用所选列的行最小值替换 NA

Replace NAs with Row Minimum for Selected Columns

假设我有一个包含多种类型列(字符、数字、ID、时间等)的数据框。我将提供一个简单的例子如下:

m <- data.frame(LETTERS[1:10], LETTERS[15:24],runif(10),runif(10),runif(10),runif(10),runif(10))
x<-c("Col1","Col2","Col3","Col4","Col5","Col6","Col7")
colnames(m)<-x
m<-as.data.frame(lapply(m, function(x) x[ sample(c(TRUE, NA), prob = c(0.75, 0.25), size = length(x), replace = TRUE) ]))

> m
   Col1 Col2       Col3       Col4       Col5       Col6       Col7
1     A    O 0.09929126 0.40435352 0.15360830 0.03830400 0.80157985
2     B    P 0.50314123 0.81725456         NA 0.07054851 0.65521042
3     C <NA> 0.75798665         NA 0.04483692 0.54671014         NA
4     D    R 0.96825047 0.01875140 0.07383107         NA 0.04498563
5  <NA>    S 0.47079716 0.04181401 0.21423046         NA 0.55493444
6     F <NA>         NA         NA         NA 0.33702657 0.54989260
7     G    U 0.71947656         NA         NA 0.99142181 0.69548691
8  <NA> <NA> 0.90518907 0.20661633 0.65788523 0.05534330 0.78420756
9     I    W 0.79208514 0.63233902         NA 0.72085080         NA
10    J    X 0.39093317 0.97107464         NA 0.86417719 0.39890170

对于 Col3-Col7,如果 NA 少于 3 个,我想用 Col3-Col7 中的最小行替换它,否则保留 NA。所以,我希望数据集如下所示:

> m
   Col1 Col2       Col3       Col4       Col5       Col6       Col7
1     A    O 0.09929126 0.40435352 0.15360830 0.03830400 0.80157985
2     B    P 0.50314123 0.81725456 0.07054851 0.07054851 0.65521042
3     C <NA> 0.75798665 0.04483692 0.04483692 0.54671014 0.04483692
4     D    R 0.96825047 0.01875140 0.07383107 0.01875140 0.04498563
5  <NA>    S 0.47079716 0.04181401 0.21423046 0.04181401 0.55493444
6     F <NA>         NA         NA         NA 0.33702657 0.54989260
7     G    U 0.71947656 0.69548691 0.69548691 0.99142181 0.69548691
8  <NA> <NA> 0.90518907 0.20661633 0.65788523 0.05534330 0.78420756
9     I    W 0.79208514 0.63233902 0.63233902 0.72085080 0.63233902
10    J    X 0.39093317 0.97107464 0.39093317 0.86417719 0.39890170

因此,除第 6 行外,每一行的值均由第 3-7 列的每行中的最小值估算。

在我的实际数据集中,对于列 18:27 之间的每一行,如果少于 4 个 NA,则替换为列 18:27 的行最小值,否则保留所有 NA。

我试过使用 dplyr pipes/mutate/replace 方法,但我不确定如何对列的子集执行此操作(我的印象是你只能使用 [= 指定一个列26=]).我尝试过的一些逻辑包括在 if 语句中 includes

rowSums(is.na(.[18:27]))<4 & rowSums(is.na(.[18:27]))>0)

我在 matrixStats 包中看到了 rowMins 函数,但我只是想知道我是否可以使用 dplyr/dataframe 而不是矩阵来做到这一点。

我建议采用 tidyverse 方法,您可以按 Col1Col2 重塑数据并分组,然后重新构建数据。由于我们将使用管道,我们还可以使用 mutate() 创建新变量,并在创建 Flag 变量并计算最小值后评估您想要的条件。接下来的代码:

library(tidyverse)
#Data
m <- structure(list(Col1 = c("A", "B", "C", "D", "<NA>", "F", "G", 
"<NA>", "I", "J"), Col2 = c("O", "P", "<NA>", "R", "S", "<NA>", 
"U", "<NA>", "W", "X"), Col3 = c(0.09929126, 0.50314123, 0.75798665, 
0.96825047, 0.47079716, NA, 0.71947656, 0.90518907, 0.79208514, 
0.39093317), Col4 = c(0.40435352, 0.81725456, NA, 0.0187514, 
0.04181401, NA, NA, 0.20661633, 0.63233902, 0.97107464), Col5 = c(0.1536083, 
NA, 0.04483692, 0.07383107, 0.21423046, NA, NA, 0.65788523, NA, 
NA), Col6 = c(0.038304, 0.07054851, 0.54671014, NA, NA, 0.33702657, 
0.99142181, 0.0553433, 0.7208508, 0.86417719), Col7 = c(0.80157985, 
0.65521042, NA, 0.04498563, 0.55493444, 0.5498926, 0.69548691, 
0.78420756, NA, 0.3989017)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8", "9", "10"))

代码:

#Reshape
m %>% pivot_longer(cols = -c(Col1,Col2)) %>%
  group_by(Col1,Col2) %>% mutate(MinVal=min(value,na.rm=T),
                                 Flag=sum(is.na(value))) %>% ungroup() %>%
  mutate(value=ifelse(is.na(value) & Flag<3,MinVal,value)) %>%
  select(-c(MinVal,Flag)) %>%
  pivot_wider(names_from = name,values_from=value)

输出:

# A tibble: 10 x 7
   Col1  Col2     Col3    Col4    Col5   Col6   Col7
   <chr> <chr>   <dbl>   <dbl>   <dbl>  <dbl>  <dbl>
 1 A     O      0.0993  0.404   0.154  0.0383 0.802 
 2 B     P      0.503   0.817   0.0705 0.0705 0.655 
 3 C     <NA>   0.758   0.0448  0.0448 0.547  0.0448
 4 D     R      0.968   0.0188  0.0738 0.0188 0.0450
 5 <NA>  S      0.471   0.0418  0.214  0.0418 0.555 
 6 F     <NA>  NA      NA      NA      0.337  0.550 
 7 G     U      0.719   0.695   0.695  0.991  0.695 
 8 <NA>  <NA>   0.905   0.207   0.658  0.0553 0.784 
 9 I     W      0.792   0.632   0.632  0.721  0.632 
10 J     X      0.391   0.971   0.391  0.864  0.399