我想以这样一种方式对频率进行分箱，使每个分箱包含大致相同的数量。到目前为止，我设法通过平均划分范围来划分频率，但我还没有找到一种自动计算 bin 大小的方法。
数据：
> dput(sumsq)
structure(list(difftim = c(-170L, -17L, 71L, -487L, -301L, -107L,
-202L, -71L, 404L, 99L, -252L, -18L, 253L, -505L, 421L, 597L,
-454L, 420L, 66L, -202L, 84L, -657L, 0L, -253L, -35L, 21L, -101L,
-152L, -33L, 150L, -253L, -185L, -137L, -100L, -370L, -67L, -218L,
-68L, -71L, 1332L, -151L, -67L, 438L, 34L, 201L, -35L, -83L,
-17L, 269L, -380L, -336L, -125L, 67L, -59L, 170L, 130L, 486L,
-118L, -16L, -33L, -73L, -50L, -404L, 302L, -51L, 302L, 17L,
-17L, 18L, -28L, 388L, 337L, 405L, -34L, -152L, 386L, 152L, 119L,
51L, -17L, 138L, 269L, 219L, 17L, -50L, 302L, 137L, 101L, 49L,
33L, 152L, -34L, 1186L, -33L, 57L, 69L, -338L, 251L, -18L, 67L,
-54L, -437L, 134L, -336L, 322L, -118L, 17L, -824L, 118L, 438L,
1L, 84L, 135L, 52L, -118L, 50L, 87L, -185L, 90L, 73L, -136L,
16L, -37L, -135L, -117L, 235L, 303L, -286L, 286L, 50L, -521L,
151L, 420L, -87L, -184L, -67L, -168L, -33L, -135L, -118L, 1379L,
256L, -34L, -84L, -17L, 135L, 254L, 811L, 168L, 403L, -443L,
-253L, 152L, -185L, -16L, 601L, -825L, -220L, -51L, -219L, 488L,
-51L, 19L, -365L, -249L, 274L, -503L, 37L, 14L, -84L, -320L,
-201L, 118L, 85L, -270L, -121L, 152L, -25L, 17L, -18L, -203L,
-33L, -236L, -371L, 252L, -438L, 219L, 1852L, 3082L, 1026L, -51L,
201L, 371L, 140L, 50L, -499L, 433L, -350L, 57L, -472L, 17L, 185L,
-51L, -118L, 174L, -49L, 185L, -118L, -185L, -337L, -121L, 166L,
-50L, -83L, 250L, 83L, -149L, 83L, -555L, -51L, -100L, -102L,
148L, 116L, 382L, -100L, -174L, 84L, 657L, -232L, -17L, -100L,
-68L, -34L, 135L, 84L, 236L, 153L, -252L, 0L, -169L, 51L, 168L,
163L, 135L, -51L, -303L, -202L, 33L, 101L, -219L, -34L, 51L,
168L, 179L, 404L, 135L, 67L, -233L, 371L, -202L, -112L, -319L,
16L, 115L, -220L, -370L, 682L, -236L, 504L, 8L, -151L, 34L, -45L,
-283L, -69L, 167L, -82L, -102L, -51L, -202L, 534L, -17L, 39L,
55L, 79L, 55L, -35L, -902L, -124L, -1L, -101L, 25L, 214L, 459L,
-367L, -186L, -104L, 50L, 261L, -474L, -168L, 16L, 17L, -209L,
-556L, -236L, -17L, -235L, -67L, -17L, -49L, 504L, -319L, 1702L,
286L, 538L, 657L, 66L, -50L, 269L, 151L, -434L, -404L, -725L,
68L, -572L, -86L, -455L, -236L, -421L, -152L, -370L, 135L, 118L,
-169L, -269L, -286L, -118L, -539L, -68L, -20L, -121L, 785L, -169L,
400L, -253L, -1L, 0L, 84L, -126L, -151L, -50L, -101L, -151L,
-163L, -50L, 0L, -100L, -169L, 497L, -34L, 170L, 154L, -505L,
-338L, -330L, 67L, 306L, -51L, -16L, -17L, 118L, -270L, -168L,
