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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] 952

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 title launchdate count
91 waiting room 1987-09-03 675
68 reclamation 1990-05-05 612
9 blueprint 1989-09-23 608
53 long division 1989-04-09 523
55 merchandise 1987-09-03 495
54 margin walker 1988-07-28 467
75 sieve-fisted find 1989-03-24 435
70 repeater 1989-07-19 427
88 turnover 1989-04-09 416
37 give me the cure 1988-03-30 401
2 and the same 1987-09-26 397
89 two beats off 1989-05-03 392
64 promises 1988-10-31 386
82 suggestion 1987-12-03 373
69 rend it 1991-12-08 354
77 song #1 1987-09-03 353
74 shut the door 1989-03-24 342
6 bad mouth 1987-10-16 312
7 bed for the scraping 1994-11-20 310
28 facet squared 1991-08-12 307
81 styrofoam 1989-07-19 294
71 reprovisional 1988-12-29 284
23 do you like me 1994-11-20 282
27 exit only 1990-07-06 282
44 instrument 1992-01-25 282
39 great cop 1991-12-08 277
66 public witness program 1993-02-05 273
73 runaway return 1990-02-11 273
84 target 1994-08-15 267
76 smallpox champion 1992-10-23 265
83 sweet and low 1992-05-15 254
13 bulldog front 1988-06-15 249
14 burning 1988-02-06 243
15 burning too 1988-07-28 238
16 by you 1993-04-24 236
8 birthday pony 1994-08-15 203
34 forensic scene 1994-08-19 203
40 greed 1989-03-24 199
49 latin roots 1990-10-01 187
51 lockdown 1987-12-03 184
46 kyeo 1987-10-07 183
18 cassavetes 1991-07-28 180
10 break 1996-08-15 179
12 brendan #1 1989-03-24 170
20 closed captioned 1997-06-18 169
4 arpeggiator 1997-05-02 163
78 stacks 1991-02-15 156
22 dear justice letter 1991-01-02 152
67 recap modotti 1997-05-01 151
38 glueman 1988-05-07 148
92 walken’s syndrome 1992-10-23 147
11 break-in 1987-10-16 146
5 back to base 1994-11-20 142
62 place position 1996-08-15 140
47 last chance for a slow dance 1991-07-28 138
72 returning the screw 1992-10-23 138
45 joe #1 1987-09-03 136
30 fell, destroyed 1993-08-16 135
29 fd 1997-05-02 122
59 number 5 1998-11-21 120
56 nice new outfit 1991-02-20 119
24 downed city 1994-11-20 116
52 long distance runner 1994-11-27 114
31 five corporations 1996-08-15 113
36 furniture 1987-09-03 108
32 floating boy 1996-10-16 107
58 no surprise 1996-09-29 97
60 oh 1998-11-29 91
3 argument 1999-08-26 76
61 pink frosty 1996-03-20 69
17 cashout 2000-06-04 67
19 caustic acrostic 1996-01-30 53
26 ex-spectator 1999-08-26 52
57 nightshop 1999-08-26 46
79 steady diet 1991-04-12 46
48 latest disgrace 1994-11-20 39
85 the kill 2001-04-05 37
86 the word 1987-09-03 37
90 version 1994-08-27 36
25 epic problem 2000-08-07 34
33 foreman’s dog 1998-05-01 34
41 guilford fall 1996-08-15 32
43 in defense of humans 1987-09-03 31
35 full disclosure 2001-04-05 29
1 23 beats off 1992-10-23 27
50 life and limb 2001-06-21 24
21 combination lock 1994-11-27 21
80 strangelight 2001-04-06 19
87 turn off your guns 1987-09-03 17
65 provisional 1989-05-03 12
63 polish 1991-03-06 6
42 hello morning 2001-04-27 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 title launchdate chosen available_rl intensity
17 cashout 2000-06-04 67 74 0.9054054
20 closed captioned 1997-06-18 169 211 0.8009479
7 bed for the scraping 1994-11-20 310 393 0.7888041
59 number 5 1998-11-21 120 157 0.7643312
68 reclamation 1990-05-05 612 820 0.7463415
4 arpeggiator 1997-05-02 163 220 0.7409091
10 break 1996-08-15 179 244 0.7336066
23 do you like me 1994-11-20 282 393 0.7175573
91 waiting room 1987-09-03 675 952 0.7090336
9 blueprint 1989-09-23 608 873 0.6964490
67 recap modotti 1997-05-01 151 221 0.6832579
3 argument 1999-08-26 76 113 0.6725664
84 target 1994-08-15 267 401 0.6658354
60 oh 1998-11-29 91 149 0.6107383
85 the kill 2001-04-05 37 62 0.5967742
