1996-01-30 51
80 steady diet 1991-04-12 46
26 ex-spectator 1999-08-26 45
57 nightshop 1999-08-26 44
48 latest disgrace 1994-11-20 39
86 the kill 2001-04-05 35
91 version 1994-08-27 34
33 foreman’s dog 1998-05-01 33
25 epic problem 2000-10-01 32
41 guilford fall 1996-08-15 32
87 the word 1987-09-03 31
1 23 beats off 1992-10-23 26
35 full disclosure 2001-04-05 26
43 in defense of humans 1987-09-03 25
21 combination lock 1994-11-27 21
50 life and limb 2001-06-21 21
81 strangelight 2001-04-06 19
88 turn off your guns 1987-09-03 15
66 provisional 1988-11-14 8
63 polish 1991-03-06 6
64 preprovisional 1988-10-31 6
42 hello morning 2001-04-27 2
94 world beat 1996-01-30 2

Performance intensity

A slightly more detailed analysis was undertaken by calculating the performance intensity of each song.

Song performance intensity = number of times a song was played / number of shows at which it was available in the repertoire.

A song was considered available in the repertoire from the first show it was performed.

The results of this analysis look like this:

knitr::kable(fugazi_song_performance_intensity, "pipe")
songid song launchdate chosen available_rl intensity
17 cashout 2000-09-30 62 66 0.9393939
20 closed captioned 1997-06-18 159 199 0.7989950
7 bed for the scraping 1994-11-20 299 377 0.7931034
59 number 5 1998-11-21 110 145 0.7586207
10 break 1996-08-15 172 229 0.7510917
69 reclamation 1990-05-05 594 795 0.7471698
4 arpeggiator 1997-05-02 154 208 0.7403846
23 do you like me 1994-11-20 272 377 0.7214854
9 blueprint 1989-11-25 584 826 0.7070218
92 waiting room 1987-09-03 633 900 0.7033333
68 recap modotti 1997-05-03 141 207 0.6811594
85 target 1994-08-15 260 385 0.6753247
3 argument 1999-08-26 66 101 0.6534653
86 the kill 2001-04-05 35 58 0.6034483
70 rend it 1991-12-08 346 585 0.5914530
53 long division 1989-04-09 498 848 0.5872642
60 oh 1998-11-29 80 137 0.5839416
62 place position 1996-08-15 133 229 0.5807860
29 fd 1997-05-02 115 208 0.5528846
55 merchandise 1987-09-03 477 900 0.5300000
34 forensic scene 1994-08-19 201 383 0.5248042
8 birthday pony 1994-08-15 199 385 0.5168831
67 public witness program 1993-02-05 264 513 0.5146199
54 margin walker 1988-08-01 430 872 0.4931193
25 epic problem 2000-10-01 32 65 0.4923077
77 smallpox champion 1992-10-23 253 516 0.4903101
76 sieve-fisted find 1989-03-24 418 853 0.4900352
71 repeater 1989-07-20 410 838 0.4892601
16 by you 1993-04-24 233 477 0.4884696
28 facet squared 1991-08-12 299 621 0.4814815
50 life and limb 2001-06-21 21 44 0.4772727
31 five corporations 1996-08-15 109 229 0.4759825
32 floating boy 1996-10-16 106 227 0.4669604
44 instrument 1992-01-25 272 583 0.4665523
89 turnover 1989-04-09 395 848 0.4658019
39 great cop 1991-12-08 268 585 0.4581197
35 full disclosure 2001-04-05 26 58 0.4482759
26 ex-spectator 1999-08-26 45 101 0.4455446
84 sweet and low 1992-05-15 249 563 0.4422735
90 two beats off 1989-05-03 371 846 0.4385343
65 promises 1988-10-15 380 869 0.4372842
57 nightshop 1999-08-26 44 101 0.4356436
2 and the same 1987-09-26 376 899 0.4182425
37 give me the cure 1988-03-30 369 887 0.4160090
58 no surprise 1996-09-29 89 228 0.3903509
83 suggestion 1987-12-03 340 895 0.3798883
75 shut the door 1989-03-24 323 853 0.3786635
27 exit only 1990-07-06 275 755 0.3642384
82 styrofoam 1990-05-17 287 788 0.3642132
5 back to base 1994-11-20 136 377 0.3607427
78 song #1 1987-09-03 322 900 0.3577778
81 strangelight 2001-04-06 19 57 0.3333333
74 runaway return 1990-02-11 265 817 0.3243574
72 reprovisional 1988-12-29 276 854 0.3231850
6 bad mouth 1987-10-16 280 897 0.3121516
30 fell, destroyed 1993-08-16 137 449 0.3051225
52 long distance runner 1994-11-27 113 376 0.3005319
24 downed city 1994-11-20 111 377 0.2944297
93 walken’s syndrome 1992-10-23 141 516 0.2732558
18 cassavetes 1991-07-28 171 632 0.2705696
61 pink frosty 1996-03-20 67 251 0.2669323
73 returning the screw 1992-10-23 134 516 0.2596899
13 bulldog front 1988-06-15 226 876 0.2579909
49 latin roots 1990-10-01 182 729 0.2496571
15 burning too 1988-08-01 215 872 0.2465596
14 burning 1988-02-06 214 890 0.2404494
79 stacks 1991-02-15 153 691 0.2214182
40 greed 1989-03-24 188 853 0.2203986
47 last chance for a slow dance 1991-07-28 136 632 0.2151899
22 dear justice letter 1991-01-02 148 694 0.2132565
19 caustic acrostic 1996-01-30 51 254 0.2007874
46 kyeo 1987-10-07 173 898 0.1926503
33 foreman’s dog 1998-05-01 33 176 0.1875000
12 brendan #1 1989-03-24 158 853 0.1852286
51 lockdown 1987-12-03 165 895 0.1843575
56 nice new outfit 1991-02-20 115 690 0.1666667
38 glueman 1988-05-12 133 884 0.1504525
11 break-in 1987-10-16 134 897 0.1493868
45 joe #1 1987-09-03 132 900 0.1466667
41 guilford fall 1996-08-15 32 229 0.1397380
36 furniture 1987-09-03 94 900 0.1044444
48 latest disgrace 1994-11-20 39 377 0.1034483
91 version 1994-08-27 34 378 0.0899471
80 steady diet 1991-04-12 46 673 0.0683507
21 combination lock 1994-11-27 21 376 0.0558511
1 23 beats off 1992-10-23 26 516 0.0503876
42 hello morning 2001-04-27 2 45 0.0444444
87 the word 1987-09-03 31 900 0.0344444
43 in defense of humans 1987-09-03 25 900 0.0277778
88 turn off your guns 1987-09-03 15 900 0.0166667
66 provisional 1988-11-14 8 863 0.0092700
63 polish 1991-03-06 6 685 0.0087591
94 world beat 1996-01-30 2 254 0.0078740
64 preprovisional 1988-10-31 6 866 0.0069284

