blocks|key|911332|text|编辑:已更新，以避免使用不明智的sapply。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|911333|因为数据框是一个列表，所以我们可以使用列表应用函数：|911334|nums+<-+unlist(lapply(x,+is.numeric))++|code-block|syntax|javascript|911335|然后进行标准子设置|911336|x[+,+nums]

##+don't+use+sapply,+even+though+it's+less+code
##+nums+<-+sapply(x,+is.numeric)|911337|对于一个更地道的现代R，我现在推荐|911338|x[+,+purrr::map_lgl(x,+is.numeric)]|911339|更少的codey，更少地反映R的特殊怪癖，更直接，更健壮地用于数据库后端的琐碎：|911340|dplyr::select_if(x,+is.numeric)|911341|较新版本的dplyr还支持以下语法：|911342|x+%25>%25+dplyr::select(where(is.numeric))|911343|entityMap^0|G|6|0|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|17|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|18|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|19|8|@]|D|@]|E|$]]|$1|O|3|P|5|J|7|1A|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|1B|8|@]|D|@]|E|$]]|$1|S|3|T|5|J|7|1C|8|@]|D|@]|E|$K|L]]|$1|U|3|V|5|6|7|1D|8|@]|D|@]|E|$]]|$1|W|3|X|5|J|7|1E|8|@]|D|@]|E|$K|L]]|$1|Y|3|Z|5|6|7|1F|8|@]|D|@]|E|$]]|$1|10|3|11|5|J|7|1G|8|@]|D|@]|E|$K|L]]|$1|12|3|-4|5|6|7|1H|8|@]|D|@]|E|$]]]|13|$]]

EDIT: updated to avoid use of ill-advised <code>sapply</code>.
Since a data frame is a list we can use the list-apply functions:
<pre><code>nums &lt;- unlist(lapply(x, is.numeric)) 
</code></pre>
Then standard subsetting
<pre><code>x[ , nums]

## don't use sapply, even though it's less code
## nums &lt;- sapply(x, is.numeric)
</code></pre>
For a more idiomatic modern R I'd now recommend
<pre><code>x[ , purrr::map_lgl(x, is.numeric)]
</code></pre>
Less codey, less reflecting R's particular quirks, and more straightforward, and robust to use on database-back-ended tibbles:
<pre><code>dplyr::select_if(x, is.numeric)
</code></pre>
Newer versions of dplyr, also support the following syntax:
<pre><code>x %&gt;% dplyr::select(where(is.numeric))
</code></pre>

blocks|key|911548|text|dplyr包的select_if()函数是一个优雅的解决方案：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|911549|library("dplyr")
select_if(x,+is.numeric)|code-block|syntax|javascript|911550|entityMap^0|7|A|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

The dplyr package's <code>select_if(</code>) function is an elegant solution:

<pre><code>library("dplyr")
select_if(x, is.numeric)
</code></pre>

blocks|key|695356|text|基础包中的Filter()是该用例的完美函数:您只需编写代码：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|695357|Filter(is.numeric,+x)|code-block|syntax|javascript|695358|它也比select_if()快得多|695359|library(microbenchmark)
microbenchmark(
++++dplyr::select_if(mtcars,+is.numeric),
++++Filter(is.numeric,+mtcars)
)|695360|(在我的电脑上)返回Filter的中位数为60微秒，返回select_if的中位数为21+000微秒(快350倍)。|695361|entityMap^0|5|8|0|0|3|B|0|0|A|6|S|9|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|W|8|@$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|10|8|@$9|11|A|12|B|C]|$9|13|A|14|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|15|8|@]|D|@]|E|$]]]|R|$]]

<code>Filter()</code> from the base package is the perfect function for that use-case:
You simply have to code: 

<pre><code>Filter(is.numeric, x)
</code></pre>

It is also much faster than <code>select_if()</code>:

<pre><code>library(microbenchmark)
microbenchmark(
 dplyr::select_if(mtcars, is.numeric),
 Filter(is.numeric, mtcars)
)
</code></pre>

returns (on my computer) a median of 60 microseconds for <code>Filter</code>, and 21 000 microseconds for <code>select_if</code> (350x faster).

blocks|key|911841|text|如果您只对列名感兴趣，则使用以下命令：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|911842|names(dplyr::select_if(train,is.numeric))|code-block|syntax|javascript|911843|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|$]]

in case you are interested only in column names then use this :

<pre><code>names(dplyr::select_if(train,is.numeric))
</code></pre>

blocks|key|695410|text|这是其他答案的替代代码：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|695411|x[,+sapply(x,+class)+==+"numeric"]|code-block|syntax|javascript|695412|使用data.table|offset|length|style|CODE|695413|x[,+lapply(x,+is.numeric)+==+TRUE,+with+=+FALSE]|695414|entityMap^0|0|0|2|A|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

This an alternate code to other answers:

