所以我有关于Sites的数据，嵌套在Class中。每个站点中都有一个时间（时间点）变量。感兴趣的数据是Count1，Total1，Count2，Total2。
我知道在整个站点的类中，对于时间Count1，Total1，Count2，Total2的值，存在整个重复集。
这是我的意思-假设我们拥有第1类的第一个站点：
Class Site Time Count1 Total1 Count2 Total2
1 a0QjvO281o1 1 8 64 4 34
1 a0QjvO281o1 2 16 64 8 34
1 a0QjvO281o1 3 16 64 8 34
1 a0QjvO281o1 4 16 64 8 34
1 a0QjvO281o1 6 8 64 4 34
而且，我注意到还有其他几个具有这种精确模式（或其他重复模式）的网站。
Class Site Time Count1 Total1 Count2 Total2
1 zlG1VmpE6QQ 1 8 64 4 34
1 zlG1VmpE6QQ 2 16 64 8 34
1 zlG1VmpE6QQ 3 16 64 8 34
1 zlG1VmpE6QQ 4 16 64 8 34
1 zlG1VmpE6QQ 6 8 64 4 34
我想在班级内确定多少个站点具有相同的模式。标记它们或将数据集减少到第一个唯一的站点模式，但是我想说出每个找到的模式适合多少个站点。
所以，这是部分数据：
df <-
structure(list(Class = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), Site = structure(c(3L,
3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 6L, 6L, 6L, 6L, 6L, 9L, 9L,
9L, 9L, 9L, 17L, 17L, 17L, 17L, 17L, 19L, 19L, 19L, 19L, 19L,
30L, 30L, 30L, 30L, 30L, 49L, 49L, 49L, 49L, 49L, 54L, 54L, 54L,
54L, 54L, 56L, 56L, 56L, 56L, 56L, 62L, 62L, 62L, 62L, 62L, 66L,
66L, 66L, 66L, 66L, 86L, 86L, 86L, 86L, 86L, 88L, 88L, 88L, 88L,
88L, 98L, 98L, 98L, 98L, 98L, 33L, 33L, 33L, 33L, 33L, 128L,
128L, 128L, 128L, 128L, 141L, 141L, 141L, 141L, 141L, 153L, 153L,
153L, 153L, 153L, 154L, 154L, 154L, 154L, 154L, 274L, 274L, 274L,
274L, 274L, 291L, 291L, 291L, 291L, 291L, 306L, 306L, 306L, 306L,
306L, 309L, 309L, 309L, 309L, 309L, 336L, 336L, 336L, 336L, 336L,
342L, 342L, 342L, 342L, 342L, 396L, 396L, 396L, 396L, 396L, 413L,
413L, 413L, 413L, 413L, 418L, 418L, 418L, 418L, 418L, 435L, 435L,
435L, 435L, 435L, 451L), .Label = c("~", "A0e3A15Lh1d", "a0QjvO281o1",
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"aUhP7zZ7LPU", "aUMEQzUKI0K", "AuP8NAgS7Th", "aUyy9i4fwhS", "AVFW2vlGxds",
"awoAlwC06Go", "awxCmxmeea2", "AWYFb5fwcYb", "Ax2Q16uPW55", "AXO6R085bth",
"Ay6W05BTgDV", "aZMeFIlkevS", "B08adcYOEl7", "b5MVFPi1inY", "B7fffQm5omx",
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"fd5zuIbL3Qd", "fDN9KAuRv2o", "FdqK3U8rDRX", "fG2ws21A6Lj", "fgDQSAYp5pj",
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"GcYMteoIkw9", "GDCM1IWa7Zh", "gdsUTJnwdzb", "Ge7oZ5R4iBk", "gEff10Pq35y",
"GFPi9bpW3sN", "ggMEnqgD9kD", "gKR0a28tTp5", "gKRGyOXbpzj", "glEuzcZNWIM",
