We can use `..` notation to find `myVector` as a vector of column positions, like it would work in `data.frame`

    mtcarsDT[, ..myVector]

According to `?data.table`

&gt; In case of overlapping variables names inside dataset and in parent scope you can use double dot prefix `..cols` to explicitly refer to &#39;cols variable parent scope and not from your dataset.

I have a DF with a huge parseable metadata as a single string column in a Dataframe, lets call it DFA, with ColmnA.

I would like to break this column, ColmnA into multiple columns thru a function, ClassXYZ = Func1(ColmnA). This function returns a class ClassXYZ, with multiple variables, and each of these variables now has to be mapped to new Column, such a ColmnA1, ColmnA2 etc.

How would I do such a transformation from 1 Dataframe to another with these additional columns by calling this Func1 just once, and not have to repeat-it to create all the columns.

Its easy to solve if I were to call this huge function every time to add a new column, but that what I wish to avoid.

Kindly please advise with a working or pseudo code.

Thanks

Sanjay

Derive multiple columns from a single column in a Spark DataFrame

I am trying to change the font size of the title of a navigation bar. I know I can set its attributes using:

    var attributes = [ NSForegroundColorAttributeName: UIColor.blackColor(), NSFontAttributeName: UIFont(name: &quot;the font name&quot;, size: 18)! ]


...

     self.navigationController?.navigationBar.titleTextAttributes = attributes


What I cannot seem to find is the correct &#39;System&#39; font name.

I was after the default, a.k.a System, font name. I tried printing all the available fonts only to discover it does not belong to a family and does not seem to have an explicit name.


How to access the iOS system font programmatically

I am trying to select those columns in a data.table whose name appears in my character vector. The operation works in a pure data.frame, but doesn&#39;t work in a data.table. Here&#39;s a reproducible example. 

    &gt; names(mtcars)
     [1] &quot;mpg&quot;  &quot;cyl&quot;  &quot;disp&quot; &quot;hp&quot;   &quot;drat&quot; &quot;wt&quot;   &quot;qsec&quot; &quot;vs&quot;   &quot;am&quot;   &quot;gear&quot;
    [11] &quot;carb&quot;
    &gt; myVector &lt;- c(&#39;disp&#39;, &#39;hp&#39;, &#39;wt&#39;)
    &gt; head(mtcars[, myVector])
                      disp  hp    wt
    Mazda RX4          160 110 2.620
    Mazda RX4 Wag      160 110 2.875
    Datsun 710         108  93 2.320
    Hornet 4 Drive     258 110 3.215
    Hornet Sportabout  360 175 3.440
    Valiant            225 105 3.460

I just made a vector which includes `disp`, `hp`, and `wt` and I selected the corresponding columns in my data.frame using that vector. Let&#39;s now make a data.table object from my data.frame and try to do the same operation. 

    &gt; library(data.table)
    &gt; mtcarsDT &lt;- data.table(mtcars)
    &gt; mtcarsDT[, myVector]
    [1] &quot;disp&quot; &quot;hp&quot;   &quot;wt&quot;  



How to select columns in data.table using a character vector of certain column names?

I am trying to select those columns in a data.table whose name appears in my character vector. The operation works in a pure data.frame, but doesn't work in a data.table. Here's a reproducible example.
<pre><code class="hljs language-csharp">> names(mtcars)
 [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
[11] "carb"
> myVector &#x3C;- c('disp', 'hp', 'wt')
> head(mtcars[, myVector])
 disp hp wt
Mazda RX4 160 110 2.620
Mazda RX4 Wag 160 110 2.875
Datsun 710 108 93 2.320
Hornet 4 Drive 258 110 3.215
Hornet Sportabout 360 175 3.440
Valiant 225 105 3.460
</code></pre>
I just made a vector which includes <code>disp</code>, <code>hp</code>, and <code>wt</code> and I selected the corresponding columns in my data.frame using that vector. Let's now make a data.table object from my data.frame and try to do the same operation.
<pre><code class="hljs language-kotlin">> library(data.table)
> mtcarsDT &#x3C;- data.table(mtcars)
> mtcarsDT[, myVector]
[1] "disp" "hp" "wt" 
</code></pre>

How can I increase the space between the keys of the legend of `ggplot2` plot?


    library(ggplot2)
    ggplot(aes(mpg, wt, colour = factor(cyl)),
           , data = mtcars) +
          geom_point() +
      theme(legend.direction = &quot;horizontal&quot;, 
            legend.position = &quot;bottom&quot;) +
      guides(color = guide_legend(nrow=2))

[![enter image description here][1]][1]


I am looking for a ggplot2 option that add a kind of vertical adjustment between (key 4 and key 6) in the plot above? Should I create a custom legend key? 