-85L, 977L, 1127L, 34L, -166L, -489L, 0L, -404L, -84L, -455L,
286L, -51L, -403L, 454L, -387L, 371L, -50L, -202L, -219L, -34L,
84L, -303L, 28L, 33L, -10L, -370L, 35L, -151L, 59L, 214L, -101L,
0L, 185L, 252L, 67L, -67L, 362L, -50L, 151L, -16L, -151L, -150L,
50L, -1227L, -320L, -112L, -60L, 85L, 135L, 99L, 20L, -807L,
-50L, 925L, 1177L, -15L, -67L, 2606L, 17L, 539L, 34L, -34L, 34L,
606L, 101L, -85L, -50L, 17L, 444L, -199L, 142L, -17L, -16L, -504L,
134L, -617L, 235L, 202L, -101L, -554L, -17L, -303L, 17L, -118L,
-185L, 0L, -67L, -50L, -118L, 590L, -537L, -80L, -119L, -286L,
354L, -488L, -235L, -336L, -101L, -185L, 169L, -370L, 218L, -101L,
16L, 336L, 0L, 387L, 168L, 219L, 118L, 202L, 236L, -185L, -134L,
-874L, -50L, 118L, 17L, 218L, -67L, 51L, -101L, 34L, 34L, -100L,
16L, 67L, 0L, 0L, -152L, 151L, 51L, 67L, -185L, -337L, 68L, 118L,
84L, 101L, -167L, -404L, 403L, -387L, -127L, 202L, -214L, 84L,
-1L, 33L, 269L, 0L, 112L, 101L, -33L, 50L, -219L, 17L, 17L, -253L,
252L, 51L, 51L, 252L, -17L, -134L, 153L, 135L, 151L, 40L, 294L,
-67L, -61L, -118L, 38L, 0L, -50L, 152L, -218L, -168L, 67L, -67L,
68L, 39L, -1L, 134L, -168L, -135L, 17L, -82L, -17L, 16L, 135L,
51L, 134L, -101L, 101L, 152L, 16L, 118L, 100L, 202L, 218L, -168L,
269L, -118L, 201L, -403L, -420L, 0L, 0L, -370L, -201L, 67L, 319L,
320L, -67L, -335L, 285L, 269L, 151L, 185L, -204L, -403L, -84L,
-111L, 1L, 369L, -67L, -50L, 150L, 0L, 319L, -168L, 16L, -117L,
-39L, -67L, -202L, 249L, -101L, -331L, -49L, -67L, 285L, -168L,
85L, -17L, 118L, 115L, -102L, -84L, 184L, 33L, -151L, -269L,
-118L, -150L, -50L, 67L, -17L, 180L, -5L, -135L, 154L, -51L,
73L, -117L, -123L, -82L, -83L, 959L, -168L, -19L, 134L, -100L,
-135L, -252L, -16L, 202L, -36L, -18L, -39L, -172L, -84L, -286L,
135L, 32L, -138L, -1429L, -84L, 117L, 51L, 134L, -134L, -134L,
151L, -34L, 0L, 689L, 386L, 134L, 117L, -100L, -17L, -69L, -34L,
118L, 50L, -117L, 286L, 68L, 95L, 98L, 204L, -16L, 17L, -168L,
-135L, 151L, -68L, 200L, 169L, 100L, 375L, -152L, -33L, 1L, 17L,
50L, 0L, -17L, 16L, -119L, 202L, 66L, -17L, 0L, 134L, 33L, 14L,
252L, 67L, 84L, -122L, -84L, -68L, 12L, -1397L, 51L, -101L, -68L,
-17L, 186L, 118L, 33L, 85L, -18L, 16L, 98L, 135L, 0L, -17L, 340L,
-35L, -34L, -34L, 336L, 85L, 101L, -98L, -1L, 251L, 101L, 151L,
-519L, -201L, -68L, -319L, -169L, -421L, -269L, -342L, 236L,
-543L, -1785L, -87L, 100L, 304L, 118L, 0L, -16L, 639L, 101L,
67L, -160L, 276L, 135L, -51L, 85L, 15L, -219L, -336L, 926L, 236L,
