blocks|key|265286|text|关键思想是您形成一组想要删除的行，并保留该集合的补数。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|265287|在R中，集合的补码由“-”运算符给出。|265288|因此，假设data.frame名为myData|offset|length|style|CODE|265289|myData[-c(2,+4,+6),+]+++#+notice+the+-|code-block|syntax|javascript|265290|当然，如果您想完全删除这些行，请不要忘记“重新分配”myData+-否则，R只会打印结果。|265291|myData+<-+myData[-c(2,+4,+6),+]|265292|entityMap^0|0|0|5|A|H|6|0|0|Q|6|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|V|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|W|8|@$F|X|G|Y|H|I]|$F|Z|G|10|H|I]]|9|@]|A|$]]|$1|J|3|K|5|L|7|11|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|12|8|@$F|13|G|14|H|I]]|9|@]|A|$]]|$1|Q|3|R|5|L|7|15|8|@]|9|@]|A|$M|N]]|$1|S|3|-4|5|6|7|16|8|@]|9|@]|A|$]]]|T|$]]

The key idea is you form a set of the rows you want to remove, and keep the complement of that set.

In R, the complement of a set is given by the '-' operator.

So, assuming the <code>data.frame</code> is called <code>myData</code>:

<pre><code>myData[-c(2, 4, 6), ] # notice the -
</code></pre>

Of course, don't forget to "reassign" <code>myData</code> if you wanted to drop those rows entirely---otherwise, R just prints the results.

<pre><code>myData &lt;- myData[-c(2, 4, 6), ]
</code></pre>

blocks|key|265357|text|您还可以使用所谓的布尔向量，也就是logical|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|265358|row_to_keep+=+c(TRUE,+FALSE,+TRUE,+FALSE,+TRUE,+FALSE,+TRUE)
myData+=+myData[row_to_keep,]|code-block|syntax|javascript|265359|请注意，!运算符充当NOT，即!TRUE+==+FALSE|265360|myData+=+myData[!row_to_keep,]|265361|与@mrwab的答案(%2B1+btw：)相比，这看起来有点麻烦，但逻辑向量可以动态生成，例如，当列值超过某个特定值时：|265362|myData+=+myData[myData$A+>+4,]
myData+=+myData[!myData$A+>+4,]+#+equal+to+myData[myData$A+<=+4,]|265363|可以将布尔向量转换为索引向量：|265364|row_to_keep+=+which(myData$A+>+4)|265365|最后，一个非常巧妙的技巧是，您不仅可以将这种子集用于提取，还可以用于赋值：|265366|myData$A[myData$A+>+4,]+<-+NA|265367|其中，列A被指定为NA+(不是数字)，其中A超过4。|265368|entityMap^0|H|7|0|0|4|1|F|E|0|0|0|0|0|0|0|0|4|1|9|2|L|1|0^^$0|@$1|2|3|4|5|6|7|14|8|@$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|17|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|18|8|@$9|19|A|1A|B|C]|$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|1D|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1E|8|@]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1F|8|@]|D|@]|E|$I|J]]|$1|S|3|T|5|6|7|1G|8|@]|D|@]|E|$]]|$1|U|3|V|5|H|7|1H|8|@]|D|@]|E|$I|J]]|$1|W|3|X|5|6|7|1I|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|H|7|1J|8|@]|D|@]|E|$I|J]]|$1|10|3|11|5|6|7|1K|8|@$9|1L|A|1M|B|C]|$9|1N|A|1O|B|C]|$9|1P|A|1Q|B|C]]|D|@]|E|$]]|$1|12|3|-4|5|6|7|1R|8|@]|D|@]|E|$]]]|13|$]]

You can also work with a so called boolean vector, aka <code>logical</code>:

<pre><code>row_to_keep = c(TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, TRUE)
myData = myData[row_to_keep,]
</code></pre>

Note that the <code>!</code> operator acts as a NOT, i.e. <code>!TRUE == FALSE</code>:

