The subset command is not necessary.  Just use data frame indexing

    studentdata[studentdata$Drink == &#39;water&#39;,]

Read the warning from `?subset`

&gt; This is a convenience function intended for use interactively.  For
&gt; programming it is better to use the standard subsetting functions like
&gt; ‘[’, and in particular the non-standard evaluation of argument
&gt; ‘subset’ can have unanticipated consequences.

Try this:

    subset(studentdata, Drink==&#39;water&#39;)

that should do it.

Thought I&#39;d update this with a `dplyr` solution

    library(dplyr)    
    filter(studentdata, Drink == &quot;water&quot;)

In other languages, a general guideline that helps produce better code is always make everything as hidden as possible. If in doubt about whether a variable should be private or protected, it&#39;s better to go with private.

Does the same hold true for Python? Should I use two leading underscores on everything at first, and only make them less hidden (only one underscore) as I need them?

If the convention is to use only one underscore, I&#39;d also like to know the rationale.

Here&#39;s a comment I left on [JBernardo&#39;s answer](https://stackoverflow.com/questions/7456807/python-name-mangling-when-in-doubt-do-what/7456958#7456958). It explains why I asked this question and also why I&#39;d like to know why Python is different from the other languages:

&gt; I come from languages that train you to think everything should be only as public as needed and no more. The reasoning is that this will reduce dependencies and make the code safer to alter. The Python way of doing things in reverse -- starting from public and going towards hidden -- is odd to me.

Python name mangling

I have an array of e-mails (it can be just 1 email, or 100 emails), and I need to send the array with an ajax request (that I know how to do), but I can only send an array that has 10 or less e-mails in it. So if there is an original array of 20 e-mails I will need to split them up into 2 arrays of 10 each. or if there are 15 e-mails in the original array, then 1 array of 10, and another array of 5. I&#39;m using jQuery, what would be the best way to do this? 

How to split a long array into smaller arrays, with JavaScript

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

I am working with the dataset <code>LearnBayes</code>. For those that want to see the actual data:
<pre><code class="hljs language-arduino">install.packages('LearnBayes')
</code></pre>
I am trying to filter out rows based on the value in the columns. For example, if the column value is "water", then I want that row. If the column value is "milk", then I don't want it. Ultimately, I am trying to filter out all individuals who's Drink column is "water".

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?

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 have a Java app which needs to perform partial least squares regression. It would appear there are no Java implementations of PLSR out there. Weka might have had something like it at some point, but it is no longer in the API. On the other hand, I have found a good R implementation, which has an added bonus to it. It was used by the people whose result I want to replicate, which means there is less chance that things will go wrong because of differences in the way PLSR is implemented.

The question is: is there a good enough (and simple to use) package that enable Java to call R, pass in some parameters to a function and read back the results? My other option is to have Java spawn R in a Process and then monitor it. Data would be read and written to disk. Which of the two would you recommend? Am I missing the obvious third option?

Java-R integration?

I need to output ggplot2 graphics from R to PNG files with transparent background. Everything is ok with basic R graphics, but no transparency with ggplot2:

    d &lt;- rnorm(100) #generating random data
    
    #this returns transparent png
    png(&#39;tr_tst1.png&#39;,width=300,height=300,units=&quot;px&quot;,bg = &quot;transparent&quot;)
    boxplot(d)
    dev.off()
    
    df &lt;- data.frame(y=d,x=1)
    p &lt;- ggplot(df) + stat_boxplot(aes(x = x,y=y)) 
    p &lt;- p + opts(
    	panel.background = theme_rect(fill = &quot;transparent&quot;,colour = NA), # or theme_blank()
    	panel.grid.minor = theme_blank(), 
    	panel.grid.major = theme_blank()
    )
    #returns white background
    png(&#39;tr_tst2.png&#39;,width=300,height=300,units=&quot;px&quot;,bg = &quot;transparent&quot;)
    p
    dev.off()

**Is there any way to get transparent background with ggplot2?**

How to make graphics with transparent background in R using ggplot2?

Commands:

    t &lt;- data.frame(v = 5:1, v2 = 9:5)
    write.csv(t, &quot;t.csv&quot;)

Resulting file:

    # &quot;&quot;,&quot;v&quot;,&quot;v2&quot;
    # &quot;1&quot;,5,9
    # &quot;2&quot;,4,8
    # &quot;3&quot;,3,7
    # &quot;4&quot;,2,6
    # &quot;5&quot;,1,5

How do I prevent first column with row index from being written to the file?