The “songid” variable indicates the raw frequency ranking of each song, allowing easy comparison between the intensity and frequency measures.

Song preferences

“We played without a setlist from the first show to the last show,” Picciotto said. “We never had a program for the night before we hit the stage. Right before we went on stage we’d get together and decide on a song to start with. From then on, we were basically improvising the set as we went.” - Guy Picciotto 25/5/2018

It is only possible to estimate a choice model from the Fugazi Live Series data because of the way that the songs were chosen quite freely as each show was performed. If fixed set lists had been used for many shows this sort of analysis probably would not be possible.

The Fugazi Live Series data includes … how many choices of songs made by the band during their live shows?

nrow(Repeatr1)
#> [1] 23280

This data was used to estimate the strength of preference for each of the songs in their live music repertoire.

Song availability was considered at both repertoire and gig level. Songs were only considered available from the time they were first played, but thereafter they were assumed to be always available. There is some evidence that certain songs were discontinued but this has not been represented here.

“To the guy who is yelling for Steady Diet, I got bad news for you. Every time before we go out for a tour, we take a week to go through every record that we’ve done, and we relearn every song and we make sure that we know everything, because we make up the sets as we go, and we relearn everything so we can play anything at anytime… but there’s three songs that we have not been able to remember how to play, one of them is Steady Diet, I am sorry to say, the other is Polish, and the other one, I can’t remember the name of, but basically, you can call out anything else, but if you call out Steady Diet, you are wasting your breath” - Guy Picciotto 27/6/2001

Within any given gig, the songs were sorted in the order that they were performed, and once a song had been played it was assumed to be unavailable for the rest of the gig. Interestingly, there were a few exceptions to this rule. One was a 1991 gig in Birmingham, Alabama, where the show notes comment “Featuring the one-time attempt of our ‘Two for Tuesday’ gag. No one appeared to notice, so we shelved the idea.” On that occasion, the song “Greed” was played twice. Another case was a 1998 gig in Richmond, Virginia where “Great Cop” was played twice due to a specific situation.

