Use the `drop=FALSE` argument to `[`.

    m &lt;- matrix(1:10,5,2)
    rownames(m) &lt;- 1:5
    colnames(m) &lt;- 1:2
    m[,1]             # vector
    m[,1,drop=FALSE]  # matrix



How can I use the database view in entity framework code first,


how to use views in code first entity framework

&gt; **Possible Duplicate:**  
&gt; [C# - Is there a better alternative than this to &amp;#39;switch on type&amp;#39;?](https://stackoverflow.com/questions/298976/c-sharp-is-there-a-better-alternative-than-this-to-switch-on-type)  

&lt;!-- End of automatically inserted text --&gt;

Hello suppose i get a big if/else on class type. it&#39;s there a way to do it with a switch case ?

Example :

    function test(object obj)
    {
    if(obj is WebControl)
    {
    
    }else if(obj is TextBox)
    {
    
    }
    else if(obj is ComboBox)
    {
    
    }


etc ...

I would like to create something like 

    switch(obj)
    {
    case is TextBox:
    break;
    case is ComboBox:
    break;
    
    }


}

Switch case on type c#

When I subset a matrix to a single column, the result is of class numeric, not matrix (i.e. myMatrix[ , 5 ] to subset to the fifth column).  Is there a compact way to subset to a single column, maintain the matrix format, and maintain the row/column names without doing something complicated like:

    matrix( myMatrix[ , 5 ] , dimnames = list( rownames( myMatrix ) , colnames( myMatrix )[ 5 ] )

How to subset matrix to one column, maintain matrix data type, maintain row/column names?

When I subset a matrix to a single column, the result is of class numeric, not matrix (i.e. myMatrix[ , 5 ] to subset to the fifth column). Is there a compact way to subset to a single column, maintain the matrix format, and maintain the row/column names without doing something complicated like:
<pre><code class="hljs language-scss">matrix( myMatrix[ , 5 ] , dimnames = list( rownames( myMatrix ) , colnames( myMatrix )[ 5 ] )
</code></pre>

I received this error message:

    Error in if (condition) { : missing value where TRUE/FALSE needed

or

    Error in while (condition) { : missing value where TRUE/FALSE needed

What does it mean, and how do I prevent it?



Error in if/while (condition) {: missing Value where TRUE/FALSE needed

I&#39;ve seen some discussions in SO and other places regarding how this should be or will be done in future versions of Roxygen2. However, I am stuck. How should I go about documenting a S4 generic, as well as its methods, using Roxygen2? A working example for a brand new generic/methods, as well as an example for extending base S4 generic would be incredibly useful. I do not want to have to make separate (mostly) redundant documentation for each S4 method of the same generic. 

Due dilligence:
I have tracked down a useful example for the &quot;extract&quot; method. However, it appears to be outdated and incomplete for my question. It uses @slot tag in the class documentation, which is not (no longer?) supported. It only shows an extension of a core S4 method &quot;[&quot;, rather than a complete Roxygen example including the documentation of the S4 generic. 

https://stackoverflow.com/questions/4396768/how-to-properly-document-s4-and-methods-using-roxygen

If I fully document a new S4 generic with title, description `@param @return @name @aliases 
@docType @rdname`, and then document the S4 method with just the corresponding `@name @aliases 
@docType @rdname`, I get the following `R CMD check` warning:

    * checking for missing documentation entries ... WARNING
    Undocumented S4 methods:
    &lt;&lt; long list of apparently undocumented methods. E.g. generic &#39;plot&#39; and siglist &#39;myClass1,ANY&#39; &gt;&gt;
    All user-level objects in a package (including S4 classes and methods)
    should have documentation entries.

