如何对android设备手势进行分组和分析？
我有这样的手势数据：如何对android设备手势进行分组和分析？,r,algorithm,dataframe,R,Algorithm,Dataframe,我有这样的手势数据： Samples Device orientation Timestamp [ms] X Y Pressure Surface Area User Label 1 1 0 68 61 0.168344286 0.22196279 32 0 1 17 68 61 0.13210589 0.168183337 32 0 1 19 91 35 0.146209159 0.257266679 32 0 1
Samples Device orientation Timestamp [ms] X Y Pressure Surface Area User Label
1 1 0 68 61 0.168344286 0.22196279 32
0 1 17 68 61 0.13210589 0.168183337 32
0 1 19 91 35 0.146209159 0.257266679 32
0 1 35 111 17 0.178064715 0.206466688 32
0 1 56 125 -8 0.148245619 0.175400006 32
0 1 88 148 -25 0.107780818 0.107866676 32
0 1 91 153 -25 0.064901965 0.034333335 32
-1 1 108 153 -25 0.064901965 0.034333335 32
1 1 0 244 267 0.38 0.04244445 4
0 1 105 244 267 0.4 0.04244445 4
0 1 122 56 359 0.193919991 0.04244445 4
-1 1 125 34 370 0.16999999 0.04244445 4
1 1 0 213 403 0.20868217 0.04344445 29
0 1 16 209 404 0.27376 0.04344445 29
0 1 20 196 405 0.357331252 0.04344445 29
0 1 24 180 407 0.374564978 0.04344445 29
0 1 39 142 410 0.469146664 0.04344445 29
0 1 67 105 414 0.336099311 0.086644455 29
0 1 97 94 418 0.192348956 0.0878889 29
-1 1 99 94 418 0.18999999 0.0878889 29
1 1 0 126 301 0.39999998 0.04544445 11
0 1 26 137 298 0.71 0.04544445 11
0 1 28 158 295 0.71 0.04544445 11
0 1 47 205 290 0.466999981 0.04544445 11
0 1 67 227 287 0.45999998 0.04544445 11
0 1 92 270 280 0.45999998 0.04544445 11
0 1 112 299 275 0.345573979 0.04544445 11
0 1 137 307 273 0.16 0.04544445 11
-1 1 139 307 273 0.16 0.04544445 11
1 1 0 111 377 0.34999998 0.04444445 30
0 1 12 93 391 0.34999998 0.04444445 30
0 1 29 43 432 0.34495998 0.087644455 30
0 1 33 20 454 0.16999999 0.04444445 30
-1 1 91 20 454 0.16999999 0.04444445 30
1 1 0 262 332 0.384664537 0.04244445 14
0 1 15 258 332 0.42 0.04244445 14
0 1 29 251 334 0.431957797 0.04244445 14
0 1 33 245 335 0.459835633 0.04244445 14
0 1 42 236 336 0.534349055 0.04244445 14
0 1 103 142 365 0.172639996 0.082266677 14
-1 1 142 132 368 0.13 0.0868889 14
1 1 0 105 340 0.17215688 0.0878889 17
0 1 16 177 328 0.17215688 0.087466678 17
0 1 29 225 321 0.17215688 0.06566668 17
0 1 65 277 314 0.12117648 0.04344445 17
-1 1 91 277 314 0.12117648 0.04344445 17
1 1 0 269 138 0.07411766 0.06566667 32
0 1 2 264 145 0.07411766 0.06566667 32
0 1 18 220 203 0.07411766 0.06566667 32
0 1 31 203 223 0.095200011 0.119711118 32
0 1 49 173 230 0.09764707 0.165133337 32
0 1 68 145 216 0.12509805 0.16566667 32
0 1 70 116 204 0.090792165 0.06846667 32
-1 1 92 115 204 0.08980393 0.06566667 32
请告知我如何将他们分组？如果我正确理解了问题，您可以尝试
库（dplyr）
最终成本百分比
分组依据（用户标签）%>%
mutate（用户\手势\索引=行\编号（），
持续时间=最大值（时间戳_ms））%>%
data.frame（）
终稿
手势速度mutate用户手势索引持续时间 Samples Device.orientation Timestamp_ms X Y Pressure Surface.Area User.Label User_gesture_index duration
1 1 1 0 68 61 0.16834429 0.22196279 32 1 108
2 0 1 17 68 61 0.13210589 0.16818334 32 2 108
3 0 1 19 91 35 0.14620916 0.25726668 32 3 108
4 0 1 35 111 17 0.17806472 0.20646669 32 4 108
5 0 1 56 125 -8 0.14824562 0.17540001 32 5 108
6 0 1 88 148 -25 0.10778082 0.10786668 32 6 108
7 0 1 91 153 -25 0.06490197 0.03433333 32 7 108
8 -1 1 108 153 -25 0.06490197 0.03433333 32 8 108
9 1 1 0 244 267 0.38000000 0.04244445 4 1 125
10 0 1 105 244 267 0.40000000 0.04244445 4 2 125
11 0 1 122 56 359 0.19391999 0.04244445 4 3 125
12 -1 1 125 34 370 0.16999999 0.04244445 4 4 125
...