The age of the songs needs considering because bands generally prioritise new material when they play live and Fugazi was no exception to this. Dummy variables (on/off) were used to represent the age of the songs at the time of each gig, as follows:

Age (years) Dummy variable
0 < age < 1 (omitted)
1 ≤ age < 2 yearsold_1
2 ≤ age < 3 yearsold_2
3 ≤ age < 4 yearsold_3
4 ≤ age < 5 yearsold_4
5 ≤ age < 6 yearsold_5
6 ≤ age < 7 yearsold_6
7 ≤ age < 8 yearsold_7
8 ≤ age yearsold_8

The above categories were defined after some experimentation to establish which categories deserved separate representation and which could be grouped together. The “less than a year old” variable was omitted because it is always necessary to omit one of each set of dummy variables in this type of model. An omitted dummy variable has a parameter of zero by definition and provides a reference point for the parameters whose values are estimated.

A dummy variable (on/off) was defined for each song, such that the corresponding parameters would represent the strength of preference for playing each song live. The dummy variable for ‘23 Beats Off’ was omitted and therefore the preference parameter for this song was zero by definition.
The formula used for the preferred model was this one:

choice ~ yearsold_1 + yearsold_2 + yearsold_3 + yearsold_4 + yearsold_5 + yearsold_6 + yearsold_7 + yearsold_8 + song2 + … + song92

The model was fitted by an optimisation process which estimated a parameter for each of the independent variables, such that the likelihood of correctly predicting the observed choices would be maximised.

The parameters related to the age of the songs support the hypothesis that recent material tended to be favoured in the band’s choices of songs to be performed.

The implied preferences for each song are shown here in descending order of preference:


