Question

我有一个包含时间数据的数据库，但是对于某些时间戳，没有可用的数据（数据库中的NA）。我想对这些值进行插值。

数据集：

 structure(list(timestamp = structure(1:7, .Label = c("21/01/2012 18:41", 
+ "21/01/2012 18:46", "21/01/2012 18:51", "21/01/2012 18:56", "21/01/2012 19:01", 
+ "21/01/2012 19:06", "21/01/2012 19:11"), class = "factor"), humid = c(47.7, 
+ 44.5, NA, 42.5, 42.5, NA, 41.6), temp = c(14.12, 15.37, NA, 16.17, 
+ 16.31, NA, 16.51)), .Names = c("timestamp", "humid", "temp"), class = "data.frame", row.names = c(NA, 
+ -7L))

看起来像这样：

         timestamp              humid               temp
1 21/01/2012 18:41 47.700000000000003 14.119999999999999
2 21/01/2012 18:46 44.500000000000000 15.369999999999999
3 21/01/2012 18:51                 NA                 NA
4 21/01/2012 18:56 42.500000000000000 16.170000000000002
5 21/01/2012 19:01 42.500000000000000 16.309999999999999
6 21/01/2012 19:06                 NA                 NA
7 21/01/2012 19:11 41.600000000000001 16.510000000000002

我已经尝试过选项A：

library(zoo)
Mz <- zoo(TEST)
index(Mz) <- Mz[,1]
Mz_approx <- na.approx(Mz, x=Mz$timestamp)

但这导致以下错误：

Error in approx(x[!na], y[!na], xout, ...) : 
  need at least two non-NA values to interpolate
In addition: Warning messages:
1: In na.approx.default(object, x = x, xout = xout, na.rm = FALSE,  :
  NAs introduced by coercion
2: In na.approx.default(object, x = x, xout = xout, na.rm = FALSE,  :
  NAs introduced by coercion
3: In xy.coords(x, y) : NAs introduced by coercion

我也尝试了选项B：

library(zoo)
Mz <- zoo(TEST)
Mz_approx <- na.approx(Mz)

但是这会导致以下错误：

Error in approx(x[!na], y[!na], xout, ...) : 
  need at least two non-NA values to interpolate
In addition: Warning message:
In xy.coords(x, y) : NAs introduced by coercion

克服这些错误并正确使用na.approx功能的最佳方法是什么？

Answer 1

read.zoo会将其转换为正确处理索引的动物园，然后可以使用na.approx。动物园附带了几个小插曲（pdf手册），包括一本专门用于read.zoo示例的完整手册，动物园帮助文件中有很多例子可供您使用。

library(zoo)

z <- read.zoo(TEST, tz = "", format = "%d/%m/%Y %H:%M")
na.approx(z)

，并提供：

                    humid  temp
2012-01-21 18:41:00 47.70 14.12
2012-01-21 18:46:00 44.50 15.37
2012-01-21 18:51:00 43.50 15.77
2012-01-21 18:56:00 42.50 16.17
2012-01-21 19:01:00 42.50 16.31
2012-01-21 19:06:00 42.05 16.41
2012-01-21 19:11:00 41.60 16.51

R - 具有缺失数据的时间序列的插值

1 个答案: