blocks|key|1436508|text|[哈德利指出这一点in+a+comment。]|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1436509|当使用序列作为迭代的索引时，最好使用seq_along()函数，而不是像1:length(x)这样的函数。|style|CODE|1436510|这里我创建了一个向量，两种方法返回相同的东西：|1436511|>+x+<-+1:10
>+1:length(x)
+[1]++1++2++3++4++5++6++7++8++9+10
>+seq_along(x)
+[1]++1++2++3++4++5++6++7++8++9+10|code-block|syntax|javascript|1436512|现在将向量设为NULL|1436513|>+x+<-+NULL
>+seq_along(x)+#+returns+an+empty+integer;+good+behavior
integer(0)
>+1:length(x)+#+wraps+around+and+returns+a+sequence;+this+is+bad
[1]+1+0|1436514|这可能会在循环中造成一些混乱：|1436515|>+for(i+in+1:length(x))+print(i)
[1]+1
[1]+0
>+for(i+in+seq_along(x))+print(i)
>|1436516|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/3094111/r-turning-list-items-into-objects^0|9|C|0|0|I|B|10|B|0|0|0|7|4|0|0|0|0^^$0|@$1|2|3|4|5|6|7|14|8|@]|9|@$A|15|B|16|1|17]]|C|$]]|$1|D|3|E|5|6|7|18|8|@$A|19|B|1A|F|G]|$A|1B|B|1C|F|G]]|9|@]|C|$]]|$1|H|3|I|5|6|7|1D|8|@]|9|@]|C|$]]|$1|J|3|K|5|L|7|1E|8|@]|9|@]|C|$M|N]]|$1|O|3|P|5|6|7|1F|8|@$A|1G|B|1H|F|G]]|9|@]|C|$]]|$1|Q|3|R|5|L|7|1I|8|@]|9|@]|C|$M|N]]|$1|S|3|T|5|6|7|1J|8|@]|9|@]|C|$]]|$1|U|3|V|5|L|7|1K|8|@]|9|@]|C|$M|N]]|$1|W|3|-4|5|6|7|1L|8|@]|9|@]|C|$]]]|X|$Y|$5|Z|10|11|C|$12|13]]]]

[Hadley pointed this out <a href="https://stackoverflow.com/questions/3094111/r-turning-list-items-into-objects">in a comment</a>.]

When using a sequence as an index for iteration, it's better to use the <code>seq_along()</code> function rather than something like <code>1:length(x)</code>. 

Here I create a vector and both approaches return the same thing:

<pre><code>&gt; x &lt;- 1:10
&gt; 1:length(x)
 [1] 1 2 3 4 5 6 7 8 9 10
&gt; seq_along(x)
 [1] 1 2 3 4 5 6 7 8 9 10
</code></pre>

Now make the vector <code>NULL</code>:

<pre><code>&gt; x &lt;- NULL
&gt; seq_along(x) # returns an empty integer; good behavior
integer(0)
&gt; 1:length(x) # wraps around and returns a sequence; this is bad
[1] 1 0
</code></pre>

This can cause some confusion in a loop:

<pre><code>&gt; for(i in 1:length(x)) print(i)
[1] 1
[1] 0
&gt; for(i in seq_along(x)) print(i)
&gt;
</code></pre>

blocks|key|1221457|text|加载数据时自动创建系数。您不假思索地将数据框中的列视为字符，这种做法很有效，直到您尝试将值更改为不是级别的值。这将生成一个警告，但会让您的数据框中包含NA+...|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1221458|当R脚本中出现意外错误时，检查一下不是这些因素造成的。|1221459|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

The automatic creation of factors when you load data. You unthinkingly treat a column in a data frame as characters, and this works well until you do something like trying to change a value to one that isn't a level. This will generate a warning but leave your data frame with NA's in it ...