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"gqNh2d7WJva", "grKa7EwswRX", "gsIY3JD3iHh", "GSWPAgMxhy2", "gsX0auFXP9m",
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"otmVyzT3xRC", "oUWkMygGP2W", "owxf1XoQ3Lu", "oYgWYWUVt2h", "OYjhvD7DqIP",
"oZfnfo46pS4", "p1NV2hE2fCZ", "p25NocgpHkc", "P2eQdjxbuZo", "p3T3oB4tfNN",
"P3Uob5UKAoM", "p4hBFnI8WIp", "p5L7w9Tjay3", "p7C2DczQikw", "P8tFheT6TtS",
"P91Rf8wCj7Z", "p9J64kFu5Fd", "PDOfJJdpbob", "pdRTIO2JqPL", "PDWC7RxX4t9",
"pEAFBcOJIVF", "PEfq6d3TONP", "PeNS8yHqYH1", "pEvaEn24SR1", "pg9F69FU9fh",
"Pi6v7zcA26e", "PibIwh4xKHI", "PicYz4ZaEkF", "PIm96jtkVB5", "pIVjHCsQgJI",
"PJI3sARzQAG", "PK027w8aZ5K", "PKfz9RYfKzF", "pl8h1HdqpFW", "pl9IGnhmOJc",
"PlISiBPN3db", "pMiRPEvyleJ", "pMtEAU5iVTB", "PnB0GLiMdBm", "PPb3XMcCAf3",
"PSdLvfFlDRF", "pthlRKVLgNp", "PTZfXfOkUR1", "pWmPB9No5RJ", "PWXwPbUM2DB",
"pxPQCkuJZrl", "PxXh1I86blw", "Pz198xRjRHD", "q2UUKkPtvll", "q4hyZcb2pgA",
"q6ke2WlwbWr", "Q75pcfnDLwr", "Q86baYhZPOB", "q8fmqtJVDhh", "qBrBhSbFC0d",
"qc9eMgI8Y95", "QCY2lUMpt7f", "QDkCAOGVng6", "QdYKp8ivavV", "qeBFicifeNz",
"QeKGz2D6wNe", "qEt7nmwua6v", "QGJz6Rv3qHU", "Qgzh7S5pLc3", "qHaaYvuNGIB",
"qiBueINJbti", "qimfq5GL5mV", "qJsVouyMqE8", "qlnxDl1BOrw", "Qlt1DOyb7iP",
"qm0fcx7VGOQ", "QMT77ObrHQa", "QOyCdSRSUXL", "Qpj3LVa0kMf", "qQ84fCTxdGh",
"QRaKmOedEZx", "qs9EipoiiBD", "qsPQEZph59z", "QTFJClMfP8c", "QtJyTjN5faU",
"qU7z54bY9jA", "QvByLV2hsHo", "QVFUUfes7vc", "QvQ5bpVOJDj", "Qwzbgh4Flmx",
"qx2DdF2CKFL", "qXdueHJNqcv", "QxSfgx5QfT7", "qxuRrLWQmXL", "Qztk8cjmz1e",
"r0ehsy1jjxa", "r2w7bZu3FsL", "R3ac44RpwRG", "r4mXVpHUWC7", "r6p12UeHOyg",
"r9efDheFtk3", "rakWSnvNhWr", "rbBZoYFr4DM", "rBtlT7YCRKx", "RdbYAXOnm2S",
"RdM4hjZsFRg", "Re2M8SlCc98", "RfmkqgjDUPL", "rgAmPaAHmNU", "rGbQXTyOdmW",
"RHpQbDCZK5O", "rhxxSbYXZRR", "RiIZqF2hfqY", "rIR8cwAz0sf", "rJ3tipUjVQ4",
"rlAmYWNUTnR", "rLiYzJJRiBA", "rLOyzoOdZqC", "RMKAo2HcVkM", "rnGH1Q5IyIU",
"robJRJuEFfM", "RovRnV9RWFd", "rpmWXDmHjsq", "rPPdTvv1QoY", "RqLdtXwHdGO",
"rR1aDWav3z1", "RrjHJQJDQSr", "rrZEwHEjjy8", "rsM3sdDc3Lk", "RsmDQZSmpD7",
"RtK3aS9WP2H", "ru8BHTnYxI3", "RU8DlKBg48x", "rUysfjKrKqk", "Rv3o89GkqWH",
"rVC8KePJHu3", "RvCLp5qbvtz", "RvQqAbOcEfA", "RW617O0UjQJ", "RWvmueaioAl",
"RxADuUq1Ba1", "RxHTSbz8VN5", "RxND5KsxzvW", "RyRJf2UHJL1", "S1Rh4YnCAAZ",
