R中64 hz的上采样

我有以下格式的数据。样本数据粘贴在这里。它基本上有3个变量开始时间、结束时间和这些时间戳之间的一组值。采样频率为64Hz

现在我需要以下格式的输出，因为采样率为64 Hz，所以两个连续的戳记之间的差值为0.0156

我尝试过使用Seq函数的各种方法，但没有成功

样本数据：

data1 <- read.table(sep="\t", text="
  start end value
  2019-04-05 19:14:44   2019-04-05 19:14:46 12.434,12.422,12.422,12.434,12.422,12.41,12.41,12.41,12.398,12.398,12.387,12.375,12.352,12.352,12.363,12.34,12.34,12.328,12.293,12.316,12.305,12.305,12.293,12.293,12.305,12.293,12.305,12.281,12.293,12.293,12.188,12.094,9.023,9.258,9.855,10.043,10.184,10.313,10.395,10.477,10.535,10.547,10.512,10.324,10.078,10.16,10.488,10.676,10.816,10.77,10.711,10.582,10.371,10.594,10.758,10.863,10.922,10.805,10.898,10.945,11.016,11.063,11.871,12.012,12.023,12.023,12.023,12.035,12.023,12.047,12.047,12.047,12.059,12.059,12.07,12.082,12.082,12.07,12.059,12.07,12.094,12.094,12.059,12.094,12.105,12.094,12.105,12.094,12.105,12.105,12.129,12.105,12.117,12.117,12.105,12.117,12.117,12.105,12.105,12.117,12.129,12.117,12.129,12.129,12.129,12.129,12.117,12.141,12.141,12.141,12.141,12.141,12.141,12.141,12.129,12.141,12.152,12.141,12.141,12.141,12.141,12.141,12.164,12.164,12.141,12.141,12.164,12.176,12.188,12.176,12.188,12.188,12.188,12.188,12.176
  2019-04-05 19:18:36   2019-04-05 19:18:39 12.504,12.527,12.516,12.539,12.516,12.504,12.527,12.504,12.527,12.516,12.539,12.516,12.516,12.527,12.516,12.527,12.504,12.539,12.516,12.516,12.527,12.516,12.527,12.504,12.527,12.516,12.516,12.516,12.527,12.516,12.516,12.34,9.492,9.715,9.879,10.078,10.184,10.23,10.254,10.23,10.324,10.605,10.852,10.957,10.945,10.934,10.828,10.629,10.453,10.723,10.887,11.016,11.039,10.945,11.063,11.133,11.191,11.227,11.285,12.188,12.211,12.211,12.211,12.223,12.234,12.234,12.258,12.258,12.258,12.27,12.27,12.281,12.281,12.281,12.27,12.281,12.293,12.293,12.281,12.305,12.293,12.281,12.293,12.305,12.293,12.305,12.305,12.305,12.293,12.316,12.305,12.316,12.316,12.305,12.316,12.305,12.305,12.316,12.328,12.328,12.34,12.34,12.328,12.34,12.305,12.328,12.328,12.328,12.34,12.305,12.352,12.34,12.328,12.352,12.328,12.34,12.305,12.328,12.316,12.305,12.316,12.328,12.352,12.34,12.375,12.328,12.352,12.34,12.352,12.363,12.363,12.375,12.352,12.375,12.352,12.352,12.352,12.363,12.363,12.352,12.387,12.363,12.363,12.352,12.363,12.363,12.34,12.375,12.352,12.363,12.352,12.363,12.363,12.352,12.363,12.34,12.375,12.352,12.352,12.34,12.352,12.352,12.328,12.363,12.328,12.352,12.34,12.352,12.34,12.328,12.363,12.328,12.352,12.328,12.352,12.352,12.328,12.352,12.34,12.363,12.34,12.352,12.34,12.34,12.352,12.316,12.352,12.352,12.352,12.34,12.34,12.363,12.34
  2019-04-05 19:20:15   2019-04-05 19:20:18 12.363,12.375,12.375,12.363,12.375,12.387,12.352,12.375,12.363,12.375,12.363,12.375,12.375,12.352,12.387,12.363,12.363,12.363,12.375,12.375,12.363,12.387,12.363,12.375,12.363,12.375,12.375,12.363,12.387,12.352,12.211,12.152,9.34,9.668,9.902,10.02,10.102,10.16,10.184,10.16,10.02,9.961,10.254,10.559,10.723,10.852,10.828,10.723,10.523,10.277,10.523,10.734,10.84,10.934,10.816,10.934,10.992,11.027,11.086,11.133,12.035,12.047,12.059,12.07,12.059,12.105,12.094,12.117,12.094,12.117,12.105,12.117,12.129,12.129,12.129,12.117,12.141,12.105,12.141,12.141,12.082,12.152,12.129,12.141,12.129,12.152,12.129,12.152,12.152,12.141,12.164,12.129,12.164,12.141,12.152,12