When something goes unexpectedly wrong in your R script, check that factors aren't to blame.

blocks|key|1436378|text|在将矩阵设置为一维的子集中忘记了drop=FALSE参数，从而也丢弃了对象类：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1436379|R>+X+<-+matrix(1:4,2)
R>+X
+++++[,1]+[,2]
[1,]++++1++++3
[2,]++++2++++4
R>+class(X)
[1]+"matrix"
R>+X[,1]
[1]+1+2
R>+class(X[,1])
[1]+"integer"
R>+X[,1,+drop=FALSE]
+++++[,1]
[1,]++++1
[2,]++++2
R>+class(X[,1,+drop=FALSE])
[1]+"matrix"
R>+|code-block|syntax|javascript|1436380|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|$]]

Forgetting the drop=FALSE argument in subsetting matrices down to single dimension and thereby dropping the object class as well:

<pre><code>R&gt; X &lt;- matrix(1:4,2)
R&gt; X
 [,1] [,2]
[1,] 1 3
[2,] 2 4
R&gt; class(X)
[1] "matrix"
R&gt; X[,1]
[1] 1 2
R&gt; class(X[,1])
[1] "integer"
R&gt; X[,1, drop=FALSE]
 [,1]
[1,] 1
[2,] 2
R&gt; class(X[,1, drop=FALSE])
[1] "matrix"
R&gt; 
</code></pre>

blocks|key|1436604|text|首先，我要说的是，我理解在二进制中表示数字的基本问题。然而，我认为可以很容易改进的一个问题是当十进制值超出R的典型表示范围时数字的表示。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1436605|x+<-+10.2+*+100
x
1020
as.integer(x)
1019|code-block|syntax|javascript|1436606|我不介意将结果表示为整数，而实际上可以将其表示为整数。例如，如果值真的是1020，那么打印x就可以了。但在这种情况下，当打印x时，像1020.0这样简单的值就会更明显地表明，该值不是整数，也不能表示为1。当有一个非常小的小数部分没有出现时，R应该默认为某种指示。|1436607|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|$]]

First, let me say that I understand fundamental problems of representing numbers in a binary system. Nevertheless, one problem that I think could be easily improved is the representation of numbers when the decimal value is beyond R's typical scope of presentation.

<pre><code>x &lt;- 10.2 * 100
x
1020
as.integer(x)
1019
</code></pre>

I don't mind if the result is represented as an integer when it really can be represented as an integer. For example, if the value really was 1020 then printing that for x would be fine. But something as simple as 1020.0 in this case when printing x would have made it more obvious that the value was not an integer and not representable as one. R should default to some kind of indication when there is an extremely small decimal component that isn't presented.

blocks|key|1436684|text|考虑NA、NaN和Inf的组合可能会很烦人。它们的行为是不同的，对其中一个的测试不一定对其他的有效：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1436685|>+x+<-+c(NA,NaN,Inf)
>+is.na(x)
[1]++TRUE++TRUE+FALSE
>+is.nan(x)
[1]+FALSE++TRUE+FALSE
>+is.infinite(x)
[1]+FALSE+FALSE++TRUE|code-block|syntax|javascript|1436686|然而，测试这些麻烦制造者最安全的方法是：|1436687|>+is.finite(x)
[1]+FALSE+FALSE+FALSE|1436688|entityMap^0|2|2|5|3|9|3|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@$9|R|A|S|B|C]|$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|Z|8|@]|D|@]|E|$I|J]]|$1|O|3|-4|5|6|7|10|8|@]|D|@]|E|$]]]|P|$]]

It can be annoying to have to allow for combinations of <code>NA</code>, <code>NaN</code> and <code>Inf</code>. They behave differently, and tests for one won't necessarily work for the others:

<pre><code>&gt; x &lt;- c(NA,NaN,Inf)
&gt; is.na(x)
[1] TRUE TRUE FALSE
&gt; is.nan(x)
[1] FALSE TRUE FALSE
&gt; is.infinite(x)
[1] FALSE FALSE TRUE
</code></pre>

However the safest way to test any of these trouble-makers is:

<pre><code>&gt; is.finite(x)
[1] FALSE FALSE FALSE
</code></pre>

blocks|key|1436408|text|忘记了strptime()和朋友返回POSIXt+POSIXlt，而length()总是为9+--转换为POSIXct会有所帮助：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1436409|R>+length(strptime("2009-10-07+20:21:22",+"%25Y-%25m-%25d+%25H:%25M:%25S"))
[1]+9
R>+length(as.POSIXct(strptime("2009-10-07+20:21:22",+"%25Y-%25m-%25d+%25H:%25M:%25S")))
[1]+1
R>+|code-block|syntax|javascript|1436410|entityMap^0|3|A|I|E|Y|8|1G|7|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]|$9|T|A|U|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|V|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|L|$]]

Forgetting that <code>strptime()</code> and friends return <code>POSIXt POSIXlt</code> where <code>length()</code> is always nine -- converting to <code>POSIXct</code> helps:

<pre><code>R&gt; length(strptime("2009-10-07 20:21:22", "%Y-%m-%d %H:%M:%S"))
[1] 9
R&gt; length(as.POSIXct(strptime("2009-10-07 20:21:22", "%Y-%m-%d %H:%M:%S")))
[1] 1
R&gt; 
</code></pre>

blocks|key|1221739|text|round函数始终四舍五入为偶数。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1221740|>+round(3.5)
[1]+4++

>+round(4.5)
[1]+4|code-block|syntax|javascript|1221741|entityMap^0|0|5|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 <code>round</code> function always rounds to the even number.