"s28njgt1wYe", "s4eb8Spa5TC", "S6gaiIWGmh9", "S6X4d5WHA1H", "sAnH4cWV41G",
"SATZgjyfpdZ", "sAyk7hwXEbV", "sBu9GwU5IKe", "SdlDgZMNxqX", "SegMIAP4dhw",
"SfB5NwJXaot", "SfPGp94cYZa", "SG0QMcMgRRq", "sG2EfH7UYLQ", "SgK14sd0Fq1",
"sgNOxONNZIv", "sGXcrRdwzAk", "shkTq2LdpXw", "si6qmHhCV9F", "skR6XpFhu3u",
"skuXY545bae", "skVM9VC2v6H", "sLkylFDaonQ", "sLQ3GDMCRSz", "sMVuTESYbpd",
"soMCF3RbHqt", "sQhxc449PV4", "sRbFOoSk7qZ", "srTptJGYtcK", "SSS1hmwqHOR",
"StSjQheznIv", "SvLVieXqQT7", "SVR6pSBhbCb", "sWlH85siDIT", "SWTCBn32M8D",
"sYBdL54a73r", "t0V5NCdjdPi", "T15MpYA7f51", "t3snPDHuVBW", "T5LdflE3Peq",
"T6RUeMH9KP0", "T6VbSgxjG4o", "t9Fl7c8SJbm", "TafeAKXESCA", "TBMPJiR0PKA",
"tcjz9dmJW4y", "tDDh1EjIZkh", "TE62MxBLgne", "TE7dhvcKVwp", "tEiDKptkacd",
"tEr481bYdow", "tfEtbnUgkGv", "TgpNd1eUCH5", "tGV21Z1HgXN", "thQZhxRh887",
"TJp862VOKlS", "TK7P7QXIDOA", "TKb3FP8mXY0", "TL5cvVAN3cA", "TM31sX4CThP",
"tMpwPcDzIfU", "tNf8m963xKK", "tnR9XvFJ5d7", "tNu7AdZ5358", "tOEYJ1EgIkn",
"TqSXqCuyodR", "tRgUTgCKu4J", "troIuBzxemz", "TSQWaAvOer5", "TsSlV9eE7Mz",
"ttKnsfno2BN", "tvoTu4cpYbh", "TWJPFfCeHES", "twyDPmlDNjH", "TyjDUvHkCAx",
"TYYPCGssY7i", "tz00ETYw78Y", "TZ307ap3HvE", "TzPwGs1AcCL", "TZxEGcWjbdk",
"u0ezFwC4OLL", "U3DjjRVyEun", "u45lZujojLF", "U6Mo4GsQKwT", "U7jt55boMwC",
"U8feQBluEhj", "UBe2SLdSmxV", "uBjjsyieqtr", "UccWk7OAtZ2", "uDXFpf8Ko6P",
"uE4KejhmDyk", "uGfkThgxZsI", "Uih0KGtvZeo", "UIyI4hkq7Bx", "UjoXPWJKPXb",
"uKFFT93nPmp", "UKSoohp2vBC", "UL70316n0C2", "UlD5QNXAW40", "uLDFnAy4ro0",
"UNxoCz1KXnW", "uOmh6keHjf6", "uormVxMEerw", "Upe0kYdbeUy", "UPSbASHNQmU",
"UQ1K5VqXqcZ", "uQvg5rWo87I", "usFB6MgBB6t", "uTeZmtXQzSN", "utgv86YyClH",
"UTmdWR44H5x", "uUmAJIXkmsO", "UUsAfkqIPhV", "Uv6Baj6YaG1", "UV9ZR51T6Ts",
"UvVxiC7b1jZ", "UW4ZNlm05Jq", "UxEq7311Xzd", "UXhcOzwv9o5", "UXSSmcXoWR8",
"v15yxuZyGjR", "V1MbBFGqwbB", "V5LD5oYeZys", "v6BprVsEEt2", "V7Hl62C5Wgz",
"Vah8YYh5HI5", "vbDOTEMQjfW", "vBjsjEqsmWL", "vBym1l507tA", "vf1kkxsjkB1",
"vFSbE4W5Kg6", "VfZPt9kXxL9", "vGLQ19KWuBv", "VHK1T5sygmw", "VLuN2iZ9oZp",
"VmwVU8HFDBn", "vnaUuR9C4FH", "vniKeY4S1Ru", "vOu023c0Snx", "vOuGO9bkEUa",
"vrQvRBzXiLv", "vRRoviRJVgX", "VS1mxlo1mVx", "VsFXXXagVmp", "VSHfWQyhzUu",
"vugElbQMtcL", "vvaX4oKLyKo", "vw87QIZ7dhk", "VWLVmvtDCSI", "vxXQe9jxSPE",