.152,12.141,12.164,12.152,12.188,12.152,12.188,12.176,12.164,12.164,12.152,12.176,12.152,12.176,12.152,12.164,12.164,12.164,12.176,12.152,12.176,12.129,12.141,12.152,12.141,12.164,12.152,12.164,12.152,12.188,12.199,12.199,12.199,12.176,12.211,12.188,12.199,12.211,12.199,12.199,12.211,12.211,12.176,12.211,12.199,12.199,12.188,12.188,12.188,12.188,12.211,12.188,12.199,12.188,12.188,12.211,12.199,12.199,12.188,12.211,12.211,12.211,12.211,12.199,12.199,12.188,12.211,12.211,12.199,12.199,12.199,12.199,12.188,12.211,12.199,12.188,12.199,12.188,12.199,12.211,12.199,12.199,12.211,12.211,12.188,12.199,12.211,12.199,12.199,12.199,12.188,12.199,12.199,12.199,12.176,12.199,12.211,12.199
  2019-04-05 19:20:47   2019-04-05 19:20:50 12.328,12.34,12.34,12.328,12.34,12.34,12.328,12.328,12.328,12.328,12.328,12.34,12.34,12.328,12.352,12.34,12.34,12.328,12.34,12.352,12.34,12.352,12.34,12.34,12.34,12.352,12.34,12.328,12.34,12.34,12.246,12.129,9.035,9.234,9.738,9.914,10.008,10.113,10.148,10.125,10.008,10.031,10.371,10.605,10.781,10.723,10.734,10.641,10.383,10.289,10.547,10.723,10.828,10.781,10.781,10.887,10.969,10.98,11.074,11.918,12,12.023,12.035,12.047,12.047,12.07,12.07,12.082,12.07,12.082,12.094,12.082,12.117,12.094,12.105,12.094,12.094,12.082,12.082,12.105,12.07,12.082,12.07,12.082,12.059,12.082,12.094,12.059,12.105,12.082,12.094,12.094,12.094,12.105,12.105,12.117,12.094,12.141,12.117,12.129,12.141,12.129,12.117,12.105,12.141,12.094,12.129,12.129,12.105,12.129,12.094,12.129,12.105,12.129,12.105,12.105,12.105,12.117,12.141,12.094,12.141,12.117,12.105,12.141,12.141,12.152,12.141,12.152,12.129,12.176,12.176,12.141,12.188,12.141,12.152,12.152,12.176,12.152,12.164,12.164,12.129,12.152,12.129,12.129,12.129,12.141,12.129,12.117,12.164,12.129,12.152,12.141,12.152,12.129,12.141,12.141,12.117,12.152,12.129,12.129,12.141,12.141,12.117,12.117,12.141,12.105,12.141,12.129,12.141,12.152,12.129,12.141,12.141,12.152,12.129,12.129,12.152,12.152,12.141,12.141,12.152,12.129,12.141,12.129,12.141,12.117,12.129,12.152,12.117,12.152,12.129,12.141,12.141,12.152,12.152,12.152,12.176,12.176,12.188,12.199,12.223,12.199,12.211,12.246,12.211,12.258,12.234,12.258,12.27,12.281,12.305,12.293,12.316,12.293,12.316,12.328,12.328,12.352,12.363
  2019-04-05 19:24:34   2019-04-05 19:24:37 12.598,12.609,12.609,12.598,12.586,12.609,12.598,12.598,12.598,12.598,12.609,12.598,12.598,12.609,12.598,12.598,12.598,12.598,12.598,12.598,12.609,12.609,12.609,12.586,12.609,12.598,12.598,12.609,12.598,12.598,12.445,12.41,9.234,9.973,10.16,10.242,10.313,10.383,10.371,10.289,10.359,10.688,10.922,11.063,11.027,11.004,10.91,10.688,10.605,10.875,11.051,11.145,11.074,11.109,11.203,11.25,11.32,11.391,12.258,12.305,12.305,12.328,12.328,12.328,12.34,12.352,12.363,12.34,12.375,12.363,12.375,12.375,12.387,12.387,12.387,12.375,12.375,12.387,12.398,12.387,12.398,12.387,12.422,12.387,12.41,12.398,12.41,12.41,12.398,12.41,12.398,12.422,12.398,12.41,12.398,12.41,12.422,12.41,12.434,12.41,12.434,12.41,12.434,12.422,12.422,12.434,12.398,12.445,12.41,12.434,12.422,12.422,12.457,12.41,12.445,12.422,12.445,12.434,12.41,12.445,12.422,12.457,12.457,12.48,12.457,12.434,12.445,12.41,12.457,12.434,12.469,12.457,12.457,12.445,12.445,12.434,12.398,12.445,12.41,12.445,12.445,12.434,12.434,