<pre><code>&gt; round(3.5)
[1] 4 

&gt; round(4.5)
[1] 4
</code></pre>

blocks|key|1436908|text|这一点太伤人了，我花了几个小时给bug-report添加评论。我没有如愿以偿，但至少下一个版本的R会产生一个错误。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1436909|R>+nchar(factor(letters))
+[1]+1+1+1+1+1+1+1+1+1+2+2+2+2+2+2+2+2+2+2+2+2+2+2+2+2+2|code-block|syntax|javascript|1436910|更新:从R+3.2.0+(可能是更早的版本)开始，这个示例现在生成一条错误消息。正如在下面的注释中提到的，因子不是向量，nchar()需要向量。|1436911|R>+nchar(factor(letters))
Error+in+nchar(factor(letters))+:+'nchar()'+requires+a+character+vector
R>+is.vector(factor(letters))
[1]+FALSE|1436912|entityMap|0|LINK|mutability|MUTABLE|url|https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=6899^0|G|A|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|10|8|@]|9|@]|C|$G|H]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

This one hurt so much that I spent hours adding comments to a <a href="https://bugs.r-project.org/bugzilla3/show_bug.cgi?id=6899" rel="nofollow noreferrer">bug-report</a>. I didn't get my wish, but at least the next version of R will generate an error.

<pre><code>R&gt; nchar(factor(letters))
 [1] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
</code></pre>

Update: As of R 3.2.0 (probably earlier), this example now generates an error message. As mentioned in the comments below, a factor is NOT a vector and nchar() requires a vector.

<pre><code>R&gt; nchar(factor(letters))
Error in nchar(factor(letters)) : 'nchar()' requires a character vector
R&gt; is.vector(factor(letters))
[1] FALSE
</code></pre>

blocks|key|1221782|text|$运算符中的部分匹配:这适用于列表，但也适用于data.frame|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1221783|df1+<-+data.frame(foo=1:10,+foobar=10:1)
df2+<-+data.frame(foobar=10:1)

df1$foo+#+Correctly+gets+the+foo+column
df2$foo+#+Expect+NULL,+but+this+returns+the+foobar+column!!!

#+So,+should+use+double+bracket+instead:
df1[["foo"]]
df2[["foo"]]|code-block|syntax|javascript|1221784|[[操作符也有一个exact标志，但幸好缺省情况下它是TRUE。|1221785|部分匹配也会影响attr|1221786|x1+<-+structure(1,+foo=1:10,+foobar=10:1)
x2+<-+structure(2,+foobar=10:1)

attr(x1,+"foo")+#+Correctly+gets+the+foo+attribute
attr(x2,+"foo")+#+Expect+NULL,+but+this+returns+the+foobar+attribute!!!

#+So,+should+use+exact=TRUE
attr(x1,+"foo",+exact=TRUE)
attr(x2,+"foo",+exact=TRUE)|1221787|entityMap^0|0|1|N|A|0|0|0|2|9|5|R|4|0|8|4|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]|$9|13|A|14|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|15|8|@$9|16|A|17|B|C]]|D|@]|E|$]]|$1|O|3|P|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|Q|3|-4|5|6|7|19|8|@]|D|@]|E|$]]]|R|$]]

Partial matching in the <code>$</code> operator:
This applies to lists, but also on <code>data.frame</code>

<pre><code>df1 &lt;- data.frame(foo=1:10, foobar=10:1)
df2 &lt;- data.frame(foobar=10:1)

df1$foo # Correctly gets the foo column
df2$foo # Expect NULL, but this returns the foobar column!!!

# So, should use double bracket instead:
df1[["foo"]]
df2[["foo"]]
</code></pre>

The <code>[[</code> operator also has an <code>exact</code> flag, but it is thankfully <code>TRUE</code> by default.

