我们可以通过以下方法解决此问题：添加具有所需数据点数量的 filter 步骤：

We can fix this by adding a filter step with the required number of data points:

library(dplyr)
dataIpol <- data %>%
  group_by(id) %>% 
  arrange(id, year) %>%
  filter(sum(!is.na(value))>=2) %>% #filter!
  mutate(valueIpol = approx(year, value, year, 
                            method = "linear", rule = 1, f = 0, ties = mean)$y)

在这里，我们将value列中的非NA项目的数量相加，并删除所有不具有&gt ; = 2 。

Here we sum the number of non-NA items in the value column, and remove any groups that do not have >=2.