title: Checking the presence of values in multiple datasets
body: <p>I have a number of tables and all the "a" columns of the tables must have identical values for the analysis I am conducting. The actual tables are very big so I will use simplified (mock) data frames.</p>
  
  <p>Let's say I have the following data:</p>
  
  <pre><code>A &lt;- data.frame(a = c(3,4,5,6,7,8), b = c(4,5,6,7,8,9), c = c(5,6,7,8,9,10))
  
  B &lt;- data.frame(a = c(2,3,4,5,6,7), b = c(4,5,6,7,8,9), c = c(5,6,7,8,9,10))
  
  C &lt;- data.frame(a = c(1,2,3,4,5,6), b = c(4,5,6,7,8,9), c = c(5,6,7,8,9,10))
  
  D &lt;- data.frame(a = c(4,5,6,7,8,9), b = c(4,5,6,7,8,9), c = c(5,6,7,8,9,10))
  </code></pre>
  
  <p>Now, each data frame has unidentical values in column "a"s. My goal is to delete the entire rows that contain different values as compared to all the other tables.</p>
  
  <p>In order to have identical values in column "a" for all tables A, B and C, I could use the following operations:</p>
  
  <pre><code>A &lt;- A[A$a %in% B$a,]
  B &lt;- B[B$a %in% A$a,]
  C &lt;- C[C$a %in% B$a,]
  B &lt;- B[B$a %in% C$a,]
  A &lt;- A[A$a %in% C$a,]
  </code></pre>
  
  <p>This is already getting very tedious as you can see. What if I throw the table D or other data frames in this mix. It's becoming almost impossible to proceed, as each table contain at least one unique value.</p>