PS: I want to increase the blank space between boxes not between labels.

the desired plot is : 

[![enter image description here][2]][2]


  [1]: http://i.stack.imgur.com/ApLwH.png
  [2]: http://i.stack.imgur.com/MVLot.png

NOTE: **No the question is not duplicated of the other question. We want here to add a vertical spacing between items that are already in multiple rows. In the other question we have 1-row legend and we want to add spaces (horizontal) between items.**


ggplot2 increase space between legend keys

Using this dummy `data.frame`

    ts &lt;- data.frame(x=1:3, y=c(&quot;blue&quot;, &quot;white&quot;, &quot;white&quot;), z=c(&quot;one&quot;, &quot;one&quot;, &quot;two&quot;))

I try and plot with category &quot;blue&quot; on top.

    ggplot(ts, aes(z, x, fill=factor(y, levels=c(&quot;blue&quot;,&quot;white&quot; )))) + geom_bar(stat = &quot;identity&quot;)
[![enter image description here][1]][1]

gives me &quot;white&quot; on top. and

    ggplot(ts, aes(z, x, fill=factor(y, levels=c(&quot;white&quot;, &quot;blue&quot;)))) + geom_bar(stat = &quot;identity&quot;)
[![enter image description here][2]][2]

reverses the colors, but still gives me &quot;white&quot; on top. How can I get &quot;blue&quot; on top?


  [1]: http://i.stack.imgur.com/CnWfE.png
  [2]: http://i.stack.imgur.com/qtnym.png

How to control ordering of stacked bar chart using identity on ggplot2

I have geocoded points in long, lat format, and I want to calculate the distance between them using R.  This seems pretty straight forward, yet I can&#39;t find a function that will do it easily.  I&#39;ve been attempting to do it with the gdistance package, but it seems very convoluted and oriented to graphing, I just need a number.  Something like `distanceBetween(pointA,pointB)` that returns a number.  

Function to calculate geospatial distance between two points (lat,long) using R

I get this warning when I am trying to generate a plot with `ggplot`.

After researching online for a while many suggested that my database contains either null values or missing data in general, which was not the case.

[In this question][1] the accepted answer says the following:

&gt; The warning means that some elements are removed because they fall out of the specified range

I was wondering what exactly does this range refer to and how can someone manually increase this range in order to avoid all warnings?


  [1]: https://stackoverflow.com/questions/21549463/the-meaning-of-warning-message-removed-4-rows-containing-missing-values-geom-p

Explain ggplot2 warning: &quot;Removed k rows containing missing values&quot;

I have this issue during package installation in R version 3.0.2 (2013-09-25) on an Ubuntu machine:

    install.packages(&quot;randomForest&quot;)
    Installing package into ‘/usr/local/lib/R/site-library’
    (as ‘lib’ is unspecified)
    Warning in install.packages :
      &#39;lib = &quot;/usr/local/lib/R/site-library&quot;&#39; is not writable

How to resolve it?

Library is not writable

I&#39;m in the process of trying out a dplyr-based workflow (rather than using mostly data.table, which I&#39;m used to), and I&#39;ve come across a problem that I can&#39;t find an equivalent dplyr solution to. I commonly run into the scenario where I need to conditionally update/replace several columns based on a single condition. Here&#39;s some example code, with my data.table solution: 

    library(data.table)

    # Create some sample data
    set.seed(1)
    dt &lt;- data.table(site = sample(1:6, 50, replace=T),
                     space = sample(1:4, 50, replace=T),
                     measure = sample(c(&#39;cfl&#39;, &#39;led&#39;, &#39;linear&#39;, &#39;exit&#39;), 50, 
                                   replace=T),
                     qty = round(runif(50) * 30),
                     qty.exit = 0,
                     delta.watts = sample(10.5:100.5, 50, replace=T),
                     cf = runif(50))
    
    # Replace the values of several columns for rows where measure is &quot;exit&quot;
    dt &lt;- dt[measure == &#39;exit&#39;, 
             `:=`(qty.exit = qty,
                  cf = 0,
                  delta.watts = 13)]

Is there a simple dplyr solution to this same problem? I&#39;d like to avoid using ifelse because I don&#39;t want to have to type the condition multiple times - this is a simplified example, but there are sometimes many assignments based on a single condition. 

