title: Average number of riders for all the days of a week using dplyr
body: <p>this is how my data looks.</p>
  <pre><code># A tibble: 4,722,462 x 5
     started_at          member_casual weekday   ride_length month 
     &lt;dttm&gt;              &lt;chr&gt;         &lt;fct&gt;           &lt;dbl&gt; &lt;fct&gt; 
   1 2020-08-20 18:08:14 member        Thursday        0.160 August
   2 2020-08-27 18:46:04 casual        Thursday        1.15  August
   3 2020-08-26 19:44:14 casual        Wednesday       2.15  August
   4 2020-08-27 12:05:41 casual        Thursday        0.801 August
   5 2020-08-27 16:49:02 casual        Thursday        0.180 August
   6 2020-08-27 17:26:23 casual        Thursday        0.691 August
   7 2020-08-26 20:14:02 casual        Wednesday       0.333 August
   8 2020-08-26 21:59:50 casual        Wednesday       0.212 August
   9 2020-08-26 19:17:42 casual        Wednesday       0.242 August
  10 2020-08-27 15:13:57 casual        Thursday        0.467 August
  # ... with 4,722,452 more rows
  </code></pre>
  <p>I want to group by 'weekday' and 'member_casual' and then summarize to get the average number of riders for each day of the week i.e., for the 'Monday' and 'casual' row: (the number of times Monday &amp; Casual appears in the data)/(the actual number of Mondays in the given timeframe). This is the closest I got to.</p>
  <pre><code>#finding the total number of weeks in the given time frame.
  weeks_ &lt;-as.numeric(difftime(max(df2$started_at),min(df2$started_at),units=&quot;weeks&quot;))
  #assuming there are only complete weeks
  df2 %&gt;% group_by(weekday,member_casual)%&gt;% summarise(&quot;Average Riders&quot;=(n()/weeks_))
  </code></pre>
  <p>This output is not exact but accurate enough since the timeframe is very large.</p>
  <pre><code>weekday   member_casual `Average Riders`
     &lt;fct&gt;     &lt;chr&gt;                    &lt;dbl&gt;
   1 Monday    casual                   4404.
   2 Monday    member                   6688.
   3 Tuesday   casual                   4279.
   4 Tuesday   member                   7289.
   5 Wednesday casual                   4434.
   6 Wednesday member                   7648.
   7 Thursday  casual                   4447.
   8 Thursday  member                   7285.
   9 Friday    casual                   5807.
  10 Friday    member                   7452.
  11 Saturday  casual                   9366.
  12 Saturday  member                   7612.
  13 Sunday    casual                   7527.
  14 Sunday    member                   6331.
  </code></pre>