df <- structure(list(Samples = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, -1L, 1L,
0L, 0L, -1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, -1L), Device.orientation = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), Timestamp_ms = c(0L, 17L, 19L, 35L, 56L, 88L, 91L,
108L, 0L, 105L, 122L, 125L, 0L, 16L, 20L, 24L, 39L, 67L, 97L,
99L), X = c(68L, 68L, 91L, 111L, 125L, 148L, 153L, 153L, 244L,
244L, 56L, 34L, 213L, 209L, 196L, 180L, 142L, 105L, 94L, 94L),
Y = c(61L, 61L, 35L, 17L, -8L, -25L, -25L, -25L, 267L, 267L,
359L, 370L, 403L, 404L, 405L, 407L, 410L, 414L, 418L, 418L
), Pressure = c(0.168344286, 0.13210589, 0.146209159, 0.178064715,
0.148245619, 0.107780818, 0.064901965, 0.064901965, 0.38,
0.4, 0.193919991, 0.16999999, 0.20868217, 0.27376, 0.357331252,
0.374564978, 0.469146664, 0.336099311, 0.192348956, 0.18999999
), Surface.Area = c(0.22196279, 0.168183337, 0.257266679,
0.206466688, 0.175400006, 0.107866676, 0.034333335, 0.034333335,
0.04244445, 0.04244445, 0.04244445, 0.04244445, 0.04344445,
0.04344445, 0.04344445, 0.04344445, 0.04344445, 0.086644455,
0.0878889, 0.0878889), User.Label = c(32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 4L, 4L, 4L, 4L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 29L)), .Names = c("Samples", "Device.orientation",
"Timestamp_ms", "X", "Y", "Pressure", "Surface.Area", "User.Label"
), row.names = c(NA, 20L), class = "data.frame")
Samples Device.orientation Timestamp_ms X Y Pressure Surface.Area User.Label User_gesture_index duration
1 1 1 0 68 61 0.16834429 0.22196279 32 1 108
2 0 1 17 68 61 0.13210589 0.16818334 32 2 108
3 0 1 19 91 35 0.14620916 0.25726668 32 3 108
4 0 1 35 111 17 0.17806472 0.20646669 32 4 108
5 0 1 56 125 -8 0.14824562 0.17540001 32 5 108
6 0 1 88 148 -25 0.10778082 0.10786668 32 6 108
7 0 1 91 153 -25 0.06490197 0.03433333 32 7 108
8 -1 1 108 153 -25 0.06490197 0.03433333 32 8 108
9 1 1 0 244 267 0.38000000 0.04244445 4 1 125
10 0 1 105 244 267 0.40000000 0.04244445 4 2 125
11 0 1 122 56 359 0.19391999 0.04244445 4 3 125
12 -1 1 125 34 370 0.16999999 0.04244445 4 4 125
...
df <- structure(list(Samples = c(1L, 0L, 0L, 0L, 0L, 0L, 0L, -1L, 1L,
0L, 0L, -1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, -1L), Device.orientation = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), Timestamp_ms = c(0L, 17L, 19L, 35L, 56L, 88L, 91L,
108L, 0L, 105L, 122L, 125L, 0L, 16L, 20L, 24L, 39L, 67L, 97L,
99L), X = c(68L, 68L, 91L, 111L, 125L, 148L, 153L, 153L, 244L,
244L, 56L, 34L, 213L, 209L, 196L, 180L, 142L, 105L, 94L, 94L),
Y = c(61L, 61L, 35L, 17L, -8L, -25L, -25L, -25L, 267L, 267L,
359L, 370L, 403L, 404L, 405L, 407L, 410L, 414L, 418L, 418L
), Pressure = c(0.168344286, 0.13210589, 0.146209159, 0.178064715,
0.148245619, 0.107780818, 0.064901965, 0.064901965, 0.38,
0.4, 0.193919991, 0.16999999, 0.20868217, 0.27376, 0.357331252,
0.374564978, 0.469146664, 0.336099311, 0.192348956, 0.18999999
), Surface.Area = c(0.22196279, 0.168183337, 0.257266679,
0.206466688, 0.175400006, 0.107866676, 0.034333335, 0.034333335,
0.04244445, 0.04244445, 0.04244445, 0.04244445, 0.04344445,
0.04344445, 0.04344445, 0.04344445, 0.04344445, 0.086644455,
0.0878889, 0.0878889), User.Label = c(32L, 32L, 32L, 32L,
32L, 32L, 32L, 32L, 4L, 4L, 4L, 4L, 29L, 29L, 29L, 29L, 29L,
29L, 29L, 29L)), .Names = c("Samples", "Device.orientation",
"Timestamp_ms", "X", "Y", "Pressure", "Surface.Area", "User.Label"
), row.names = c(NA, 20L), class = "data.frame")
df看起来我需要一个窗口函数之类的东西。。。但是它能是什么呢？到目前为止你都试过什么？请使用dput（）
显示您的数据或数据框的定义@DanielPetterson教授，如果这有助于你解决问题，那么你也许应该这样做，这样问题就可以被认为是完结了。