<pre><code>x[, sapply(x, class) == "numeric"]
</code></pre>

with a <code>data.table</code>

<pre><code>x[, lapply(x, is.numeric) == TRUE, with = FALSE]
</code></pre>

blocks|key|911947|text|iris+%25>%25+dplyr::select(where(is.numeric))+#as+per+most+recent+updates|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|911948|purrr的另一个选择是否定discard函数：|unstyled|offset|length|style|CODE|911949|iris+%25>%25+purrr::discard(~!is.numeric(.))|911950|如果需要数值列的名称，可以添加names或colnames|911951|iris+%25>%25+purrr::discard(~!is.numeric(.))+%25>%25+names|911952|entityMap^0|0|0|5|E|7|0|0|F|5|L|8|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|T|8|@$G|U|H|V|I|J]|$G|W|H|X|I|J]]|9|@]|A|$]]|$1|K|3|L|5|6|7|Y|8|@]|9|@]|A|$B|C]]|$1|M|3|N|5|F|7|Z|8|@$G|10|H|11|I|J]|$G|12|H|13|I|J]]|9|@]|A|$]]|$1|O|3|P|5|6|7|14|8|@]|9|@]|A|$B|C]]|$1|Q|3|-4|5|F|7|15|8|@]|9|@]|A|$]]]|R|$]]

<pre><code>iris %&gt;% dplyr::select(where(is.numeric)) #as per most recent updates
</code></pre>
Another option with <code>purrr</code> would be to negate <code>discard</code> function:
<pre><code>iris %&gt;% purrr::discard(~!is.numeric(.))
</code></pre>
If you want the names of the numeric columns, you can add <code>names</code> or <code>colnames</code>:
<pre><code>iris %&gt;% purrr::discard(~!is.numeric(.)) %&gt;% names
</code></pre>

blocks|key|695619|text|库PCAmixdata具有拆分函数splitmix，它可以拆分给定数据帧"YourDataframe“的定量(数值数据)和定性(分类数据)，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|695620|install.packages("PCAmixdata")
library(PCAmixdata)
split+<-+splitmix(YourDataframe)
X1+<-+split$X.quanti(Gives+numerical+columns+in+the+dataset)+
X2+<-+split$X.quali+(Gives+categorical+columns+in+the+dataset)|code-block|syntax|javascript|695621|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|$]]

The library PCAmixdata has functon splitmix that splits quantitative(Numerical data) and qualitative (Categorical data) of a given dataframe "YourDataframe" as shown below: 

<pre><code>install.packages("PCAmixdata")
library(PCAmixdata)
split &lt;- splitmix(YourDataframe)
X1 &lt;- split$X.quanti(Gives numerical columns in the dataset) 
X2 &lt;- split$X.quali (Gives categorical columns in the dataset)
</code></pre>

blocks|key|911875|text|library(purrr)
x+<-+x+%25>%25+keep(is.numeric)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|911876|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>library(purrr)
x &lt;- x %&gt;% keep(is.numeric)
</code></pre>

blocks|key|695521|text|如果你有很多因子变量，你可以使用select_if函数。安装dplyr包。有许多通过满足条件来分隔数据的函数。您可以设置条件。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|695522|像这样使用。|695523|categorical<-select_if(df,is.factor)
str(categorical)|code-block|syntax|javascript|695524|entityMap^0|G|9|0|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|R|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|S|8|@]|D|@]|E|$K|L]]|$1|M|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|N|$]]

If you have many factor variables, you can use <code>select_if</code> funtion.
install the dplyr packages. There are many function that separates data by satisfying a condition. you can set the conditions.

Use like this.

<pre><code>categorical&lt;-select_if(df,is.factor)
str(categorical)
</code></pre>

blocks|key|911867|text|另一种方式可以是：-|type|unstyled|depth|inlineStyleRanges|entityRanges|data|911868|#extracting+numeric+columns+from+iris+datset
(iris[sapply(iris,+is.numeric)])|code-block|syntax|javascript|911869|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|$]]

Another way could be as follows:-

<pre><code>#extracting numeric columns from iris datset
(iris[sapply(iris, is.numeric)])
</code></pre>

blocks|key|695798|text|Numerical_variables+<-+which(sapply(df,+is.numeric))
#+then+extract+column+names+
Names+<-+names(Numerical_variables)|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|695799|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>Numerical_variables &lt;- which(sapply(df, is.numeric))
# then extract column names 
Names &lt;- names(Numerical_variables)
</code></pre>

blocks|key|695688|text|这不能直接回答问题，但可能非常有用，特别是当您想要除id列和因变量之外的所有数值列的内容时。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|695689|numeric_cols+<-+sapply(dataframe,+is.numeric)+%25>%25+which+%25>%25+
+++++++++++++++++++names+%25>%25+setdiff(.,+c("id_variable",+"dep_var"))

dataframe+%25<>%25+dplyr::mutate_at(numeric_cols,+function(x)+your_function(x))|code-block|syntax|javascript|695690|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|$]]

This doesn't directly answer the question but can be very useful, especially if you want something like all the numeric columns except for your id column and dependent variable. 

<pre><code>numeric_cols &lt;- sapply(dataframe, is.numeric) %&gt;% which %&gt;% 
 names %&gt;% setdiff(., c("id_variable", "dep_var"))

dataframe %&lt;&gt;% dplyr::mutate_at(numeric_cols, function(x) your_function(x))
</code></pre>

Suppose, you have a data.frame like this: 

<pre><code>x &lt;- data.frame(v1=1:20,v2=1:20,v3=1:20,v4=letters[1:20])
</code></pre>

How would you select only those columns in x that are numeric?

Selecting only numeric columns from a data frame

假设你有一个这样的data.frame：x <- data.frame(v1=1:20,v2=1:20,v3=1:20,v4=letters[1:20])如何只选择x中的那些数字列？

问仅从数据框中选择数字列
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回答 12

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