It depends on how you’re running R.

In the terminal, the “normal” way is &lt;kbd&gt;Control&lt;/kbd&gt;+&lt;kbd&gt;C&lt;/kbd&gt; –&#160;the “cancel” key combination.

In the graphical R application and in RStudio, it’s &lt;kbd&gt;Escape&lt;/kbd&gt;.

IShouldBuyABoat was right the problem was that I accidentally included a back tick, this caused the parser to require a second back tick to close the string before evaluating the string or returning the proper prompt. 

I used Escape key as suggested by Konrad Rudolph, although I was not in R studio and this returned the prompt. 

Thanks for all the help, and sorry I didn&#39;t post my answer earlier. This is my first question and I didn&#39;t realize that I was supposed to do that. 


If you are using terminal you can quit using q()

I am using RStudio 3.3.1  and had a the same problem. Press Escape key for worked. Please try this and let me know if I  can help more...

I haven&#39;t been able to find a good explanation of decltype. Please tell me, as a beginning programmer, what it does and why it is useful.


For example, I am reading a book that asked the following question. Can someone explain to me the answer and why, along with some good (beginner-level) examples?


&gt; What would be the type of each variable and what value would each variable have when the code finishes?


&gt;     int a = 3, b = 4;    
&gt;     decltype(a) c = a;
    
&gt;     decltype((b)) d = a; 
    
&gt;     ++c; 
    
&gt;     ++d;

A line-by-line explanation would be very helpful.

What is decltype and how is it used?

I&#39;d like to understand better why choose `int` over `unsigned`?

Personally, I&#39;ve never liked signed values unless there is a valid reason for them.  e.g. count of items in an array, or length of a string, or size of memory block, etc., so often these things cannot possibly be negative.  Such a value has no possible meaning.  Why prefer `int` when it is misleading in all such cases?

I ask this because both Bjarne Stroustrup and Chandler Carruth gave the advice to prefer `int` over `unsigned` [here (approx 12:30&#39;)][1].

I can see the argument for using `int` over `short` or `long` - `int` is the &quot;most natural&quot; data width for the target machine architecture.

But signed over unsigned has always annoyed me.  Are signed values genuinely faster on typical modern CPU architectures?  What makes them better?


  [1]: http://channel9.msdn.com/Events/GoingNative/2013/Interactive-Panel-Ask-Us-Anything

Why prefer signed over unsigned in C++?

I&#39;m fairly new to R and I made a type-o while entering some code and now instead of getting the &quot;&gt;&quot; prompt I get a &quot;+&quot; prompt. I appear to be stuck in some kind of function that is looking for input, but I can&#39;t seem to get out of it. I figure that there must be a better way to fix this problem then closing R and rerunning my script but I haven&#39;t been able to find anything on google (likely because I don&#39;t know the right search terms). 
 
Thanks for your help, sorry it&#39;s a trivial question

The code I entered was as follows: 

    &gt; fit = lm(x~`y)
    + fit = lm(x~y)
    + fit
    + summary(fit)
    + 
    + 
    + end
    + ;
    + break
    + )
    + 
    + 
    + 
    +           
    +      
    + quit()
    + 
    + quit
    + break()
    + abline(b0hat,b1hat)
    + return(null)
    + 



ending &quot;+&quot; prompt in R

I'm fairly new to R and I made a type-o while entering some code and now instead of getting the ">" prompt I get a "+" prompt. I appear to be stuck in some kind of function that is looking for input, but I can't seem to get out of it. I figure that there must be a better way to fix this problem then closing R and rerunning my script but I haven't been able to find anything on google (likely because I don't know the right search terms).
Thanks for your help, sorry it's a trivial question
The code I entered was as follows:
<pre><code class="hljs language-markdown">> fit = lm(x~`y)
+ fit = lm(x~y)
+ fit
+ summary(fit)
+ 
+ 
+ end
+ ;
+ break
+ )
+ 
+ 
+ 
+     
+   
+ quit()
+ 
+ quit
+ break()
+ abline(b0hat,b1hat)
+ return(null)
+ 
</code></pre>

I have a sparse data set, one whose number of columns vary in length, in a csv format.  Here is a sample of the file text.
                                                                              
    12223, University
    12227, bridge, Sky
    12828, Sunset
    13801, Ground
    14853, Tranceamerica
    14854, San Francisco
    15595, shibuya, Shrine
    16126, fog, San Francisco
    16520, California, ocean, summer, golden gate, beach, San Francisco

When I use 

    read.csv(&quot;data.txt&quot;, header = F)



R will interpret the data set as having 3 columns because the size is determined from the first 5 rows.  Is there anyway to force r to put the data in more columns? 