53 long division 1989-04-09 523 889 0.5883015
69 rend it 1991-12-08 354 608 0.5822368
62 place position 1996-08-15 140 244 0.5737705
29 fd 1997-05-02 122 220 0.5545455
55 merchandise 1987-09-03 495 952 0.5199580
66 public witness program 1993-02-05 273 534 0.5112360
50 life and limb 2001-06-21 24 47 0.5106383
34 forensic scene 1994-08-19 203 399 0.5087719
54 margin walker 1988-07-28 467 921 0.5070575
8 birthday pony 1994-08-15 203 401 0.5062344
76 smallpox champion 1992-10-23 265 537 0.4934823
70 repeater 1989-07-19 427 876 0.4874429
75 sieve-fisted find 1989-03-24 435 894 0.4865772
28 facet squared 1991-08-12 307 645 0.4759690
16 by you 1993-04-24 236 498 0.4738956
25 epic problem 2000-08-07 34 72 0.4722222
88 turnover 1989-04-09 416 889 0.4679415
35 full disclosure 2001-04-05 29 62 0.4677419
44 instrument 1992-01-25 282 606 0.4653465
31 five corporations 1996-08-15 113 244 0.4631148
26 ex-spectator 1999-08-26 52 113 0.4601770
39 great cop 1991-12-08 277 608 0.4555921
32 floating boy 1996-10-16 107 242 0.4421488
89 two beats off 1989-05-03 392 887 0.4419391
83 sweet and low 1992-05-15 254 585 0.4341880
37 give me the cure 1988-03-30 401 939 0.4270501
64 promises 1988-10-31 386 911 0.4237102
2 and the same 1987-09-26 397 951 0.4174553
57 nightshop 1999-08-26 46 113 0.4070796
58 no surprise 1996-09-29 97 243 0.3991770
82 suggestion 1987-12-03 373 947 0.3938754
74 shut the door 1989-03-24 342 894 0.3825503
77 song #1 1987-09-03 353 952 0.3707983
27 exit only 1990-07-06 282 780 0.3615385
5 back to base 1994-11-20 142 393 0.3613232
81 styrofoam 1989-07-19 294 876 0.3356164
6 bad mouth 1987-10-16 312 949 0.3287671
73 runaway return 1990-02-11 273 845 0.3230769
71 reprovisional 1988-12-29 284 896 0.3169643
80 strangelight 2001-04-06 19 61 0.3114754
24 downed city 1994-11-20 116 393 0.2951654
52 long distance runner 1994-11-27 114 392 0.2908163
30 fell, destroyed 1993-08-16 135 467 0.2890792
18 cassavetes 1991-07-28 180 656 0.2743902
92 walken’s syndrome 1992-10-23 147 537 0.2737430
13 bulldog front 1988-06-15 249 925 0.2691892
61 pink frosty 1996-03-20 69 266 0.2593985
15 burning too 1988-07-28 238 921 0.2584148
14 burning 1988-02-06 243 942 0.2579618
72 returning the screw 1992-10-23 138 537 0.2569832
49 latin roots 1990-10-01 187 753 0.2483400
40 greed 1989-03-24 199 894 0.2225951
78 stacks 1991-02-15 156 715 0.2181818
22 dear justice letter 1991-01-02 152 718 0.2116992
47 last chance for a slow dance 1991-07-28 138 656 0.2103659
19 caustic acrostic 1996-01-30 53 269 0.1970260
51 lockdown 1987-12-03 184 947 0.1942978
46 kyeo 1987-10-07 183 950 0.1926316
12 brendan #1 1989-03-24 170 894 0.1901566
33 foreman’s dog 1998-05-01 34 188 0.1808511
56 nice new outfit 1991-02-20 119 714 0.1666667
38 glueman 1988-05-07 148 935 0.1582888
11 break-in 1987-10-16 146 949 0.1538462
45 joe #1 1987-09-03 136 952 0.1428571
41 guilford fall 1996-08-15 32 244 0.1311475
36 furniture 1987-09-03 108 952 0.1134454
48 latest disgrace 1994-11-20 39 393 0.0992366
90 version 1994-08-27 36 394 0.0913706
79 steady diet 1991-04-12 46 697 0.0659971
21 combination lock 1994-11-27 21 392 0.0535714
1 23 beats off 1992-10-23 27 537 0.0502793
42 hello morning 2001-04-27 2 48 0.0416667
86 the word 1987-09-03 37 952 0.0388655
43 in defense of humans 1987-09-03 31 952 0.0325630
87 turn off your guns 1987-09-03 17 952 0.0178571
65 provisional 1989-05-03 12 887 0.0135287
63 polish 1991-03-06 6 709 0.0084626

The “songid” variable is each song’s stable identity (alphabetical rank among all classified songs, from songidlookup) - it’s a consistent key for joining fugazi_song_counts and fugazi_song_performance_intensity together, not a ranking by frequency or intensity itself.