Prevent row names to be written to file when using write.csv

I need to restrict input into a TextField to integers. Any advice?


What is the recommended way to make a numeric TextField in JavaFX?

I have an OData service where I&#39;m trying to filter by a list of IDs; the SQL equivalent would be something like:

    SELECT * FROM MyTable WHERE TableId IN (100, 200, 300, 400)

The property I&#39;m trying to filter on is typed as an Int32. I&#39;ve tried the following, which gives me an error &quot;Operator &#39;add&#39; incompatible with operand types &#39;Edm.String&#39; and &#39;Edm.Int32&#39;&quot;:

    string ids = &quot;,100,200,300,400,&quot;;
    from m in provider.Media where ids.Contains(&quot;,&quot; + t.media_id + &quot;,&quot;)

as well as

    string ids = &quot;,100,200,300,400,&quot;;
    from m in provider.Media where ids.Contains(&quot;,&quot; + t.media_id.ToString() + &quot;,&quot;)

and

    string ids = &quot;,100,200,300,400,&quot;;
    from m in provider.Media where ids.Contains(&quot;,&quot; + Convert.ToString(t.media_id) + &quot;,&quot;)

and

    string ids = &quot;,100,200,300,400,&quot;;
    from m in provider.Media where ids.Contains(string.Concat(&quot;,&quot;, t.media_id, &quot;,&quot;))

As you can see, currently I&#39;m using LINQ to query the service.

Is there a way I can do what I&#39;m trying to, or am I stuck constructing a text filter and using AddQueryOption, and iterating through the list and manually adding &quot;or media_id eq 100&quot; clauses?

OData &quot;where ID in list&quot; query

I need to filter an array to remove the elements that are lower than a certain threshold. My current code is like this:

    threshold = 5
    a = numpy.array(range(10)) # testing data
    b = numpy.array(filter(lambda x: x &gt;= threshold, a))

The problem is that this creates a temporary list, using a filter with a lambda function (slow).

As this is a quite simple operation, maybe there is a numpy function that does it in an efficient way, but I&#39;ve been unable to find it.

I&#39;ve thought that another way to achieve this could be sorting the array, finding the index of the threshold and returning a slice from that index onwards, but even if this would be faster for small inputs (and it won&#39;t be noticeable anyway), its definitively asymptotically less efficient as the input size grows.

Any ideas? Thanks!

**Update**: I took some measurements too, and the sorting+slicing was still twice as fast than the pure python filter when the input was 100.000.000 entries.


    In [321]: r = numpy.random.uniform(0, 1, 100000000)
    
    In [322]: %timeit test1(r) # filter
    1 loops, best of 3: 21.3 s per loop
    
    In [323]: %timeit test2(r) # sort and slice
    1 loops, best of 3: 11.1 s per loop
    
    In [324]: %timeit test3(r) # boolean indexing
    1 loops, best of 3: 1.26 s per loop



Efficient thresholding filter of an array with numpy

Is there a reason there&#39;s

    Lists.transform()

but no

    Lists.filter()

?

How do I filter a list correctly? I could use

    new ArrayList(Collection2.filter())

of course, but this way it&#39;s not guaranteed that my ordering stays the same, if I understand correctly.

Guava: Why is there no Lists.filter() function?

I don&#39;t have much experience with SQLAlchemy and I have a problem, which I can&#39;t solve. I tried searching and I tried a lot of code.
This is my Class (reduced to the most significant code):

    class Patient(Base):
        __tablename__ = &#39;patients&#39;
        id = Column(Integer, primary_key=True, nullable=False)
        mother_id = Column(Integer, ForeignKey(&#39;patients.id&#39;), index=True)
        mother = relationship(&#39;Patient&#39;, primaryjoin=&#39;Patient.id==Patient.mother_id&#39;, remote_side=&#39;Patient.id&#39;, uselist=False)
        phenoscore = Column(Float)

and I would like to query all patients, whose mother&#39;s phenoscore is (for example) `== 10`

As told, I tried a lot of code, but I don&#39;t get it. The logically solution, in my eyes, would be

    patients = Patient.query.filter(Patient.mother.phenoscore == 10)

because, you can access `.mother.phenoscore` for each element when outputting but, this code doesn&#39;t do it.