Thanks in advance for the help!

dplyr mutate/replace several columns on a subset of rows

Suppose I have two *data.table*&#39;s:

A:

      A  B
    1: 1 12
    2: 2 13
    3: 3 14
    4: 4 15

B:

       A  B
    1: 2 13
    2: 3 14

and I have the following code: 

    merge_test = merge(dataA, dataB, by=&quot;A&quot;, all.data=TRUE)

I get:

       A B.x B.y
    1: 2  13  13
    2: 3  14  14


However, I want all the rows in dataA in the final merged table. Is there a way to do this?

Left join using data.table

I am having difficulty grasping the essence of the `setDT()` function. As I read code on SO, I frequently come across the use of `setDT()` to create a data.table. Of course the use of `data.table()` is ubiquitous. I feel like I solidly comprehend the nature of `data.table()` yet the relevance of `setDT()` eludes me. `?setDT` tells me this:

&gt; `setDT` converts lists (both named and unnamed) and data.frames to data.tables _by reference_. 

as well as:

&gt;In `data.table` parlance, all `set*` functions change their input by reference. That is, no copy is made at all, other than temporary working memory, which is as large as one column.

So this makes me think I should only use `setDT()` to make a data.table, right? Is `setDT()` simply a list to data.table converter?  

    library(data.table)
    
    a &lt;- letters[c(19,20,1,3,11,15,22,5,18,6,12,15,23)]
    b &lt;- seq(1,41,pi)
    ab &lt;- data.frame(a,b)
    d &lt;- data.table(ab)
    e &lt;- setDT(ab)

    str(d)
    #Classes ‘data.table’ and &#39;data.frame&#39;:	13 obs. of  2 variables:
    # $ a: Factor w/ 12 levels &quot;a&quot;,&quot;c&quot;,&quot;e&quot;,&quot;f&quot;,..: 9 10 1 2 5 7 11 3 8 4 ...
    # $ b: num  1 4.14 7.28 10.42 13.57 ...
    # - attr(*, &quot;.internal.selfref&quot;)=&lt;externalptr&gt;
            
    str(e)
    #Classes ‘data.table’ and &#39;data.frame&#39;:	13 obs. of  2 variables:
    # $ a: Factor w/ 12 levels &quot;a&quot;,&quot;c&quot;,&quot;e&quot;,&quot;f&quot;,..: 9 10 1 2 5 7 11 3 8 4 ...
    # $ b: num  1 4.14 7.28 10.42 13.57 ...
    # - attr(*, &quot;.internal.selfref&quot;)=&lt;externalptr&gt;

Seemingly no difference in this instance. In another instance the difference is evident:

    ba &lt;- list(a,b)
    f &lt;- data.table(ba)
    g &lt;- setDT(ba)

    str(f)
    #Classes ‘data.table’ and &#39;data.frame&#39;:	2 obs. of  1 variable:
    # $ ba:List of 2
    #  ..$ : chr  &quot;s&quot; &quot;t&quot; &quot;a&quot; &quot;c&quot; ...
    #  ..$ : num  1 4.14 7.28 10.42 13.57 ...
    # - attr(*, &quot;.internal.selfref&quot;)=&lt;externalptr&gt;
    
    str(g)
    #Classes ‘data.table’ and &#39;data.frame&#39;:	13 obs. of  2 variables:
    # $ V1: chr  &quot;s&quot; &quot;t&quot; &quot;a&quot; &quot;c&quot; ...
    # $ V2: num  1 4.14 7.28 10.42 13.57 ...
    # - attr(*, &quot;.internal.selfref&quot;)=&lt;externalptr&gt;

When should I use `setDT()`? What makes `setDT()` relevant? Why not just make the original `data.table()` function capable of doing what `setDT()` is able to do?





 



 

        

 



Content Type	Original Author	Original Content on Stackoverflow
Question	coneyhelixlake	View Question on Stackoverflow
Solution 1 - R	akrun	View Answer on Stackoverflow

How to select columns in data.table using a character vector of certain column names?

R Problem Overview

R Solutions

Solution 1 - R

How to access the iOS system font programmatically

Derive multiple columns from a single column in a Spark DataFrame

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