将空白更改为NA的功能 [英] Function to change blanks to NA

问题描述

我正在尝试编写一个将空字符串转换为NA的函数.我的专栏之一的摘要如下:

I'm trying to write a function that turns empty strings into NA. A summary of one of my column looks like this:

      a   b 
 12 210 468 

我想将12个空值更改为NA.我还想将其他空值更改为NA，因此我从这里到那里借了一些东西来解决这个问题:

I'd like to change the 12 empty values to NA. I also have a few other factor columns for which I'd like to change empty values to NA, so I borrowed some stuff from here and there to come up with this:

# change nulls to NAs
nullToNA <- function(df){

  # split df into numeric & non-numeric functions
  a<-df[,sapply(df, is.numeric), drop = FALSE]
  b<-df[,sapply(df, Negate(is.numeric)), drop = FALSE]

  # Change empty strings to NA
  b<-b[lapply(b,function(x) levels(x) <- c(levels(x), NA) ),] # add NA level
  b<-b[lapply(b,function(x) x[x=="",]<- NA),]                 # change Null to NA

  # Put the columns back together
  d<-cbind(a,b)
  d[, names(df)]
}

但是，我遇到此错误:

> foo<-nullToNA(bar)  
Error in x[x == "", ] <- NA : incorrect number of subscripts on matrix  
Called from: FUN(X[[i]], ...)

我尝试了以下答案:将所有0值替换为NA ，但它会将我所有的列都更改为数值.

I have tried the answer found here: Replace all 0 values to NA but it changes all my columns to numeric values.

推荐答案

您可以直接索引与逻辑条件匹配的字段.所以你可以这样写:

You can directly index fields that match a logical criterion. So you can just write:

df[is_empty(df)] = NA

is_empty是您的比较对象，例如df == "":

Where is_empty is your comparison, e.g. df == "":

df[df == ""] = NA

但是请注意，is.null(df)将不起作用，并且反而会很奇怪 ¹ .我建议不要合并不同类型的列的逻辑！相反，请分别处理它们.

But note that is.null(df) won’t work, and would be weird anyway¹. I would advise against merging the logic for columns of different types, though! Instead, handle them separately.

¹ 您几乎永远不会在表中遇到NULL，因为只有在基础向量是list时，该表才有效.您可以创建具有此约束的矩阵和data.frames，但是is.null(df)永远不会是TRUE，因为NULL值包装在列表中).

¹ You’ll almost never encounter NULL inside a table since that only works if the underlying vector is a list. You can create matrices and data.frames with this constraint, but then is.null(df) will never be TRUE because the NULL values are wrapped inside the list).

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