"vy0hyVTrTom", "Vy1JFQbsNBB", "vzGc2nPWraO", "vzVRv2jtJxL", "w0aCC4wNNzW",
"W1wtZLbWuY0", "w2yXiR4CyWt", "w539HzekPQh", "w55gRgLikEN", "WBhss2tvLa8",
"WcPEy9epMgd", "WCSGolF5yhy", "wdcS5ORWZte", "WDyq0ryAjpn", "webeuXrveDi",
"WeSJR8GDPmC", "WFApCUf18Lp", "wfFCmvMEGOQ", "WFiPvuGJf9O", "WggRnJplCQI",
"wgqFTVU7Iky", "wIMmZwl1gpX", "WjCGPzMzLVr", "WJfiDULf7ZC", "wkl1yyAzga3",
"wlspYUyDoQM", "wm060hpEM7g", "wMPB6u0GZDL", "Wn07Tbv74qp", "wNha3idA7l6",
"WnZVpXq5XCO", "wOe4JHkqbUm", "Wog7gclb7TJ", "wq4bmXnJK45", "Wq4O1nlYk1C",
"wqUwUpMD2mJ", "wrGYa8E94Yc", "WSAfRmiEJOF", "wSP90pEfCng", "wSW662GVwZP",
"wtoXU3G9YIy", "WtPSqPwjH2f", "wtV2TtEPCCZ", "Wtw2jbyaHz2", "WUChzooYWJ1",
"WUFgPdTN02g", "wUQiuRjZxiO", "Ww9Rq2KLlqV", "WWabB2sc4B7", "wxKEHpSLvib",
"wXnoTA2MDy9", "WYk4A1fVYD7", "WYMXHupBG7P", "wzD83xmvR3b", "WzemydwRD0R",
"X4ZVDdDd2xa", "X6efCWparbb", "X6uv3PName4", "X7deWPhTiIy", "X8TsrtMQFiu",
"X8UmaBiq1yy", "xbJCVaOZWp5", "XbjRzgMPN24", "XbubJh2yjOw", "XcqBCAaLcq5",
"xd8LIlN7N8h", "XdKVljaiZ9j", "xeEUMp35d5m", "XeUDpg1CTKf", "xf9Q4yYDlq5",
"XFEHZnnEGkT", "xFO9GKAXi1n", "xfxtwRZ7Ejp", "xhOpIbHQy8I", "XjBkSXvZLOZ",
"xjfIPJ04cET", "XLt8l1uPicg", "xlYle4v5GZ8", "xmJNiAbmSfe", "XnkRi1jTMKr",
"XPhxWI0fDyq", "XqDQsrhQ7W5", "Xsd3yzbnFOf", "XTF6vymtG8J", "xuovzIjWZUG",
"Xv1I8z1cK76", "XvVmyn071HT", "XxBMueAFsnk", "xxVZKlzMYJJ", "xyr4dO4G3tW",
"y4rr2PbfufS", "yaa2uBLsdRa", "YBG39jGSV17", "yDcnCB4aZEX", "YDuoFIKpONe",
"YdWxRCaQR2D", "yfgSogitBGX", "YFi06xiFHWs", "YFi2V7qfmJf", "yfpM2zJ3Zuc",
"ygTl7hih5qi", "YGtrgJxKWiU", "yIcfnuZhejK", "YIxt0WtezdT", "yJ014QFEqru",
"yJO8QTnBF3o", "yKfdWuLsdDx", "ylMgcLnwgce", "YNy9ymD2A8p", "yONz8gph9A7",
"YowwYq8CIXJ", "YPsxC0bl7T2", "YQP6diqjJAl", "YqR6LoSk2Ed", "yqwh11CvYXU",
"YRemZ3p9bFA", "ySxRSgTOeqD", "yTvx2IJ0w0z", "ytwga9hKjVj", "YtyO06HBaVr",
"YvEkkZlNeCK", "yVFdJkYsLK5", "yvoQHXHGvbT", "YVT9zsaVBzp", "YWbmL6VK8R6",
"Ywm8eA9tZHe", "yXady1QV27H", "yY7MHufA6C9", "yYG52aLO1GK", "yYgG4h097xR",
"YyhPAO5yx22", "Yz5yhyHf7Ul", "z2cGjpx37Mw", "Z42m6cWsI9m", "z4DptoHrJnb",
"z4kLOdnL1Op", "z5tZes2s49Z", "z5WklS85YjT", "z6bId6qlNk4", "Z6ZZLw50mAM",
"z8MwD6T43n2", "z8UkGdr2xNs", "Z90jET09ZrD", "zaeb1Zos2Mu", "ZBkpY2KdibX",
"Zc0BcScQDBU", "zCjn57zZQVN", "ZcrdEBruDka", "ZCT4YbaBFUb", "ZdVIx83rdI7",