-320L, 200L, -33L, -50L, -611L, -353L, -67L, -17L, -160L, 17L,
-269L, -286L, -151L, 135L, 183L, -387L, -101L, -169L, -219L,
17L, -137L, -33L, -101L, -80L, 269L, -17L, 252L, -67L, -66L,
118L, -324L, -100L, -135L, 235L, -17L, 68L, -488L, 387L, 205L,
-117L, -455L, 0L, 455L, -125L, -370L, -34L, 185L, -34L, -1L,
-101L, -123L, -729L, 504L, 0L, 123L, 352L, 218L, 134L, 588L,
537L, -68L, 202L, 135L, 101L, -285L, 23L, -201L, 363L, 65L, 202L,
-211L, 34L, 0L, -134L, -219L, 17L, 420L, 50L, -1L, 319L, -48L,
151L, 17L, 152L, 945L, 46L, -33L, 232L, 16L, 15L, -304L, -168L,
151L, -55L, 51L, 0L, 33L, 50L, -183L, 806L, -422L, -185L, -102L,
-151L, 66L, -148L, 353L, -337L, -14L, 67L, -185L, -17L, -118L,
-134L, 134L, 18L, -168L, 6L, 588L, 235L, -232L, 253L, 67L, -51L,
-151L, -118L, -135L, 212L, -131L, -135L, -18L, 102L, 56L, -100L,
84L, 84L, 150L, 50L, 17L, -35L, 136L, 34L, -147L, -35L, 51L,
-51L, 0L, -118L, 16L, -118L, -67L, -67L, -123L, -84L, 168L, -17L,
-353L, 51L, -84L, 17L, -101L, -17L, 202L, -220L, -1L, -159L,
304L, -792L, -219L, 92L, -185L, 31L, -252L, 572L, -421L, 33L,
1128L, -270L, 0L, 235L, -639L, -54L, 135L, 34L, 236L, -152L,
124L, 33L, -17L, -67L, 202L, 723L, -34L, 82L, 201L, 1L, -169L,
110L, -348L, -11L, 606L, -17L, -394L, -118L, 673L, 35L, 51L,
-842L, 118L, 68L, -13L, -33L, 84L, -67L, -320L, 17L, 168L, -16L,
84L, -268L, -201L, -487L, -68L, 17L, -34L, 336L, -56L, 67L, 101L,
1L, 0L, -101L, 68L, -16L, 16L, -101L, 67L, 33L, 0L, 34L, 51L,
-18L, 52L, 353L, -153L, 117L, -17L, 605L, 252L, 219L, 438L, -31L,
-219L, -101L, 135L, 34L, 51L, 34L, 1497L, -487L, -85L, -137L,
-67L, -135L, -51L, 202L, 51L, -1110L, -67L, -1043L, -308L, -185L,
135L, -202L, -354L, -253L, 84L, -185L, -50L, -354L, 0L, -33L,
7L, -252L, -134L, -284L, 34L, -219L, -219L, -303L, -287L, -135L,
102L, -35L, 860L, -34L, 118L, 68L, -61L, -264L, -168L, 47L, 236L,
-287L, -103L, 17L, -95L, -607L, 438L, -388L, 17L, -287L, -68L,
-253L, 34L, -113L, -101L, 168L, 1885L, 168L, 708L, 135L, 387L,
118L, 556L, 219L, 68L, 51L, 68L, 202L, -370L, -572L, 487L, 0L,
-253L, 405L, 118L, -269L, 741L, -185L, -152L, -84L, -167L, 169L,
102L, 573L, -151L, 235L, 820L, 169L, -186L, 539L, -58L, 4L, 100L,
101L, -573L, -118L, -17L, -34L, -252L, 84L, 1L, 101L, -286L,
269L, 270L, 370L, -168L, 152L, -83L, 34L, -50L, -84L, -51L, 84L,
118L, 51L, 253L, 67L, -117L, 87L, 52L, -319L, -1L, 51L, 149L,
68L, 219L, 589L, -67L, -219L, 689L, 17L, -17L, -219L, -202L,
555L, 806L, -151L, 353L, 673L, -252L, 151L, 438L, 403L, 50L,
