blocks|key|1746345|text|将Col2分隔为单独的列，然后为每一行创建序列：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1746346|library(dplyr)
library(tidyr)
DF+%25>%25+
+++separate(Col2,+into+=+c("parity",+"X1",+"from",+"X2",+"to"))+%25>%25+
+++group_by(Col1)+%25>%25+
+++do(data.frame(Col2+=+seq(.$from,+.$to,+2)))+%25>%25
+++ungroup|code-block|syntax|javascript|1746347|注1|1746348|可重现形式的输入DF假定为：|offset|length|style|CODE|1746349|DF+<-+structure(list(Col1+=+c("a",+"b",+"c"),+Col2+=+c("odd+from+1+to+9",+
"even+from+2+to+14",+"even+from+30+to+50")),+.Names+=+c("Col1",+
"Col2"),+row.names+=+c(NA,+-3L),+class+=+"data.frame")|1746350|注2|1746351|tidyr的下一个版本支持在into矢量中使用NA来表示要忽略的字段，因此可以编写上面的separate语句：|1746352|+++separate(Col2,+into+=+c("parity",+NA,+"from",+NA,+"to"))+%25>%25+|1746353|entityMap^0|0|0|0|8|2|0|0|0|E|4|18|8|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|10|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|11|8|@$K|12|L|13|M|N]]|9|@]|A|$]]|$1|O|3|P|5|D|7|14|8|@]|9|@]|A|$E|F]]|$1|Q|3|R|5|6|7|15|8|@]|9|@]|A|$]]|$1|S|3|T|5|6|7|16|8|@$K|17|L|18|M|N]|$K|19|L|1A|M|N]]|9|@]|A|$]]|$1|U|3|V|5|D|7|1B|8|@]|9|@]|A|$E|F]]|$1|W|3|-4|5|6|7|1C|8|@]|9|@]|A|$]]]|X|$]]

<p>separate Col2 into individual columns and then for each row create the sequence:</p>

<pre><code>library(dplyr)
library(tidyr)
DF %&gt;% 
   separate(Col2, into = c("parity", "X1", "from", "X2", "to")) %&gt;% 
   group_by(Col1) %&gt;% 
   do(data.frame(Col2 = seq(.$from, .$to, 2))) %&gt;%
   ungroup
</code></pre>

<h2>Note 1</h2>

<p>The input <code>DF</code> in reproducible form is assumed to be:</p>

<pre><code>DF &lt;- structure(list(Col1 = c("a", "b", "c"), Col2 = c("odd from 1 to 9", 
"even from 2 to 14", "even from 30 to 50")), .Names = c("Col1", 
"Col2"), row.names = c(NA, -3L), class = "data.frame")
</code></pre>

<h2>Note 2</h2>

<p>The next version of tidyr supports NA in the <code>into</code> vector to denote fields to ignore so the <code>separate</code> statement above could be written:</p>

<pre><code>   separate(Col2, into = c("parity", NA, "from", NA, "to")) %&gt;% 
</code></pre>


blocks|key|7108|text|使用tidyverse|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|7109|library(tidyverse)++++
df+%25>%25+mutate(Col2+=+map(str_split(Col2,"+"),
+++++++++++++++++++++++++~seq(as.numeric(.[3]),as.numeric(.[5]),2)))+%25>%25
++unnest|code-block|syntax|javascript|7110|或者，借用@g-grothendieck的解决方案中的separate，可读性更好一些：|7111|df+%25>%25
++separate(Col2,as.character(1:5),convert=TRUE)+%25>%25
++transmute(Col1,Col2+=+map2(`3`,`5`,seq,2))+%25>%25
++unnest|7112|entityMap^0|2|9|0|0|R|8|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|Y|8|@]|D|@]|E|$]]]|P|$]]

<p>with <code>tidyverse</code>:</p>

<pre><code>library(tidyverse)    
df %&gt;% mutate(Col2 = map(str_split(Col2," "),
                         ~seq(as.numeric(.[3]),as.numeric(.[5]),2))) %&gt;%
  unnest
</code></pre>

<p>Or maybe a bit more readable, borrowing <code>separate</code> from @g-grothendieck 's solution:</p>

<pre><code>df %&gt;%
  separate(Col2,as.character(1:5),convert=TRUE) %&gt;%
  transmute(Col1,Col2 = map2(`3`,`5`,seq,2)) %&gt;%
  unnest
</code></pre>


blocks|key|1785653|text|这里有一个使用base+R的选项。我们使用gregexpr/regmatches将'Col2‘中的数字元素提取到一个list中，然后使用seq获取2的元素序列，并将其stack到data.frame|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1785654|res+<-+stack(setNames(lapply(regmatches(DF$Col2,+gregexpr("\\d%2B",+DF$Col2)),+function(x)
+++++seq(as.numeric(x[1]),+as.numeric(x[2]),+by+=+2)),+DF$Col1))[2:1]
colnames(res)+<-+colnames(DF)
head(res)
#++Col1+Col2
#1++++a++++1
#2++++a++++3
#3++++a++++5
#4++++a++++7
#5++++a++++9
#6++++b++++2|code-block|syntax|javascript|1785655|entityMap^0|7|6|L|J|1M|4|1W|3|2B|5|2H|A|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]|$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|L|$]]

<p>Here is an option using <code>base R</code>.  We extract the numeric elements in 'Col2' using <code>gregexpr/regmatches</code> into a <code>list</code>, then the get the sequence of elements by 2 with <code>seq</code> and <code>stack</code> it to <code>data.frame</code></p>

<pre><code>res &lt;- stack(setNames(lapply(regmatches(DF$Col2, gregexpr("\\d+", DF$Col2)), function(x)
     seq(as.numeric(x[1]), as.numeric(x[2]), by = 2)), DF$Col1))[2:1]
colnames(res) &lt;- colnames(DF)
head(res)
#  Col1 Col2
#1    a    1
#2    a    3
#3    a    5
#4    a    7
#5    a    9
#6    b    2
</code></pre>


<p>Suppose you have something like this:</p>

<pre><code>Col1 Col2
a    odd from 1 to 9
b    even from 2 to 14
c    even from 30 to 50
...
</code></pre>

<p>I would like to expand the rows by separating the interval into individual row, so:</p>

<pre><code>Col1 Col2
a    1
a    3
a    5
...
b    2
b    4
b    6
...
c    30
c    32
c    34
...
</code></pre>

<p>Note that when it says "even from", the lower and upper bounds are also even, and the same goes for odd numbers.</p>


Turn singular row with interval into multiple rows which equal the interval

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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假设你有这样的东西：Col1 Col2a    odd from 1 to 9b    even from 2 to 14c    even from 30 to 50...我想通过将间隔分成单独的行来扩展行，因此：Col1 Col2a    1a    3a    5...b    2b    4b    6...c...

问将具有间隔的单行转换为等于间隔的多行
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问将具有间隔的单行转换为等于间隔的多行EN