R 使用na.spline（）和case_when（）按组插值和外推

我有一些日期丢失，我想插值和外推每个组内的值，即使我只有一个值可用

#create an example
library(zoo)
library(tidyverse)

df <- data.frame(a = c("group1","group1","group1","group1","group2","group2","group2","group2","group3","group3"), b = c(1,2,NA,4,1,NA,NA,NA,NA,NA))
        a  b
1  group1  1
2  group1  2
3  group1 NA
4  group1  4
5  group2  1
6  group2 NA
7  group2 NA
8  group2 NA
9  group3 NA
10 group3 NA

首先，我尝试按组使用na.spline

df %>% group_by(a) %>% mutate(b_interpolated = na.spline(b, na.rm = FALSE))

告诉我这个错误：

Error: Problem with `mutate()` input `test`.
x zero non-NA points
ℹ Input `test` is `na.spline(b, na.rm = FALSE)`.
ℹ The error occurred in group 3: a = "group3".

所以我尝试在任何值可用时使用na.spline

#Interpolate and extrapolate test
test <- df %>% group_by(a) %>% mutate(test = case_when(all(is.na(b)) == TRUE ~ "empty",
                                                       all(is.na(b)) == FALSE ~ "ok"))

如果使用na.approx，则无法推断group2，因为只有一个值

df %>% group_by(a) %>% mutate(b_interpolated = na.approx(b, na.rm = FALSE, rule = 2))
   a          b b_interpolated
   <chr>  <dbl>          <dbl>
 1 group1     1              1
 2 group1     2              2
 3 group1    NA              3
 4 group1     4              4
 5 group2     1              1
 6 group2    NA             NA
 7 group2    NA             NA
 8 group2    NA             NA
 9 group3    NA             NA
10 group3    NA             NA

df%>%group_by（a）%>%mutate（b_interpolated=na.近似值（b，na.rm=FALSE，rule=2））
a b_插值
1组1
2组1 2
3组1 NA 3
4组1 4 4
5组2 1
6组2 NA NA
7第2组不适用
8组2 NA NA
9组3 NA NA
10组3 NA NA

---

我不明白为什么在给出错误时使用case\u，我肯定我遗漏了一些东西…

这看起来像是na.spline的错误。用这个来解决它

对于na.approx，我们使用na.fill在开始和结束时将数据扩展到na。na.fill的第二个参数是一个3向量，它给出了左端、内部NAs和右端的替换规则。它可以循环使用，因此我们可以省略右端

na_spline <- function(x) if (all(is.na(x))) NA else na.spline(x, na.rm = FALSE)
na_approx <- function(x) na.fill(na.approx(x, na.rm = FALSE), c("extend", NA))

df %>%
  group_by(a) %>%
  mutate(spline = na_spline(b), approx = na_approx(b)) %>%
  ungroup

na_样条线%
变异（样条曲线=na_样条曲线（b），近似值=na_近似值（b））%>%
解组

给予：

# A tibble: 10 x 4
   a          b spline approx
   <chr>  <dbl>  <dbl>  <dbl>
 1 group1     1      1      1
 2 group1     2      2      2
 3 group1    NA      3      3
 4 group1     4      4      4
 5 group2     1      1      1
 6 group2    NA      1      1
 7 group2    NA      1      1
 8 group2    NA      1      1
 9 group3    NA     NA     NA
10 group3    NA     NA     NA

#一个tible:10 x 4
b样条近似
1组1
2组1 2 2
3组1 NA 3 3
4组1 4 4
5组2 1
6组2 NA 1
7组2 NA 1
8组2 NA 1
9第3组不适用
10第3组不适用

df %>% group_by(a) %>% mutate(b_interpolated = na.approx(b, na.rm = FALSE, rule = 2))
   a          b b_interpolated
   <chr>  <dbl>          <dbl>
 1 group1     1              1
 2 group1     2              2
 3 group1    NA              3
 4 group1     4              4
 5 group2     1              1
 6 group2    NA             NA
 7 group2    NA             NA
 8 group2    NA             NA
 9 group3    NA             NA
10 group3    NA             NA

na_spline <- function(x) if (all(is.na(x))) NA else na.spline(x, na.rm = FALSE)
na_approx <- function(x) na.fill(na.approx(x, na.rm = FALSE), c("extend", NA))

df %>%
  group_by(a) %>%
  mutate(spline = na_spline(b), approx = na_approx(b)) %>%
  ungroup

# A tibble: 10 x 4
   a          b spline approx
   <chr>  <dbl>  <dbl>  <dbl>
 1 group1     1      1      1
 2 group1     2      2      2
 3 group1    NA      3      3
 4 group1     4      4      4
 5 group2     1      1      1
 6 group2    NA      1      1
 7 group2    NA      1      1
 8 group2    NA      1      1
 9 group3    NA     NA     NA
10 group3    NA     NA     NA