线性插值时间序列中的缺失值 [英] linear interpolate missing values in time series

问题描述

我想在 data.frame 中添加最小和最大日期之间的所有缺失日期，并对所有缺失值进行线性插值，例如

I would like to add all missing dates between min and max date in a data.frame and linear interpolate all missing values, like

df <- data.frame(date = as.Date(c("2015-10-05","2015-10-08","2015-10-09",
                                  "2015-10-12","2015-10-14")),       
                 value = c(8,3,9,NA,5))

      date value
2015-10-05     8
2015-10-08     3
2015-10-09     9
2015-10-12    NA
2015-10-14     5

      date value approx
2015-10-05     8      8
2015-10-06    NA   6.33
2015-10-07    NA   4.67
2015-10-08     3      3
2015-10-09     9      9
2015-10-10    NA   8.20
2015-10-11    NA   7.40
2015-10-12    NA   6.60
2015-10-13    NA   5.80
2015-10-14     5      5

dplyr 和 approx 是否有明确的解决方案?(我不喜欢我的 10 行 for 循环代码.)

Is there a clear solution with dplyr and approx? (I do not like my 10 line for loop code.)

推荐答案

这是一种方法.我使用第一个和最后一个日期创建了一个带有日期序列的数据框.使用 dplyr 包中的 full_join()，我合并了数据框和 mydf.然后我使用 zoo 包中的 na.approx() 来处理 mutate() 部分中的插值.

Here is one way. I created a data frame with a sequence of date using the first and last date. Using full_join() in the dplyr package, I merged the data frame and mydf. I then used na.approx() in the zoo package to handle the interpolation in the mutate() part.

mydf <- data.frame(date = as.Date(c("2015-10-05","2015-10-08","2015-10-09",
                                    "2015-10-12","2015-10-14")),       
                   value = c(8,3,9,NA,5))

library(dplyr)
library(zoo)

data.frame(date = seq(mydf$date[1], mydf$date[nrow(mydf)], by = 1)) %>%
full_join(mydf, by = "date") %>%
mutate(approx = na.approx(value))

#         date value   approx
#1  2015-10-05     8 8.000000
#2  2015-10-06    NA 6.333333
#3  2015-10-07    NA 4.666667
#4  2015-10-08     3 3.000000
#5  2015-10-09     9 9.000000
#6  2015-10-10    NA 8.200000
#7  2015-10-11    NA 7.400000
#8  2015-10-12    NA 6.600000
#9  2015-10-13    NA 5.800000
#10 2015-10-14     5 5.000000

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