"zEQXA689E4a", "ZfjQmCjVKRF", "zfutn6ulVcO", "zFzYdXMnPoP", "zG4JqtM8wHO",
"ZGyAErBl5PS", "ZifoCg4OvIj", "ZJ6MAab9PJE", "ZKVzRmYkKzQ", "zlG1VmpE6QQ",
"zN6xXPgmzqK", "zOfDRrZmbQO", "zOGa9wLHDFE", "zQmuipEUYbz", "zR7UekDUG3X",
"zrs6iFpEtF1", "ZrUjQFzR1gM", "zTnxsAMqHRP", "Zu7gpmcwfqY", "zvOkAI9ewwE",
"zvv07VAowTS", "ZWAdop7zYgJ", "ZWAEE8DrywN", "zxIlF5RwQFi", "ZXONCt7P01p"
), class = "factor"), Time = c(1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L,
4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L,
6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L,
1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L,
2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L,
3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L,
4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L,
6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L,
1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L,
2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L, 2L, 3L, 4L, 6L, 1L),
Count1 = c(8L, 16L, 16L, 16L, 8L, 12L, 24L, 24L, 24L, 12L,
8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L,
16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 12L,
24L, 24L, 24L, 12L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L,
16L, 8L, 12L, 24L, 24L, 24L, 12L, 8L, 16L, 16L, 16L, 8L,
12L, 24L, 24L, 24L, 12L, 8L, 16L, 16L, 16L, 8L, 8L, 16L,
16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L,
8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L,
16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L,
16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L,
8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L,
16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L, 8L, 16L, 16L, 16L, 8L,
8L), Total1 = c(64L, 64L, 64L, 64L, 64L, 96L, 96L, 96L, 96L,
96L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 96L, 96L, 96L, 96L, 96L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 96L, 96L, 96L, 96L, 96L, 64L, 64L,
64L, 64L, 64L, 96L, 96L, 96L, 96L, 96L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L,
64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L, 64L), Count2 = c(4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 3L, 7L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 3L, 8L, 8L, 8L, 4L, 3L, 7L, 8L, 8L, 4L, 2L,