Partial matching also affects <code>attr</code>:

<pre><code>x1 &lt;- structure(1, foo=1:10, foobar=10:1)
x2 &lt;- structure(2, foobar=10:1)

attr(x1, "foo") # Correctly gets the foo attribute
attr(x2, "foo") # Expect NULL, but this returns the foobar attribute!!!

# So, should use exact=TRUE
attr(x1, "foo", exact=TRUE)
attr(x2, "foo", exact=TRUE)
</code></pre>

blocks|key|1221900|text|零长度向量有一些怪癖：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1221901|R>+kk=vector(mode="numeric",length=0)
R>+kk
numeric(0)
R>+sum(kk)
[1]+0
R>+var(kk)
[1]+NA|code-block|syntax|javascript|1221902|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|$]]

Zero-length vectors have some quirks:

<pre><code>R&gt; kk=vector(mode="numeric",length=0)
R&gt; kk
numeric(0)
R&gt; sum(kk)
[1] 0
R&gt; var(kk)
[1] NA
</code></pre>

blocks|key|1221853|text|在使用列表时，有几个不直观的东西：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1221854|当然，[和[[之间的区别需要一些时间来适应。对于列表，[返回一个元素列表(可能是1个)，而[[返回列表中的元素。|offset|length|style|CODE|1221855|列表创建：|1221856|#+When+you're+used+to+this:
x+<-+numeric(5)+#+A+vector+of+length+5+with+zeroes
#+...+this+might+surprise+you
x+<-+list(5)++++#+A+list+with+a+SINGLE+element:+the+value+5
#+This+is+what+you+have+to+do+instead:
x+<-+vector('list',+5)+#+A+vector+of+length+5+with+NULLS|code-block|syntax|javascript|1221857|那么，如何在列表中插入NULL呢？|1221858|x+<-+list("foo",+1:3,+letters,+LETTERS)+#+A+sample+list
x[[2]]+<-+1:5++++++++#+Put+1:5+in+the+second+element
#+The+obvious+way+doesn't+work:+
x[[2]]+<-+NULL+++++++#+This+DELETES+the+second+element!
#+This+doesn't+work+either:+
x[2]+<-+NULL+++++++#+This+DELETES+the+second+element!

#+The+solution+is+NOT+very+intuitive:
x[2]+<-+list(NULL)+#+Put+NULL+in+the+second+element

#+Btw,+now+that+we+think+we+know+how+to+delete+an+element:
x+<-+1:10
x[[2]]+<-+NULL++#+Nope,+gives+an+ERROR!
x+<-+x[-2]++++#+This+is+the+only+way+for+atomic+vectors+(works+for+lists+too)|1221859|最后是一些高级的东西，比如通过嵌套列表进行索引：|1221860|x+<-+list(a=1:3,+b=list(c=42,+d=13,+e="HELLO"),+f='bar')
x[[c(2,3)]]+#+HELLO+(first+selects+second+element+and+then+it's+third+element)
x[c(2,3)]+++#+The+second+and+third+elements+(b+and+f)|1221861|entityMap^0|0|3|1|5|2|R|1|19|2|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|Z|8|@$D|10|E|11|F|G]|$D|12|E|13|F|G]|$D|14|E|15|F|G]|$D|16|E|17|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|18|8|@]|9|@]|A|$]]|$1|J|3|K|5|L|7|19|8|@]|9|@]|A|$M|N]]|$1|O|3|P|5|6|7|1A|8|@]|9|@]|A|$]]|$1|Q|3|R|5|L|7|1B|8|@]|9|@]|A|$M|N]]|$1|S|3|T|5|6|7|1C|8|@]|9|@]|A|$]]|$1|U|3|V|5|L|7|1D|8|@]|9|@]|A|$M|N]]|$1|W|3|-4|5|6|7|1E|8|@]|9|@]|A|$]]]|X|$]]

Working with lists, there are a couple of unintuitive things:

Of course, the difference between <code>[</code> and <code>[[</code> takes some getting used to. For lists, the <code>[</code> returns a list of (potentially 1) elements whereas the <code>[[</code> returns the element inside the list.

List creation:

<pre><code># When you're used to this:
x &lt;- numeric(5) # A vector of length 5 with zeroes
# ... this might surprise you
x &lt;- list(5) # A list with a SINGLE element: the value 5
# This is what you have to do instead:
x &lt;- vector('list', 5) # A vector of length 5 with NULLS
</code></pre>

So, how to insert NULL into a list?

