blocks|key|1197637|text|您可以使用by()函数：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1197638|by(dataFrame,+seq_len(nrow(dataFrame)),+function(row)+dostuff)|code-block|syntax|javascript|1197639|但是，像这样直接迭代行并不是您想要的；您应该尝试向量化。我可以问一下循环中的实际工作是做什么的吗？|1197640|entityMap|0|LINK|mutability|MUTABLE|url|http://stat.ethz.ch/R-manual/R-devel/library/base/html/by.html^0|5|4|5|4|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@$9|X|A|Y|1|Z]]|E|$]]|$1|F|3|G|5|H|7|10|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|11|8|@]|D|@]|E|$]]|$1|M|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

You can use the <a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/by.html" rel="nofollow noreferrer"><code>by()</code></a> function:
<pre><code>by(dataFrame, seq_len(nrow(dataFrame)), function(row) dostuff)
</code></pre>
But iterating over the rows directly like this is rarely what you want to; you should try to vectorize instead. Can I ask what the actual work in the loop is doing?

blocks|key|1412973|text|你可以尝试一下，使用apply()函数|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1412974|>+d
++name+plate+value1+value2
1++++A++++P1++++++1++++100
2++++B++++P2++++++2++++200
3++++C++++P3++++++3++++300

>+f+<-+function(x,+output)+{
+wellName+<-+x[1]
+plateName+<-+x[2]
+wellID+<-+1
+print(paste(wellID,+x[3],+x[4],+sep=","))
+cat(paste(wellID,+x[3],+x[4],+sep=","),+file=+output,+append+=+T,+fill+=+T)
}

>+apply(d,+1,+f,+output+=+'outputfile')|code-block|syntax|javascript|1412975|entityMap|0|LINK|mutability|MUTABLE|url|http://stat.ethz.ch/R-manual/R-devel/library/base/html/apply.html^0|A|7|A|7|0|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]]|D|@$9|V|A|W|1|X]]|E|$]]|$1|F|3|G|5|H|7|Y|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|L|$M|$5|N|O|P|E|$Q|R]]]]

You can try this, using <a href="http://stat.ethz.ch/R-manual/R-devel/library/base/html/apply.html" rel="noreferrer"><code>apply()</code></a> function

<pre><code>&gt; d
 name plate value1 value2
1 A P1 1 100
2 B P2 2 200
3 C P3 3 300

&gt; f &lt;- function(x, output) {
 wellName &lt;- x[1]
 plateName &lt;- x[2]
 wellID &lt;- 1
 print(paste(wellID, x[3], x[4], sep=","))
 cat(paste(wellID, x[3], x[4], sep=","), file= output, append = T, fill = T)
}

&gt; apply(d, 1, f, output = 'outputfile')
</code></pre>

blocks|key|1413011|text|首先，Jonathan关于向量化的观点是正确的。如果您的getWellID()函数是矢量化的，那么您可以跳过循环，只使用cat或write.csv：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1413012|write.csv(data.frame(wellid=getWellID(well$name,+well$plate),+
+++++++++value1=well$value1,+value2=well$value2),+file=outputFile)|code-block|syntax|javascript|1413013|如果getWellID()没有被矢量化，那么Jonathan建议使用by或者knguyen建议使用apply应该是可行的。|offset|length|style|CODE|1413014|否则，如果你真的想使用for，你可以这样做：|1413015|for(i+in+1:nrow(dataFrame))+{
++++row+<-+dataFrame[i,]
++++#+do+stuff+with+row
}|1413016|您也可以尝试使用foreach包，尽管它要求您熟悉该语法。下面是一个简单的例子：|1413017|library(foreach)
d+<-+data.frame(x=1:10,+y=rnorm(10))
s+<-+foreach(d=iter(d,+by='row'),+.combine=rbind)+%25dopar%25+d|1413018|最后一种选择是使用plyr包之外的函数，在这种情况下，约定与apply函数非常相似。|1413019|library(plyr)
ddply(dataFrame,+.(x),+function(x)+{+#+do+stuff+})|1413020|entityMap^0|0|0|Y|2|1D|5|0|B|3|0|0|8|7|0|0|9|4|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@$I|13|J|14|K|L]|$I|15|J|16|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|17|8|@$I|18|J|19|K|L]]|9|@]|A|$]]|$1|O|3|P|5|D|7|1A|8|@]|9|@]|A|$E|F]]|$1|Q|3|R|5|6|7|1B|8|@$I|1C|J|1D|K|L]]|9|@]|A|$]]|$1|S|3|T|5|D|7|1E|8|@]|9|@]|A|$E|F]]|$1|U|3|V|5|6|7|1F|8|@$I|1G|J|1H|K|L]]|9|@]|A|$]]|$1|W|3|X|5|D|7|1I|8|@]|9|@]|A|$E|F]]|$1|Y|3|-4|5|6|7|1J|8|@]|9|@]|A|$]]]|Z|$]]