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] 24568

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 title Estimate z-value
1 7 bed for the scraping 3.6086886 17.7473206
2 68 reclamation 3.6041238 18.0365751
3 10 break 3.5612232 16.7066562
4 23 do you like me 3.4346337 16.8314134
5 20 closed captioned 3.3199736 15.3773712
6 17 cashout 3.2403719 13.2097176
7 62 place position 3.1978593 14.7669130
8 91 waiting room 3.1516072 15.3019763
9 84 target 3.1375366 15.3977028
10 67 recap modotti 3.1161374 14.3701092
11 59 number 5 3.0190723 13.3709669
12 9 blueprint 2.9912505 14.9161528
13 75 sieve-fisted find 2.9531415 14.5113535
14 69 rend it 2.8989151 14.4723545
15 55 merchandise 2.8953058 13.9756657
16 4 arpeggiator 2.8596784 13.2512392
17 54 margin walker 2.8157008 13.7793466
18 28 facet squared 2.7669101 13.7084453
19 88 turnover 2.7575923 13.5405387
20 8 birthday pony 2.7526631 13.3192153
21 60 oh 2.7408498 11.8203565
22 53 long division 2.7151304 13.4041460
23 3 argument 2.7096246 11.3806705
24 66 public witness program 2.6961330 13.3488556
25 85 the kill 2.6150860 9.6423459
26 29 fd 2.6055077 11.8205662
27 76 smallpox champion 2.5809319 12.7655862
28 34 forensic scene 2.5670313 12.4244316
29 16 by you 2.5426366 12.4989675
30 2 and the same 2.5314142 12.1476531
31 31 five corporations 2.5213837 11.4459728
32 44 instrument 2.4937480 12.3402397
33 50 life and limb 2.4927268 8.3604923
34 32 floating boy 2.4567042 11.0865822
35 35 full disclosure 2.4552050 8.6192480
36 77 song #1 2.4359085 11.6584575
37 37 give me the cure 2.4054965 11.6485470
38 89 two beats off 2.4018132 11.7814558
39 39 great cop 2.4012566 11.8759698
40 26 ex-spectator 2.4003750 9.5736906
41 70 repeater 2.3394339 11.5476366
42 82 suggestion 2.3157598 11.1646430
43 58 no surprise 2.2798002 10.1946959
44 25 epic problem 2.2554190 8.2456870
45 6 bad mouth 2.2507575 10.7552364
46 64 promises 2.2069306 10.7689369
47 5 back to base 2.1987583 10.3564868
48 74 shut the door 2.1843697 10.6553701
49 27 exit only 2.1737359 10.6643155
50 81 styrofoam 2.1662961 10.5588062
51 83 sweet and low 2.1217459 10.4682680
52 57 nightshop 2.0692549 8.0939269
53 73 runaway return 2.0173052 9.8414339
54 13 bulldog front 1.9417767 9.2843087
55 24 downed city 1.9129866 8.8604672
56 92 walken’s syndrome 1.8887340 9.0160740
57 15 burning too 1.8811958 8.9863433
58 14 burning 1.8785531 8.9095708
59 71 reprovisional 1.8645493 9.0188331
60 18 cassavetes 1.8352500 8.8555996
61 30 fell, destroyed 1.8280580 8.6552143
62 52 long distance runner 1.8127303 8.3825481
63 72 returning the screw 1.7709465 8.4115932
64 61 pink frosty 1.7675462 7.6343068
65 49 latin roots 1.7603124 8.4708514
66 40 greed 1.7441696 8.3011711
67 80 strangelight 1.7052959 5.4185020
68 22 dear justice letter 1.5984698 7.5989853
69 78 stacks 1.5549731 7.4073981
70 19 caustic acrostic 1.5430873 6.4116237
71 51 lockdown 1.5222364 7.1103687
72 12 brendan #1 1.5069287 7.1035642
73 47 last chance for a slow dance 1.4727829 6.9740537
74 46 kyeo 1.3873312 6.4531617
75 56 nice new outfit 1.3104772 6.1058090
76 11 break-in 1.2358574 5.6720477
77 33 foreman’s dog 1.2093225 4.5345895
78 65 provisional 1.1804772 3.1435445
79 38 glueman 1.1417943 5.2796981