12.434,12.445,12.41,12.445,12.434,12.422,12.445,12.422,12.445,12.434,12.469,12.445,12.445,12.434,12.434,12.445,12.434,12.445,12.434,12.457,12.445,12.445,12.457,12.422,12.457,12.422,12.457,12.434,12.445,12.457,12.445,12.457,12.422,12.457,12.445,12.457,12.434,12.445,12.469,12.445,12.445,12.445,12.457,12.445,12.422,12.445,12.445,12.469,12.445,12.48,12.48,12.48,12.492,12.516,12.527,12.516,12.551,12.551,12.563,12.574,12.586,12.574,12.598,12.621,12.609,12.621,12.633,12.633,12.656,12.668,12.668,12.668,12.68,12.668,12.68
  2019-04-05 19:28:06   2019-04-05 19:28:09 12.539,12.563,12.551,12.539,12.551,12.539,12.563,12.539,12.551,12.551,12.551,12.551,12.539,12.563,12.539,12.563,12.539,12.551,12.551,12.539,12.563,12.551,12.563,12.539,12.551,12.551,12.539,12.551,12.539,12.563,12.539,12.363,8.941,9.34,9.961,10.148,10.242,10.336,10.523,10.77,10.91,10.945,10.957,10.934,10.828,10.594,10.441,10.734,10.922,11.063,11.133,11.051,10.852,10.746,10.945,11.063,11.156,10.992,11.098,11.168,11.203,11.309,12.199,12.258,12.234,12.27,12.281,12.293,12.293,12.293,12.293,12.305,12.316,12.328,12.316,12.316,12.316,12.34,12.328,12.328,12.34,12.328,12.328,12.34,12.34,12.328,12.328,12.34,12.34,12.34,12.34,12.34,12.352,12.363,12.352,12.352,12.352,12.352,12.352,12.34,12.352,12.363,12.363,12.363,12.363,12.387,12.375,12.363,12.375,12.387,12.363,12.375,12.375,12.387,12.363,12.398,12.375,12.387,12.352,12.375,12.375,12.363,12.387,12.375,12.387,12.375,12.398,12.422,12.41,12.41,12.387,12.422,12.422,12.422,12.422,12.422,12.434,12.41,12.434,12.398,12.41,12.41,12.398,12.398,12.363,12.398,12.387,12.398,12.387,12.387,12.387,12.387,12.398,12.387,12.41,12.387,12.398,12.398,12.387,12.375,12.375,12.41,12.387,12.375,12.387,12.398,12.41,12.363,12.398,12.387,12.41,12.375,12.387,12.41,12.387,12.387,12.375,12.41,12.375,12.398,12.387,12.375,12.41,12.387,12.398,12.375,12.41,12.398,12.387,12.422,12.398,12.434,12.434,12.469,12.48,12.469,12.48,12.492,12.492,12.504,12.516,12.527,12.551,12.551,12.551,12.574,12.574,12.598,12.598,12.609,12.609,12.609,12.633,12.621,12.656,12.633,12.68,12.633,12.691,12.668,12.68,12.703,12.691,12.703,12.691,12.727,12.715,12.715,12.75", header=TRUE, stringsAsFactors=FALSE)
data1$start <- as.POSIXct(data1$start)
data1$end <- as.POSIXct(data1$end)


data2<-data1%>%group_by(start time,end 

time)%>%mutate(time=seq(from=as.POSIXct(starttime, format="%Y-%m-%d 

%H:%M:%OS",tz="UTC"),                                                             

to=as.POSIXct(endtime, format="%Y-%m-%d %H:%M:%OS", tz="UTC"), by=0.0156))


time                            value

2019-04-05 19:14:44            12.3

2019-04-05 19:14:455625        11.9

2019-04-05 19:14.47125          12.6

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---

这是你想要的吗？ 使用1/64并不总是生成正确数量的行

data2 <- data1 %>%
  mutate(
    measures = map(value, ~str_split(., ",", simplify = TRUE)),
    n_measures = map_int(measures, length),
    start = as.numeric(start) * 1000,
    end = as.numeric(end) * 1000,
    time = pmap(list(start, end, n_measures), ~seq(..1, ..2, length.out = ..3)),
    paired = map2(time, measures, ~data.frame(time = .x, measure = as.numeric(.y)))
  ) %>%
  pull(paired) %>%
  bind_rows()

---

使用不同的值提供相同的时间戳。。。不是预期的输出：我认为这是由于打印输出中的舍入，如果检查它们是否彼此不相等，如果增加一个时间戳，则可以看到不同的值