First, Jonathan's point about vectorizing is correct. If your getWellID() function is vectorized, then you can skip the loop and just use cat or write.csv:

<pre><code>write.csv(data.frame(wellid=getWellID(well$name, well$plate), 
 value1=well$value1, value2=well$value2), file=outputFile)
</code></pre>

If getWellID() isn't vectorized, then Jonathan's recommendation of using <code>by</code> or knguyen's suggestion of <code>apply</code> should work. 

Otherwise, if you really want to use <code>for</code>, you can do something like this:

<pre><code>for(i in 1:nrow(dataFrame)) {
 row &lt;- dataFrame[i,]
 # do stuff with row
}
</code></pre>

You can also try to use the <code>foreach</code> package, although it requires you to become familiar with that syntax. Here's a simple example:

<pre><code>library(foreach)
d &lt;- data.frame(x=1:10, y=rnorm(10))
s &lt;- foreach(d=iter(d, by='row'), .combine=rbind) %dopar% d
</code></pre>

A final option is to use a function out of the <code>plyr</code> package, in which case the convention will be very similar to the apply function. 

<pre><code>library(plyr)
ddply(dataFrame, .(x), function(x) { # do stuff })
</code></pre>

blocks|key|1413056|text|既然你要求R等同于其他语言，我就试着这么做。虽然我还没有真正看过哪种技术在R中更有效，但似乎是有效的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1413057|>+myDf+<-+head(iris)
>+myDf
Sepal.Length+Sepal.Width+Petal.Length+Petal.Width+Species
1++++++++++5.1+++++++++3.5++++++++++1.4+++++++++0.2++setosa
2++++++++++4.9+++++++++3.0++++++++++1.4+++++++++0.2++setosa
3++++++++++4.7+++++++++3.2++++++++++1.3+++++++++0.2++setosa
4++++++++++4.6+++++++++3.1++++++++++1.5+++++++++0.2++setosa
5++++++++++5.0+++++++++3.6++++++++++1.4+++++++++0.2++setosa
6++++++++++5.4+++++++++3.9++++++++++1.7+++++++++0.4++setosa
>+nRowsDf+<-+nrow(myDf)
>+for(i+in+1:nRowsDf){
%2B+print(myDf[i,4])
%2B+}
[1]+0.2
[1]+0.2
[1]+0.2
[1]+0.2
[1]+0.2
[1]+0.4|code-block|syntax|javascript|1413058|但是对于分类列，它将获取一个数据框，如果需要，可以使用as.character()对其进行类型转换。|1413059|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|$]]

Well, since you asked for R equivalent to other languages, I tried to do this. Seems to work though I haven't really looked at which technique is more efficient in R.

<pre><code>&gt; myDf &lt;- head(iris)
&gt; myDf
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
4 4.6 3.1 1.5 0.2 setosa
5 5.0 3.6 1.4 0.2 setosa
6 5.4 3.9 1.7 0.4 setosa
&gt; nRowsDf &lt;- nrow(myDf)
&gt; for(i in 1:nRowsDf){
+ print(myDf[i,4])
+ }
[1] 0.2
[1] 0.2
[1] 0.2
[1] 0.2
[1] 0.2
[1] 0.4
</code></pre>

For the categorical columns though, it would fetch you a Data Frame which you could typecast using as.character() if needed.