It looked at first as though none of my S4 methods documented in this fashion with roxygen2 had actually worked. However, so far, I&#39;ve noticed that my extensions of the core method &quot;show&quot; do not have an associated error, even though they were documented in exactly the same way as the rest. Here is an example of the complete roxygen documentation I included above one of the show methods, that did NOT generate the missing-documentation error:

    #&#39; @name show
    #&#39; @aliases show,myClass2-method
    #&#39; @docType methods
    #&#39; @rdname show-methods

Thus, I&#39;m at a loss. As you can see, I&#39;ve included the convention for aliases for S4 methods described in [the S4 documentation section of the R package manual](http://cran.r-project.org/doc/manuals/R-exts.html#Documenting-S4-classes-and-methods), namely that methods should have an alias with the following name (without space):

    generic,signature_list-method.

Somehow, this is not completely being understood by `R CMD check`.

Finally, after building the documentation using: 

    library(&quot;devtools&quot;)
    library(&quot;roxygen2&quot;)
    document(&quot;mypkgname&quot;)
    check_doc(&quot;mypkgname&quot;)

and building the package, I get functioning documentation. Any titles from a specific method&#39;s documentation winds up in the &#39;Description&#39; field, rather awkwardly. So roxygen2 obviously did something with each method&#39;s documentation and is on the right track. However, it is not enough to avoid a large and troubling warning from 
    
    &gt; R CMD check mypkgname

I&#39;ve looked at the Rd files, but I know even less about them to quickly see what&#39;s the problem is, and I anyway want to know the roxygen2 solution so that I will not have to manipulate the Rd files directly after each documentation revision.

So this is a multipart question:

1. What is the current recommended approach for both S4 generic and S4 method documentation with roxygen2?

2. Is there a good example available somewhere that shows the complete details?

3. What might be the cause, and solution, to the &lt;R CMD check mypkgname&gt; warning that documentation for most S4 methods is missing (even though the methods with &quot;missing&quot; documentation actually have had their doc parsed by roxygen2 and the resulting Rd files are at least good enough to work in the package after `R CMD build mypkgname`) ?







How to properly document S4 methods using roxygen2

For documenting classes with roxygen(2), specifying a title and description/details appears to be the same as for functions, methods, data, etc. However, slots and inheritance are their own sort of animal. What is the best practice -- current or planned -- for documenting S4 classes in roxygen2?

Due Diligence:

I found mention of an `@slot` tag in early descriptions of roxygen. 
[A 2008 R-forge mailing list post](http://lists.r-forge.r-project.org/pipermail/roxygen-devel/2008-November/000009.html) 
seems to indicate that this is dead, 
and there is no support for `@slot` in roxygen:

Is this true of roxygen2? The previously-mentioned post suggests a user should instead make their own itemized list with LaTeX markup. E.g. a new S4 class that extends the `&quot;character&quot;` class would be coded and documented like this:

    #&#39; The title for my S4 class that extends \code{&quot;character&quot;} class.
    #&#39;
    #&#39; Some details about this class and my plans for it in the body.
    #&#39;
    #&#39; \describe{
    #&#39;    \item{myslot1}{A logical keeping track of something.}
    #&#39;
    #&#39;    \item{myslot2}{An integer specifying something else.}
    #&#39; 
    #&#39;    \item{myslot3}{A data.frame holding some data.}
    #&#39;  }
    #&#39; @name mynewclass-class
    #&#39; @rdname mynewclass-class
    #&#39; @exportClass mynewclass
    setClass(&quot;mynewclass&quot;,
    	representation(myslot1=&quot;logical&quot;,
    		myslot2=&quot;integer&quot;,
    		myslot3=&quot;data.frame&quot;),
    	contains = &quot;character&quot;
    )

However, although this works, this `\describe` , `\item` approach for documenting the slots seems inconsistent with the rest of roxygen(2), in that there are no `@`-delimited tags and slots could go undocumented with no objection from `roxygenize()`. It also says nothing about a consistent way to document inheritance of the class being defined. I imagine dependency still generally works fine (if a particular slot requires a non-base class from another package) using the `@import` tag. 

So, to summarize, what is the current best-practice for roxygen(2) slots?