How can you read a CSV file in R with different number of columns

Suppose I have a vector `v`, how do I get its reverse, i.e. last element first?

The first thing that comes to me is `v[length(v):1]`, but it returns NA when `v` is `numeric(0)`, while user normally expect sorting nothing returns nothing, not sorting nothing returns the unavailable thing - it does make a big difference in my case.

How to reverse order a vector?

How can I remove empty elements from a list that contain zero length pairlist as
`character(0)`, `integer(0)` etc...

    list2
    # $`hsa:7476`
    # [1] &quot;1&quot;,&quot;2&quot;,&quot;3&quot;
    # 
    # $`hsa:656`
    # character(0)
    #
    # $`hsa:7475`
    # character(0)
    #
    # $`hsa:7472`
    # character(0)

I don&#39;t know how to deal with them. I mean if `NULL` it is much simpler. How can I remove these elements such that just `hsa:7476` remains in the list.

Remove empty elements from list with character(0)

Say I have large datasets in R and I just want to know whether two of them they are the same. I use this often when I&#39;m experimenting different algorithms to achieve the same result. For example, say we have the following datasets:

    df1 &lt;- data.frame(num = 1:5, let = letters[1:5])
    df2 &lt;- df1
    df3 &lt;- data.frame(num = c(1:5, NA), let = letters[1:6])
    df4 &lt;- df3

So this is what I do to compare them:

    table(x == y, useNA = &#39;ifany&#39;)

Which works great when the datasets have no NAs:

    &gt; table(df1 == df2, useNA = &#39;ifany&#39;)
    TRUE 
      10 

But not so much when they have NAs:

    &gt; table(df3 == df4, useNA = &#39;ifany&#39;)
    TRUE &lt;NA&gt; 
      11    1 

In the example, it&#39;s easy to dismiss the `NA` as not a problem since we know that both dataframes *are* equal. The problem is that `NA == &lt;anything&gt;` yields `NA`, so whenever one of the datasets has an `NA`, it doesn&#39;t matter what the other one has on that same position, the result is always going to be `NA`.

So using `table()` to compare datasets doesn&#39;t seem ideal to me. **How can I better check if two data frames are identical?**

P.S.: Note this is not a duplicate of https://stackoverflow.com/questions/12567524/r-comparing-several-datasets, https://stackoverflow.com/questions/7459138/comparing-2-datasets-in-r or https://stackoverflow.com/questions/8872333/compare-datasets-in-r

How to check if two data frames are equal

I&#39;m writing a Shiny app for visualizing insurance benefit plans at my company. Here is what I&#39;d like to happen:

- I&#39;ll have a `selectInput` or `sliderInput` where the user will choose the number of individuals on their medical plan
- A matching number of double sided sliders will appear (one for each member)
- They can then input their estimates for best/worst case medical expenses for each member on their plan
- I have code that will take those estimates and create side by side plots illustrating the forecast cost on the three plan offerings so they can decide which one is least expensive based on their estimates

Here&#39;s my current `ui.R` file with hard coded inputs, simulating a family of four:

    shinyUI(pageWithSidebar(
      
      headerPanel(&quot;Side by side comparison&quot;),
      
      sidebarPanel(
        
        selectInput(inputId = &quot;class&quot;, label = &quot;Choose plan type:&quot;,
                    list(&quot;Employee only&quot; = &quot;emp&quot;, &quot;Employee and spouse&quot; = &quot;emp_spouse&quot;,
                         &quot;Employee and child&quot; = &quot;emp_child&quot;, &quot;Employee and family&quot; = &quot;emp_fam&quot;)),
        
        sliderInput(inputId = &quot;ind1&quot;, label = &quot;Individual 1&quot;,
                    min = 0, max = 20000, value = c(0, 2500), step = 250),
        
        sliderInput(inputId = &quot;ind2&quot;, label = &quot;Individual 2&quot;,
                    min = 0, max = 20000, value = c(0, 2500), step = 250),
        
        sliderInput(inputId = &quot;ind3&quot;, label = &quot;Individual 3&quot;,
                    min = 0, max = 20000, value = c(0, 2500), step = 250),
        
        sliderInput(inputId = &quot;ind4&quot;, label = &quot;Individual 4&quot;,
                    min = 0, max = 20000, value = c(0, 2500), step = 250)
        ),
      
      mainPanel(
        tabsetPanel(  
        tabPanel(&quot;Side by Side&quot;, plotOutput(outputId = &quot;main_plot&quot;, width = &quot;100%&quot;)),
        tabPanel(&quot;Summary&quot;, tableOutput(outputId = &quot;summary&quot;))
      )
    )))


Here&#39;s what it looks like (the transparent end sections are the result of HSA contributions from two of the plans. I thought it was a nice way to show both the premiums and medical expenses while showing the impact of the company HSA contribution. Thus, you&#39;d just compare the length of the solid colors).

