title: make a mean of different subset from a dataframe
body: <p>i have a dataframe with a large number of rows and column, that contains differents calculus of efforts  and measures of 30 people doing 6 differents activities.</p>
  
  <p>I would like calculate the mean of each variable for each people and each activity and summarize it in a table...</p>
  
  <p>My solution in my mind is to make two loops and to proceed it, but there is not an other solution, faster, to proceed it... I discovered the packages, dplyr, tidyr, plyr and reshape2 recently, and i think i can use it to find the solution, but i don't find... </p>
  
  <p>Can you help me ? </p>
  
  <pre><code> subject id_activity activity tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z tGravityAcc-mean()-X tGravityAcc-mean()-Y tGravityAcc-mean()-Z tBodyAccJerk-mean()-X tBodyAccJerk-mean()-Y tBodyAccJerk-mean()-Z
  1        1           1  WALKING         0.2885845      -0.020294171       -0.13290514            0.9633961           -0.1408397           0.11537494            0.07799634           0.005000803         -0.0678308080
  2        1           1  WALKING         0.2784188      -0.016410568       -0.12352019            0.9665611           -0.1415513           0.10937881            0.07400671           0.005771104          0.0293766330
  3        1           1  WALKING         0.2796531      -0.019467156       -0.11346169            0.9668781           -0.1420098           0.10188392            0.07363596           0.003104037         -0.0090456308
  4        1           1  WALKING         0.2791739      -0.026200646       -0.12328257            0.9676152           -0.1439765           0.09985014            0.07732061           0.020057642         -0.0098647722
  5        1           1  WALKING         0.2766288      -0.016569655       -0.11536185            0.9682244           -0.1487502           0.09448590            0.07344436           0.019121574          0.0167799790
  6        1           1  WALKING         0.2771988      -0.010097850       -0.10513725            0.9679482           -0.1482100           0.09190972            0.07793244           0.018684046          0.0093444336
  7        1           1  WALKING         0.2794539      -0.019640776       -0.11002215            0.9679295           -0.1442821           0.09314463            0.08217077          -0.017014670         -0.0157981660
  8        1           1  WALKING         0.2774325      -0.030488303       -0.12536043            0.9684915           -0.1467054           0.09170816            0.07236423           0.008747856         -0.0044681354
  9        1           1  WALKING         0.2772934      -0.021750698       -0.12075082            0.9684812           -0.1543740           0.08511826            0.07528437           0.030762704          0.0112119500
  10       1           1  WALKING         0.2805857      -0.009960298       -0.10606516            0.9684180           -0.1563020           0.08087447            0.07636932           0.012518906          0.0030843751
  11       1           1  WALKING         0.2768803      -0.012721805       -0.10343832            0.9692027           -0.1523614           0.08125808            0.07139686           0.016842441          0.0010303821
  12       1           1  WALKING         0.2762282      -0.021441302       -0.10820234            0.9692533           -0.1500638           0.08293121            0.07608451          -0.002311558         -0.0076736296
  13       1           1  WALKING         0.2784570      -0.020414761       -0.11273172            0.9689963           -0.1523621           0.08315080            0.07710200           0.017027167         -0.0009852394
  14       1           1  WALKING         0.2771750      -0.014712802       -0.10675647            0.9690440           -0.1541413           0.08181960            0.07761238           0.019489223          0.0152076830
  15       1           1  WALKING         0.2979457       0.027093908       -0.06166812            0.9448949           -0.2926233          -0.02143552            0.06665616          -0.068367084         -0.0336076010
  </code></pre>
  
  <p>there are 10 299 rows and 56 columns, i didn't put you all column, just a subset to see how it seems like... Sorry for my english ^^</p>