There seem to be three options to consider at the moment:

&gt;  - A -- Itemized list (as example above).
&gt;  - B -- `@slot` ... but with extra tags/implementation I missed. I was    unable to get @slot to work with roxygen / roxygen2 in versions where 
&gt; it was included as a replacement for the itemized list in the example 
&gt; above. Again, the example above does work with roxygen(2). 
&gt;  - C -- Some alternative tag for specifying slots, like `@param`, that    would accomplish the same thing.

I&#39;m borrowing/extending this question from a post I made to the `roxygen2` development page on [github](https://github.com/klutometis/roxygen/issues/34).


How to properly document S4 class slots using Roxygen2?

I am working with the dataset `LearnBayes`. For those that want to see the actual data: 

    install.packages(&#39;LearnBayes&#39;)

I am trying to filter out rows based on the value in the columns. For example, if the column value is &quot;water&quot;, then I want that row. If the column value is &quot;milk&quot;, then I don&#39;t want it. Ultimately, I am trying to filter out all individuals who&#39;s Drink column is &quot;water&quot;.




Filtering a data frame by values in a column

It would be helpful if someone can illustrate this with a simple example?

Also, where would it be useful to use `parent.frame()` instead of `parent.env()` and vice versa.



What is the difference between parent.frame() and parent.env() in R; how do they differ in call by reference?

Is it possible in R to say - I want all indices from position `i` to the end of vector/matrix?
Say I want a submatrix from 3rd column onwards. I currently only know this way:

    A = matrix(rep(1:8, each = 5), nrow = 5) # just generate some example matrix...
    
    A[,3:ncol(A)] # get submatrix from 3rd column onwards

But do I really need to write `ncol(A)`? Isn&#39;t there any elegant way how to say &quot;from the 3rd column onwards&quot;? Something like `A[,3:]`? (or `A[,3:...]`)?


Elegant indexing up to end of vector/matrix

The [documentation][1] is pretty vague as to what is actually happening when these methods are used.  Can someone explain how Matrix actually affects the Bitmap that it&#39;s being set to? They use the term concatenate in there, but I&#39;m unclear on how that term applies to coordinate data (having only used it in regard to string manipulation before).


  [1]: http://developer.android.com/reference/android/graphics/Matrix.html

Looking for an explanation of post/pre/set Translate (in Matrix object) and how to use them

The [Eigen][1] library can map existing memory into Eigen matrices.

    float array[3];
    Map&lt;Vector3f&gt;(array, 3).fill(10);
    int data[4] = 1, 2, 3, 4;
    Matrix2i mat2x2(data);
    MatrixXi mat2x2 = Map&lt;Matrix2i&gt;(data);
    MatrixXi mat2x2 = Map&lt;MatrixXi&gt;(data, 2, 2);

My question is, how can we get c array (e.g. float[] a) from eigen matrix (e.g. Matrix3f m)? What it the real layout of eigen matrix? Is the real data stored as in normal c array?


  [1]: http://eigen.tuxfamily.org/

Convert Eigen Matrix to C array

I&#39;m a physicist, and have been learning some programming, and have come across a lot of people using quaternions for rotations instead of writing things in matrix/vector form. 

In physics, there are very good reasons we don&#39;t use quaternions (despite the bizarre story that&#39;s occasionally told about Hamilton/Gibbs/etc). Physics requires that our descriptions have good analytic behavior (this has a precisely defined meaning, but in some rather technical ways that go far beyond what&#39;s taught in normal intro classes, so I won&#39;t go into any detail). It turns out that quaternions don&#39;t have this nice behavior, and so they aren&#39;t useful, and vectors/matrices do, so we use them.

However, restricted to rigid rotations and descriptions that do not use any analytic structures, 3D rotations can be equivalently described either way (or a few other ways).