<pre><code>myData = myData[!row_to_keep,]
</code></pre>

This seems a bit cumbersome in comparison to @mrwab's answer (+1 btw :)), but a logical vector can be generated on the fly, e.g. where a column value exceeds a certain value:

<pre><code>myData = myData[myData$A &gt; 4,]
myData = myData[!myData$A &gt; 4,] # equal to myData[myData$A &lt;= 4,]
</code></pre>

You can transform a boolean vector to a vector of indices:

<pre><code>row_to_keep = which(myData$A &gt; 4)
</code></pre>

Finally, a very neat trick is that you can use this kind of subsetting not only for extraction, but also for assignment:

<pre><code>myData$A[myData$A &gt; 4,] &lt;- NA
</code></pre>

where column <code>A</code> is assigned <code>NA</code> (not a number) where <code>A</code> exceeds 4.

blocks|key|265448|text|按行号删除的问题|type|unstyled|depth|inlineStyleRanges|entityRanges|data|265449|对于快速和肮脏的分析，您可以根据最上面的答案按编号删除data.frame的行。也就是说，|265450|newdata+<-+myData[-c(2,+4,+6),+]+|code-block|syntax|javascript|265451|但是，如果您正在尝试编写健壮的数据分析脚本，通常应该避免按数字位置删除行。这是因为数据中行的顺序将来可能会更改。data.frame或数据库表的一般原则是行的顺序不重要。如果顺序很重要，则应该将其编码到data.frame中的实际变量中。|265452|例如，假设您导入了一个数据集，并在检查数据并确定了要删除的行的行号后，按数字位置删除了行。但是，在以后的某一时刻，您可以查看原始数据，并对数据进行重新排序。您的行删除代码现在将删除错误的行，更糟糕的是，您不太可能收到任何警告这种情况已经发生的错误。|265453|更好的策略|265454|更好的策略是根据行的实质性和稳定属性删除行。例如，如果您有一个唯一标识每个案例的id列变量，则可以使用该变量。|offset|length|style|CODE|265455|newdata+<-+myData[+!(myData$id+%25in%25+c(2,4,6)),+]|265456|其他时候，可以指定正式的排除条件，并且可以使用R中的许多子设置工具之一来排除基于该规则的情况。|265457|entityMap^0|0|0|0|0|0|0|14|2|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|11|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|12|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|13|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|14|8|@]|9|@]|A|$]]|$1|M|3|N|5|6|7|15|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|16|8|@$Q|17|R|18|S|T]]|9|@]|A|$]]|$1|U|3|V|5|F|7|19|8|@]|9|@]|A|$G|H]]|$1|W|3|X|5|6|7|1A|8|@]|9|@]|A|$]]|$1|Y|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|Z|$]]

<h3>Problems with deleting by row number</h3>

For quick and dirty analyses, you can delete rows of a data.frame by number as per the top answer. I.e.,

<pre><code>newdata &lt;- myData[-c(2, 4, 6), ] 
</code></pre>

However, if you are trying to write a robust data analysis script, you should generally avoid deleting rows by numeric position. This is because the order of the rows in your data may change in the future. A general principle of a data.frame or database tables is that the order of the rows should not matter. If the order does matter, this should be encoded in an actual variable in the data.frame.

For example, imagine you imported a dataset and deleted rows by numeric position after inspecting the data and identifying the row numbers of the rows that you wanted to delete. However, at some later point, you go into the raw data and have a look around and reorder the data. Your row deletion code will now delete the wrong rows, and worse, you are unlikely to get any errors warning you that this has occurred.

<h3>Better strategy</h3>

A better strategy is to delete rows based on substantive and stable properties of the row. For example, if you had an <code>id</code> column variable that uniquely identifies each case, you could use that.