blocks|key|1413173|text|我对非向量化期权的时间性能很好奇。为此，我使用了由knguyen定义的函数f。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1413174|f+<-+function(x,+output)+{
++wellName+<-+x[1]
++plateName+<-+x[2]
++wellID+<-+1
++print(paste(wellID,+x[3],+x[4],+sep=","))
++cat(paste(wellID,+x[3],+x[4],+sep=","),+file=+output,+append+=+T,+fill+=+T)
}|code-block|syntax|javascript|1413175|和一个类似于他的例子中的数据帧：|1413176|n+=+100;+#number+of+rows+for+the+data+frame
d+<-+data.frame(+name+=+LETTERS[+sample.int(+25,+n,+replace=T+)+],
++++++++++++++++++plate+=+paste0(+"P",+1:n+),
++++++++++++++++++value1+=+1:n,
++++++++++++++++++value2+=+(1:n)*10+)|1413177|我包含了两个向量化函数(肯定比其他函数更快)，以便将cat()方法与write.table()方法进行比较...|1413178|library("ggplot2")
library(+"microbenchmark"+)
library(+foreach+)
library(+iterators+)

tm+<-+microbenchmark(S1+=
+++++++++++++++++++++++apply(d,+1,+f,+output+=+'outputfile1'),
+++++++++++++++++++++S2+=+
+++++++++++++++++++++++for(i+in+1:nrow(d))+{
+++++++++++++++++++++++++row+<-+d[i,]
+++++++++++++++++++++++++#+do+stuff+with+row
+++++++++++++++++++++++++f(row,+'outputfile2')
+++++++++++++++++++++++},
+++++++++++++++++++++S3+=+
+++++++++++++++++++++++foreach(d1=iter(d,+by='row'),+.combine=rbind)+%25dopar%25+f(d1,"outputfile3"),
+++++++++++++++++++++S4=+{
+++++++++++++++++++++++print(+paste(wellID=rep(1,n),+d[,3],+d[,4],+sep=",")+)
+++++++++++++++++++++++cat(+paste(wellID=rep(1,n),+d[,3],+d[,4],+sep=","),+file=+'outputfile4',+sep='\n',append=T,+fill+=+F)+++++++++++++++++++++++++++
+++++++++++++++++++++},
+++++++++++++++++++++S5+=+{
+++++++++++++++++++++++print(+(paste(wellID=rep(1,n),+d[,3],+d[,4],+sep=","))+)
+++++++++++++++++++++++write.table(data.frame(rep(1,n),+d[,3],+d[,4]),+file='outputfile5',+row.names=F,+col.names=F,+sep=",",+append=T+)
+++++++++++++++++++++},
+++++++++++++++++++++times=100L)
autoplot(tm)|1413179|结果图像显示，对于非矢量化版本，apply提供了最佳性能，而write.table()似乎优于cat()。|1413180|📷|atomic|offset|length|1413181|​|1413182|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/5e50987b9fdac7a6f3c335cbe82357a6.png|imageAlt^0|0|0|0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|17|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|18|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|1A|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|1B|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|1C|8|@]|9|@]|A|$]]|$1|Q|3|R|5|S|7|1D|8|@]|9|@$T|1E|U|1F|1|1G]]|A|$]]|$1|V|3|W|5|6|7|1H|8|@]|9|@]|A|$]]|$1|X|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]]|Y|$Z|$5|10|11|12|A|$13|14|15|-4]]]]

I was curious about the time performance of the non-vectorised options.
For this purpose, I have used the function f defined by knguyen

<pre><code>f &lt;- function(x, output) {
 wellName &lt;- x[1]
 plateName &lt;- x[2]
 wellID &lt;- 1
 print(paste(wellID, x[3], x[4], sep=","))
 cat(paste(wellID, x[3], x[4], sep=","), file= output, append = T, fill = T)
}
</code></pre>

and a dataframe like the one in his example:

<pre><code>n = 100; #number of rows for the data frame
d &lt;- data.frame( name = LETTERS[ sample.int( 25, n, replace=T ) ],
 plate = paste0( "P", 1:n ),
 value1 = 1:n,
 value2 = (1:n)*10 )
</code></pre>

I included two vectorised functions (for sure quicker than the others) in order to compare the cat() approach with a write.table() one...