<pre><code>x &lt;- list("foo", 1:3, letters, LETTERS) # A sample list
x[[2]] &lt;- 1:5 # Put 1:5 in the second element
# The obvious way doesn't work: 
x[[2]] &lt;- NULL # This DELETES the second element!
# This doesn't work either: 
x[2] &lt;- NULL # This DELETES the second element!

# The solution is NOT very intuitive:
x[2] &lt;- list(NULL) # Put NULL in the second element

# Btw, now that we think we know how to delete an element:
x &lt;- 1:10
x[[2]] &lt;- NULL # Nope, gives an ERROR!
x &lt;- x[-2] # This is the only way for atomic vectors (works for lists too)
</code></pre>

Finally some advanced stuff like indexing through a nested list:

<pre><code>x &lt;- list(a=1:3, b=list(c=42, d=13, e="HELLO"), f='bar')
x[[c(2,3)]] # HELLO (first selects second element and then it's third element)
x[c(2,3)] # The second and third elements (b and f)
</code></pre>

blocks|key|1222163|text|R中最大的混乱之一是[i,+drop+=+TRUE]确实降低了因子水平，而[i,+j,+drop+=+TRUE]没有！|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1222164|>+df+=+data.frame(a+=+c("europe",+"asia",+"oceania"),+b+=+c(1,+2,+3))
>+df$a[1:2,+drop+=+TRUE]
[1]+europe+asia++
Levels:+asia+europe++++++++++<----+drops+factor+levels,+works+fine
>+df[1:2,,+drop+=+TRUE]$a
[1]+europe+asia++
Levels:+asia+europe+oceania++<----+does+not+drops+factor+levels!|code-block|syntax|javascript|1222165|有关更多信息，请参阅：drop+=+TRUE+doesn't+drop+factor+levels+in+data.frame+while+in+vector+it+does|1222166|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/q/14123792/684229^0|A|G|11|J|0|0|B|24|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$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|L|5|6|7|10|8|@]|D|@$9|11|A|12|1|13]]|E|$]]|$1|M|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

One of the big confusion in R is that <code>[i, drop = TRUE]</code> does drop factor levels, but <code>[i, j, drop = TRUE]</code> does not!

<pre><code>&gt; df = data.frame(a = c("europe", "asia", "oceania"), b = c(1, 2, 3))
&gt; df$a[1:2, drop = TRUE]
[1] europe asia 
Levels: asia europe &lt;---- drops factor levels, works fine
&gt; df[1:2,, drop = TRUE]$a
[1] europe asia 
Levels: asia europe oceania &lt;---- does not drops factor levels!
</code></pre>

For more info see: <a href="https://stackoverflow.com/q/14123792/684229">drop = TRUE doesn&#39;t drop factor levels in data.frame while in vector it does</a>

blocks|key|1436568|text|我从今天开始:+qnorm()获取概率，pnorm()获取分位数。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1436569|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Mine from today: qnorm() takes Probabilities and pnorm() takes Quantiles.

blocks|key|1222020|text|对我来说，这是一种违反直觉的方式，当您使用write.csv将data.frame导出到文本文件时，然后再导入它，您需要添加一个额外的参数来获得完全相同的data.frame，如下所示：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1222021|write.csv(m,+file+=+'m.csv')
read.csv('m.csv',+row.names+=+1)+#+Note+the+row.names+argument|code-block|syntax|javascript|1222022|我还在SO中发布了this+question，并被@BenBolker建议作为对这个问题的回答。|1222023|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/questions/12512062/why-write-csv-and-read-csv-are-not-consistent^0|L|9|0|0|9|D|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|X|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|Y|8|@]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|M|3|-4|5|6|7|12|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

For me it is the counter intuitive way in which when you export a data.frame to a text file using <code>write.csv</code>, then to import it afterwards you need to add an additional argument to get exactly the same data.frame, like this:

<pre><code>write.csv(m, file = 'm.csv')
read.csv('m.csv', row.names = 1) # Note the row.names argument
</code></pre>

I also posted <a href="https://stackoverflow.com/questions/12512062/why-write-csv-and-read-csv-are-not-consistent">this question</a> in SO and was suggested as an answer to this Q by @BenBolker.