<pre><code>newdata &lt;- myData[ !(myData$id %in% c(2,4,6)), ]
</code></pre>

Other times, you will have a formal exclusion criteria that could be specified, and you could use one of the many subsetting tools in R to exclude cases based on that rule.

blocks|key|482437|text|在数据框中创建id列或使用任何列名来标识行。使用索引删除是不公平的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|482438|使用subset函数创建新框架。|offset|length|style|CODE|482439|updated_myData+<-+subset(myData,+id!=+6)
print+(updated_myData)

updated_myData+<-+subset(myData,+id+%25in%25+c(1,+3,+5,+7))
print+(updated_myData)|code-block|syntax|javascript|482440|entityMap^0|0|2|6|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@$D|Q|E|R|F|G]]|9|@]|A|$]]|$1|H|3|I|5|J|7|S|8|@]|9|@]|A|$K|L]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

Create id column in your data frame or use any column name to identify the row. Using index is not fair to delete. 

Use <code>subset</code> function to create new frame.

<pre><code>updated_myData &lt;- subset(myData, id!= 6)
print (updated_myData)

updated_myData &lt;- subset(myData, id %in% c(1, 3, 5, 7))
print (updated_myData)
</code></pre>

blocks|key|482578|text|从employee.data中删除Dan+-无需管理新的data.frame。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|482579|employee.data+<-+subset(employee.data,+name!="Dan")|code-block|syntax|javascript|482580|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Delete Dan from employee.data - No need to manage a new data.frame.

<pre><code>employee.data &lt;- subset(employee.data, name!="Dan")
</code></pre>

blocks|key|265697|text|下面是一个快速而又脏的函数，用于按索引删除行。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|265698|removeRowByIndex+<-+function(x,+row_index)+{
++nr+<-+nrow(x)
++if+(nr+<+row_index)+{
++++print('row_index+exceeds+number+of+rows')
++}+else+if+(row_index+==+1)
++{
++++return(x[2:nr,+])
++}+else+if+(row_index+==+nr)+{
++++return(x[1:(nr+-+1),+])
++}+else+{
++++return+(x[c(1:(row_index+-+1),+(row_index+%2B+1):nr),+])
++}
}|code-block|syntax|javascript|265699|它的主要缺陷是它的row_index参数不遵循作为值的向量的R模式。可能还有其他问题，因为我只花了几分钟的时间来编写和测试它，并且在过去的几周里才开始使用R。任何对此的评论和改进都将非常受欢迎！|265700|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Here's a quick and dirty function to remove a row by index.

<pre><code>removeRowByIndex &lt;- function(x, row_index) {
 nr &lt;- nrow(x)
 if (nr &lt; row_index) {
 print('row_index exceeds number of rows')
 } else if (row_index == 1)
 {
 return(x[2:nr, ])
 } else if (row_index == nr) {
 return(x[1:(nr - 1), ])
 } else {
 return (x[c(1:(row_index - 1), (row_index + 1):nr), ])
 }
}
</code></pre>

It's main flaw is it the row_index argument doesn't follow the R pattern of being a vector of values. There may be other problems as I only spent a couple of minutes writing and testing it, and have only started using R in the last few weeks. Any comments and improvements on this would be very welcome!

I have a data frame named "mydata" that looks like this this: 

<pre><code> A B C D 
1. 5 4 4 4 
2. 5 4 4 4 
3. 5 4 4 4 
4. 5 4 4 4 
5. 5 4 4 4 
6. 5 4 4 4 
7. 5 4 4 4 
</code></pre>

I'd like to delete row 2,4,6. For example, like this:

<pre><code> A B C D
1. 5 4 4 4 
3. 5 4 4 4 
5. 5 4 4 4 
7. 5 4 4 4 
</code></pre>

How do I delete rows in a data frame?

数据分析

数据库

我有一个名为"mydata“的数据框，如下所示：   A  B  C   D 1. 5  4  4   4 2. 5  4  4   4 3. 5  4  4   4 4. 5  4  4   4 5. 5  4  4   4 6. 5  4  4   4 7. 5  4  4   4 我想删除第2,4,6行。例如，如...

问如何删除数据框中的行？
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