80 45 joe #1 1.1365963 5.1699528
81 41 guilford fall 1.0707057 4.0218200
82 36 furniture 0.9459643 4.2226754
83 48 latest disgrace 0.7339073 2.9092202
84 90 version 0.5578754 2.1796928
85 79 steady diet 0.3055302 1.2545455
86 21 combination lock 0.0719275 0.2458331
87 1 23 beats off 0.0000000 NA
88 86 the word -0.3078846 -1.1674949
89 42 hello morning -0.4059818 -0.5490568
90 43 in defense of humans -0.4062096 -1.5017400
91 87 turn off your guns -1.0216074 -3.2351004
92 63 polish -1.8376209 -4.0656032

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(title, chosen, intensity, rating) %>% arrange(desc(rating)), "pipe")
title chosen intensity rating
bed for the scraping 310 0.7888041 1.0000000
reclamation 612 0.7463415 0.9991618
break 179 0.7336066 0.9912848
do you like me 282 0.7175573 0.9680417
closed captioned 169 0.8009479 0.9469889
cashout 67 0.9054054 0.9323732
place position 140 0.5737705 0.9245674
waiting room 675 0.7090336 0.9160750
target 267 0.6658354 0.9134915
recap modotti 151 0.6832579 0.9095624
number 5 120 0.7643312 0.8917402
blueprint 608 0.6964490 0.8866318
sieve-fisted find 435 0.4865772 0.8796346
rend it 354 0.5822368 0.8696781
merchandise 495 0.5199580 0.8690154
arpeggiator 163 0.7409091 0.8624738
margin walker 467 0.5070575 0.8543991
facet squared 307 0.4759690 0.8454406
turnover 416 0.4679415 0.8437297
birthday pony 203 0.5062344 0.8428247
oh 91 0.6107383 0.8406556
long division 523 0.5883015 0.8359333
argument 76 0.6725664 0.8349223
public witness program 273 0.5112360 0.8324451
the kill 37 0.5967742 0.8175641
fd 122 0.5545455 0.8158054
smallpox champion 265 0.4934823 0.8112930
forensic scene 203 0.5087719 0.8087407
by you 236 0.4738956 0.8042616
and the same 397 0.4174553 0.8022010
five corporations 113 0.4631148 0.8003593
instrument 282 0.4653465 0.7952851
life and limb 24 0.5106383 0.7950976
floating boy 107 0.4421488 0.7884835
full disclosure 29 0.4677419 0.7882082
song #1 353 0.3707983 0.7846652
give me the cure 401 0.4270501 0.7790812
two beats off 392 0.4419391 0.7784049
great cop 277 0.4555921 0.7783027
ex-spectator 52 0.4601770 0.7781408
repeater 427 0.4874429 0.7669514
suggestion 373 0.3938754 0.7626046
no surprise 97 0.3991770 0.7560020
epic problem 34 0.4722222 0.7515254
bad mouth 312 0.3287671 0.7506695
promises 386 0.4237102 0.7426224
back to base 142 0.3613232 0.7411219
shut the door 342 0.3825503 0.7384800
exit only 282 0.3615385 0.7365275
styrofoam 294 0.3356164 0.7351615
sweet and low 254 0.4341880 0.7269816
nightshop 46 0.4070796 0.7173437
runaway return 273 0.3230769 0.7078052
bulldog front 249 0.2691892 0.6939374
downed city 116 0.2951654 0.6886512
walken’s syndrome 147 0.2737430 0.6841981
burning too 238 0.2584148 0.6828141
burning 243 0.2579618 0.6823288
reprovisional 284 0.3169643 0.6797576
cassavetes 180 0.2743902 0.6743779
fell, destroyed 135 0.2890792 0.6730574
long distance runner 114 0.2908163 0.6702431
returning the screw 138 0.2569832 0.6625711
pink frosty 69 0.2593985 0.6619468
latin roots 187 0.2483400 0.6606186
greed 199 0.2225951 0.6576546
strangelight 19 0.3114754 0.6505170
dear justice letter 152 0.2116992 0.6309026
stacks 156 0.2181818 0.6229161
caustic acrostic 53 0.1970260 0.6207338
lockdown 184 0.1942978 0.6169053
