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如何在dplyr中使用带有条件语句的mutate_at（）内的approx（）？

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我想使用dplyr，piping和approx（）来插入缺失值 .

数据：

test <- structure(list(site = structure(c(3L, 3L, 3L, 3L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L), .Label = c("lake", "stream", "wetland"), class = "factor"), 
    depth = c(0L, -3L, -4L, -8L, 0L, -1L, -3L, -5L, 0L, -2L, 
    -4L, -6L), var1 = c(1L, NA, 3L, 4L, 1L, 2L, NA, 4L, 1L, NA, 
    NA, 4L), var2 = c(1L, NA, 3L, 4L, NA, NA, NA, NA, NA, 2L, 
    NA, NA)), .Names = c("site", "depth", "var1", "var2"), class = "data.frame", row.names = c(NA, 
-12L))

此代码有效：

library(tidyverse)

# interpolate missing var1 values for each site using approx()
test_int <- test %>% 
  group_by(site) %>% 
  mutate_at(vars(c(var1)),
            funs("i" = approx(depth, ., depth, rule=1, method="linear")[["y"]]))

但是，如果代码遇到不具有至少2个非NA值的分组（site＆var），则代码不再有效，例如，

# here I'm trying to interpolate missing values for var1 & var2
test_int2 <- test %>% 
  group_by(site) %>% 
  mutate_at(vars(c(var1, var2)),
            funs("i" = approx(depth, ., depth, rule=1, method="linear")[["y"]]))

R适当地抛出此错误：mutate_impl（.data，dots）中的错误：评估错误：需要至少两个非NA值进行插值 .

如何包含条件语句或过滤器，以便它只尝试插入站点至少有2个非NA值并跳过其余值或返回NA的情况？

r dplyr

1 回答

  • 1

    这将做你想要的......

    test_int2 <- test %>% 
             group_by(site) %>% 
             mutate_at(vars(c(var1, var2)),
                       funs("i"=if(sum(!is.na(.))>1) 
                                  approx(depth, ., depth, rule=1, method="linear")[["y"]] 
                                else 
                                  NA))

test_int2
# A tibble: 12 x 6
# Groups:   site [3]
      site depth  var1  var2 var1_i var2_i
    <fctr> <int> <int> <int>  <dbl>  <dbl>
 1 wetland     0     1     1    1.0    1.0
 2 wetland    -3    NA    NA    2.5    2.5
 3 wetland    -4     3     3    3.0    3.0
 4 wetland    -8     4     4    4.0    4.0
 5    lake     0     1    NA    1.0     NA
 6    lake    -1     2    NA    2.0     NA
 7    lake    -3    NA    NA    3.0     NA
 8    lake    -5     4    NA    4.0     NA
 9  stream     0     1    NA    1.0     NA
10  stream    -2    NA     2    2.0     NA
11  stream    -4    NA    NA    3.0     NA
12  stream    -6     4    NA    4.0     NA
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