title: How to recursively join nested lists that share one column name in R?
body: <p>I have a large nested data set with the following structure:</p>
  <pre><code>&gt; str(Normalized_All)
  List of 48
   $ Traces/Sample10_1_D.csv:'data.frame':    2988 obs. of  2 variables:
    ..$ Time_min    : num [1:2988] 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 ...
    ..$ Sample10_1_D: num [1:2988] 0 0 0 0 0 0 0 0 0 0 ...
   $ Traces/Sample10_1_L.csv:'data.frame':    2965 obs. of  2 variables:
    ..$ Time_min    : num [1:2965] 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 ...
    ..$ Sample10_1_L: num [1:2965] 0 0 0 0 0 0 0 0 0 0 ...
   $ Traces/Sample10_1_R.csv:'data.frame':    2962 obs. of  2 variables:
    ..$ Time_min    : num [1:2962] 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 ...
    ..$ Sample10_1_R: num [1:2962] 0 0 0 0 0 0 0 0 0 0 ...
  </code></pre>
  <p>I want to essentially full_join all the data into one tibble, but essentially by recursively using dplyr::full_join(x,y, by=&quot;Time_min&quot;). I want to use full_join because not every Time_min column is the same length, but there are many overlapping time points. Each 'Sample' Column has a unique column name. I essentially want to minimize rows with many NAs.</p>
  <p>Is there an elegant way to do this? Preferably using dplyr or related tidyverse packages.</p>