-235L, -252L, 84L, -235L, -336L, 182L, 1059L, 84L, -505L, 169L,
134L, -168L, 404L), ratperc = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,
0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0, 0, 0, 0, 0, 0, 75.6, 0, 89.6,
24.8, -100, -100, 75.6, 100, 100, -100, -100, -100, -100, -100,
-100, 75.6, 98.4, 98.4, -51.2, -51.2, 0.8, 0.8, 0.4, 0.4, 0.4,
0.4, 75.2, -100, -100, -100, 1.2, -0.4, -0.4, -0.4, -0.4, 100,
100, -1.6, 0, 0, 0, 0, -100, 0.4, 100, 0.4, 0.4, 100, -0.4, -78.4,
0.4, 100, 100, 100, 100, -100, 23.6, 61.2, 61.2, 69.2, 75.6,
75.6, 75.6, 75.6, 75.6, 98, 98, 98, -75.2, -75.2, 47.2, 47.2,
47.2, 47.2, 76.8, 97.6, -71.6, -71.6, -71.6, -71.6, 24, 52, 52,
52, 75.2, 75.2, -77.6, 25.2, 47.2, 76.4, 76.4, 76.4, 76.4, 76.4,
76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, 76.4, -73.2,
-73.2, -73.2, -73.2, 0.8, 0.8, 75.2, 75.2, 75.2, 75.2, 75.2,
75.2, 0.4, 0.4, 0.4, 0.4, 0.4, -100, -100, -100, -100, -100,
73.2, 2, -0.8, -0.8, -0.8, -100, -0.4, -0.4, 50.4, 50.4, 50.4,
50.4, 50.4, 50.4, -76.4, 99.6, 99.6, -76.4, 100, 100, 50.4, 1.2,
28, -1.2, 93.6, 41.2, 1.6, 24.8, -1.6, 0, 0, 24.8, -24, 26, 50.8,
2, 28, 36.4, 24, -43.6, 33.6, 61.2, 81.2, 86.8, 34, -51.6, -2,
28.4, 2, 82, 41.6, 25.6, 82, 0.8, 92, 1.2, 86.4, 54, 96, 0.4,
-54.4, 1.2, -93.2, -49.2, -98.4, -2, -77.2, 93.2, 23.6, 78.8,
42.4, 0.4, 2.8, 70.8, 24.4, 2.4, 62, 92.8, 16.4, -61.2, 24.4,
-77.2, -0.4, 74.8, 3.6, 82, 82, 18, 54, 9.2, 55.2, 96.4, 96.4,
90, 90, -84.4, -84.4, -2.8, -2, -90.4, 2.4, 34.8, 24, -1.6, -16.8,
2.8, 2.4, -83.2, 22.4, 22.4, -1.6, -1.6, 60, -2.4, 2.4, 2, 0.8,
-22.8, 2, -1.6, 25.2, 2, 2, -52.8, -1.2, -1.2, 3.2, -74.4, 3.2,
3.2, -78.4, 0.4, -2.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, -79.2, -0.8,
-0.8, -0.8, -0.8, -0.8, -3.2, 41.2, -0.8, -0.8, -0.8, -0.8, -83.2,
-1.6, -1.6, 0.4, 0.4, 0.4, -90, -1.6, -1.6, -1.6, -1.6, -1.6,
-1.6, -1.6, -1.6, -1.6, -1.6, -1.6, 77.6, -79.6, 80.8, -81.6,
-93.2, -100, 8.4, 75.6, 82.8, 67.2, -27.2, 78.8, 65.6, 84.8,
73.6, 46.8, -62.4, 57.2, 74, 13.6, -0.8, 32.8, -27.2, 6.4, -67.2,
79.2, -64, 58, -40.4, 64, 8, 60, 76.8, -24.8, -52.4, 56.8, 75.6,
38.4, -50.4, -72.8, -83.6, 24, 34.8, 54.4, -54, 67.6, 78.4, -41.6,
-64.4, -83.6, -93.6, 76.8, -2.4, -19.2, -54, -38, 5.2, 52.4,
64.8, 42.4, 77.6, -46.4, -74.8, -60.4, -83.2, -56.4, -34.8, -16.8,
21.2, 40, 59.2, 0.4, -17.6, 24.4, -14.4, 35.2, -26.8, 42, 44,
-1.2, -35.6, 10.8, -19.6, -35.2, 22.4, -18.4, 27.6, -9.6, 43.2,
