两个数据帧中的NAs的条件替换 [英] Conditional replacement of NAs in two dataframes R

问题描述

可能简单但棘手的问题，特别是对于较大的数据集。给定两个相同尺寸的数据框（ df1 ， df2 ）：

Probably simple but tricky question especially for larger data sets. Given two dataframes (df1,df2) of equal dimensions as below:

 head(df1)
          a          b  c
1 0.8569720 0.45839112 NA
2 0.7789126 0.36591578 NA
3 0.6901663 0.88095485 NA
4 0.7705756 0.54775807 NA
5 0.1743111 0.89087819 NA
6 0.5812786 0.04361905 NA

head(df2)
           a         b  c
1 0.21210312 0.7670091 NA
2 0.19767464 0.3050934  1
3 0.08982958 0.4453491  2
4 0.75196925 0.6745908  3
5 0.73216793 0.6418483  4
6 0.73640209 0.7448011  5

如果可以在 if（all（is.na（df1））中找到所有列，在这种情况下 c ，转到 df2 并将匹配列（ c ）中的所有值设置为 NAs 。

How can one find all columns where if(all(is.na(df1)), in this case c, go to df2and set all values in matching column (c) to NAs.

期望的输出

head(df3)
           a         b  c
1 0.21210312 0.7670091 NA
2 0.19767464 0.3050934 NA
3 0.08982958 0.4453491 NA
4 0.75196925 0.6745908 NA
5 0.73216793 0.6418483 NA
6 0.73640209 0.7448011 NA

我的实际数据框超过 140000 列。

My actual dataframes have more than 140000 columns.

推荐答案

我们可以在否定的逻辑矩阵（是）上使用 colSums na（df1）），否定（！） vector'，使0个非NA元素变为TRUE，所有其他FALSE，使用此对'df2'的列进行子集并将其分配给NA。

We can use colSums on the negated logical matrix (is.na(df1)), negate (!) thevector` so that 0 non-NA elements becomes TRUE and all others FALSE, use this to subset the columns of 'df2' and assign it to NA.

df2[!colSums(!is.na(df1))] <- NA
df2
#           a         b  c
#1 0.21210312 0.7670091 NA
#2 0.19767464 0.3050934 NA
#3 0.08982958 0.4453491 NA
#4 0.75196925 0.6745908 NA
#5 0.73216793 0.6418483 NA
#6 0.73640209 0.7448011 NA

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或另一个选项是循环查看列，并检查是否全部 e创建一个逻辑向量，用于对'df2'的列进行子集并将其分配给NA

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Or another option is to loop over the columns and check whether all the elements are NA to create a logical vector for subsetting the columns of 'df2' and assigning it to NA

df2[sapply(df1, function(x) all(is.na(x)))] <- NA

---

如果这些是大数据集，另一个选项将是从 data.table

---

If these are big datasets, another option would be set from data.table (should be more efficient as this does the assignment in place)

library(data.table)
setDT(df2)
j1 <-  which(sapply(df1, function(x) all(is.na(x))))

for(j in j1){
   set(df2, i = NULL, j = j, value = NA)
}

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