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Plotting-data-with-ggplot2.html
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Plotting-data-with-ggplot2.html
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<meta name="author" content="Hefin Rhys" />
<meta name="date" content="2021-07-01" />
<title>R for cytometry course</title>
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o/jNPY4W82Os88wiJ34K4tdAIQjAOQkx8YArcM2PaAOjSZBL8uolzAJFFvGDXd8ej67P2AvKpUkOYghcnK7zl300RBcsExwzJ/hbrd7GuYBwhgAIYtbTx/3+d4klJ3gtKCQnGIz9InYZEzqG8EkjSzNavCB/cXYlcQshhyMsZrI6PYLWc3lOG/vlA4rHr/3uTFD3r38/r+3fMKOke9W4oJ9G566u7au84CpOz/ct5R99wF7W6dIYjjnawrHIAh3hlungFOWgXoyzVKbHOr1eD19Il6vISsrrU8kSzbY+0QMGpdjgYh60zDTHJKHoyP4404pw27zB4o1o62gq+BLL299am8j+zv774zj995/dgTOZsOfWr3rnTWPj2h8qGbo1/M//kYYvmxfms7TtPrM54E7ns4vwBw0rFy/aNJjRRVTet31OgCBPABhongUDOCAzuE0h6gnxChToCJ1ulB0iH0jeqvscFBZotflk+hMQ5oJDqhrC/l//FxmAUlGYeK5Z6Jl5MDec2yJQdc+l5ViNduL1avoZ805eGll04jy6COKheT8S+U6kQwdw+lW6nPpXF4qtEoBziwAye3mMnRLkqlPRLqZdQlsKxTcLghkqhzjrLL5M+WgUwldSkjbL1HPLrCf51d8MHbv66zu/mcGl5Kz0YNZ0+mcf759kbEB29qGGrZiYWop2b2R9fYqnKnlWOVzqXqgNfQIB5LtRr8fQLLT7CyT0ZLaL2K0WFzU5e0TcfmojkckcgvcyhJ4pNlr8Bd63VyEhIbiGhfIBFGTq8R9lqcWB2Dl1G79Rn/9i8n08OU3L/760UX2E369YuvqVUPrI9VryFR8CXc5V/rYefbW7svv/YNdxUHv/OnFVQ1V8yse2Dde0UcAIY/zU4L0sA1FEQg3jJT0jVAJFBlqbOOrALk1dCOmkuHNF+mpaKOYunHhldNAlZhEyFGpz4R20C+c47Vmu+6gqXo9lewuq5TfXrLnZORk9Ink5JjAlNwvYvJBoF8E5N8qd9nN3jrmj7mOx8OPLDXqolpgwv0zZkpuzaeTynf+vWjNvnr22b+bsfDJR7+e+cL6dQ1bXlu3CDvOWfHIMytnrhJPHt7x4L7eg/48+8C5U0euLuu/f8ozr1xteHTRssdGru8V3kwfeHTMsN937/zksLEzFdlO5NQpNsMLWdAtnJlizzQYAAQu26AljUvWZbEQlyuJi1Ymcr8Iaal2jjKNg5qJ9Ctqx02jMyDFKHJw8TpUIvjHKhXZQlZ0/Iwe1eO++6/RVHpg2mv/uPbBuguPMtfKLU+tuXfjkIFraEVzg2tlMuZg6O57/vXBP1C3kZ3H9od2PPV81RMVE/aNAy3HEcaokRS34Ta+LAA8XotzQMRiizkRDVfN87X0JXae6NzkVR6Znehb6J8XL+Y3IKovXMjn0oEDMrkmmc2iXu9yGm0DIkab6hgTZklwj/T6FDccpXsmn6Rjlxv+knyrTFMR8+U/cF9+DiRwh/UCiChwdeXD58cDhSwsRjeikNNcTo83/0AtP2DDKLywji1nhxSezMTjgo9eVHOy3LBbJgIQ0OsEsToiIFRHrIjI4wHOlfxEz6a4ZOTXTLq9eTjdTofW1bEH6up+g5GIBDhGEr2BkRNVlMZTa/P3HKVyrMMKrF3H/KPYUAWjlGsXaRnXrxTIhrJwqp/bMtnphFYWIdgGoLWtddqASGuPzdA7YhNaqFZLvVJSEa48LZwUd4YSN4mJ+aq/ctSSXgtmD6gf2emV91/9KNj38bHd9l3PX0tq19dMnzFw3OSsgsWjj+zqPXn0w4On3e9nZ+NJLYFZ1yqkQ2ITFEM5zzwyA+1KLJ1kVwpAjsvSTgx3S+rQQeiisxv5Ky+9kGbnqUmllmSFEhOP6/G4ug6C2nJQUPdSt0td36R1IFMgbsUalrqlQAbw4KK1v1BwIH/udKqm8NCQbeMHP2LUtVk3rv7Fb4712N3Tt/DeaWvZt3+8wA7swe6Y/5cvjv3I1rHJn+AyhLM44ODVn14/7bBUDpq/hpxb8c388XfdM+rU3veu+Tws17Pv7O79aFvzMnvxc3aaHRq8sAZX4jgUsP7CfvYntoNhGYquJiAAAKJNPAIyWLjk0ojFqENR0SwqyILNaiG9I0bRYhFECoKD518xh6iplZYz+5W8H0OIlBsz/tURB6IHmnaT7itJORvb6A94cnbjGZYvHrnSg0zENwfPGTGddQIKJwCEo9xyW8ALGdA7nO0UUg1Wn89iEGQLjwd01iRrUlXEarWAxVcVsTjAWxUBevt4QnM9/gxBMbluwe4SAjxpj/mcgN0ef3cCt2IAhVVLsR/7+TIjjZjU9PTeY1ew4I9/Ovhn8cCeI/Nf9BnK2Pk3/kZ7TF00+6HoquhndauXPAGAMIdb09Oqr8gOu6jFpbdQb5IDekccglHi/HK2DL+4emRymUNIE3+Ro3WokKfbtNP37Cs0/7rxjQ0X2Cvs2Rex/NNLuysbxBB7lX3FPmdvl64rwyU44QusOVSzuj8AUTgmDuEc04FdsYcWQQ8COJyiuSoiUsFSFREct4ppwc9rSBlA+ZuAPZTBx2Az2Uo2CY/hIHysic/1z59PI/dU5CtWz+aJB9gi9gKmYebVKZgHgMq89Bc+r1GJWSSDAQXQoWAyS/reEUlCQsTeEUKRr3B03DZmUZBwxy/6S/MZmh+dTYZHt5OF4oH1LKc+eilhJj0UhpMlAKQ6pAbjTRPxSW45Q0CbAac3asPzwaNfrY9LTuyi2ilOhUvnI8SSohNapUJK7wiAaDLZe0dMgujtHRGdt4+8/HaphRyV9+rq5lT1xe9nfPc0a2IrDuKQL//9bve3DrL/so/Qj0kbVrGXCYuWZWXjUhzzD7xn/+D6GvYau8Q+Ze8H8LUY7WK6yuVQ2KdHBJ0giCCaTTraO6LTiQaJoshJV81RgnG/Qbydi5f/DYnpjc2ssZGSRrI3Ws1z7dXkYQC8NoLNxfFqVpwaNht1OotVT4GzFDJj9GrpGI15+JJiPpxLMg0v6dVv9AONx9jclFWuR6fyFGvI0TNxvRC+UjHmnkjBViRGg4Ix0Yn6RGzLWkgJZRVRDKHw1TvRrzc2NpL1J6JN5M0l0dc5snnk4+jCBF0QIT1soQCCJCMFzgtw3EBXxTekkO0+0aio0pV/bIp9V+KIgpPrUZJOFCUev/JSmsuNBjuVjDK1gKQgp2DnLbuZlRjwuJUAn2MY4nce4COtZjadZSsCntbhh6zRomMm0bbpo+bh4oGrVQLPOume7Uev/BCXo1IDsUG7sFsvcaytVpDB7jBS2aqjKCdypaUI4xPzabNJKZdj+WvNn+tsW4/RVB2xkGeEk582NR/nE3ZMwaxy2guAqFp99FZ5bu+IXqDW3hHqvLVNiOltBiTmueJRtpW9oZgjHIE9sBOOujo9+v1/fvn5h/9Eeb77LHuYa+94HIt1bArbxs6yU1iIuRjEAnYqZp+E8erqdUBRONnA+c75DE6XQaiKGAySLDuqIjKVEtavhpXmSgW/mlplYChutYXx7Ay7tLsRZ5PWUePGL949euKoYPr7t1HOh2jK6mdXrVC5wHaoXLBCCp+Zp8MeAIEa+OqmZtns6x0xC7KTL2yZM+MtlRs3J6I2pViG8q258sX7OOxndrH0tpz5ki3rzuqxivyf/DnN+WMCN1SGs8yIxKS3y0aDQdYTwePVm8EMVRGzmVDK5UepkSi6cntnp2Ku8ktw20SOf5bGNm4BcRXyGdhfcfkJ9jQ7/VXTzl2vfEZGRLeJB94/zf4+LjqZjFi9cuWqJwDVHIFw29ha4V6a0wSQ5BSFrGxTGvV4uH30CFSfoEoJiY4mt0CGlozy8D+o5jgx+6jmBbwy4BEI+9d3rHnZ0I/GN+7usnL1ey+xM389WLx/1+INHRbWXfoDLjz+6Z07su+YN73vyIFFvd959sV3qtf2nfFA35F3FQw8AoDgABCGcv7JvJ7iABSRUp1epgK3CYLmFeJ5qGYSi7k3IEsbWYFQyQrE9PWqJzjM14yPj2OHrLDdhgYZZafDrqOCmQ8UpzGUuFzsLkUnVHMYs4uij/2F/cJfFxrfee3ld8QDzf2vsC8wo5nuaa44+Mabh+ghQAAA4XW1/pMcNqJgMuooCJQqiPLlrxWvQhjgF8//SgXTwej3O6M/NmF1x8zWHdVaFh/5uU3bnwXkmg1yXz6aT6km+QwpyW6LRdQn2Q0U9TGTotqUGOKqNclWAjJldKcyenwSZ0h8cyc75y5CT3v2xU42u+nL9p6UYpSa0Nne7yy+1EQ/7PaW6/dbm0N88llHNx18ic5qnrv59RXv0YUK93QAQr1q9QNhhyCJ3ORLiskXFJMvtDT5KhocAz63Yu7rj/PIY0oTXmKdjuAkfHg/60QWROeQZnI4+gq5M9oX4lybrUY5GWGrIBJRpnoDiChTUeOcJmE+qKL+GCJdcNEhlrSb+Q6T8+R887zoCZJPFyv1ZQBBscZ6pWKmQyqDLKBgMIoCNwcUdUrMcuuKmVot8AvlzU6qi9roq82/0LSFwoaNC69OAIQGdoRMVnSRY2mRUFAYoxcJlTDIOdBSfeJRD5nMSvEEu4B+dkS6svyKX6HWC0A+i1c2Kd5c2XRy3h0mgYbo/4spg/KNEDuCzdrMFFACSacHOUgFevPMXj5rMb9CfMoLfOrSA+KF5b9KyigFJCgExOMgQVJYD1TWiQQEwrO+G5rpVFUTC3DfaPxsA1vG9pEg3dQ8jnwV9QJea2Zv0k3XKtUKsJLHIlEqwBgjmU/LQUfRp9mbCwCxTjhHHZIf9OA8AILRID2BkJ+s1ZoxwDW1OMStBHU83G1fm5MZ0+4QzhUdK3f33F8MRKk50lPCUEXzoVc4K1NnTEvz+Rw6yqMpYkzrFSFGI7jd1ooIt4LJFRHRA24o/98LVH4tX7NllapJZ7zS6LZn8QVeLKsVKjrQrxv43GPPvUychyc/VveH0F3HR77xCrNs/mPDWy89tOWB3js3Y1+b1GPe7Jq5dxTuORZ11TZuHC3LD00fOhwI7OVWtVZygRPSeVUt0+D1Wq2mVGqiGX4zmNwOu8HOhccRljzgqoiArYV5DSXF1SDB1sddEk825YBijeRQiVcrvHAqyJ5Pv/3+k0l/7GwKzGzQ6Wa811i/qXFjfb0wlJ1jP/DXxwMGLpdcbNHcsTuWvv7ll29fOPPJXwAQpnMOLxWGxbIaK6VuPU3ySmaOmQ0cHDPPzVmNGM9qlJ1DHgNzu6hmOGTcZXYV9f8d8HTbUOn8QrbvuW11Tz3swiw0oRPvyPQu96Sywe