brendan #1 170 0.1901566 0.6140947
last chance for a slow dance 138 0.2103659 0.6078251
kyeo 183 0.1926316 0.5921353
nice new outfit 119 0.1666667 0.5780241
break-in 146 0.1538462 0.5643231
foreman’s dog 34 0.1808511 0.5594510
provisional 12 0.0135287 0.5541547
glueman 148 0.1582888 0.5470521
joe #1 136 0.1428571 0.5460977
guilford fall 32 0.1311475 0.5339995
furniture 108 0.1134454 0.5110957
latest disgrace 39 0.0992366 0.4721598
version 36 0.0913706 0.4398384
steady diet 46 0.0659971 0.3935052
combination lock 21 0.0535714 0.3506133
23 beats off 27 0.0502793 0.3374066
the word 37 0.0388655 0.2808758
hello morning 2 0.0416667 0.2628641
in defense of humans 31 0.0325630 0.2628222
turn off your guns 17 0.0178571 0.1498287
polish 6 0.0084626 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(alt1)`
#> Joining with `by = join_by(alt2)`
mycomparisons <- mycomparisons %>%
  select(title1, title2, mycoef1, mycoef2, mycoefdiff, myz) %>%
  rename(coef1 = mycoef1, coef2 = mycoef2, coefdiff = mycoefdiff, z = myz)
knitr::kable(mycomparisons, format = "pipe", digits = 3)
title1 title2 coef1 coef2 coefdiff z
waiting room bulldog front 3.152 1.942 1.21 15.94

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=nrow(summary), by=8))
mycomparisons <- rankr(coeftable = results_ml_Repeatr4, vcovmat = vcovmat_ml_Repeatr4, mysongidlist = songstobecompared)
#> Joining with `by = join_by(alt1)`
#> Joining with `by = join_by(alt2)`
mycomparisons <- mycomparisons %>%
  select(title1, title2, mycoef1, mycoef2, mycoefdiff, myz) %>%
  rename(coef1 = mycoef1, coef2 = mycoef2, coefdiff = mycoefdiff, z = myz)
knitr::kable(mycomparisons, format = "pipe", digits = 3)
title1 title2 coef1 coef2 coefdiff z
waiting room and the same 3.152 2.531 0.620 9.542
and the same turnover 2.531 2.758 -0.226 -3.056
turnover styrofoam 2.758 2.166 0.591 7.672
styrofoam steady diet 2.166 0.306 1.861 11.634
steady diet returning the screw 0.306 1.771 -1.465 -8.527
returning the screw instrument 1.771 2.494 -0.723 -6.877
instrument fell, destroyed 2.494 1.828 0.666 6.210
fell, destroyed place position 1.828 3.198 -1.370 -10.675
place position arpeggiator 3.198 2.860 0.338 2.884
arpeggiator the kill 2.860 2.615 0.245 1.295
the kill hello morning 2.615 -0.406 3.021 4.159

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")
rid release_title first_debut last_debut release_date songs count shows intensity rating
9 the argument 1998-11-29 2001-06-21 2001-10-16 10 475 87 0.5415 0.7906
8 end hits 1996-01-30 1998-05-01 1998-04-24 13 1429 235 0.4738 0.7824
4 repeater 1987-09-03 1989-09-23 1990-03-01 11 4062 893 0.4135 0.7681
1 fugazi 1987-09-03 1988-06-15 1988-11-19 7 2401 940 0.3638 0.7331
7 red medicine 1993-04-24 1994-11-27 1995-05-12 13 2104 408 0.3955 0.7210
6 in on the killtaker 1991-07-28 1993-02-05 1993-06-18 12 2642 587 0.3736 0.7188
2 margin walker 1987-09-26 1989-05-03 1989-06-15 6 1684 922 0.3027 0.7088
3 3 songs 1987-09-03 1987-10-16 1989-12-01 3 635 950 0.2227 0.6317
5 steady diet of nothing 1987-10-07 1991-04-12 1991-08-01 11 2539 781 0.2847 0.6143
10 furniture 1987-09-03 2001-04-27 2001-10-16 3 230 385 0.3065 0.5552
11 first demo 1987-09-03 1987-09-03 2014-11-18 3 85 951 0.0298 0.2312