title: Make dataframe or rank adjusted R-squared - R
body: <p>This question is basically an extention of something I asked before: <a href="https://stackoverflow.com/questions/50564111/how-to-only-print-adjusted-r-squared-of-regression-model">How to only print (adjusted) R-squared of regression model?</a></p>
  
  <p>I want to make a linear regression model to predict concentrations with 150 potential predictors. I want to perform a manual stepwise forward procedure. The dataset looks more or less like this: </p>
  
  <pre><code>df = data.frame(
  Site = c("A", "B", "C", "D"),
  Concentration = c(2983, 9848, 2894, 8384),
  Var1 = c(12, 23, 34, 45),
  Var2 = c(23, 34, 45, 56))
  </code></pre>
  
  <p>I use the following code to make a univariate model for every possible predictor and check the adjusted R-squared. </p>
  
  <pre><code>for (j in names(df)){
  model &lt;- lm(Concentration ~ df[[j]], data = df)
  print(j)
  print(summary(model)$adj.r.squared)
  
  [1] "site"
    r.squared adj.r.squared
  1 0.02132635    -0.9573473
  </code></pre>
  
  <p>It is however, a lot of work to check the adjusted R-squared for 150 variables. </p>
  
  <p>Is it possible to either make a dataframe with all adjusted R-squared values and each corresponding variable name? </p>
  
  <p>Or to rank the adjusted R-squared values, so the highest value is first (and corresponding variable name printed with it)?</p>
  
  <p>I am very curious to hear if something like this is possible. It would help me enormously.</p>
  
  <p>Thanks in advance!</p>