myresults <- fugazi_song_preferences %>%
  arrange(desc(Estimate))
knitr::kable((myresults), "pipe")
rank_rating songid song Estimate z-value
1 10 break 3.6151309 16.6149760
2 7 bed for the scraping 3.6132708 17.4354500
3 69 reclamation 3.5928734 17.6389784
4 23 do you like me 3.4384435 16.5323445
5 20 closed captioned 3.3332829 15.1087393
6 17 cashout 3.3219397 13.1021613
7 62 place position 3.2284656 14.5986343
8 92 waiting room 3.1528596 14.9893938
9 85 target 3.1522621 15.1840506
10 68 recap modotti 3.1322854 14.1235711
11 59 number 5 3.0227345 13.0589993
12 9 blueprint 3.0136458 14.7683753
13 76 sieve-fisted find 2.9638769 14.2762975
14 55 merchandise 2.9579034 13.9887751
15 70 rend it 2.9307233 14.3643152
16 4 arpeggiator 2.8769889 13.0497540
17 8 birthday pony 2.7843413 13.2304591
18 28 facet squared 2.7812830 13.5277056
19 54 margin walker 2.7807233 13.3239780
20 89 turnover 2.7520394 13.2424418
21 53 long division 2.7064274 13.0951452
22 67 public witness program 2.7001402 13.1214187
23 3 argument 2.6976917 10.9563104
24 60 oh 2.6808937 11.2149156
25 86 the kill 2.6648060 9.5871521
26 29 fd 2.6162995 11.6136202
27 34 forensic scene 2.6080878 12.4033604
28 16 by you 2.5793459 12.4574970
29 77 smallpox champion 2.5674365 12.4542531
30 31 five corporations 2.5599379 11.3953052
31 2 and the same 2.5470885 11.9690939
32 32 floating boy 2.5350336 11.2458226
33 44 instrument 2.5012510 12.1464337
34 50 life and limb 2.4112048 7.7422559
35 39 great cop 2.4106031 11.7012988
36 78 song #1 2.3951887 11.2108337
37 37 give me the cure 2.3843146 11.2974018
38 90 two beats off 2.3827643 11.4523925
39 26 ex-spectator 2.3681516 9.0955783
40 35 full disclosure 2.3611212 7.9244607
41 71 repeater 2.3311568 11.2820890
42 25 epic problem 2.3195059 8.2603483
43 82 styrofoam 2.2764726 10.9591809
44 83 suggestion 2.2722860 10.7145833
45 58 no surprise 2.2666353 9.8883853
46 65 promises 2.2545649 10.7843545
47 5 back to base 2.2011881 10.1678255
48 6 bad mouth 2.1873359 10.2145383
49 27 exit only 2.1731676 10.4641283
50 75 shut the door 2.1671432 10.3554887
51 57 nightshop 2.1616521 8.2716626
52 84 sweet and low 2.1421206 10.3799135
53 74 runaway return 2.0130082 9.6354315
54 24 downed city 1.9052989 8.6529808
55 13 bulldog front 1.8992168 8.8755212
56 30 fell, destroyed 1.8904206 8.8200245
57 93 walken’s syndrome 1.8861810 8.8328817
58 72 reprovisional 1.8805356 8.9193094
59 52 long distance runner 1.8486327 8.4095765
60 81 strangelight 1.8242021 5.7338584
61 15 burning too 1.8215669 8.5020613
62 18 cassavetes 1.8201967 8.6109538
63 61 pink frosty 1.8066725 7.6620996
64 14 burning 1.8013983 8.3358598
65 73 returning the screw 1.7811400 8.3074786
66 49 latin roots 1.7608370 8.3176852
67 40 greed 1.7271599 8.0502161
68 22 dear justice letter 1.6086562 7.5093759
69 19 caustic acrostic 1.5749119 6.4180914
70 79 stacks 1.5714261 7.3543751
71 47 last chance for a slow dance 1.4995064 6.9805344
72 12 brendan #1 1.4703437 6.7793896
73 51 lockdown 1.4685222 6.6895343
74 46 kyeo 1.4059058 6.4009275
75 56 nice new outfit 1.3095043 5.9881733
76 33 foreman’s dog 1.2630294 4.6491262
77 11 break-in 1.2197548 5.4668722
78 45 joe #1 1.1729447 5.2280109
79 41 guilford fall 1.1498609 4.2708032
80 38 glueman 1.0976805 4.9506273
81 36 furniture 0.8682549 3.7616061
82 48 latest disgrace 0.7790864 3.0530077
83 66 provisional 0.5551207 1.3531239
84 91 version 0.5451926 2.0813508
85 80 steady diet 0.3469293 1.4073716
86 21 combination lock 0.1140464 0.3864588
87 1 23 beats off 0.0000000 NA
88 42 hello morning -0.2863910 -0.3865418
89 87 the word -0.4329009 -1.5582092
90 43 in defense of humans -0.5602311 -1.9489160
91 88 turn off your guns -1.0811282 -3.2703801
92 94 world beat -1.7996925 -2.4477619
93 63 polish -1.8009262 -3.9702490
94 64 preprovisional -2.0608606 -4.5226576

It is hard to say exactly whose preferences are represented by these results. It seems reasonable to assume that they mainly represent the band’s preferences, more often than not Ian MacKaye and Guy Picciotto, but the preferences of the audience may also have influenced the choice of the songs that were performed, directly or indirectly.

“We played without a setlist from the first show to the last show. We never had a program for the night before we hit the stage. Right before we went on stage we’d get together and decide on a song to start with. From then on, we were basically improvising the set as we went. That meant, before we went on tour, we had to have these insanely long rehearsals where we relearned very piece of music that we knew so that everyone was ready. So, every night was completely different show. You could pick from over 100 songs. The only methodology we had was that we alternated singing. Once Ian was wrapping up his song, I knew that I had to have a song ready to go for my thing.” - Guy Picciotto, 25/5/2018 Source: https://web.archive.org/web/20201123023401/https://www.abc.net.au/doublej/music-reads/features/fugazi-the-past-the-future-and-the-ethos-that-drove-them/10265848

“Do you like me?”

The following table shows ratings based on the preferences described in the section above, together with the indicators described in previous sections: performance counts and intensities. The ratings are simply the preferences normalised in such a way that the highest preference has a value of 1 and the lowest a value of 0. This way it will be easy to scale these values for comparison with ratings defined on other intervals.