![shiny-example][1]

I&#39;ve seen examples [like this](https://gist.github.com/wch/4211337) where the UI input itself is fixed (in this case, one `checkboxGroupInput` exists, but its contents are tailored based on the choice from another UI input), but I&#39;ve not seen examples of tailoring *the number* (or, say, type) of input elements spawned as the result of another UI input&#39;s contents.

Any suggestions on this (is it even possible)?

---

My last resort will be to create, say, 15 input sliders and initialize them to zero. My code will work just fine, but I&#39;d like to clean up the interface by not having to create that many sliders just for the occasional user who has a very large family.

---

**Update based on Kevin Ushay&#39;s answer**

I tried to go the `server.R` route and have this:

    shinyServer(function(input, output) {
      
      output$sliders &lt;- renderUI({
        members &lt;- as.integer(input$members) # default 2
        max_pred &lt;- as.integer(input$max_pred) # default 5000
        lapply(1:members, function(i) {
          sliderInput(inputId = paste0(&quot;ind&quot;, i), label = paste(&quot;Individual&quot;, i),
                      min = 0, max = max_pred, value = c(0, 500), step = 100)
        })
        
      })
    
    })

Immediately afterwards, I try and extract the values out of `input` for each individual&#39;s expenses:

    expenses &lt;- reactive({
        members &lt;- as.numeric(input$members)
        
        mins &lt;- sapply(1:members, function(i) {
          as.numeric(input[[paste0(&quot;ind&quot;, i)]])[1]
        })
        
        maxs &lt;- sapply(1:members, function(i) {
          as.numeric(input[[paste0(&quot;ind&quot;, i)]])[2]
        })
        
        expenses &lt;- as.data.frame(cbind(mins, maxs))
    })

Lastly, I have two functions that create objects to store a data frame for plotting based on the low and high medical expense estimates. They&#39;re called `best_case` and `worst_case` and both need the `expenses` object to work, so I call it as my first line as I learned from [this question](https://stackoverflow.com/questions/17683933/set-global-object-in-shiny)

    best_case &lt;- reactive({
        
        expenses &lt;- expenses()
    
        ...
    
    )}

I got some errors, so I used `browser()` to step through the `expenses` bit and noticed peculiar things like `input$ind1` not seeming to exist from within the `expenses` function.

I also played around with various `print()` statements in it to see what was happening. The most striking is when I do `print(names(input))` as the very first line in the function:

    [1] &quot;class&quot;    &quot;max_pred&quot; &quot;members&quot; 

    [1] &quot;class&quot;    &quot;ind1&quot;     &quot;ind2&quot;     &quot;max_pred&quot; &quot;members&quot; 

I get *two* outputs, which I believe is due to the defining of `expenses` and subsequent calling of it. Strangely... I don&#39;t get a third when `worst_case` uses the exact same `expenses &lt;- expense()` line.

If I do something like `print(expenses)` inside of my `expenses` function, I also get duplicates:

    # the first
      mins maxs
    1   NA   NA
    2   NA   NA

    # the second
      mins maxs
    1    0  500
    2    0  500

Any tips on why my `input` elements for `ind1` and `ind2` wouldn&#39;t show up until `expenses` is called the second time and thus prevent the data frame from being created correctly?

  [1]: http://i.stack.imgur.com/ZRhRS.png

Create dynamic number of input elements with R/Shiny

I want to look at the source code for a function to see how it works. I know I can print a function by typing its name at the prompt:

    &gt; t
    function (x) 
    UseMethod(&quot;t&quot;)
    &lt;bytecode: 0x2332948&gt;
    &lt;environment: namespace:base&gt;

In this case, what does `UseMethod(&quot;t&quot;)` mean?  How do I find the source code that&#39;s actually being used by, for example: `t(1:10)`?

Is there a difference between when I see `UseMethod` and when I see `standardGeneric` and `showMethods`, as with `with`?

    &gt; with
    standardGeneric for &quot;with&quot; defined from package &quot;base&quot;
    
    function (data, expr, ...) 
    standardGeneric(&quot;with&quot;)
    &lt;bytecode: 0x102fb3fc0&gt;
    &lt;environment: 0x102fab988&gt;
    Methods may be defined for arguments: data
    Use  showMethods(&quot;with&quot;)  for currently available ones.