-31.2, 45.2, 23.6, -16.4, 28.8, 40.4, 25.6, -8, 15.6, 11.2, -17.2,
15.6, -17.6, 18, 24, -9.6, -34.8, 12.4, -17.2, 36.4, -9.2, -35.2,
-19.6, 10.4, -15.6, -30.4, 30.8, 16.4, -14.8, -26.4, -34.4, 52.8,
34.4, 55.6, 21.2, 41.2, 52, 36.8, 50, 15.6, 36, 53.6, -22.8,
14.8, 25.2, -13.2, -18.8, 32, 20.8, -6.8, -16.4, -27.6, 14.4,
26.8, 38, -28.4, 19.6, -23.6, 18.4, -19.6, 11.6, 0, 0, 0, -26,
-52.4, -24.4, 2, 19.6, -10.8, 3.6, 3.6, -25.2, 28.4, 12, -11.2,
3.2, 37.2, 26, 0.8, 47.6, -17.2, 2.4, -12, -52.4, 0.8, 28.4,
-12, 36.4, 2.4, 50.4, -16, 24.4, -2.4, -2.4, 15.2, -1.6, -1.6,
-1.6, 24.4, -36, 33.2, 1.2, 1.2, -48.8, -22.4, -1.2, -100, -1.6,
-1.6, -26.4, 28, -47.6, 86, -1.6, -1.6, -1.6, -1.6, -1.6, 41.6,
-16, 29.6, -14.8, 3.2, 3.2, 100, 0.8, 0.8, 0.8, 0.8, 25.6, 24.8,
-28, 0.8, -39.2, -97.6, -97.6, -50, 0, 0, 49.6, 0.8, 54, 25.6,
-1.2, -1.2, -90.8, 4.4, 4.4, 41.6, -40.8, -6, -6, 51.6, -8.4,
0, 0, 0, -60, 2.8, -52.4, 1.6, 1.6, 1.6, 18.8, 24.4, -0.4, -0.4,
-0.4, -0.4, -51.6, -0.4, -0.4, -0.4, 26, 0, 18, -42.4, -1.6,
-0.4, 60.4, -2.8, -2.8, -2.8, 76, 2.8, 2.8, -29.2, -23.2, 23.6,
-26.8, 0.4, 0.4, -40.8, -3.6, -47.6, 27.6, -2.4, -2.4, -76, -2,
-2, -2, -30.8, 26.8, -4.4, -4.4, -4.4, 3.6, -0.8, -0.8, 67.2,
-1.2, -48.8, 63.2, -42, 50, 30.8, 57.6, -48.8, -48.8, 41.6, -39.2,
-39.2, -35.6, 40, -44, -39.6, -39.6, -50.8, 0, -48.8, 40, -53.2,
52, -47.2, -47.2, -46, 26.4, -29.2, 0, -46.8, -46.8, 34.8, -43.6,
0, 39.2, 0.4, -48.4, 0, -23.6, 29.2, 29.2, -53.2, -53.2, 19.2,
46.4, 46.4, -2, 36, 2, -25.2, -50, -1.6, -2, 35.2, -32.8, 31.2,
-43.2, 46, -28.8, -0.4, -50.4, 0.8, -43.6, 0.4, 27.6, -37.6,
-37.6, 37.6, -50, 40.8, -0.8, -50.4, -49.6, 45.6, 45.6, -48.8,
-0.8, -54, -54, 43.2, -48.8, 46.4, -42.8, 54, -54.4, 34.8, 0.4,
0.4, 0.4, 0.8, -50.4, -50.8, -50.8, 51.6, -68.8, 0.8, 52, -42,
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到目前为止进行分箱的方法......
timevec = seq(min(sums$difftim), max(sums$difftim), by=round( (max(sums$difftim) - min(sums$difftim))/8 ) )
#cut
sumstab = xtabs(~ratperc +cut(difftim, timevec ), dat=sumsid1)
#to dataframe
sumstab = data.frame(sumstab)
#rename
colnames(sumstab) = c("rating", "range", "freq")
#convert rating to numeric
sumstab$rating = as.numeric(as.character(sumstab$rating))
#prepare data for summary
sumstab$fresum = sumstab$rating * sumstab$freq
sumstab2 = ddply(sumstab, .(range), summarise, meanrat = sum(fresum)/sum(freq), no_obs = sum(freq))
我认为最简单的方法是找到一种需要一种新方法来计算timevec我用于cut数据的向量。但是，如果有人知道做整个事情的更好方法，请告诉我。谢谢！