9+2mlNGRBlVqGU88fB+dM97E+VvGCx2CV7ht/htgIgmqhez9mjt1FnRYR6bscerSYTkLTqvTcUDPLPA6osi+JOiG7ST//n2W+/++TCTLMsNCxmTzdu3Ny4evOmNS9gNlr5647tA/rh0V+/mfny+4Gv3r54+i+fxLF0cN44IRk6hdOTDF4jpdzqtkrxGit4uRskyaUyyqIw6paZQyiRZQ632++JsUuivNbh53Kb+x/2JYp/e/+7qFl8eecf/zBk65bfb7WQLstc2AZl1GMH9v3fJxx/p2pttp/+c/eGrS8oUksFoBYpHVxK3cVlMjkJ4UaSuj0GvhQMgKIsVkScspUqq0GtY98IAxWmOZS1p2QNgeJSXkPW3DX3mE+zrxreeANH3lObN6LH8KHopW83l9G3+3TugmsDC9PnPNkLgEKQuYQCzplcKIVu8HC4a56vQ5YpvYtY4ESnSHIzW6Vn+Qzd72xlLbYWV0R0nXpFDJm6XKvOqvPk5pJekVxrm/JekTY2T7teEU9KnHUa+zj/8pXd+rzbxD1uragaVBdAqDC+jaAUkrJv/OXKcGMXmJOnbhQXF/F3QsHJVnf87VhB3sSqoa/te5X9jf3r7FdPzMgtC/ccNOnTtwb3ZPb6ZWdOPLzh7amPD50/4z8/1T4uVE5ICkzt9ewxXYdBbfPqVx54ddvqMauTndXFnYfmBnY+2PS66ypEhs2ZFOn5IO08/ZFvfn4cEPYCCD24nnuUzM5i0nFz7dF7vEkWvcMhVEQcNgOA3q0Y7xjlCatesVT2mALbtRUfM1P06cfm/+GZhgadoWD/jBMnyJuLfn/kk+jrfHXnDOow4N5XP4gWAxDYDoDjxAtAwcr9tZ3PJCDa7Ga5MmImVlQ04/3EwqZSIqAJJVQc3NDQ1CG3TceObXI7CJWYU1Zc0qFDaSkAubaKudSxTZAEd4Q9TqPRrNP5kj22yognrLcC1z6ISzW5xSTOhATTljhb3v2det7Zv/eNGZnLt9g16B6h+aqNHZHv0yaP8TSV89QGJTzetxgMRqNOEkSdYHeYAGw2nY7KRje1xiKGfD5zeUyFyuJsRTUiQi0bdclYkzcER73JeuD5E2zOnB07dKSgy2icydpGlxLpQTZOcjW/XTo9NjcO5nNT4GQCoiASQHfca2tMVBjHYVRo6SRfJQGoCAfcdruDiz+gdwRo66xWHrfb4RPMPm5p0302p1UPDkUPuCLEt534Igi1bHVIVIgEzfAqepHh1bRDypryyOa1DVNmblnVsDhFl79rIuIAXcHhmYdfJicWLNj3cnSLcv/zx9HjQmV99dDDg8e8+heuMZq2cnxdUBBOApeiri69x23S22xcWW02g/V2ytpSV72Jmrp7m4JG6NDUt95RNPXwJ+q8d0XUSWM2dhSfU9EknsU6wSyDnOwzeLgds1GbYvxvmcVylSHFilGFxE4PYRT74fKaf/wOTZcvobX5lZ3PPffii88/10Cy2I/swyeR/AFNmMfeZ1f/8rfzH545p1j5vdyW1apU+6E8nOEzCrKsS3foHJkBwQhWq7siYrXprboUaHXDzMdZ0GLBqpaeO2hPAhMUr62Y+gRHrThpU8Niry7c+PBf/+f7yzvryabGFc8+6xowcMRg1kUqqh9azT5h/1GcNr14+GTWl29fevfUeYVXHNNSlVexqMKW6qHJyT6bL8OfnOK1pqalecxOp8wtv80MFRHz/+Y2VT5yJ1l63Ul6r3vQ0njtQyL9GzaIW15cvXnjnI8uf/fJ57P0SQsajObpM/d9mHXp3YunT59birloRDO2a6z/9T38eEzFCzE9okGOpw1ywy6zXm8wEF4DsZrB4FYtg03rc2nRkaE5IY15ZEfvjt4eRQtfaahz6rrsFoaZNlk/fTbaJFSenDQjlrnS6XyW1twOtIplrqLzeuZaEfHYJKq/rj/5t8pdueG5kbsG25Hfpq50+j/e/+tjA/bXzF82+dmN88r/evSPL3Z6ftEjj7Yds+J13jSzsaHnpjbt7h4Uvrdr2aAH+yzaXLm4R1W3O7p2KO71FCCkX/uG7BQrwKPWJlwu3jPioEKS1+C0OXtFLGGbVeaCkj1xU3kqIVjV5ONWqo52xVGXhtxKNuHyEMcdA5NSJuSy17ZurRiBXdlrw2vN8lyzHQeQZdU9/83mRWePngiAsIOvrjKhElx8fh86ZZPJ4DS4PSaz2aZzWdVV7TFqEbMS/4daVmW0rJcrhBY127EvX9TPNNQl6UP7Z7zztlAZLeMO6GMSvnpozV2Dj54hp7RcjgiVau+HAQ0ms6hHK6jhiJZl+NX0NFTicIYQt7ER+76ptuiMte/tYyP4oI/8o0cx9iPtrx6K5UpSgI/Winsblz4lNc3rsZipYBZ0yQ7ubnTuxCyYK7c2A1U2Z2Rlk8LhUHSq1BmbsoRPKeSfcBbp2qSdPsY+3jNxsk5nLHCcaHqjg0snBF7dzc6QBZ3OvHR/dK5QyUaz6j5l+4tJbXTp7trW9eRvHClACAIIOpXGzLBdFiVAUWlxQZ3RLaD1pnQ4ngmjmhUfYgteQT9m/JktwFVH2Cn27hFSQLxsGO6IfhU9jUdYD0AgfL1LfHw3z/sVMqnHK5jB7OBLO0UHfIJCVam1GRJo46KKOdrSUrLvuwFOnfnuS/tYTsWfl/StKu2xq3cXzuCVn9wf+pn87mrGy5vtC03HtkAsZ6YPCZW3yJl7RUQr6npF0P2/5cz0oeZ/ksHR0+TL6D5y31Q6eN685sPxrixetlPl5/YlJxu9AFbZRbmnpqlpTq09K3F7TdV/bpXcPJZTfEtxCddDvj7d3EK4ZLfHjedrpx794PFH58/49MClCxdM44aRZaRxE+aPjywnw0Zg4ebdS6Xj7NzZoCl4FhAvMxuZrfluorSo0RSABN+tlHzx8nKeJv3cDAiV7Ijaw5Oq4OwWDQ4H8UFqqsXiE2laujso0QScEzYFFXSDxYr7U7DPVNCV5Dj2pcRw4eKhDx+Z/9jjp45OnvHwVFIePIvB49LSPRvZ+yPvJcsjvOq5cRenZNg4zJn2qEvdpyXVQg6tAS/XAzu1JvkcpuoIdVglCaojEuTngS3pjfw38rSkOlOZT8nQVNOmbD9lKoU5HFg8t2TMUz2mRrqPyi95omTcisrHK/sMJSfuLFn/UKvsVinhsvqH/RkZSeoOPFuKdcJwrcuYCALV8343AGpSu4xtNPOWXcZcCQNO1/Xt0PNKk/Gszp3Ly0IVZPfVC2Lfxb3C5ZVhQDjK7fd5dVemazjNozNTahCARxo62irVJxKnwUz4SzDKgg+07k9ljt9sw2apra1KOJCldLR6NAOuqD89OWHNwpPHcdniPisKChY+tHv7My8sX/FdifTO+xlov4LNXXfvoH7vstCH5z462QkQypUYSDzBpV4Zzk5y6s3mZI+dGD1OMS3dlORL6h/R+3xOcNr6RpxJIPa5uRWkRdPQzZ6Nm29lf5Lfinl2ypuduEqQxqONXTatnD0HG9jQblU05erVU2+99f/EEzUL+/1uGTs397MxS+7YtDz/xwtzsfO+U4psZqMkeIVtnHNByAibW0GmBSxtctLd7iwZeNSYn1gJchaVBku9il8r9co82Ja9clCxDnKwNLs0IXQ6VLV4+OLx8+eOq7t/UVXVgmF14+YuGrN42MKqeVtnzHh627QZW8mHj01aNmxh794Lhz059ZEFD/CHvfj7JZN+N2XbM1Onbd8BiscDEJT9Fw8MDrdzWGSj0WYS9URPTS6LW/YmGSwW2So5HBScbqsz3UmsTqvThG7JlATlWg+33RHrzL7lpjuGUOGj1uaovjBEKnH2HjYCJfY6dmGv72BvYGd+ARu7j1wgZ5vZ3Ma57Ec08RslQBKsgaxUVYkkUR726QUqUDlmFjgmiYqtbgjFLYRiI5p/YebmnxVpXPuF1kupUABdeGdcdiE4pdy0Dj5fmkmCgNS13E07lbRqK/n1/mCviN+tt/WK6OGGznh/s4t9I39VVFmLztSUlwuwZdCiRC2l/Kk33lG0dHD/qprTbw5/ZmTxqMV9Z8yYvelw/cCqjf/+6K9P9H9t4KLl7R+cvmJR99W/f6Ggbs3LPQbRnMF1WW0mD5q1NDW4IJjSKdy5prTH+klDl+fctXrZxm5rs9r27dWuY8e8oqHTRvWb0MVZPfnuKWXOMUCwWLTQ8eKH6u5TWpiTanKAI8lnpW495N90QCAhzctKeI/FxVnZpaXZWcU4pzgrq7Q0K6tYnFrUrl1RYUFBYfwOQGEM7xzvEdt5hxKeSwWDXmrNT0936a1esbSDZAKH1ZRuIuCwOYjJYXKk5AWcoRQByhNPBdhblgFRMxHuG90bnN2obu8KDjc3eYHM1py5DiFU2NqhNXTQOXMWz10weE77sRWvffDZq0880vHB5vXv4PB3les1tv2D02z76xP2YNvdezD3pT3s7N497JOXhMCeTTu3t/2dq9X3n575qfMjIXZI/Q7b/u6brOGD0zj0rT+wD/+wB3P2xr8GQKCCushU8W1OdzqUhlt5pRQDokeJazP8rQwGh88D1EYJNTvSOakf3feGku9qVGpqG4xTV8ojfbXWGSt18iYUtdZJXEnDlt0/edPztWvHjM+btnB+HauecmLUlAeov2bk6HHjJkhCcGFoRIcJs1jnI2OaCgRBqd8NhFraSI+CBGbICTupxI21YNTrBbMkWKwmUYegHGS5WbPRiyhjVuw2EAfPVEriM1kjLsUhtexzTK9lO0kQ1/dk29mzvXB9yo23qh9EHfeDXhAhJWwiKKAki0J1RCSQr20nattixUJOXfM71Bv9Hhc+CdeuaV3LRAIbAAjXdUoX16r7wqGgF3iOLui5Zpn1JodXKu1gsnFoi9Pi0DmtjnQHAR63E4fT4bythikCCP22ZKVVoUS+hp0Bqm51Fnr+L2UjHz5YPXLwfRNx36B+l3eeXrwWxYbNVy/8n+pGrtwd7tNtSfXsNFaLo9jTdPZ89ub/pXB47YrkEiRpzW3r+oJ09UfBJLnmAoG5dBi5LJ5U83Z/2GIGp7L7nGwzHPNQhS3J7yWaAKe27LkytvA6c/fPn39g4Oqa+fun195VPX3qwLunC2vmH9i/oGZlTdOCgdOm3l0zdZoiv/GASic8yQYLAMhwBiA6Q93NqCLLub9OUmpcstOLaHGCwAsItnQvZqjyadHEUVx6cz+0JMt+sjy645vIQH91edGont0XbPj9msiaPXiIVI2/NHhk35IePbMLh0yeP6V6/ZPPA4KflKlzBqAsnGkVRaCONIPUOstxn/MhJ+nrRKMzxUmcTl2yP92s88eVhKvIfTe2KDHRmKtlyd/2PpPpA3vsPbRzw4w1sz/8snbmA6Or7+w+pUPP8mXDl2wVvqx+wJu//YmVHWb32L5q0oAeXXrkBYa2LZl5056LnkfvwhP6xD0X5YAIN3pyAOvaT85494494cnCD133dnN3O1oEqNZDegiV4IHicLJoMOhs4HS6dC6+LeC2ulLMRKks6LWkMWHX6XqfaELKyMnTOhsGs13PNCxJNkz+Z/0Qg6GhAeewK698pKaNLwyr2caOScrsU1mzMEJygRWCYYcgIoBopDa7TidSq4jaQa/8RJkG7MortqVTEvILI6Z9PL1rzacn//ov0pY1S3t/raYhx5WrKDBA2ED6Yh0dqvitsEECMJuofkCEQsyAJOqq2jzatUOseZR82L1nz+7xMwlZzIVNAOBQIge7xQhgUfrILXa7jtog/71CzQq3qDNoZYbSkOzBpo31obZtOw24a8BDQx4ubWIXRk7UT9S1Kckrtu+bHgSEvqQKP1d3kPleHwFKDSZuX2mGBGlK3sc5EGO7FpnEzw8MXLlQ8pQsvpNv4K4ld9471NP2/hFAoDt1kaPi26q3zgo7lONnEnBvHfMfbr3iP964r4XTTjgzJSYsWHJ0V/3qF3eu3/B8lN07fsKwYRMeGCZM3nHw8LPP7T+w/TH+b/YjjwCBau4hdsY9BF+ZRr1AgMrEoJdu5R/4fBhELEUxdqM72c5aTGef1+IQVnvjPTGxCb3wfhzek01IufGW24c+AOIZzq8gnCYLACAbHrsGKMNHNDV6EPR/osTBA8ziYuCw7Tjs+ThseQz2CwV2Ou3PYeV9xMZBVchkAMkvnuAQM34FFf4CxEZ9KD5qXmxUIBBiM2mNMBxSoY3Sba1zpQWwlbVVwCXk5EIqmmhqKj93lzEgkm2zG3tH7IEWecP9w+9rGZ4ohslCYnXDUm9MGF2J0ihbnJBfkf59Rs7q4vv9Y9X1ozq9+dbRTwPhSMnYbk2zOnXtXqqkXKHH1tZM7NOvw5ip2e0XjzjcWDEhMjB/yIz70jFvcU/eGRvmVKrdoPJ0bltbq9R1v/YaDgTdn4hNzIa84ltA1MLCGETS7SCOQSAGkdoSIv86xGsg3HKMrOsQE6CUQxiaKGmtgtyAkWIwIMNxKIN5QK4xAIk3MIIVnNA/fAdPM+wIOhPaRNEtuvROycm7kHm7iMHM7wabASUqOtByowkglmHm5an5G8bOiYau9y/SAF7vYVQ2zqR5UUeUXdxLDtMT0SMkNXqR9Lhag0cfURpetbZG/AvZr2jRHOZSOkc5ztkqzrMIAf55rM9N5VmbON8PqhxBs8aRmyFqoTwG4b4dxLFrV2MQyS0hsq5DTACHylWC/hhXgUA+gFip9id54Z5wod3t1glmAKcgCUk+rogS11erXC6/JJ+WL8jcIsuyoNfbqiJ6Kri17tNEXW55EDWhHZV7uVhLarxnM5QhVqpNqbM3bcJ9eBf+bn/07S9xNlt4lIyKtaWSunqyntWxHSQcba5nhhhNYrmqS+3jurSmJdWx7jiVLwUx3sKsmLb5bgdRi4YYhP92EMegKQaR3RIiX4PgeGy65RhZ1yEmwMdxnW4b5z7CQrQJJmEDGMEX1st6ino0mXXgy0+0x2rMHLeOu0ewbTh8BHua7RiLw9m2MThS2DCa/3fbaLyfPTsaR+CIsWwrAOXzv877434CJ6RAQFkZnnRvmsAPExtcAA6rqFMCF0+a32f2945YHTpRoDazQHnjnES1lrm3+Fq4+YgL/ygm0lglwc7fxSoM1BZEj3qKzovZ1zsLv1479tEH9ykddGe2jnx04rGmh6Mjpu/9zy/NwbFk68SdWpPhmOUDNr2FDyl9dMMXV699l61D26bmvgOVZjp2ZRN9qTc7xVdOrI9LlUxpXLoVMfk7Nb7fDFELp2MQKbeDOAZzYhAZLSGyrkNMgA3xlRNMtEfCbHWUTvF5CmKjOFSQeO/frHjvH9+pMOtFUbKDBB6vWeALiC8fs96sl2LdkZoVarkRrHVH8v9lCDcaJGexM+zzQ42NZ9GHnuYrO3mL5LvvUdvFy4zXWq/B6ei/V+5Y9yQAqv0oW6R0aK94ppxcMTUAXpMJUu25YkGhw5Hbrl12RaQd5LrV3S5tj+vm0xpaZCBL2vZIQjWCo6Q2/2lnOTKUqE/1UYJv5ZAOKb36Lxv32p+OTCrfUnn27ofnjujZq094yVz2TcPf/v7+58IPi6dX3OnPyC0L3b917LZdPTcF8w/0mVQxcHZN+cTisqHF1YMuXO0r7Nv3562c52pXkOTnPL8TACXovgLUVWlXOH6L57V56vN2t3t+7FP1eajFc/Gz689fe+UW3xc/vP58whegruiOKsCNGRZehzj+cwyiTQwCqAIhKbtXOVDENWdkOJQLre3tedlIaF+WlJTe3ghi5y4pbYNtKyK+AqGgV6RD66BdECyZQU+xzqKriLgsNtBaO9R97viBxZsNL1corarUot3Jy/+qHSkOv7bLFExMz5TiAMaaVIb/wg7NmPnUc0VVb4+a/3xO8a6Hj/0reqcOO967tWbwurHswpy73lz03Mt7Jg1ZtfPpwzvoK7OWGon8BOY/+yddrEUqp/ie+4eMYP/9+yRWGwjyVpav5k5sXH9/5MVNo2XdQ6Sw4ektO5V1zXc4lW4kzreeMU+JFaqnVDtxVIn1ikl8vyqRVppEbn5e21993vp2z4/9rD7PafGcS1R7PsEQk1d7TaLX/gqAo9URXolZHHYXKGOgqI3xIgApTICovZYRgzDHIa79iUMMSoA4xl6IQTg0iG84RDrHQ4OYwA4CqBbHZ9d89VRlx1zyq6euqsJ5fsnUqhXwYN5jsTttkj7YRp9eETFSj91nsfLIR0+9LqSttY3QmLJw6/3b430QyITiIlAqxdlBMcj/lHpUk+6gRVqnV4kwil39+e/sK5T/9sUYXdkp9n3vr4YN77ll3OW+pzc8v7NpC3vppe0vPUtC7Ev2FzR/cQmlWcInr25+cGHXgtrefZ6cNHMlm8b+taaRbXjh4Aku21jXgbraqmOrzaLyJC1RNqNUrt0Vk/1HquySb/e8drD6PPN2z4+p45Ngi+d8fu35a9/f4vtcJtrzCSkx3Wh3fS2Ph2YhR9gJVO1CD4WTPAaDTSACKjsZTifKZjMqJ/QQ8tX1yhOfG8nPjUN6iccXE96Pp8ejezqVFHXsFCrqot3J8iefZP/q3KW8Y1m4nPwYfwOUY3tEGCUsjvv7PvxEa3orl8vQ6iZn76u47uxt1M+b2Kjnf3P2ZWVxBdGcfXw7QXSpTl4Si1SnX6L2X2yaUjNt+Dw0Xd40o6Z25NzmV4rxTJ9pvAljfYjl95r63Iuxboyetf0XbEBQGjL6zuy7cMOvu8aRRcWffLRjTHRO6DzXjNjutSq5e2KSf0PVDI8mmZuf107VNOfWz4851OeBFs+5ZLXnE/yxtZarrfrYDqw6wr2xGWIjpKsAWu+I2t+VyXex0jOkFJfNZpfsrQMOsKeYPHqqT+NdjB7q5euvRZPnb3oYUWsXUUomXo/W9JUVbx7J4HugOKR748Sz333/yd8fMwk63mSElTs38OYRzF9LmyID2Efsvwpjn83sV86KdcDaFQ1NOXQi58u3ce/ZMxo1nF6Nmgn7Y/TmxejV+puEyuv9TaJArLfsb+Iw6gkU6UvxFLggHe4Ot0uSrE5nKpjtqZKY4bc6eDxpBaOR51hGGj+Vwg8UUAc4b5zk4det2ia1fWVJO2TlvZF9aafq7NnSl1EYN4y9zJ7BYRgeN5RaonxdR8+Rfs09fmXXEH+ecs89LqzDiTgeF3ljSZmwlZ1m55QTGn6hNi32qy1yujAU0iAXCmBQuG26zkI8nqx8t7tVlk4oDOW1Mbbh0RHvSCKixdiunWg32pIyxcyKCIieFj7YoVjVRAeseV9R9a0q5rdyvYktTFkxnyvWs/Nzup6pu8B+ROnrBae6djz2+InL0aAOq4Y/e8+QDVf9G154buPm5xvWCb3mrjKRjN+7vp4xEwtQh3q8Y+a0KbPYz19MYDO5tw1mkLIPz3985rOPP/10x9NP7wBEE68Q7pH8YFF6wGWwWXmN0KJs3CSfKkwsE/Igzx1QzhIE0DR3nLfB89CcmUMWLuFF2u+WPJGTu3C+t3TBoiIAgpP5iG2lhdp+kEMyxSpMejflw753u9KSrHUfcfpp29njxj46a8zY3z3YPRTq3rmsqJu4b9TM2lGjps8c3qFLlw78AkQdn+k78TN1N5wPn+Szg2gC/nKrZc73En4mKLYb3o4vKU6BwvQ0olRTQpJEXXkDB/TOLAxZRpmn39tucP/KjIL21tHmqcL5rLZZnbvMquO3Tl1n1aldEci5Ff/FEyCCePMvngykw+K/eMIh5f8VUtYgffQ49lB7+R0HUNTpQenhP6WBBkscHEs5y+QZ1WF29yx63DMUTVyicNM3RdTpRZly061Rq55Od5RisXIk/bGKDPGARzmLjqmfcouq/e4LkcAKAEQZizSpY1khOWwS0KwXbHbQUZP2M1+x3pUgbyrhA/vjeGG9tcNjs9M6maNnb2B4FnXTeR1Tw7TF6DZldL0ZRcHuMIs2WRn9LW10DWe/ei9JQJ4ELUkjOsxJ7m6+QYbnXvbTY2Ow6D6FHh/7lTTBZZSVLOtqB8g4iCCHzeZK+dC1Y38ymWJ3vb5SBnteXszG7cAfyXB6EYzgPBD/URrIP3Wr6u+OqQ9OmDF94qRp5JtZj/9u9sx5C/icym8TiHvgB8gGOwAEwU4c/M4nELJA1RaoJelK5ZPTbBAIlYikk0WuCInpvPM3e2CJ+16ASv2UpGqjUBAIkMRRWhRNSeqtK6QAyGYBkJXxUyYgEkE7ZYLxAQJIVjbPWkkXx4+ZIJRzr1gnnuT0TQ2Xp3rTPZ5kI5Hl5NZ2wZDslYJtjN4kb/+ILklMTUvtHyFp1rT0tPw0qqdJaUlpzsxM6BvJlJ0W3iDhg5ZN3bwwdMsfKruRW2ZQbuRlt9evdcorVpPyolGwuJT/dUDsCHUKOz4AWfRHQvA065Z1snHLxtW7/oddaNewgZANO4LY+n9OPN+rQSxmD80rC7ed1/Rm9/puaEacl3tH9TwUsfXIpYPVzprl6o4iBXdYT0AUtDAtYc3y+EuJtrjkUwGEVlI650ylKvE+5ABA/HNTwuf9lc+BgItUcf0/AgZwQedwuks0ypTyaYjSqY+iqLe60l3E5aIWOZ1mxPuV70toergeGwR4g0v8V2eKi0otVJZJ05xV7GHcsHQO+0ESk9LSjDup6913x/KzVKdeX9THFGzb1v5TDDfpQ45bECoJ9+43cBcf0nCXXr/F8/43notvxJ6rVEnqc1TWG05X9cp+AAQRKWiHl2Knck80KgqljCAC4Aq1QvJpPHP6XaxCImp1FiUv6pwAUXstt2Ud9NrbHGJCAsQx9ufEKktsFtJBzroOMYF9EK/V+GK1mv8PflNJUQAAAAABAAAAARmahXJJOF8PPPUACQgAAAAAAMk1MYsAAAAAyehMTPua/dUJoghiAAAACQACAAAAAAAAeAFjYGRg4Oj9u4KBgXPN71n/qjkXAUVQwU0Ap6sHhAB4AW2SA6wYQRRF786+2d3atm3b9ldQ27atsG6D2mFt2zaC2ra2d/YbSU7u6C3OG7mIowAgGQFlKIBldiXM1CVQQRZiurMEffRtDLVOYqbqhBBSS/ohgnt9rG+ooxYiTOXDMvUBGbnWixwgPUgnUoLMJCOj5n1IP3Oe1ImajzZpD0YOtxzG6rSALoOzOiUm6ps4K8NJPs6vc/4cZ1UBv4u85FoRnHWr4azjkRqYKFej8hP3eqCfDER61uyT44DbBzlkBTwZD8h8/sMabOD3ZmFWkAiUs5f4f2SFNZfv6iTPscW+jOHynEzEcLULuaQbivCdW5SDNcrx50uFYLzFHYotZl1umvNM1tgNWX+V/3gdebi3ThTgVEMWKYci4kHZhxBie3TYx3rHbGr+Pdo7x4dIHTKe5DFn+O/j+W2VnE3ooW6isf0LIUENvZs1gf/LHojJwdpplCP5gn/5gi26FoYa19ZVFOJ6Sxuoz/q2Ti20IKVJdnqvYJwnhfPH/2f6YHoQF30aZaK9J8T026RxH5fA/WPW/8IW4zkpnIfoFLifGB86v0ffm5nbyRs5iaHR3hNBD0HSfTzoPugRM+hdN0x052KoHLBS0tdgpidAiEesDsgWYO73RWQz2LWIwjqnMe/uYISQtlbyf2NlT9Q9PoBcBnrO6I5ELoMeyHkNnIXGdv809H/DXNOTeAEc0jWMJFcQxvFnto/5LjEvHrdbmh2Kji9aPL4839TcKPNAa6mlZUyOmZk6lzbPJ3bo56//Cz+Vaqqrat5rY8x7xnzxl3nvo+27jFnz8c/mI9Nmh2XBdMsilrBitsnD9rI8aiN5DI/jSftC9mIf9pMfIB4kHiI+hWfQY5aPAYYYYYwpcyfpMMX0aZzBWZzDeVygchGXcBlX8ApexWt4HW/gLbzNbnfwLt7DJ/p0TX4+Uucji1hCnY/U+cijVB7D46jzkb3Yh/3kB4gHiYeIT+EZ9JjlY4AhRhhjytxJOkwxfRpncBbncB4XqFzEJVzGFbyCV/EaXscbeAtvs9sdvIv3cjmftWavuWs2mg6byt3ooIsFOyx77Kos2kiWsIK/UVPDOjawiQmO4CgdxnAcJzClz2PVbNKsy2ZzvoncjQ66qE2kNpHaRJawgr9RU8M6NrCJCY6gNpFjOI4TmNIn36TNfGSH5RrssKtyN+59b410iF0sUFO0l2UJtY/8jU9rWMcGNjHBEUypf0z8mm7vZLvZaC/LzdhmV2XBvpBF25IlLJOvEFfRI+NjgCFGGGNK5Rs6Z7Ij/45yNzro4m9Ywzo2sIkJjuBj2ZnvLDdjGxntLLWzLGGZfIW4ih4ZHwMMMcIYUyq1s8xkl97bH0y3JkZyM36j/+58rvTQxwBDjDDGNzyVyX35Ccjd6KCLv2EN69jAJiY4go/lfr05F+Ua7CCzGx10sYA9tiWLxCWs2BfyN+Ia1rGBTUxwBEfpMIbjOIEpfdjHvGaTd9LJb0duRp2S1O1I3Y4sYZl8hbiKHhkfAwwxwhhTKt/QOZPfmY3//Ss3Y5tNpTpL9ZQeGR8DDDHCGN/wbCbdfHO5GbW51OZSm8sSlslXiKvokfExwBAjjDGlUpvLTBY0K5KbiDcT672SbXZY6k7lbnTQxQI1h+1FeZTKY3gcT2KvTWUf9pMZIB4kHiI+xcQzxGfpfA7P4wW8yG4eT/kYYIgRxvgb9TWsYwObmOAITlI/xf7TOIOzOIfzuEDlIi7hMq7gFbyK1/A63sBbeJtvdwfv4j28zyaP8QmVL/imL/ENJ5PJHt3RqtyMbbYlPfQxwBAjjPEN9ZksqkMqN6PuV7bZy7LDtuRudNDFwzx1FI/hcTzJp73Yh/3kB4gHiYeIT+EZ9JjlY4AhRhjjb1TWsI4NbGKCIzjJlCmcxhmcxTmcxwVcxCVcxhW8glfxGl7HG3gLbzPxDt7Fe/gY/+egvq0YCAEoCNa1n+KVyTUl3Q0uIhoe+3DnRfV7nXGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOMc5zjHOc5xjnOc4xznOMc5znGOc5zjHOc4xznOcY5znOM8XZouTZemS1OAKcAUYAowBZgCTAHm3x31O7p3vNf5c1iXeBkEAQDFcbsJX0IqFBwK7tyEgkPC3R0K7hrXzsIhePPK/7c77jPM1yxSPua0WmuDzNcuNmuLtmq7sbyfsUu7De/xu9fvvvDNfN3ioN9j5pq0ximd1hmd1TmlX7iky7qiq7qmG3pgXYd6pMd6oqd6pud6oZd6pdd6p/f6oI/6pC/KSxvf9F0/1LFl1naRcwwzrAu7AHNarbW6oEu6rCu6qmu6ob9Y7xu+kbfHH1ZopCk25RVrhXKn4LCO6KiOGfvpd+R3is15xXmVWKGRptgaysQKpUwc1hEdVcpEysTI7xTbKHMcKzTSFDtCmVihkab4z0FdI0QQBAEUbRz6XLh3Lc7VcI/WN54IuxXFS97oH58+MBoclE1usbHHW77wlW985wcHHHLEMSecsUuPXMNRqfzib3pcllj5xd+0lSVW5nNIL3nF6389h+Y5NG3Thja0oQ1taEMb2tCGNrQn+QwjrcwxM93gJre4Y89mvsdb3vGeD3zkE5/5wle+8Z0fHHDIEceccMaOX67wNz3747gObCQAQhCKdjlRzBVD5be7rwAmfOMQsUvPLj279OzSYBks49Ibl97In/HCuNDGO+NOW6qlWqqlWqqlWqqlWqqYUkwpphTzifnEfII92IM92IM92IM92IM92IM92I/D4/A4PA6Pw+PwODwOj8M/f7kaaDXQyt7K3mqglcCVwNVAq4FWA60GWglZCVkJWQlZCVkJWQlZDbQyqhpoNdAPh3NAwCAAwwDM+7b2sg8kCjIO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO4zAO47AO67AO67AO67AO67AO67AO67AO67AO67AO67AO67AO63AO53AO53AO53AO53AO53AO53AO53AO53AO53AO53AO5xCHOMQhDnGIQxziEIc4xCEOcYhDHOIQhzjEIQ5xiEMd6lCHOtShDnWoQx3qUIc61KEOdahDHepQhzrUoQ6/h+P6RpIjiKEoyOPvCARUoK9LctP5ZqXTop7q/6H/0H+4P9yfPz82bdm2Y9ee/T355bS3/divDW9reFtDb4beDL0ZejP0ZujN0JuhN0Nvht4MvRl6M/Rm6M3w1of3PVnJSlaykpWsZCUrWclKVrKSlaxkJStZySpWsYpVrGIVq1jFKlaxilWsYhWrWMUqVrGa1axmNatZzWpWs5rVrGY1q1nNalazmtWsYQ1rWMMa1rCGNaxhDWtYwxrWsIY1rGENa1nLWtaylrWsZS1rWcta1rKWtaxlLWtZyzrWsY51rGMd61jHOtaxjnWsYx3rWMc61rEeTf1o6kdTP/84rpMqCKAYhmH8Cfy2JjuLCPiYPDH1Y+rH1I+pH1M/pn5M/Zh6FEZhFEZhFEZhFEZhFEZhFFZhFVZhFVZhFVZhFVZhFVbhFE7hFE7hFE7hFE7hFE7hFCKgCChPHQFlc7I52ZxsTgQUAUVAEVAEFAFFQBFQBBQBRUARUAQUAUVAEVAEFAFFQBFQti5bl63L1mXrsnXZuggoAoqAIqAIKAKKgCKgCCgCioAioAgoAoqAIqAIKAKKgCKgCCgCyt5GQBFQBPTlwD7OEIaBKAxSOrmJVZa2TsJcwJ6r0/+9sBOGnTDshOF+DndyXG7k7vfh9+n35fft978Thp2wKuqqqKtarmq58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<section class="page-header">
<h1 class="title toc-ignore project-name">R for cytometry course</h1>
<h3 class="subtitle project-tagline">Part 2: Plotting data with ggplot2</h3>
<h4 class="author project-author">Hefin Rhys</h4>
<h4 class="date project-date">2021-07-01</h4>
</section>
<div id="TOC" class="toc">
<div class="toc-box">
<ul>
<li><a href="#what-is-ggplot2"><span class="toc-section-number">1</span> What is ggplot2?</a></li>
<li><a href="#installing-and-loading-the-packages"><span class="toc-section-number">2</span> Installing and loading the packages</a></li>
<li><a href="#loading-package-datasets"><span class="toc-section-number">3</span> Loading package datasets</a></li>
<li><a href="#plotting-with-base-r"><span class="toc-section-number">4</span> Plotting with base R</a></li>
<li><a href="#plotting-with-ggplot2"><span class="toc-section-number">5</span> Plotting with ggplot2</a><ul>
<li><a href="#exploring-additional-aesthetic-mappings"><span class="toc-section-number">5.1</span> Exploring additional aesthetic mappings</a></li>
<li><a href="#exploring-additional-geoms-for-scatter-plots"><span class="toc-section-number">5.2</span> Exploring additional geoms for scatter plots</a></li>
<li><a href="#line-plots"><span class="toc-section-number">5.3</span> Line plots</a></li>
<li><a href="#bar-boxplot-and-violin-plots"><span class="toc-section-number">5.4</span> Bar, boxplot, and violin plots</a></li>
<li><a href="#factorial-bar-graphs"><span class="toc-section-number">5.5</span> Factorial bar graphs</a></li>
<li><a href="#facetting"><span class="toc-section-number">5.6</span> Facetting</a></li>
<li><a href="#customizing-labels"><span class="toc-section-number">5.7</span> Customizing labels</a></li>
<li><a href="#customising-the-look-of-your-plot-with-themes"><span class="toc-section-number">5.8</span> Customising the look of your plot with themes</a><ul>
<li><a href="#pre-built-themes"><span class="toc-section-number">5.8.1</span> Pre-built themes</a></li>
<li><a href="#customising-themes"><span class="toc-section-number">5.8.2</span> Customising themes</a></li>
</ul></li>
<li><a href="#saving-plots"><span class="toc-section-number">5.9</span> Saving plots</a></li>
</ul></li>
</ul>
</div>
</div>
<section class="main-content">
<div id="what-is-ggplot2" class="section level1">