In other cases, I can see that R functions are being called, but I can&#39;t find the source code for those functions.

    &gt; ts.union
    function (..., dframe = FALSE) 
    .cbind.ts(list(...), .makeNamesTs(...), dframe = dframe, union = TRUE)
    &lt;bytecode: 0x36fbf88&gt;
    &lt;environment: namespace:stats&gt;
    &gt; .cbindts
    Error: object &#39;.cbindts&#39; not found
    &gt; .makeNamesTs
    Error: object &#39;.makeNamesTs&#39; not found

How do I find functions like `.cbindts` and `.makeNamesTs`?

In still other cases, there&#39;s a bit of R code, but most of work seems to be done somewhere else.

    &gt; matrix
    function (data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL) 
    {
        if (is.object(data) || !is.atomic(data)) 
            data &lt;- as.vector(data)
        .Internal(matrix(data, nrow, ncol, byrow, dimnames, missing(nrow), 
            missing(ncol)))
    }
    &lt;bytecode: 0x134bd10&gt;
    &lt;environment: namespace:base&gt;
    &gt; .Internal
    function (call)  .Primitive(&quot;.Internal&quot;)
    &gt; .Primitive
    function (name)  .Primitive(&quot;.Primitive&quot;)

How do I find out what the `.Primitive` function does?  Similarly, some functions call `.C`, `.Call`, `.Fortran`, `.External`, or `.Internal`.  How can I find the source code for those?

How can I view the source code for a function?

What is the most effective (ie efficient / appropriate) way to clean up a factor containing multiple levels that need to be collapsed? That is, how to combine two or more factor levels into one.

Here&#39;s an example where the two levels &quot;Yes&quot; and &quot;Y&quot; should be collapsed to &quot;Yes&quot;, and &quot;No&quot; and &quot;N&quot; collapsed to &quot;No&quot;: 
  
    ## Given: 
    x &lt;- c(&quot;Y&quot;, &quot;Y&quot;, &quot;Yes&quot;, &quot;N&quot;, &quot;No&quot;, &quot;H&quot;)   # The &#39;H&#39; should be treated as NA

    ## expectedOutput
    [1] Yes  Yes  Yes  No   No   &lt;NA&gt;
    Levels: Yes No  # &lt;~~ NOTICE ONLY **TWO** LEVELS

----

One option is of course to clean the strings before hand using `sub` and friends. 

Another method, is to allow duplicate label, then drop them

    ## Duplicate levels ==&gt; &quot;Warning: deprecated&quot;
    x.f &lt;- factor(x, levels=c(&quot;Y&quot;, &quot;Yes&quot;, &quot;No&quot;, &quot;N&quot;), labels=c(&quot;Yes&quot;, &quot;Yes&quot;, &quot;No&quot;, &quot;No&quot;))

    ## the above line can be wrapped in either of the next two lines
    factor(x.f)      
    droplevels(x.f) 

However, **is there a more effective way**? 
 
---

While I know that the `levels` and `labels` arguments should be vectors, I experimented with lists and named lists and named vectors to see what happens
Needless to say, none of the following got me any closer to my goal. 

      factor(x, levels=list(c(&quot;Yes&quot;, &quot;Y&quot;), c(&quot;No&quot;, &quot;N&quot;)), labels=c(&quot;Yes&quot;, &quot;No&quot;))
      factor(x, levels=c(&quot;Yes&quot;, &quot;No&quot;), labels=list(c(&quot;Yes&quot;, &quot;Y&quot;), c(&quot;No&quot;, &quot;N&quot;)))

      factor(x, levels=c(&quot;Y&quot;, &quot;Yes&quot;, &quot;No&quot;, &quot;N&quot;), labels=c(Y=&quot;Yes&quot;, Yes=&quot;Yes&quot;, No=&quot;No&quot;, N=&quot;No&quot;))
      factor(x, levels=c(&quot;Y&quot;, &quot;Yes&quot;, &quot;No&quot;, &quot;N&quot;), labels=c(Yes=&quot;Y&quot;, Yes=&quot;Yes&quot;, No=&quot;No&quot;, No=&quot;N&quot;))
      factor(x, levels=c(&quot;Yes&quot;, &quot;No&quot;), labels=c(Y=&quot;Yes&quot;, Yes=&quot;Yes&quot;, No=&quot;No&quot;, N=&quot;No&quot;))