Is there a (direct) possibility to filter by an attribute of a relationship (without writing the SQL Statement, or an extra join-statement), I need this kind of filter more than one time.

Even if there is no easy solution, I am happy to get all answers.

SqlAlchemy - Filtering by Relationship Attribute

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

How can I merge the columns of two data frames, containing a distinct set of columns but *some* rows with the same names? The fields for rows that don&#39;t occur in both data frames should be filled with zeros:

    &gt; d
        a   b   c   d   e   f   g   h   i  j
    1 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10
    2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9  1
    &gt; e
       k  l  m  n  o  p  q  r  s  t
    1 11 12 13 14 15 16 17 18 19 20
    3 21 22 23 24 25 26 27 28 29 30
    &gt; de
        a   b   c   d   e   f   g   h   i  j  k  l  m  n  o  p  q  r  s  t
    1 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10 11 12 13 14 15 16 17 18 19 20
    2 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9  1  0  0  0  0  0  0  0  0  0  0
    3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0  0 21 22 23 24 25 26 27 28 29 30

Merge data frames based on rownames in R

I have a data frame, which looks like that: 

        P1  P2  P3  T1  T2  T3  I1  I2
    1   2   3   5   52  43  61  6   &quot;b&quot;
    2   6   4   3   72  NA  59  1   &quot;a&quot;
    3   1   5   6   55  48  60  6   &quot;f&quot;
    4   2   4   4   65  64  58  2   &quot;b&quot;

I want to sort it by I1 in descending order, and rows with the same value in I1 by I2 in ascending order, getting the rows in the order `1 3 4 2`. But the `order` function seems to only take one `decreasing` argument, which is then `TRUE` or `FALSE` for all ordering vectors at once. How do I get my sort correct?



How to order a data frame by one descending and one ascending column?

I have huge dataframe like this:

    SN = c(1:100, 1:100, 1:100, 1:100)  
    class = c(rep(&quot;A1&quot;, 100), rep(&quot;B2&quot;, 100), rep(&quot;C3&quot;, 100), rep(&quot;D4&quot;, 100)) # total 6000 levels 
    myvar = rnorm(400)
    mydf = data.frame(SN, class, myvar) 
    
I want to &quot;unmelt&quot; to a table with each level as single column and myvar in filled:

    
    SN          A1            B2          C3         D4       .............and so on for all 6000 

How can I achieve this, I know it is simple question, but I could not figure out.







Rearrange dataframe to a table, the opposite of &quot;melt&quot;

I want to perform my own complex operations on financial data in dataframes in a sequential manner.

For example I am using the following MSFT CSV file taken from [Yahoo Finance](http://finance.yahoo.com/q/hp?s=MSFT):

    Date,Open,High,Low,Close,Volume,Adj Close
    2011-10-19,27.37,27.47,27.01,27.13,42880000,27.13
    2011-10-18,26.94,27.40,26.80,27.31,52487900,27.31
    2011-10-17,27.11,27.42,26.85,26.98,39433400,26.98
    2011-10-14,27.31,27.50,27.02,27.27,50947700,27.27
    
    ....

I then do the following:

    #!/usr/bin/env python
    from pandas import *
    
    df = read_csv(&#39;table.csv&#39;)
    
    for i, row in enumerate(df.values):
        date = df.index[i]
        open, high, low, close, adjclose = row
        #now perform analysis on open/close based on date, etc..

Is that the most efficient way? Given the focus on speed in pandas, I would assume there must be some special function to iterate through the  values in a manner that one also retrieves the index (possibly through a generator to be memory efficient)? `df.iteritems` unfortunately only iterates column by column.


Content Type	Original Author	Original Content on Stackoverflow
Question	user722224	View Question on Stackoverflow
Solution 1 - R	adamleerich	View Answer on Stackoverflow
Solution 2 - R	Dave Kincaid	View Answer on Stackoverflow
Solution 3 - R	rrs	View Answer on Stackoverflow

Filtering a data frame by values in a column

R Problem Overview

R Solutions

Solution 1 - R

Solution 2 - R

Solution 3 - R

How to split a long array into smaller arrays, with JavaScript

Python name mangling

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