Generally, we just want a mapping of a point X = (x, y, z) to a new point X&#39; = (x&#39;, y&#39;, z&#39;) subject to the constraint that X&lt;sup&gt;2&lt;/sup&gt; = X&#39;&lt;sup&gt;2&lt;/sup&gt;. And there are lots of things that do this. 

The naive way is to just draw the triangles this defines and use trigonometry, or use the isomorphism between a point (x, y, z) and a vector (x, y, z) and the function f(X) = X&#39; and a matrix MX = X&#39;, or using quaternions, or projecting out components of the old vector along the new one using some other method (x, y, z)&lt;sup&gt;T&lt;/sup&gt;.(a,b,c) (x&#39;,y&#39;,z&#39;), etc.

From a math point of view, these descriptions are all equivalent in this setting (as a theorem). They all have the same number of degrees of freedom, the same number of constraints, etc.

So why do quaternions seem to preferred over vectors?

The usual reasons I see are no gimbal lock, or numerical issues.

The no gimbal lock argument seems odd, since this is only a problem of euler angles. It is also only a coordinate problem (just like the singularity at r=0 in polar coordinates (the Jacobian looses rank)), which means it is only a local problem, and can be resolved by switching coordinates, rotating out of the degeneracy, or using two overlapping coordinate systems. 

I&#39;m less sure about numerical issues, since I don&#39;t know in detail how both of these (and any alternatives) would be implemented. I&#39;ve read that re-normalizing a quaternion is easier than doing that for a rotation matrix, but this is only true for a general matrix; a rotation has additional constraints that trivializes this (which are built into the definition of quaternions) (In fact, this has to be true since they have the same number of degrees of freedom). 

So what is the reason for the use of quaternions over vectors or other alternatives? 

Why are quaternions used for rotations?

I have a `data.frame` that looks like this.

    x a 1 
    x b 2 
    x c 3 
    y a 3 
    y b 3 
    y c 2 

I want this in matrix form so I can feed it to heatmap to make a plot.  The result should look something like:

        a    b    c
    x   1    2    3
    y   3    3    2

I have tried `cast` from the reshape package and I have tried writing a manual function to do this but I do not seem to be able to get it right.


Reshape three column data frame to matrix (&quot;long&quot; to &quot;wide&quot; format)

We have a data frame from a CSV file. The data frame `DF` has columns that contain observed values and a column (`VaR2`) that contains the date at which a measurement has been taken. If the date was not recorded, the CSV file contains the value `NA`, for missing data.

    Var1  Var2 
    10    2010/01/01
    20    NA
    30    2010/03/01

We would like to use the subset command to define a new data frame `new_DF` such that it only contains rows that have an `NA&#39;` value from the column (`VaR2`). In the example given, only Row 2 will be contained in the new `DF`.

The command
```r
new_DF&lt;-subset(DF, DF$Var2==&quot;NA&quot;) 
```
does not work, the resulting data frame has no row entries.

If in the original CSV file the Value `NA` are exchanged with  `NULL`, the same command produces the desired result: 
```r
new_DF &lt;- subset(DF, DF$Var2==&quot;NULL&quot;)
``` 

How can I get this method working, if for the character string the value `NA` is provided in the original CSV file? 

Subset of rows containing NA (missing) values in a chosen column of a data frame

![enter image description here][1]

I wish to filter a data frame based on conditions in several columns. For example, how can I delete rows if column A = B and Column E = 0.

  [1]: http://i.stack.imgur.com/iyJCo.png

Subset data frame based on multiple conditions

I&#39;m trying to write a custom filter method that takes an arbitrary number of *kwargs* and returns a list containing the elements of a database-like list that contain those *kwargs*.

For example, suppose `d1 = {&#39;a&#39;:&#39;2&#39;, &#39;b&#39;:&#39;3&#39;}` and `d2` = the same thing. `d1 == d2` results in True. But suppose `d2` = the same thing plus a bunch of other things. My method needs to be able to tell if **d1 in d2**, but Python can&#39;t do that with dictionaries.