Cleaning up factor levels (collapsing multiple levels/labels)

I am writing codes to export database from R into Excel, I have been trying others codes including:

    write.table(ALBERTA1, &quot;D:/ALBERTA1.txt&quot;, sep=&quot;\t&quot;)
    write.csv(ALBERTA1,&quot;:\ALBERTA1.csv&quot;)
    your_filename_in_R = read.csv(&quot;ALBERTA1.csv&quot;)
    your_filename_in_R = read.csv(&quot;ALBERTA1.csv&quot;)
    write.csv(df, file = &quot;ALBERTA1.csv&quot;)
    your_filename_in_R = read.csv(&quot;ALBERTA1.csv&quot;)
    write.csv(ALBERTA1, &quot;ALBERTA1.csv&quot;)
    write.table(ALBERTA1, &#39;clipboard&#39;, sep=&#39;\t&#39;)
    write.table(ALBERTA1,&quot;ALBERTA1.txt&quot;)
    write.table(as.matrix(ALBERTA2),&quot;ALBERTA2.txt&quot;)
    write.table(as.matrix(vecm.pred$fcst$Alberta_Females[,1]), &quot;vecm.pred$fcst$Alberta_Females[,1].txt&quot;)
    write.table(as.matrix(foo),&quot;foo.txt&quot;)
    write.xlsx(ALBERTA2, &quot;/ALBERTA2.xlsx&quot;)
    write.table(ALBERTA1, &quot;D:/ALBERTA1.txt&quot;, sep=&quot;\t&quot;).
    
Other users of this forum advised me this:
    
    
    write.csv2(ALBERTA1, &quot;ALBERTA1.csv&quot;)
    write.table(kt, &quot;D:/kt.txt&quot;, sep=&quot;\t&quot;, row.names=FALSE)

![enter image description here][1]

You can see on the pictures the outcome I have got from this codes above. But this numbers can&#39;t be used to make any further operations such as addition with other matrices. 

Has someone experienced this kind of problems?

  [1]: http://i.stack.imgur.com/69zFS.jpg

Export data from R to Excel

Someone should have asked this already, but I couldn&#39;t find an answer. Say I have:
  
    x = data.frame(q=1,w=2,e=3, ...and many many columns...)  

what is the most elegant way to rename an arbitrary subset of columns, whose position I don&#39;t necessarily know, into some other arbitrary names?

e.g. Say I want to rename `&quot;q&quot;` and `&quot;e&quot;` into `&quot;A&quot;` and `&quot;B&quot;`, what is the most elegant code to do this?

Obviously, I can do a loop:

    oldnames = c(&quot;q&quot;,&quot;e&quot;)
    newnames = c(&quot;A&quot;,&quot;B&quot;)
    for(i in 1:2) names(x)[names(x) == oldnames[i]] = newnames[i]

But I wonder if there is a better way? Maybe using some of the packages? (`plyr::rename` etc.)


Rename multiple columns by names

I&#39;m trying to transfer my understanding of plyr into dplyr, but I can&#39;t figure out how to group by multiple columns.

    # make data with weird column names that can&#39;t be hard coded
    data = data.frame(
      asihckhdoydkhxiydfgfTgdsx = sample(LETTERS[1:3], 100, replace=TRUE),
      a30mvxigxkghc5cdsvxvyv0ja = sample(LETTERS[1:3], 100, replace=TRUE),
      value = rnorm(100)
    )
    
    # get the columns we want to average within
    columns = names(data)[-3]
    
    # plyr - works
    ddply(data, columns, summarize, value=mean(value))
    
    # dplyr - raises error
    data %.%
      group_by(columns) %.%
      summarise(Value = mean(value))
    #&gt; Error in eval(expr, envir, enclos) : index out of bounds

What am I missing to translate the plyr example into a dplyr-esque syntax?

**Edit 2017**: Dplyr has been updated, so a simpler solution is available. See the currently selected answer.

Content Type	Original Author	Original Content on Stackoverflow
Question	TMin	View Question on Stackoverflow
Solution 1 - R	Konrad Rudolph	View Answer on Stackoverflow
Solution 2 - R	TMin	View Answer on Stackoverflow
Solution 3 - R	Pramodya Abeysinghe	View Answer on Stackoverflow
Solution 4 - R	Amir	View Answer on Stackoverflow

ending "+" prompt in R

R Problem Overview

R Solutions

Solution 1 - R

Solution 2 - R

Solution 3 - R

Solution 4 - R

Why prefer signed over unsigned in C++?

What is decltype and how is it used?

Attributions