R 中 2 种子集数据方法的不同结果 [英] Different results for 2 subset data methods in R

问题描述

我正在对我的数据进行子集化，对于以下代码，我得到了不同的结果:

I'm subseting my data, and I'm getting different results for the following codes:

subset(df, x==1)
df[df$x==1,]

x 的类型是 integer

我做错了吗?提前致谢

推荐答案

没有示例数据，很难说出您的问题是什么.但是，我的预感是以下内容可能可以解释您的问题:

Without example data, it is difficult to say what your problem is. However, my hunch is that the following probably explains your problem:

df <- data.frame(quantity=c(1:3, NA), item=c("Coffee", "Americano", "Espresso", "Decaf"))
df
quantity      item
       1    Coffee
       2 Americano
       3  Espresso
      NA     Decaf

让我们用 [

df[df$quantity == 2,]
 quantity      item
        2 Americano
       NA      <NA>

现在让我们用 subset 子集:

Now let's subset with subset:

subset(df, quantity == 2)
quantity      item
       2 Americano

我们看到根据 NA 值的处理方式，子设置输出存在差异.我认为这如下:使用 subset，您明确说明您想要条件可验证为真的子集.df$quantity==2 生成真/假陈述的向量，但在数量缺失的情况下，不可能分配 TRUE 或 FALSE.这就是为什么我们得到以下带有 NA 的输出:

We see that there is a difference in sub-setting output depending on how NA values are treated. I think of this as follows: With subset, you are explicitly stating you want the subset for which the condition is verifiably true. df$quantity==2 produces a vector of true/false-statements, but where quantity is missing, it is impossible to assign TRUE or FALSE. This is why we get the following output with an NA at the end:

df$quantity==2
[1] FALSE  TRUE FALSE    NA

函数 [ 接受这个向量，但不明白如何处理 NA，这就是为什么我们得到 NA Decaf 而不是 NA Decaf代码>NA .如果您更喜欢使用 [，则可以使用以下代码:

The function [ takes this vector but does not understand what to do with NA, which is why instead of NA Decaf we get NA <NA>. If you prefer using [, you could use the following instead:

df[which(df$quantity == 2),]
quantity      item
       2 Americano

这将逻辑条件 df$quantity == 2 转换为向量或行号，其中逻辑条件可验证"满足.

This translates the logical condition df$quantity == 2 into a vector or row numbers where the logical condition is "verifiably" satisfied.

这篇关于R 中 2 种子集数据方法的不同结果的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！