整个 data.frame R 的分段插值

Question

我有一个来自使用特殊压缩算法的来源的数据集。简而言之，只有当斜率（变化率）的变化大于一定百分比（比如 5%）时，才会记录新的测量值。

但是，对于我目前正在进行的分析，我需要定期获取值。我可以使用 approx、approxfun 或 spline 对不同的变量与时间进行分段插值（下面数据中的 tme），但我想这样做对于单次拍摄的所有变量（data.table 的列）。

library(data.table)
q = setDT(
structure(list(tme = structure(c(1463172120, 1463173320, 1463175720, 
1463180520, 1463182920, 1463187720, 1463188920, 1463190120, 1463191320, 
1463192520, 1463202180, 1463203380, 1463204580, 1463205780, 1463206980, 
1463208180, 1463218980, 1463233440, 1463244240, 1463245440, 1463246640, 
1463247840, 1463249040, 1463250240, 1463251440, 1463252640, 1463253840, 
1463255040, 1463256240, 1463316360, 1463317560, 1463318760, 1463319960, 
1463321160, 1463322360, 1463323560, 1463324760, 1463325960, 1463327160, 
1463328360, 1463329560, 1463330760, 1463331960), class = c("POSIXct", 
"POSIXt"), tzone = "America/Montreal"), rh = c(50.36, 47.31, 
46.39, 46.99, 47.89, 50.37, 51.29, 51.92, 54.97, 67.64, 69.38, 
68.96, 69.89, 56.66, 51.23, 55.38, 64.36, 50.72, 31.33, 31.38, 
32.65, 33.15, 33.05, 31.87, 32.58, 32.65, 31.06, 29.82, 28.72, 
67.95, 66.68, 64.66, 62.12, 59.86, 58.11, 57.41, 56.5, 56.16, 
55.69, 54.57, 53.89, 53.81, 52.01), degc = c(30.0055555555556, 
30.3611111111111, 30.6611111111111, 30.5833333333333, 30.2666666666667, 
28.6888888888889, 28.2555555555556, 28.0722222222222, 27.4944444444444, 
25.0722222222222, 24.8111111111111, 24.7166666666667, 24.1666666666667, 
25.4111111111111, 25.5222222222222, 24.3555555555556, 22.7722222222222, 
25.5222222222222, 27.8111111111111, 27.9888888888889, 28.0277777777778, 
28.1333333333333, 28.5333333333333, 28.7, 28.85, 29.1555555555556, 
28.8388888888889, 29.5111111111111, 29.6722222222222, 22.3888888888889, 
22.5722222222222, 22.9444444444444, 23.3722222222222, 23.6777777777778, 
23.8777777777778, 24.2055555555556, 24.6888888888889, 24.9777777777778, 
25.3888888888889, 25.8, 26.1, 26.1555555555556, 26.7388888888889
)), .Names = c("tme", "rh", "degc"), row.names = c(NA, -43L), class = c("data.table", 
"data.frame")))

q 是我查询的数据集。以下是适用于单个变量的方法（本例中为 degc）：

interpolate_degc <- approxfun(x = q$tme, y = q$degc, method = "linear")

# To get the uniform samples: 
width <- "10 mins"
new_times <- seq.POSIXt(from = q$tme[1], to = q$tme[nrow(q)], by = width)
new_degc <- interpolate_degc(new_times)

我想一次对所有变量执行此操作，最好使用 data.table。

Answer 1

这似乎有效：

cols = c("rh", "degc")
DT = q[.(seq(min(tme), max(tme), by="10 mins")), on=.(tme)]
DT[, (cols) := lapply(cols, function(z) with(q, 
  approxfun(x = tme, y = get(z), method = "linear")
)(tme))]

                     tme     rh     degc
  1: 2016-05-13 16:42:00 50.360 30.00556
  2: 2016-05-13 16:52:00 48.835 30.18333
  3: 2016-05-13 17:02:00 47.310 30.36111
  4: 2016-05-13 17:12:00 47.080 30.43611
  5: 2016-05-13 17:22:00 46.850 30.51111
 ---                                    
263: 2016-05-15 12:22:00 54.026 26.04000
264: 2016-05-15 12:32:00 53.866 26.11667
265: 2016-05-15 12:42:00 53.826 26.14444
266: 2016-05-15 12:52:00 53.270 26.33056
267: 2016-05-15 13:02:00 52.370 26.62222

通常，当您想遍历列时，lapply 或 Map 就可以了。

工作原理： 在 with(q, ...)、tme 和 get(z) 中引用 q 的列，但在外部其中，我们正在查看 DT 的列（在本例中只是 tme）。

做同样事情的另一种方法：

q[, {
  tt = seq(min(tme), max(tme), by="10 mins")

  c(
    .(tme = tt), 
    lapply(.SD, function(z) approxfun(x = tme, y = z, method="linear")(tt))
  )
}, .SDcols=cols]

Answer 2

对于时间序列，我喜欢使用专门的包，如 xts 和 zoo:

library(xts)
ts <- merge(xts(x = q[,-1], order.by = q[,1]), new_times)
head(ts)
#>                        rh     degc
#> 2016-05-13 16:42:00 50.36 30.00556
#> 2016-05-13 16:52:00    NA       NA
#> 2016-05-13 17:02:00 47.31 30.36111
#> 2016-05-13 17:12:00    NA       NA
#> 2016-05-13 17:22:00    NA       NA
#> 2016-05-13 17:32:00    NA       NA
head(na.approx(ts))
#>                         rh     degc
#> 2016-05-13 16:42:00 50.360 30.00556
#> 2016-05-13 16:52:00 48.835 30.18333
#> 2016-05-13 17:02:00 47.310 30.36111
#> 2016-05-13 17:12:00 47.080 30.43611
#> 2016-05-13 17:22:00 46.850 30.51111
#> 2016-05-13 17:32:00 46.620 30.58611
head(na.spline(ts))
#>                           rh     degc
#> 2016-05-13 16:42:00 50.36000 30.00556
#> 2016-05-13 16:52:00 48.52407 30.20524
#> 2016-05-13 17:02:00 47.31000 30.36111
#> 2016-05-13 17:12:00 46.62601 30.47791
#> 2016-05-13 17:22:00 46.33972 30.56219
#> 2016-05-13 17:32:00 46.30857 30.62093

整个 data.frame R 的分段插值

Piecewise interpolation for entire data.frame R

r

data.table

interpolation

piecewise