title: Different results from dplyr filter starting with identical data
body: <p>When I tried to answer <a href="https://stackoverflow.com/questions/29009261/i-can-not-use-filter-after-mutate-in-dplyr">this question</a>, I came across some very strange behavior. Below I define the same data twice, once just as a <code>data.frame</code> and the second time using <code>mutate</code>. I check that the results are identical. Then I try to do the same filtering operation. For the first data set this works, but for the second (identical) data set it fails.  Can anybody figure out why. </p>
  
  <p>It seems that part of the reason for this difference is the use of <code>ñ</code>. But I don't understand why that is a problem for the second data set, but not for the first. </p>
  
  <pre><code># define the same data twice
  datos1 &lt;- data.frame(año = 2001:2005, gedad = c(letters[1:5]), año2 = 2001:2005)  
  datos2 &lt;- data.frame(año = 2001:2005, gedad = c(letters[1:5])) %&gt;% mutate(año2 = año) 
  # check that they are identical
  identical(datos1, datos2)
  # do same operation
  datos1 %&gt;% filter(año2 &gt;= 2003)
  ## año gedad año2
  ## 1 2003     c 2003
  ## 2 2004     d 2004
  ## 3 2005     e 2005
  datos2 %&gt;% filter(año2 &gt;= 2003)
  ## Error in filter_impl(.data, dots) : object 'año2' not found
  </code></pre>
  
  <p>Note: I don't believe that this is a duplicate of the original question because I ask why this difference occurs and the original post asked how to fix it. </p>
  
  <p>EDIT: Since @Khashaa could not reproduce the error, here is my <code>sessionInfo()</code> output: </p>
  
  <pre><code>sessionInfo()
  ## R version 3.1.2 (2014-10-31)
  ## Platform: x86_64-w64-mingw32/x64 (64-bit)
  ## 
  ## locale:
  ## [1] LC_COLLATE=German_Switzerland.1252  LC_CTYPE=German_Switzerland.1252    LC_MONETARY=German_Switzerland.1252
  ## [4] LC_NUMERIC=C                        LC_TIME=German_Switzerland.1252    
  ## 
  ## attached base packages:
  ## [1] stats     graphics  grDevices utils     datasets  methods   base     
  ## 
  ## other attached packages:
  ## [1] dplyr_0.4.1
  ## 
  ## loaded via a namespace (and not attached):
  ## [1] assertthat_0.1  DBI_0.3.1       lazyeval_0.1.10 magrittr_1.5    parallel_3.1.2  Rcpp_0.11.4     tools_3.1.2  
  </code></pre>