<h1><span class="header-section-number">1</span> What is ggplot2?</h1>
<p>R is great for creating plots and comes with its own base functions for doing so. But creating and customising complex plots with base R can be a little cumbersome. Instead, you can achieve beautiful, publication-ready plots much faster and more easily using the <em>ggplot2</em> plotting package.</p>
<p>The “gg” in ggplot2 stands for “grammar of graphics”, which is a school of thought that says any plot can be constructed by adding sequential layers of elements, such as axes, dots, and lines. The ggplot2 package makes constructing plots in this way very easy.</p>
</div>
<div id="installing-and-loading-the-packages" class="section level1">
<h1><span class="header-section-number">2</span> Installing and loading the packages</h1>
<p>We’ll start by installing the ggplot2 package from CRAN, and also the <em>palmerpenguins</em> package, that comes with some cute example data we will plot.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a><span class="co">#install.packages("ggplot2")</span></span>
<span id="cb1-2"><a href="#cb1-2"></a><span class="co">#install.packages("palmerpenguins")</span></span>
<span id="cb1-3"><a href="#cb1-3"></a><span class="kw">library</span>(ggplot2)</span>
<span id="cb1-4"><a href="#cb1-4"></a><span class="kw">library</span>(palmerpenguins)</span></code></pre></div>
</div>
<div id="loading-package-datasets" class="section level1">
<h1><span class="header-section-number">3</span> Loading package datasets</h1>
<p>R comes with a large variety of example datasets you can freely load and experiment with. Executing <code>data()</code> will open a new tab in RStudio listing all the datasets available in base R and the loaded packages. You can search for datasets in a particular package by using the <code>package</code> argument. To load a dataset, simply pass it as the argument to the <code>data()</code> function, and it will be loaded into your environment. Below, we load the <em>penguins</em> dataset.</p>
<blockquote>
<p>Note that the penguins object is a <em>tibble</em> not a data.frame. A tibble can largely be treated the same as a data.frame, but it is more pleasing and easier to read when printed.</p>
</blockquote>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="kw">data</span>()</span>
<span id="cb2-2"><a href="#cb2-2"></a><span class="kw">data</span>(<span class="dt">package =</span> <span class="st">"palmerpenguins"</span>)</span>
<span id="cb2-3"><a href="#cb2-3"></a><span class="kw">data</span>(penguins)</span>
<span id="cb2-4"><a href="#cb2-4"></a>penguins</span></code></pre></div>
<pre><code>## # A tibble: 344 x 8
## species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
## <fct> <fct> <dbl> <dbl> <int> <int>
## 1 Adelie Torgersen 39.1 18.7 181 3750
## 2 Adelie Torgersen 39.5 17.4 186 3800
## 3 Adelie Torgersen 40.3 18 195 3250
## 4 Adelie Torgersen NA NA NA NA
## 5 Adelie Torgersen 36.7 19.3 193 3450
## 6 Adelie Torgersen 39.3 20.6 190 3650
## 7 Adelie Torgersen 38.9 17.8 181 3625
## 8 Adelie Torgersen 39.2 19.6 195 4675
## 9 Adelie Torgersen 34.1 18.1 193 3475
## 10 Adelie Torgersen 42 20.2 190 4250
## # ... with 334 more rows, and 2 more variables: sex <fct>, year <int></code></pre>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="kw">str</span>(penguins)</span></code></pre></div>
<pre><code>## tibble[,8] [344 x 8] (S3: tbl_df/tbl/data.frame)
## $ species : Factor w/ 3 levels "Adelie","Chinstrap",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ island : Factor w/ 3 levels "Biscoe","Dream",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ bill_length_mm : num [1:344] 39.1 39.5 40.3 NA 36.7 39.3 38.9 39.2 34.1 42 ...
## $ bill_depth_mm : num [1:344] 18.7 17.4 18 NA 19.3 20.6 17.8 19.6 18.1 20.2 ...
## $ flipper_length_mm: int [1:344] 181 186 195 NA 193 190 181 195 193 190 ...
## $ body_mass_g : int [1:344] 3750 3800 3250 NA 3450 3650 3625 4675 3475 4250 ...
## $ sex : Factor w/ 2 levels "female","male": 2 1 1 NA 1 2 1 2 NA NA ...
## $ year : int [1:344] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...</code></pre>
</div>
<div id="plotting-with-base-r" class="section level1">
<h1><span class="header-section-number">4</span> Plotting with base R</h1>
<p>We’re not going to spend time learning to plot in base R, but just to give you a flavour, here are some examples. Just calling <code>plot()</code> on a data.frame will plot each variable against every other variable. To create a scatter plot, we must supply vectors representing the x and y axes, and (optionally) a vector for the colours.</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1"></a><span class="kw">plot</span>(penguins)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="kw">plot</span>(penguins<span class="op">$</span>bill_length_mm, penguins<span class="op">$</span>bill_depth_mm, <span class="dt">col =</span> penguins<span class="op">$</span>species)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="plotting-with-ggplot2" class="section level1">
<h1><span class="header-section-number">5</span> Plotting with ggplot2</h1>
<p>When creating a plot with ggplot2, we always start with the <code>ggplot()</code> function. The first argument is the data, the second argument is another function, <code>aes()</code>. This function allows us to specify the plot’s <em>aesthetic mappings</em>.</p>
<p>An aesthetic mapping is the link between a graphical aspect of the plot (such as the x or y coordinate, or the colour of the dots) and a variable in the data. The example below maps the bill_length_mm variable to the x aesthetic, maps the bill_depth_mm variable to the y aesthetic, and maps the species variable to the col (colour) aesthetic.</p>