blocks|key|1222085|text|apply函数集不仅适用于矩阵，而且可以扩展到多维数组。在我的研究中，我经常有一个例如大气温度的数据集。从现在开始，它被存储在一个维度为x,y,level,time的多维数组中，称为multi_dim_array。一个样机示例如下：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1222086|multi_dim_array+=+array(runif(96+*+48+*+6+*+100,+-50,+50),+
++++++++++++++++++++++++dim+=+c(96,+48,+6,+100))
>+str(multi_dim_array)
#+++++x+++++y+++++lev++time++++
+num+[1:96,+1:48,+1:6,+1:100]+42.4+16+32.3+49.5+24.9+...|code-block|syntax|javascript|1222087|使用apply+one可以很容易地获得：|1222088|#+temporal+mean+value
>+str(apply(multi_dim_array,+4,+mean))
+num+[1:100]+-0.0113+-0.0329+-0.3424+-0.3595+-0.0801+...
#+temporal+mean+value+per+gridcell+(x,y+location)
>+str(apply(multi_dim_array,+c(1,2),+mean))
+num+[1:96,+1:48]+-1.506+0.4553+-1.7951+0.0703+0.2915+...
#+temporal+mean+value+per+gridcell+and+level+(x,y+location,+level)
>+str(apply(multi_dim_array,+c(1,2,3),+mean))
+num+[1:96,+1:48,+1:6]+-3.839+-3.672+0.131+-1.024+-2.143+...
#+Spatial+mean+per+level
>+str(apply(multi_dim_array,+c(3,4),+mean))
+num+[1:6,+1:100]+-0.4436+-0.3026+-0.3158+0.0902+0.2438+...|1222089|这使得apply的margin参数看起来不那么违反直觉。首先，为什么不使用"row“和"col”而不是1和2。但它也适用于更多维数组的事实清楚地说明了为什么像这样使用margin是首选的。|1222090|entityMap^0|0|5|1W|E|2J|F|0|0|2|5|0|0|3|5|9|6|2B|6|0^^$0|@$1|2|3|4|5|6|7|S|8|@$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|L|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1B|8|@]|D|@]|E|$]]]|R|$]]

The <code>apply</code> set of functions does not only work for matrices, but scales up to multi-dimensional array's. In my research I often have a dataset of for example temperature of the atmosphere. This is stored in a multi-dimensional array with dimensions <code>x,y,level,time</code>, from now on called <code>multi_dim_array</code>. A mockup example would be:

<pre><code>multi_dim_array = array(runif(96 * 48 * 6 * 100, -50, 50), 
 dim = c(96, 48, 6, 100))
&gt; str(multi_dim_array)
# x y lev time 
 num [1:96, 1:48, 1:6, 1:100] 42.4 16 32.3 49.5 24.9 ...
</code></pre>

Using <code>apply</code> one can easily get the:

<pre><code># temporal mean value
&gt; str(apply(multi_dim_array, 4, mean))
 num [1:100] -0.0113 -0.0329 -0.3424 -0.3595 -0.0801 ...
# temporal mean value per gridcell (x,y location)
&gt; str(apply(multi_dim_array, c(1,2), mean))
 num [1:96, 1:48] -1.506 0.4553 -1.7951 0.0703 0.2915 ...
# temporal mean value per gridcell and level (x,y location, level)
&gt; str(apply(multi_dim_array, c(1,2,3), mean))
 num [1:96, 1:48, 1:6] -3.839 -3.672 0.131 -1.024 -2.143 ...
# Spatial mean per level
&gt; str(apply(multi_dim_array, c(3,4), mean))
 num [1:6, 1:100] -0.4436 -0.3026 -0.3158 0.0902 0.2438 ...
</code></pre>

This makes the <code>margin</code> argument to <code>apply</code> seem much less counter intuitive. I first though, why not use "row" and "col" instead of 1 and 2. But the fact that it also works for array's with more dimensions makes it clear why using <code>margin</code> like this is preferred.

blocks|key|1222317|text|在使用比较运算符时，which.min和which.max函数的预期相反，甚至可能给出不正确的答案。例如，试图找出排序数字列表中的哪个元素是小于阈值的最大数字。(即在100到200的序列中，这是小于110的最大数字)|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1222318|set.seed(420)
x+=+seq(100,+200)
which(x+<+110)
>+[1]++1++2++3++4++5++6++7++8++9+10
which.max(x+<+110)
>+[1]+1
which.min(x+<+110)
>+[1]+11
x[11]
>+[1]+110
max(which(x+<+110))
>[1]+10
x[10]
>+[1]+109|code-block|syntax|javascript|1222319|entityMap^0|A|9|K|9|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