Context:

I have a Word class, and each object has properties like `word`, `definition`, `part_of_speech`, and so on. I want to be able to call a filter method on the main list of these words, like `Word.objects.filter(word=&#39;jump&#39;, part_of_speech=&#39;verb-intransitive&#39;)`. I can&#39;t figure out how to manage these keys and values at the same time. But this could have larger functionality outside this context for other people.

How to check if one dictionary is a subset of another larger dictionary?

&gt; **Possible Duplicate:**  
&gt; [Standard way to remove multiple elements from a dataframe](https://stackoverflow.com/questions/7494848/standard-way-to-remove-multiple-elements-from-a-dataframe)  

&lt;!-- End of automatically inserted text --&gt;

I know in R that if you are searching for a subset of another group or matching based on id you&#39;d use something like

    subset(df1, df1$id %in% idNums1)

My question is how to do the opposite or choose items NOT matching a vector of ids.

I tried using `!` but get the error message

    subset(df1, df1$id !%in% idNums1)

I think my backup is to do sometime like this:

    matches &lt;- subset(df1, df1$id %in% idNums1)
    nonMatches &lt;- df1[(-matches[,1]),]

but I&#39;m hoping there&#39;s something a bit more efficient.


R Not in subset

When I need to filter a data.frame, i.e., extract rows that meet certain conditions, I prefer to use the `subset` function:

    subset(airquality, Month == 8 &amp; Temp &gt; 90)

Rather than the `[` function:

    airquality[airquality$Month == 8 &amp; airquality$Temp &gt; 90, ]

There are two main reasons for my preference:

 1. I find the code reads better, from left to right. Even people who know nothing about R could tell what the `subset` statement above is doing.

 2. Because columns can be referred to as variables in the `select` expression, I can save a few keystrokes. In my example above, I only had to type `airquality` once with `subset`, but three times with `[`.

So I was living happy, using `subset` everywhere because it is shorter and reads better, even advocating its beauty to my fellow R coders. But yesterday my world broke apart. While reading the `subset` documentation, I notice this section:

&gt;Warning
&gt;
&gt;This is a convenience function intended for use interactively. For programming it is better to use the standard subsetting functions like [, and in particular the non-standard evaluation of argument subset can have unanticipated consequences.

Could someone help clarify what the authors mean?

First, what do they mean by &quot;*for use interactively*&quot;? I know what an interactive session is, as opposed to a script run in BATCH mode but I don&#39;t see what difference it should make.

Then, could you please explain &quot;*the non-standard evaluation of argument subset*&quot; and why it is dangerous, maybe provide an example?


Why is `[` better than `subset`?

Say I have a `data.frame` object:

    df &lt;- data.frame(name=c(&#39;black&#39;,&#39;black&#39;,&#39;black&#39;,&#39;red&#39;,&#39;red&#39;),
                     type=c(&#39;chair&#39;,&#39;chair&#39;,&#39;sofa&#39;,&#39;sofa&#39;,&#39;plate&#39;),
                     num=c(4,5,12,4,3))

Now I want to count the number of rows (observations) of for each combination of `name` and `type`. This can be done like so:

    table(df[ , c(&quot;name&quot;,&quot;type&quot;)])

or possibly also with `plyr`, (though I am not sure how).

However, how do I get the results incorporated into the original data frame?  So that the results will look like this:

    df
    #    name  type num count
    # 1 black chair   4     2
    # 2 black chair   5     2
    # 3 black  sofa  12     1
    # 4   red  sofa   4     1
    # 5   red plate   3     1

where `count` now stores the results from the aggregation.

A solution with `plyr` could be interesting to learn as well, though I would like to see how this is done with base R.