<p>This function creates the plotting space and axes, but in order to see our data visualised we must add a geom (short for geometric object) layer. To add dots or points, we add a <code>geom_point()</code> layer to the plot, using the <code>+</code> symbol.</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(<span class="dt">x =</span> bill_length_mm, <span class="dt">y =</span> bill_depth_mm, <span class="dt">col =</span> species)) <span class="op">+</span></span>
<span id="cb8-2"><a href="#cb8-2"></a><span class="st"> </span><span class="kw">geom_point</span>()</span></code></pre></div>
<p><img 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/7H372RVyj/5s/H/UOdfQvq/ITn6v2g19+cNW8PBSdiUbz88iyvoBiD5kn0EvTDH968bFvOl4+bq4Nj8eP8/iBot0AUdPnQ3PtBx/dP3okfr08mb7LWg0tLpkXdjSaoA5ojdbuEZNxqM4Ket2bel+PFtosX49H2ZSdov0AUdHE/6Osv/vqNFPTm+1Px4/bX00iC7k5iUtHsRuriXVCP1O0ivHmrPRRUOwYVz3OKT3UQo86Xx/JWpDsvekGfuj7juXo/6HUn6Nuv5NLNd0Nf/1mLQ7YzvaCeOwZvQkWqx7RV3azP4tseXprZt6Bdg9kL6tR6ClYfmusE7RvQ5vrr00kryvkPFS1oIPlbUJ2g56K9a/v4cQx6de/NOAa9Gj8Ux0XQ4WbQ5SRpNwLt2I1DWeshfAy6e4ExaJRED0E//avwqB1ttl19P4sXn/Gwm8W7fYrD6mmmTtDXzyar4ggazGg4LnVGSfQQlAfNJOlq3oLuenXRkt7+lvs0kx4IGjmxYEG7IWh/SvSL3UQ+Tj34AkEjJxYg6PluDOp0AjVOPXhjHoOGUqNO/Il6CfK0oG7EqYcLtwc8aIkarBPxo6MjNVC7D+3BD7H1tIjj7wlhI4KSiFMPbo/I0RI1WJ94OzramTMGavehPTrXnRzV/p4QNiOo7ObdmtE49QBBvdiKoOfdNzI4fUJTnHqAoF5sRFBlFr9GnHq4wBjUBwiqEnb8KyeFHK/8LK+OLhNDLyDtqHHOzZ+olyB1Fy+/kSFFF7+Ti9LFq5vN7w2dJQZfgt9Ro078iXoJkreg6uV4I0GHD0FrS9RLkHwWTyDo8CFobYl6CfZOUOXmo0Z933zr3XLMOdtyTOxeRRqD0qZELolaiHOnzQh6fnBwfO50m55XlajqNbr3DYZqVmtWNXxtp6aItJNKLolaqGefShVUfDdSq1P/JKfyQKfLE54zQV/e+5+nx45f5+Vz1BBUTdRSn6BttaiCyvPqL+VNytrdqYJ+fHoszjTFO80EQdVELdUJKutlKWj3sQ2fnh9/ePT34gKluFf5D/8mL1VeiXm4ePT497/9891X4mni4+bjn/5dcxNzUkHVgaFmDKrbzLC3blXDN/ocAqdgDOou6O6Zjg8Pd097dK/EI0n9I8gPj6XI8kG6e29U9+aXOkUXn+lS51SrNROHFyYNG+sWnkW0XR2lJ5qhXl+aJ4ZcnRr2ZRF0GCuKrrx71KN/1X8kzuRjcFplReP46cdlEzqfJF113z2fQdDFhNzYlw8vjB15Y93Cr4gPrNfvqYlmyFfoZ4kh1/d3+3KMQeVTnVpBxUeEjM8nteNU2eMLa18ub1Uq5XY7CDqhQkF1s/jJGHQpaNNMnvAUTWffrNpaUAhqLCIEJQs6m8XPBBWj01FQ+f/PX3x8el/zKPLqY8cJBTWMQZWhJ8ag9sQMY1C9oNPzoPMWVPbpbeN693fRnJ4fiOeSP/7xHyyz+OF+UKc7lr2rYH7cwwt9e+fRCrJPaGu8MMmf6Ccoid2Hic4o5X5QCFp2ol4CCApBC0nUS8AqqJ5iHvnQm0gfR0LQKIl6CVIL2p0HzfvQnAaSpbNEjnmS65G6zaDEJASCEijlNNMKtH5+mshypsnxSN3m+PI0DgQlUMGXKEBQKxBUJU496ICgViCoSpx60IIxqA0IqhKnHrzZqcjrEyHQnRoT9RJAUFfGzpy1RyYEEqgxUS8BBHUFgkZO1EsAQV2BoJET9RJsTVD13iVn9GPQFQknb62r2rjGOANBCZR0ol656TM00a2ZtGzVrG3gdQvedgS90n1eN+3rZIu5H/QCgvqTX9CjoyNV0Ct5z+fy1g5PQalEqAcI6kt2QeUFiKWg3TcXiieT+i86ls8cy9f9lx07fOdxSYKGjEH1iW72YAxqT/QRdGwq+y863j19PHzRnMP3zZXUxWtQTbW5m/Z2O6+HQJTE4E+xLVTQ4anO8YuOJ1/V2a7sf7gJSiVOPSxQ+3pr759UUL/HlJaJ4Z8Dnl1Q7Rh093zx+EXHk0eTPv34ov8BQYOAoHLZIqhuFt+NQfuWspk+Hu/TgsrPvSmti4egfonhsJxmkp/YLf6z+6Lj4bMaPMagVOLUwxKMQf0Sg+E5Dyo+FayfrXcfJCL+/+l5tbP4IhMbskA2ays46FKvJJX5TFLexIbaBVv7/QoOulBBS/gir+ISIahc1pJY0DK+yKu0RAgql7VA0AISPceg5n3Ud/o1/nOljQjaqZnns5nYSDuLN2JuddV3+jUBZ5u2IGiZlzoLSISgcjkXOM0UJxCCMgFBIwVuYgyaAG9BWbm8vMxdhEiIz2lQXwJnimhBuT6te0xkJChw0m2PLyvoNvagBeWsBwgaCARV4awHCBoIBFVxO07He4/036DgRVpBrfOao9HL4KmQGQiq4nSYHndvhramSQV1OTM0bBN+MskMBFVxOkwICkEDgaAhgRA0OmWMQSm7WChsDDrZBmNQH4qYxTvhKG5Bs3hFxSP91wyG07BbD0H19WDGtesvR1ClMx9W8AvKP26AoPp6MANBKb8qGAiqrwczEJTyq4KBoPp6WAFjUDMYg6pw1QPfRc4hkRneQPXKEgeYxasw1QPnZfgukTOMPfDoaGcoZ7cMQVWY6gGCcgBBVZjqAYJyAEFVuOqhhjGoahLdrfE6ku8YdGV7CKoSpx4KTNR9cAO99Zvuwf0YHgTVEaceCkyEoHI5FxDUHghBISgfhY9B+0QfMAYlEaceyknc6RASODo1tauZrPGeyc92hKAqceqhmMSxQw0InM3YFy1ot8b7XNN8RwiqEqceikmEoIvlXEBQPRB0sZwLCGoAY9D5ci4gqF/guljr77oW0V1eCKoSpx4KTNQGrnfNlo7bsYiE7h+CqsSphwITIahczgUE9QqEoKlgFpR6b1Ktgs7Hh4pIGIOywSso+e7OagWdQj1XVMFBQ1BDPYQDQaMkcjpHAoKGB0LQiGAMyhBIPNtewUHvraCh9VBeYsgZeQPlHzQENdVDcYlB55MMFH/QmkRO50hA0HUgaL+cCwi6DgTtl3MBQS1gDNot5wKCBgTq76azccT/ScsQVCVOPRSYaA403I9sod/W+1ZlHRBUJU49FJgIQeVyLiCofyAETQAE7THqwjEG1d37BEGdgKAd5gaNoYiz8GGBswmFoCpx6iFbIgRdT+R0jgQE7YCg64mczpGAoD0+Y1C/8MnMiiFaAkFV4tRDgYlKoFUs29ypgoOGoIZ6KDBxGWjtmq1nnyo4aAhqqIcCEyGoXM7FqqDX35w1zdvDw8Mvz8TizXeHT94N78WphwITIahczsWaoO+lmK9P+sXbX06at18Nb8aphwITh8Cda8OFoMV2R/1nganflzDf82L6vXBME6VtCvr6i7+2Lejtr6f98s0PZ12bKolTDwUm9oHz1lBtG492LDdRXvhdInUoIyM1CNp18W2/fngoG9Hrb981N98LXT9rSVK6gpAuGZZ2a6ard6+VF+NWagxYYBX0+uvTvhV9/2QQVBDnH2qBiWhB5XIu7JMkgRyHji2oIE49FJiojEE1S8OapcT6FxiDEiAIuu0x6JRArSo46IoEFR377W9Cy9tfnm15Fj8S2jFXcNAVCSrOg35xOsyXcB70AoImBVeS6IEQNCEQ1CMQY9B0QFCHQK2Q7pYut6zgoCGooR4KTNR9mewFpZ9XtqzgoCGooR4KTISgcjkXENQeCEEhKB/mRNrUZrc1xqByORfbEZR2cmjcukad+BM5nSMBQW1b16gTfyKncyQgqG3rGnXiT+R0jsR2BA0YgzJTYyKncyQ2JGhQ4HhL5/xd9Z665RumRE4gqEqceigwUQb2Pb4yTFDvSl6+kaKIEFRDnHooMBGCyuVcQFCnQAiaCwjqFogxaCYgqG1636huDq2pYw6uJAUAQW0nSNXe/WiHUw6uxYcAQSGoSyKncyQgKAR1SeR0jgQEnXyIkj7Qbwy63DysiBYgqEqcesiUuGaooal0a3f5irgKBFWJUw+ZEiGoLZHTORIQVABBbYmczpGAoBKHMehy61U/caKeCwhqYCfYaqBBw3V5G67PDJskcgNB9fVQTOLYRa8FGjpyS/ffWN6nA0FV4tRDMYkQdLGcCwiqB4IulnMBQQ1gDDpfzsWmBZ16YnRmFmi6WLQWsJ5I3ts1MQwIqq+HpInTntbc604DZ1vNdyF025oiBnb6EFQlTj0kTYSg7omczpGAoOprcyAETc6WBfUegx7pnv9QAoxXQzEGJbBpQT0D3Zq7oyOToRUcNAQ11EOBiRBULucCgtIDIWhCIKhHoNuAkTQGDQOCqsSph0yJa8a5BoadqA8DgqrEqYc8iat9tmNg2GmmQCCoSpx6yJMIQa2JnM6RgKAXENQhkdM5EhBUcLR78l11rBm3WVUQY9AoQNAB02mhZv1tDwo6aOdETudIQNABCLqWyOkcCQg6AEHXEjmdIwFBdwSOQd0p6aBdEzmdIwFB19lNnxw2c6X4g9YkcjpHAoKucjRi3cw5tPSD1iVyOkcCgq4CQYflXEDQVSDosJwLb0E3Qiue/M+Rw2YgAvvdgq61a66tXtPnrGxNnN2jBSWw14KuOeh8WrOZdPQrUZ5F5AGCqsSpB95ECMqWyOkcCQjqEAhBISgboWNQZSXGoHI5F/stKB1V2+KKmCWR0zkSEHQOBNUncjpHAoLOgaD6RE7nSEDQBdoxKC81JnI6R2JrgtLvmDMF+t97B0EJbExQj3s6DYEeSZbEACCoSpx6iJ0IQT0TOZ0jAUE9AyFoGjYmKMagnomczpHYmqB61mQrpIiZEzmdIwFBL3g+WYRAjYmczpGAoBcQ1CGR0zkSEPQCgjokcjpHAoIKVu/KCyiMnkIOmpTI6RwJCLpKwMkkM6UftC6R0zkSEHQVCDos5wKCrgJBh+VcQNB1MAbtl3MBQcmBwc5WcNAQ1FAPBSYuA8N7/QoOGoIa6qHARAgql3MBQamBEDQpEJQciDFoSjYuqINsuYtYRiKncyS2LahLd12jTvyJnM6RgKAQ1CWR0zkSEBSCuiRyOkdi24JiDOqayOkciY0LmiOwykRO50hA0OSBVSZyOkcCgiYPrDKR0zkSEDR5YJWJnM6RgKDJA6tM5HSOBARNHlhlIqdzJCBo8sAqEzmdIwFBkwdWmcjpHAkImjywykRO50hA0OSBVSZyOkcCgiYPrDKR0zkSEDR5YJWJnM6RgKDJA6tM5HSOBARNHlhlIqdzJCBo8sAqEzmdIwFBkwdWmcjpHAlvQUvls9wFsFNBEcspIwRNTwVFLKeMEDQ9FRSxnDJC0PRUUMRyyrh3goL9AoKCooGgoGggKCgaCAqKZq8Evf3lpGluvjt88i53SQy8PTw8/PKs6CK2lXj4xWk51bhXgr49PJGSvv0qd0kMvD4R/y26iKKM75+8K6aM+yTo9T/980lz88NZc/3NWe6yaLn99VT8KLmIsnBNQWXcI0Fvf/2v9p/99bfvmpvvT3MXRkvbbR4ell3EtnD/Kbr4Ysq4R4K+fSb6pbZ7KqNmNVx/fSpa0ZKL2Fz/Rf4LKqaM+yNoW6u3hbegktcnRRexL1wxZdwfQcUM+fDwWTGDJxOvT4ou4s2/SDOLKeP+CNp00+PbX56VMf3UIPrN29/OSi6inMXLzqiQMu6doMWcwNPRtvIlnWPU0hbuy7NyyrhXgoL9A4KCooGgoGggKCgaCAqKBoKCotm4oB8+fzH87P63fEPH//336tuAEwg6eekmqHgLgqYCgk5eQtDy2LygPx0c3Huj7+I/PT84uPuqffnzw4OD46b5+PTgzs+Pfm8XHsv9xLrd5j/L1XLD2QIIY+uCPrz35tPz+1pBxfrm/N4bsU1zfvfVx6ePW0fvvpItaL9umtOcH3QrZwt5jmt/2Lygx5MZ0lzQK2HXx6fHwzZy+XwQ9Hg+PpDLQ9h0Ic9x7Q9bF/RRL6FG0PMDyWO5Viy3TaLYYTcGXQ5Zh//MFvIc1/4AQc2CCiGbUTUImoOtCzo6pXbxd17MtpFd/NVdCJqUrQu6Oklqm8zW0kG1YZLUNrgQNBlbF1ScLrpvuJIkTjPdmbSW4jTTT20z+vLgPgRNxcYFJXPVD0xBIiCoM2JMKs+NgoRA0Bni6o/gjq5rFued7rtvDjiAoKBoICgoGggKigaCgqKBoKBoICgoGggKiub/AfT0DDbX1iu2AAAAAElFTkSuQmCC" /><!-- --></p>
<div id="exploring-additional-aesthetic-mappings" class="section level2">
<h2><span class="header-section-number">5.1</span> Exploring additional aesthetic mappings</h2>
<p>We’ve already seen we can map our variables to the x, y, and col aesthetics, but we can map variables to other aspects of our plots. You can find out what aesthetic mappings a geom can/needs to use by using the help system (e.g. <code>?geom_point()</code>).</p>
<p>Let’s draw the same plot as above, but this time map the size of each dot to the body_mass_g variable.</p>
<blockquote>
<p>Note that ggplot2 takes care of the legends for you. Base R doesn’t do this.</p>
</blockquote>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(<span class="dt">x =</span> bill_length_mm, </span>
<span id="cb9-2"><a href="#cb9-2"></a> <span class="dt">y =</span> bill_depth_mm, </span>
<span id="cb9-3"><a href="#cb9-3"></a> <span class="dt">col =</span> species, </span>
<span id="cb9-4"><a href="#cb9-4"></a> <span class="dt">size =</span> body_mass_g)) <span class="op">+</span></span>
<span id="cb9-5"><a href="#cb9-5"></a><span class="st"> </span><span class="kw">geom_point</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>We could also indicate the sex of the penguin using different shapes. We can change the size of the points by using the <code>size</code> argument.</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(<span class="dt">x =</span> bill_length_mm, </span>
<span id="cb10-2"><a href="#cb10-2"></a> <span class="dt">y =</span> bill_depth_mm, </span>
<span id="cb10-3"><a href="#cb10-3"></a> <span class="dt">col =</span> species, </span>
<span id="cb10-4"><a href="#cb10-4"></a> <span class="dt">shape =</span> sex)) <span class="op">+</span></span>
<span id="cb10-5"><a href="#cb10-5"></a><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">size =</span> <span class="dv">3</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>There are others, but these are the main ones you’ll need for the majority of plots.</p>
</div>
<div id="exploring-additional-geoms-for-scatter-plots" class="section level2">
<h2><span class="header-section-number">5.2</span> Exploring additional geoms for scatter plots</h2>
<p>ggplot2 has some other geoms that are useful for scatter plots. The <code>geom_density_2d()</code> geom is useful for highlighting regions of high density (especially when there are many data). The <code>stat_ellipse()</code> stat (a geom that shows some kind of statistical summary of the data) shows a confidence ellipse (95% by default) around groups of observations.</p>
<blockquote>
<p>Note that you can store a ggplot as an object, and then add geoms to it later!</p>
</blockquote>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1"></a>p1 <-<span class="st"> </span><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(<span class="dt">x =</span> bill_length_mm, <span class="dt">y =</span> bill_depth_mm, <span class="dt">col =</span> species))</span>
<span id="cb11-2"><a href="#cb11-2"></a></span>
<span id="cb11-3"><a href="#cb11-3"></a>p1 <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>() </span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a>p1 <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span><span class="kw">geom_density_2d</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1"></a>p1 <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span><span class="kw">stat_ellipse</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="line-plots" class="section level2">
<h2><span class="header-section-number">5.3</span> Line plots</h2>
<p>When wanting to represent how a quantity changes over time, we will often employ line graphs. As our penguins dataset doesn’t lend itself to this kind of plotting, we’re going to load a new dataset, containing repeated circumference measurements made on 5 orange trees many years apart.</p>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a><span class="kw">data</span>(Orange)</span>
<span id="cb14-2"><a href="#cb14-2"></a>Orange</span></code></pre></div>
<pre><code>## Tree age circumference
