通过线性插值查找缺失值（时间序列）

为了完整起见，这里是使用的解决方案data.table。

OP提供了2015年至2017年每个月的数据点。他尚未定义值所属的月份。此外，他还没有指定他期望的插值类型。

因此，给定的数据如下所示（仅v1出于简化目的而显示）：

请注意，故意将每月值分配给该月的第一天。

有多种插值数据的方法。我们将看看其中两个。

分段常数插值

由于每月仅给出一个数据点，因此我们可以安全地假设该值代表相应月份的每一天：

（标有geom_step()）

For interpolation, the base R function approx() is used. approx() is applied on all value columns v1, v2, v3 with help of lapply().

But first we need to turn the year-month into a full-flegded date (including day). The first day of the month has been chosen deliberately. Now, the data points in df1 are attributed to the dates 2015-01-01 to 2017-12-01. Note, that there is no given value for 2017-12-31 or 2018-01-01.

library(data.table)
library(magrittr)
# create date (assuming the 1st of month)
setDT(df1)[, date := as.IDate(paste(Year, Month, 1, sep = "-"))]
# create sequence of days covering the whole period
ds <- seq(as.IDate("2015-01-01"), as.IDate("2017-12-31"), by = "1 day")
# perform interpolation
cols = c("v1", "v2", "v3")
results <- df1[, c(.(date = ds), lapply(.SD, function(y) 
  approx(x = date, y = y, xout = ds, method = "constant", rule = 2)$y)), 
  .SDcols = cols]
results

            date       v1       v2       v3
   1: 2015-01-01 15072.73 2524.102 17596.83
   2: 2015-01-02 15072.73 2524.102 17596.83
   3: 2015-01-03 15072.73 2524.102 17596.83
   4: 2015-01-04 15072.73 2524.102 17596.83
   5: 2015-01-05 15072.73 2524.102 17596.83
  ---                                      
1092: 2017-12-27 17002.14 3328.890 20331.03
1093: 2017-12-28 17002.14 3328.890 20331.03
1094: 2017-12-29 17002.14 3328.890 20331.03
1095: 2017-12-30 17002.14 3328.890 20331.03
1096: 2017-12-31 17002.14 3328.890 20331.03

By specifying rule = 2, approx() was told to use the last given values (the ones for 2017-12-01) to complete the sequence up to 2017-12-31.

The result can be plotted on top of the given data points.

Piecewise linear interpolation

为了绘制线段，必须给出两点。为了绘制36个间隔（月）的线段，我们需要37个数据点。不幸的是，OP仅给出了36个数据点。我们将需要2018年1月1日的其他数据点以绘制上个月的线。

在这种情况下，一种选择是假设上个月的值是恒定的。这是什么approx()做的时候method = "linear"，并rule = 2指定。

library(data.table)
library(magrittr)
# create date (assuming the 1st of month)
setDT(df1)[, date := as.IDate(paste(Year, Month, 1, sep = "-"))]
# create sequence of days covering the whole period
ds <- seq(as.IDate("2015-01-01"), as.IDate("2017-12-31"), by = "1 day")
# perform interpolation
cols = c("v1", "v2", "v3")
results <- df1[, c(.(date = ds), lapply(.SD, function(y) 
  approx(x = date, y = y, xout = ds, method = "linear", rule = 2)$y)), 
  .SDcols = cols]
results

            date       v1       v2       v3
   1: 2015-01-01 15072.73 2524.102 17596.83
   2: 2015-01-02 15078.43 2526.462 17604.89
   3: 2015-01-03 15084.14 2528.822 17612.96
   4: 2015-01-04 15089.84 2531.182 17621.02
   5: 2015-01-05 15095.54 2533.542 17629.08
  ---                                      
1092: 2017-12-27 17002.14 3328.890 20331.03
1093: 2017-12-28 17002.14 3328.890 20331.03
1094: 2017-12-29 17002.14 3328.890 20331.03
1095: 2017-12-30 17002.14 3328.890 20331.03
1096: 2017-12-31 17002.14 3328.890 20331.03

在样本数据集中，2016和2017的值相当平坦。无论如何，上个月的恒定插值并不引人注目。