Count number of rows per group and add result to original data frame

I wonder how to add regression line equation and R^2 on the `ggplot`. My code is:

    library(ggplot2)

    df &lt;- data.frame(x = c(1:100))
    df$y &lt;- 2 + 3 * df$x + rnorm(100, sd = 40)
    p &lt;- ggplot(data = df, aes(x = x, y = y)) +
                geom_smooth(method = &quot;lm&quot;, se=FALSE, color=&quot;black&quot;, formula = y ~ x) +
                geom_point()
    p

Any help will be highly appreciated.


Add regression line equation and R^2 on graph

In a ```data.frame``` (or ```data.table```), I would like to &quot;fill forward&quot; NAs with the closest previous non-NA value. A simple example, using vectors (instead of a `data.frame`) is the following:

    &gt; y &lt;- c(NA, 2, 2, NA, NA, 3, NA, 4, NA, NA)

I would like a function `fill.NAs()` that allows me to construct `yy` such that:

    &gt; yy
    [1] NA NA NA  2  2  2  2  3  3  3  4  4

I need to repeat this operation for many (total ~1 Tb) small sized `data.frame`s (~30-50 Mb), where a row is NA is all its entries are. What is a good way to approach the problem? 

The ugly solution I cooked up uses this function:

    last &lt;- function (x){
        x[length(x)]
    }    
    
    fill.NAs &lt;- function(isNA){
	if (isNA[1] == 1) {
		isNA[1:max({which(isNA==0)[1]-1},1)] &lt;- 0 # first is NAs 
												  # can&#39;t be forward filled
	}
	isNA.neg &lt;- isNA.pos &lt;- isNA.diff &lt;- diff(isNA)
	isNA.pos[isNA.diff &lt; 0] &lt;- 0
	isNA.neg[isNA.diff &gt; 0] &lt;- 0
	which.isNA.neg &lt;- which(as.logical(isNA.neg))
	if (length(which.isNA.neg)==0) return(NULL) # generates warnings later, but works
	which.isNA.pos &lt;- which(as.logical(isNA.pos))
	which.isNA &lt;- which(as.logical(isNA))
	if (length(which.isNA.neg)==length(which.isNA.pos)){
		replacement &lt;- rep(which.isNA.pos[2:length(which.isNA.neg)], 
									which.isNA.neg[2:max(length(which.isNA.neg)-1,2)] - 
									which.isNA.pos[1:max(length(which.isNA.neg)-1,1)])		
		replacement &lt;- c(replacement, rep(last(which.isNA.pos), last(which.isNA) - last(which.isNA.pos)))
	} else {
		replacement &lt;- rep(which.isNA.pos[1:length(which.isNA.neg)], which.isNA.neg - which.isNA.pos[1:length(which.isNA.neg)])		
		replacement &lt;- c(replacement, rep(last(which.isNA.pos), last(which.isNA) - last(which.isNA.pos)))
	}
	replacement
    }


The function `fill.NAs` is used as follows:

    y &lt;- c(NA, 2, 2, NA, NA, 3, NA, 4, NA, NA)
    isNA &lt;- as.numeric(is.na(y))
    replacement &lt;- fill.NAs(isNA)
    if (length(replacement)){
	which.isNA &lt;- which(as.logical(isNA))
	to.replace &lt;- which.isNA[which(isNA==0)[1]:length(which.isNA)]
	y[to.replace] &lt;- y[replacement]
    } 
***Output***

    &gt; y
    [1] NA  2  2  2  2  3  3  3  4  4  4

... which seems to work. But, man, is it ugly! Any suggestions?


Replacing NAs with latest non-NA value

How can I list the distinct values in a vector where the values are replicative? I mean, similarly to the following SQL statement:

    SELECT DISTINCT product_code
    FROM data

Content Type	Original Author	Original Content on Stackoverflow
Question	Suraj	View Question on Stackoverflow
Solution 1 - R	Joshua Ulrich	View Answer on Stackoverflow

How to subset matrix to one column, maintain matrix data type, maintain row/column names?

R Problem Overview

R Solutions

Solution 1 - R

Switch case on type c#

how to use views in code first entity framework

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