<pre><code>library("ggplot2")
library( "microbenchmark" )
library( foreach )
library( iterators )

tm &lt;- microbenchmark(S1 =
 apply(d, 1, f, output = 'outputfile1'),
 S2 = 
 for(i in 1:nrow(d)) {
 row &lt;- d[i,]
 # do stuff with row
 f(row, 'outputfile2')
 },
 S3 = 
 foreach(d1=iter(d, by='row'), .combine=rbind) %dopar% f(d1,"outputfile3"),
 S4= {
 print( paste(wellID=rep(1,n), d[,3], d[,4], sep=",") )
 cat( paste(wellID=rep(1,n), d[,3], d[,4], sep=","), file= 'outputfile4', sep='\n',append=T, fill = F) 
 },
 S5 = {
 print( (paste(wellID=rep(1,n), d[,3], d[,4], sep=",")) )
 write.table(data.frame(rep(1,n), d[,3], d[,4]), file='outputfile5', row.names=F, col.names=F, sep=",", append=T )
 },
 times=100L)
autoplot(tm)
</code></pre>

The resulting image shows that apply gives the best performance for a non-vectorised version, whereas write.table() seems to outperform cat().
<img src="https://i.stack.imgur.com/hHQ3M.png" alt="ForEachRunningTime">

blocks|key|1197949|text|我使用这个简单的实用函数：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1197950|rows+=+function(tab)+lapply(
++seq_len(nrow(tab)),
++function(i)+unclass(tab[i,,drop=F])
)|code-block|syntax|javascript|1197951|或者一种更快、更不清晰的形式：|1197952|rows+=+function(x)+lapply(seq_len(nrow(x)),+function(i)+lapply(x,"[",i))|1197953|这个函数只是将一个data.frame拆分成一个行列表。然后你可以在这个列表上做一个普通的"for“：|1197954|tab+=+data.frame(x+=+1:3,+y=2:4,+z=3:5)
for+(A+in+rows(tab))+{
++++print(A$x+%2B+A$y+*+A$z)
}++++++++|1197955|问题中的代码只需进行最小的修改即可工作：|1197956|for+(well+in+rows(dataFrame))+{
++wellName+<-+well$name++++#+string+like+"H1"
++plateName+<-+well$plate++#+string+like+"plate67"
++wellID+<-+getWellID(wellName,+plateName)
++cat(paste(wellID,+well$value1,+well$value2,+sep=","),+file=outputFile)
}|1197957|entityMap^0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|Y|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|10|8|@]|9|@]|A|$]]|$1|Q|3|R|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|S|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|T|$]]

I use this simple utility function:

<pre><code>rows = function(tab) lapply(
 seq_len(nrow(tab)),
 function(i) unclass(tab[i,,drop=F])
)
</code></pre>

Or a faster, less clear form:

<pre><code>rows = function(x) lapply(seq_len(nrow(x)), function(i) lapply(x,"[",i))
</code></pre>

This function just splits a data.frame to a list of rows. Then you can make a normal "for" over this list:

<pre><code>tab = data.frame(x = 1:3, y=2:4, z=3:5)
for (A in rows(tab)) {
 print(A$x + A$y * A$z)
} 
</code></pre>

Your code from the question will work with a minimal modification:

<pre><code>for (well in rows(dataFrame)) {
 wellName &lt;- well$name # string like "H1"
 plateName &lt;- well$plate # string like "plate67"
 wellID &lt;- getWellID(wellName, plateName)
 cat(paste(wellID, well$value1, well$value2, sep=","), file=outputFile)
}
</code></pre>

blocks|key|1413327|text|您可以使用purrrlyr包中的by_row函数来执行以下操作：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1413328|myfn+<-+function(row)+{
++#row+is+a+tibble+with+one+row,+and+the+same+
++#number+of+columns+as+the+original+df
++#If+you'd+rather+it+be+a+list,+you+can+use+as.list(row)
}

purrrlyr::by_row(df,+myfn)|code-block|syntax|javascript|1413329|默认情况下，从myfn返回的值被放入名为.out的df中的一个新list+column中。|1413330|如果这是您想要的唯一输出，您可以编写purrrlyr::by_row(df,+myfn)$.out|1413331|entityMap|0|LINK|mutability|MUTABLE|url|http://r4ds.had.co.nz/many-models.html#list-columns-1^0|5|8|G|6|0|0|7|4|K|4|W|B|0|0|I|V|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]|$9|Z|A|10|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|11|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@$9|17|A|18|1|19]]|E|$]]|$1|M|3|N|5|6|7|1A|8|@$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|1D|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

