title: Append several tables into one with one different column in R
body: <p>I have a data frame after making several tables I would like to create a data frame that combines all  tables into one data frame in order to export to excel. The only issue is the first variable is different in each table so <code>bind_rows</code> will not work.</p>
  <p>Dummy sample data:</p>
  <pre><code>df1 = data.frame(Id = c(11:16), date = seq(as.Date(&quot;2015-01-01&quot;),as.Date(&quot;2015-01-6&quot;),1))
  df2 = data.frame(HH_size = c(1:6 ), date = seq(as.Date(&quot;2015-01-01&quot;),as.Date(&quot;2015-01-6&quot;),1) )
  </code></pre>
  <p>let's say I made these tables</p>
  <pre><code>df11&lt;- df1 %&gt;%
    dplyr::group_by(date) %&gt;%
    count(Id) %&gt;%
    tidyr::spread(date,n)
  
  df22&lt;- df2 %&gt;%
    dplyr::group_by(date) %&gt;%
    count(HH_size) %&gt;%
    tidyr::spread(date,n)
  
  
  df11
  
           Id `2015-01-01` `2015-01-02` `2015-01-03` `2015-01-04` `2015-01-05` `2015-01-06`
         &lt;int&gt;        &lt;int&gt;        &lt;int&gt;        &lt;int&gt;        &lt;int&gt;        &lt;int&gt;        &lt;int&gt;
  1         11            1           NA           NA           NA           NA           NA
  2         12           NA            1           NA           NA           NA           NA
  3         13           NA           NA            1           NA           NA           NA
  4         14           NA           NA           NA            1           NA           NA
  5         15           NA           NA           NA           NA            1           NA
  6         16           NA           NA           NA           NA           NA            1
  </code></pre>
  <h1>This will not work</h1>
  <pre><code>list &lt;- c(&quot;df11&quot; , &quot;df22&quot;)
  list %&gt;% map_df(bind_rows)
  Error: Argument 1 must have names
  </code></pre>
  <p>here is my desired output:</p>
  <pre><code>   label      cat `2015-01-01` `2015-01-02` `2015-01-03` `2015-01-04` `2015-01-05` `2015-01-06`
  
     Id         11            1           NA           NA           NA           NA           NA
     Id         12           NA            1           NA           NA           NA           NA
     Id         13           NA           NA            1           NA           NA           NA
     Id         14           NA           NA           NA            1           NA           NA
     Id         15           NA           NA           NA           NA            1           NA
     Id         16           NA           NA           NA           NA           NA            1
  HH_size       1            1           NA           NA           NA           NA           NA
  HH_size       2           NA            1           NA           NA           NA           NA
  HH_size       3           NA           NA            1           NA           NA           NA
  HH_size       4           NA           NA           NA            1           NA           NA
  HH_size       5           NA           NA           NA           NA            1           NA
  HH_size       6           NA           NA           NA           NA           NA            1
  </code></pre>