knitr::kable(summary %>% select(song, chosen, intensity, rating) %>% arrange(desc(rating)), "pipe")
song chosen intensity rating
break 172 0.7510917 1.0000000
bed for the scraping 299 0.7931034 0.9996723
reclamation 594 0.7471698 0.9960787
do you like me 272 0.7214854 0.9688711
closed captioned 159 0.7989950 0.9503438
cashout 62 0.9393939 0.9483454
place position 133 0.5807860 0.9318771
waiting room 633 0.7033333 0.9185567
target 260 0.6753247 0.9184515
recap modotti 141 0.6811594 0.9149320
number 5 110 0.7586207 0.8956312
blueprint 584 0.7070218 0.8940300
sieve-fisted find 418 0.4900352 0.8852616
merchandise 477 0.5300000 0.8842092
rend it 346 0.5914530 0.8794206
arpeggiator 154 0.7403846 0.8699537
birthday pony 199 0.5168831 0.8536309
facet squared 299 0.4814815 0.8530921
margin walker 430 0.4931193 0.8529935
turnover 395 0.4658019 0.8479400
long division 498 0.5872642 0.8399040
public witness program 264 0.5146199 0.8387963
argument 66 0.6534653 0.8383650
oh 80 0.5839416 0.8354055
the kill 35 0.6034483 0.8325711
fd 115 0.5528846 0.8240252
forensic scene 201 0.5248042 0.8225785
by you 233 0.4884696 0.8175147
smallpox champion 253 0.4903101 0.8154165
five corporations 109 0.4759825 0.8140954
and the same 376 0.4182425 0.8118316
floating boy 106 0.4669604 0.8097077
instrument 272 0.4665523 0.8037559
life and limb 21 0.4772727 0.7878915
great cop 268 0.4581197 0.7877855
song #1 322 0.3577778 0.7850698
give me the cure 369 0.4160090 0.7831540
two beats off 371 0.4385343 0.7828808
ex-spectator 45 0.4455446 0.7803063
full disclosure 26 0.4482759 0.7790677
repeater 410 0.4892601 0.7737886
epic problem 32 0.4923077 0.7717359
styrofoam 287 0.3642132 0.7641543
suggestion 340 0.3798883 0.7634167
no surprise 89 0.3903509 0.7624211
promises 380 0.4372842 0.7602946
back to base 136 0.3607427 0.7508906
bad mouth 280 0.3121516 0.7484501
exit only 275 0.3642384 0.7459539
shut the door 323 0.3786635 0.7448926
nightshop 44 0.4356436 0.7439251
sweet and low 249 0.4422735 0.7404841
runaway return 265 0.3243574 0.7177370
downed city 111 0.2944297 0.6987606
bulldog front 226 0.2579909 0.6976891
fell, destroyed 137 0.3051225 0.6961394
walken’s syndrome 141 0.2732558 0.6953924
reprovisional 276 0.3231850 0.6943978
long distance runner 113 0.3005319 0.6887772
strangelight 19 0.3333333 0.6844730
burning too 215 0.2465596 0.6840087
cassavetes 171 0.2705696 0.6837673
pink frosty 67 0.2669323 0.6813846
burning 214 0.2404494 0.6804554
returning the screw 134 0.2596899 0.6768863
latin roots 182 0.2496571 0.6733093
greed 188 0.2203986 0.6673760
dear justice letter 148 0.2132565 0.6464979
caustic acrostic 51 0.2007874 0.6405528
stacks 153 0.2214182 0.6399387
last chance for a slow dance 136 0.2151899 0.6272679
brendan #1 158 0.1852286 0.6221300
lockdown 165 0.1843575 0.6218090
kyeo 173 0.1926503 0.6107772
nice new outfit 115 0.1666667 0.5937932
foreman’s dog 33 0.1875000 0.5856052
break-in 134 0.1493868 0.5779810
joe #1 132 0.1466667 0.5697340
guilford fall 32 0.1397380 0.5656671
glueman 133 0.1504525 0.5564739
furniture 94 0.1044444 0.5160535
latest disgrace 39 0.1034483 0.5003438
provisional 8 0.0092700 0.4608853
version 34 0.0899471 0.4591362
steady diet 46 0.0683507 0.4242061
combination lock 21 0.0558511 0.3831766
23 beats off 26 0.0503876 0.3630838
hello morning 2 0.0444444 0.3126273
the word 31 0.0344444 0.2868150
in defense of humans 25 0.0277778 0.2643819
turn off your guns 15 0.0166667 0.1726099
world beat 2 0.0078740 0.0460128
polish 6 0.0087591 0.0457954
preprovisional 6 0.0069284 0.0000000

Breaking ranks

The rank order of songs derived from the ratings is not very strong. Some of the differences between the ratings are very small and the differences between the ratings of adjacent songs in the table turned out to be insignificant. The rankr function makes it easy to test which differences between song ratings are significant and which are not. For instance, do the results really indicate that “Bed for the Scraping” was preferred over “Reclamation”?