You can use the <code>by_row</code> function from the package <code>purrrlyr</code> for this:

<pre><code>myfn &lt;- function(row) {
 #row is a tibble with one row, and the same 
 #number of columns as the original df
 #If you'd rather it be a list, you can use as.list(row)
}

purrrlyr::by_row(df, myfn)
</code></pre>

By default, the returned value from <code>myfn</code> is put into a new <a href="http://r4ds.had.co.nz/many-models.html#list-columns-1" rel="noreferrer">list column</a> in the df called <code>.out</code>. 

If this is the only output you desire, you could write <code>purrrlyr::by_row(df, myfn)$.out</code>

blocks|key|1413383|text|我认为使用basic+R实现这一点的最佳方法是：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1413384|for(+i+in+rownames(df)+)
+++print(df[i,+"column1"])|code-block|syntax|javascript|1413385|与for(+i+in+1:nrow(df))-approach相比的优点是，如果df为empty和nrow(df)=0，您就不会遇到麻烦。|offset|length|style|CODE|1413386|entityMap^0|0|0|1|L|14|2|1D|A|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]|$I|T|J|U|K|L]|$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|X|8|@]|9|@]|A|$]]]|N|$]]

I think the best way to do this with basic R is:

<pre><code>for( i in rownames(df) )
 print(df[i, "column1"])
</code></pre>

The advantage over the <code>for( i in 1:nrow(df))</code>-approach is that you do not get into trouble if <code>df</code> is empty and <code>nrow(df)=0</code>.

blocks|key|1198116|text|你可以为list对象做一些事情，|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1198117|data("mtcars")
rownames(mtcars)
data+<-+list(mtcars+,mtcars,+mtcars,+mtcars);data

out1+<-+NULL+
for(i+in+seq_along(data))+{+
++out1[[i]]+<-+data[[i]][rownames(data[[i]])+!=+"Volvo+142E",+]+}+
out1|code-block|syntax|javascript|1198118|或数据帧，|1198119|data("mtcars")
df+<-+mtcars
out1+<-+NULL+
for(i+in+1:nrow(df))+{
++row+<-+rownames(df[i,])
++#+do+stuff+with+row
++out1+<-+df[rownames(df)+!=+"Volvo+142E",]
++
}
out1+|1198120|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

you can do something for a list object,
<pre><code>data(&quot;mtcars&quot;)
rownames(mtcars)
data &lt;- list(mtcars ,mtcars, mtcars, mtcars);data

out1 &lt;- NULL 
for(i in seq_along(data)) { 
 out1[[i]] &lt;- data[[i]][rownames(data[[i]]) != &quot;Volvo 142E&quot;, ] } 
out1
</code></pre>
Or a data frame,
<pre><code>data(&quot;mtcars&quot;)
df &lt;- mtcars
out1 &lt;- NULL 
for(i in 1:nrow(df)) {
 row &lt;- rownames(df[i,])
 # do stuff with row
 out1 &lt;- df[rownames(df) != &quot;Volvo 142E&quot;,]
 
}
out1 
</code></pre>

I have a dataframe, and for each row in that dataframe I have to do some complicated lookups and append some data to a file.

The dataFrame contains scientific results for selected wells from 96 well plates used in biological research so I want to do something like:

<pre><code>for (well in dataFrame) {
 wellName &lt;- well$name # string like "H1"
 plateName &lt;- well$plate # string like "plate67"
 wellID &lt;- getWellID(wellName, plateName)
 cat(paste(wellID, well$value1, well$value2, sep=","), file=outputFile)
}
</code></pre>

In my procedural world, I'd do something like:

<pre><code>for (row in dataFrame) {
 #look up stuff using data from the row
 #write stuff to the file
}
</code></pre>

What is the "R way" to do this?

For each row in an R dataframe

我有一个数据帧，对于该数据帧中的每一行，我必须执行一些复杂的查找，并将一些数据附加到文件中。dataFrame包含了在生物研究中使用的96个井板中选定的井的科学结果，所以我想做一些类似的事情：for (well in dataFrame) { wellName <- well$name # string lik...

问对于R数据帧中的每一行
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