4L, 4L, 4L, 2L, 3L, 5L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 3L, 6L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 3L, 4L, 6L, 6L, 2L, 2L, 4L, 4L, 4L, 2L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 4L,
8L, 8L, 8L, 4L, 3L, 8L, 8L, 8L, 4L, 4L, 8L, 8L, 8L, 4L, 3L,
8L, 8L, 8L, 4L, 3L, 5L, 7L, 8L, 3L, 4L, 8L, 8L, 8L, 4L, 4L
), Total2 = c(34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 32L, 32L, 32L, 32L, 32L, 34L, 34L, 34L, 34L, 34L, 33L,
33L, 33L, 33L, 33L, 32L, 32L, 32L, 32L, 32L, 16L, 16L, 16L,
16L, 16L, 30L, 30L, 30L, 30L, 30L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 31L, 31L,
31L, 31L, 31L, 34L, 34L, 34L, 34L, 34L, 22L, 22L, 22L, 22L,
22L, 16L, 16L, 16L, 16L, 16L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L,
34L, 34L, 34L, 34L, 34L, 34L, 34L, 34L, 33L, 33L, 33L, 33L,
33L, 34L, 34L, 34L, 34L, 34L, 33L, 33L, 33L, 33L, 33L, 28L,
28L, 28L, 28L, 28L, 34L, 34L, 34L, 34L, 34L, 34L)), row.names = c(1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L,
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L,
55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L,
68L, 69L, 70L, 71L, 72L, 73L, 74L, 75L, 1041L, 1042L, 1043L,
1044L, 1045L, 1046L, 1047L, 1048L, 1049L, 1050L, 1051L, 1052L,
1053L, 1054L, 1055L, 1056L, 1057L, 1058L, 1059L, 1060L, 1061L,
1062L, 1063L, 1064L, 1065L, 1066L, 1067L, 1068L, 1069L, 1070L,
1071L, 1072L, 1073L, 1074L, 1075L, 1076L, 1077L, 1078L, 1079L,
1080L, 1081L, 1082L, 1083L, 1084L, 1085L, 1086L, 1087L, 1088L,
1089L, 1090L, 1091L, 1092L, 1093L, 1094L, 1095L, 1096L, 1097L,
1098L, 1099L, 1100L, 1101L, 1102L, 1103L, 1104L, 1105L, 1106L,
1107L, 1108L, 1109L, 1110L, 1111L, 1112L, 1113L, 1114L, 1115L,
1116L), class = "data.frame")
也许我们可以做
library(dplyr)
library(stringr)
df %>%
group_by(Class, Site) %>%
summarise_at(vars(-Time), str_c, collapse="") %>%
group_by(Class, Count1, Total1, Count2, Total2) %>%
mutate(ind = group_indices()) %>%
ungroup %>%
select(Class, Site, ind) %>%
left_join(df)
或与data.table类似的逻辑
library(data.table)
setDT(df)[df[, lapply(.SD, paste, collapse=""),
.(Class, Site), .SDcols = patterns('Count|Total')][,
ind := .GRP, by = Count1:Total2][, .(Class, Site, ind)], on = .(Class, Site)]