songstobecompared <- songstobecompared <- summary %>% slice(seq(from=1, to=2, by=1))
mycomparisons <- rankr(coeftable = results_ml_Repeatr4, vcovmat = vcovmat_ml_Repeatr4, mysongidlist = songstobecompared)
#> Joining with `by = join_by(songid1)`
#> Joining with `by = join_by(songid2)`
mycomparisons <- mycomparisons %>%
  select(song1, song2, mycoef1, mycoef2, mycoefdiff, myz) %>%
  rename(coef1 = mycoef1, coef2 = mycoef2, coefdiff = mycoefdiff, z = myz)
knitr::kable(mycomparisons, format = "pipe", digits = 3)
song1 song2 coef1 coef2 coefdiff z
waiting room bulldog front 3.148 1.899 1.249 1.72

A z-statistic of 1.96 or greater indicates a difference that is statistically significant with 95% confidence. The difference between ‘Bed for the Scraping’ and ‘Reclamation’ is not statistically significant. In fact, none of the differences between adjacent songs are statistically significant. However, some of the differences between songs further apart on the table are significant, as can be seen below.

songstobecompared <- songstobecompared <- songstobecompared <- summary %>% slice(seq(from=1, to=92, by=8))
mycomparisons <- rankr(coeftable = results_ml_Repeatr4, vcovmat = vcovmat_ml_Repeatr4, mysongidlist = songstobecompared)
#> Joining with `by = join_by(songid1)`
#> Joining with `by = join_by(songid2)`
mycomparisons <- mycomparisons %>%
  select(song1, song2, mycoef1, mycoef2, mycoefdiff, myz) %>%
  rename(coef1 = mycoef1, coef2 = mycoef2, coefdiff = mycoefdiff, z = myz)
knitr::kable(mycomparisons, format = "pipe", digits = 3)
song1 song2 coef1 coef2 coefdiff z
waiting room and the same 3.148 2.545 0.603 0.836
and the same turnover 2.545 2.751 -0.206 -0.782
turnover styrofoam 2.751 2.274 0.477 1.352
styrofoam steady diet 2.274 0.345 1.929 7.433
steady diet returning the screw 0.345 1.781 -1.436 -6.141
returning the screw instrument 1.781 2.500 -0.719 -4.192
instrument fell, destroyed 2.500 1.891 0.609 5.104
fell, destroyed place position 1.891 3.231 -1.340 -11.316
place position arpeggiator 3.231 2.880 0.351 2.709
arpeggiator the kill 2.880 2.668 0.212 0.902
the kill hello morning 2.668 -0.284 2.952 12.658

So, the ranks should not be interpreted rigidly. Any two of the adjacent songs in the table could be interchanged and the resulting ranking would be just as valid.

Rating releases

The song ratings calculated using the Fugazi Live Series (FLS) data were used to calculate average ratings for the band’s studio releases. The results are shown below.

releases_data <- releases_summary 
knitr::kable(releases_data %>% arrange(desc(rating)), "pipe")
releaseid release first_debut last_debut release_date songs count shows intensity rating
9 the argument 1998-11-29 2001-06-21 2001-10-16 10 430 79 0.5412 0.8000
8 end hits 1996-01-30 1998-05-01 1998-04-24 13 1361 221 0.4795 0.7961
4 repeater 1987-09-03 1990-05-17 1990-03-01 11 3887 846 0.4175 0.7784
1 fugazi 1987-09-03 1988-06-15 1988-11-19 7 2195 889 0.3519 0.7357
7 red medicine 1993-04-24 1994-11-27 1995-05-12 13 2055 392 0.4023 0.7352
6 in on the killtaker 1991-07-28 1993-02-05 1993-06-18 12 2559 565 0.3762 0.7305
2 margin walker 1987-09-26 1988-11-14 1989-06-15 6 1574 878 0.2985 0.6989
3 3 songs 1987-09-03 1987-10-16 1989-12-01 3 588 898 0.2182 0.6445
5 steady diet of nothing 1987-10-07 1991-04-12 1991-08-01 11 2455 752 0.2858 0.6305
10 furniture 1987-09-03 2001-04-27 2001-10-16 3 206 363 0.3025 0.5753
11 first demo 1987-09-03 1987-09-03 2014-11-18 3 71 899 0.0263 0.2414
13 unreleased 1988-10-31 1996-01-30 NA 2 8 560 0.0074 0.0228