title: Crash logs for R/Rstudio/Rstudio-server after suspected memory issue
body: <p>I have a long running process in R (via Rstudio-server), which I suspect may have a memory problem, eventuating in the R session crashing. Unfortunately I am not around to monitor exactly what is going on: do crash logs exist and where do I find them if they do?</p>
  
  <p>My setup is as follows:</p>
  
  <ul>
  <li>Rstudio-server installed in ubuntu 12.04, on a vmmare player virtual machine.</li>
  <li>I access the r session from firefox, on a windows 7 installation.</li>
  <li><p>I leave the program running overnight, and come back to find the following error messagein the rstudio interface:</p>
  
  <p>The previous R session was abnormally terminated due to an unexpected crash.
  You may have lost workspace data as a result of this crash.</p></li>
  </ul>
  
  <p>It appears that the following code is causing the problem (not a reproducible sample). The code takes a list of regression formulas (about 250k), a data frame of 1500 rows by 70 columns, and also allows you to specify the number of cores to be used in the calculation:</p>
  
  <pre><code>get_models_rsquared = function(combination_formula,df,cores = 1){
    if (cores == "ALL"){cores &lt;- detectCores()}
    require(parallel) #if parallel processing is not required, mclapply should be changed to lapply.
  
    #using mclapply to calculate linear models in parallel,
    #storing adjusted r-squared and number of missing rows in a list
    combination_fitted_models_rsq = mclapply(seq_along(combination_formula), function(i) 
      list(summary(lm(data = df, combination_formula[[i]]))$adj.r.squared,
           length(summary(lm(data = df, combination_formula[[i]]))$na.action)), mc.cores = cores  )
  
    #now store the adjusted r-squared and missing rows of data
    temp_1 = lapply(seq_along(combination_fitted_models_rsq), function(i) 
      combination_fitted_models_rsq[[i]][[1]])
    temp_1 = as.numeric(temp_1)
  
    temp_2 = lapply(seq_along(combination_fitted_models_rsq), function(i) 
      combination_fitted_models_rsq[[i]][[2]])
    temp_2 = as.numeric(temp_2)
  
    #this is the problematic line
    temp_3 =  lapply(seq_along(combination_formula), function(i) {
      length(attributes(terms.formula(combination_formula[[i]]))$term.labels)
    }#tells you number of predictors in each formula used for linear regression
     )#end lapply
    result = data.frame(temp_1,temp_2,temp_3)
    names(result) = c("rsquared","length.na","number_of_terms")
    return(result)
  }
  </code></pre>
  
  <p>The calculation of <code>temp_3</code> seems to give the problems when the function is called. However, it all works properly if you take the code for <code>temp_3</code> out of the function and calculate it <strong>after</strong> running the function.</p>