<code>which.min</code> and <code>which.max</code> function opposite expectations when using comparison operator and can even give incorrect answers. So for example trying to figure out which element in a list of sorted numbers is the largest number that is less than a threshold. (i.e. in a sequence from 100 to 200 which is the largest number that is less than 110)
<pre><code>set.seed(420)
x = seq(100, 200)
which(x &lt; 110)
&gt; [1] 1 2 3 4 5 6 7 8 9 10
which.max(x &lt; 110)
&gt; [1] 1
which.min(x &lt; 110)
&gt; [1] 11
x[11]
&gt; [1] 110
max(which(x &lt; 110))
&gt;[1] 10
x[10]
&gt; [1] 109
</code></pre>

blocks|key|1437157|text|那些肮脏的东西确实很难找到！像这样剪切多行表达式：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1437158|K+<-+hyperpar$intcept.sigma2
++++++++%2B+cov.NN.additive(x1$env,+x2+=+NULL,+sigma2_int+=+hyperpar$env.sigma2_int,+sigma2_slope+=+hyperpar$env.sigma2_slope)
++++++++%2B+hyperpar$env.sigma2+*+K.cache$k.env|code-block|syntax|javascript|1437159|R将只计算第一行，而其他两行将被浪费掉！它不会说任何警告，什么都不会说！这对毫无戒心的用户来说是相当糟糕的背信弃义。它实际上必须写成这样：|1437160|K+<-+hyperpar$intcept.sigma2+%2B
++++++++cov.NN.additive(x1$env,+x2+=+NULL,+sigma2_int+=+hyperpar$env.sigma2_int,+sigma2_slope+=+hyperpar$env.sigma2_slope)+%2B
++++++++hyperpar$env.sigma2+*+K.cache$k.env|1437161|这不是一种很自然的写作方式。|1437162|entityMap^0|0|0|0|0|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]]|$1|G|3|H|5|6|7|Q|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|S|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

The dirties gotcha that can be really hard to find! Cutting multi-line expressions like this one:

<pre><code>K &lt;- hyperpar$intcept.sigma2
 + cov.NN.additive(x1$env, x2 = NULL, sigma2_int = hyperpar$env.sigma2_int, sigma2_slope = hyperpar$env.sigma2_slope)
 + hyperpar$env.sigma2 * K.cache$k.env
</code></pre>

R will only evaluate the first line, and the other two will just go waste! And it will not say any warning, nothing! This is pretty nasty treachery on unsuspecting user. It must actually be written like this:

<pre><code>K &lt;- hyperpar$intcept.sigma2 +
 cov.NN.additive(x1$env, x2 = NULL, sigma2_int = hyperpar$env.sigma2_int, sigma2_slope = hyperpar$env.sigma2_slope) +
 hyperpar$env.sigma2 * K.cache$k.env
</code></pre>

which is not quite natural way of writing.

blocks|key|1222272|text|这一个!|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1222273|all(c(1,2,3,4)+==+NULL)
$[1]+TRUE|code-block|syntax|javascript|1222274|我在代码中进行了这样的检查，我真的需要两个表具有相同的列名：|1222275|stopifnot(all(names(x$x$env)+==+names(x$obsx$env)))|1222276|但是当x$x$env甚至不存在时，检查就通过了(计算结果为真)！|offset|length|style|CODE|1222277|entityMap^0|0|0|0|0|3|7|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|W|8|@$M|X|N|Y|O|P]]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|Z|8|@]|9|@]|A|$]]]|R|$]]

This one!

<pre><code>all(c(1,2,3,4) == NULL)
$[1] TRUE
</code></pre>

I had this check in my code, I really need both tables to have the same column names:

<pre><code>stopifnot(all(names(x$x$env) == names(x$obsx$env)))
</code></pre>

But the check passed (evaluated to TRUE) when <code>x$x$env</code> didn't even exist!

Is there a certain R-gotcha that had you really surprised one day? I think we'd all gain from sharing these.

Here's mine: in list indexing, <code>my.list[[1]]</code> is not <code>my.list[1]</code>. Learned this in the early days of R.

What's the biggest R-gotcha you've run across?

有没有哪一天真的让你大吃一惊？我想我们都会从分享这些东西中获益。下面是我的:在列表索引中，my.list[[1]]不是my.list[1]。在R的早期就学会了这一点。

问你遇到过的最大的问题是什么？
EN

回答 18

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