用周围环境值的平均值替换缺失值 [英] Replace missing values with the mean of surroundings values

问题描述

我的数据集如下所示(我们称之为a"):

My dataset looks like the following (let's call it "a"):

date value
2013-01-01 12.2
2013-01-02 NA
2013-01-03 NA
2013-01-04 16.8
2013-01-05 10.1
2013-01-06 NA
2013-01-07 12.0

我想用最近的环境值(系列中的前一个和下一个值)的平均值替换 NA.

I would like to replace the NA by the mean of the closest surroundings values (the previous and the next values in the series).

我尝试了以下操作，但我不相信输出...

I tried the following but I am not convinced by the output...

miss.val = which(is.na(a$value))
library(zoo)
z = zoo(a$value, a$date)
z.corr = na.approx(z)
z.corr[(miss.val - 1):(miss.val + 1), ]

推荐答案

Using na.locf (Last Observation Carried Forward) from package zoo:

Using na.locf (Last Observation Carried Forward) from package zoo:

R> library("zoo")
R> x <- c(12.2, NA, NA, 16.8, 10.1, NA, 12.0)
R> (na.locf(x) + rev(na.locf(rev(x))))/2
[1] 12.20 14.50 14.50 16.80 10.10 11.05 12.00

(如果 x 的第一个或最后一个元素是 NA，则不起作用)

(does not work if first or last element of x is NA)

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