## 1 1 118 30
## 2 1 484 58
## 3 1 664 87
## 4 1 1004 115
## 5 1 1231 120
## 6 1 1372 142
## 7 1 1582 145
## 8 2 118 33
## 9 2 484 69
## 10 2 664 111
## 11 2 1004 156
## 12 2 1231 172
## 13 2 1372 203
## 14 2 1582 203
## 15 3 118 30
## 16 3 484 51
## 17 3 664 75
## 18 3 1004 108
## 19 3 1231 115
## 20 3 1372 139
## 21 3 1582 140
## 22 4 118 32
## 23 4 484 62
## 24 4 664 112
## 25 4 1004 167
## 26 4 1231 179
## 27 4 1372 209
## 28 4 1582 214
## 29 5 118 30
## 30 5 484 49
## 31 5 664 81
## 32 5 1004 125
## 33 5 1231 142
## 34 5 1372 174
## 35 5 1582 177</code></pre>
<p>Once again, we start with our <code>ggplot()</code> call, supplying Orange as the data, and using the <code>aes()</code> function to map age to the x axis, circumference to the y axis, and I use the <code>group</code> aesthetic to tell ggplot2 I want a separate line for each Tree. If I want a straight line to connect each observation, I can use the <code>geom_line()</code> geom, but if I want a smooth trend line across all the values, I can use the <code>geom_smooth()</code> geom.</p>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1"></a>p2 <-<span class="st"> </span><span class="kw">ggplot</span>(Orange, <span class="kw">aes</span>(age, circumference, <span class="dt">group =</span> Tree))</span>
<span id="cb16-2"><a href="#cb16-2"></a>p2 <span class="op">+</span><span class="st"> </span><span class="kw">geom_line</span>() <span class="op">+</span><span class="st"> </span><span class="kw">geom_point</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1"></a>p2 <span class="op">+</span><span class="st"> </span><span class="kw">geom_smooth</span>(<span class="dt">se =</span> <span class="ot">FALSE</span>) </span></code></pre></div>
<pre><code>## `geom_smooth()` using method = 'loess' and formula 'y ~ x'</code></pre>
<p><img 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" /><!-- --></p>
<p>If, instead, we want least squares linear regression lines, we can set the argument <code>method = lm</code> (for linear model) and we will get a separate line per group or colour.</p>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(bill_length_mm, bill_depth_mm, <span class="dt">col =</span> species)) <span class="op">+</span></span>
<span id="cb19-2"><a href="#cb19-2"></a><span class="st"> </span><span class="kw">geom_point</span>() <span class="op">+</span></span>
<span id="cb19-3"><a href="#cb19-3"></a><span class="st"> </span><span class="kw">geom_smooth</span>(<span class="dt">method =</span> <span class="st">"lm"</span>)</span></code></pre></div>
<pre><code>## `geom_smooth()` using formula 'y ~ x'</code></pre>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="bar-boxplot-and-violin-plots" class="section level2">
<h2><span class="header-section-number">5.4</span> Bar, boxplot, and violin plots</h2>
<p>When plotting a categorical variable against a continuous one, we often make use of bar plots, boxplots, and violin plots.</p>
<p>Let’s start by creating a bar chart to show the frequency of each penguin species. By default <code>geom_bar()</code> will assume it is being used to display frequencies, so we only need to specify the x aesthetic mapping.</p>
<blockquote>
<p>Note we can set the fill and colour of geoms manually. The fill is the internal colour, and the col is the colour of the border. The size argument for bars specifies the width of the border.</p>
</blockquote>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1"></a><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(species)) <span class="op">+</span><span class="st"> </span></span>
<span id="cb21-2"><a href="#cb21-2"></a><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">fill =</span> <span class="st">"lightblue"</span>, <span class="dt">col =</span> <span class="st">"black"</span>, <span class="dt">size =</span> <span class="dv">1</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Let’s store a ggplot as an object so we can experiment with a few things.</p>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a>p3 <-<span class="st"> </span><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(species, bill_length_mm))</span></code></pre></div>
<p>Often, we want our bars to indicate a summary statistic of a group, like a mean or median. To do this, we have to tell ggplot we’re plotting a summary statistic using the argument <code>stat = summary</code>, and then use the <code>fun</code> argument to specify what summary statistic we wish to plot.</p>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a>p3 <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"summary"</span>, </span>
<span id="cb23-2"><a href="#cb23-2"></a> <span class="dt">fun =</span> <span class="st">"mean"</span>, </span>
<span id="cb23-3"><a href="#cb23-3"></a> <span class="dt">fill =</span> <span class="st">"lightblue"</span>, </span>
<span id="cb23-4"><a href="#cb23-4"></a> <span class="dt">col =</span> <span class="st">"black"</span>, </span>
<span id="cb23-5"><a href="#cb23-5"></a> <span class="dt">size =</span> <span class="dv">1</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>It’s bad practice to show only summary statistics, and showing the raw points along with the mean bars is better.</p>
<blockquote>
<p>Note that to avoid the points sitting on top of each other, we can add a small amount of “jitter” to help us see their distribution.</p>
</blockquote>
<blockquote>
<p>Also note that the order of the geoms is important! Try placing the geom_point layer before the geom_bar layer and see what happens!</p>
</blockquote>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1"></a>p3 <span class="op">+</span><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"summary"</span>, </span>
<span id="cb24-2"><a href="#cb24-2"></a> <span class="dt">fun =</span> <span class="st">"mean"</span>, </span>
<span id="cb24-3"><a href="#cb24-3"></a> <span class="dt">fill =</span> <span class="st">"lightblue"</span>, </span>
<span id="cb24-4"><a href="#cb24-4"></a> <span class="dt">col =</span> <span class="st">"black"</span>, </span>
<span id="cb24-5"><a href="#cb24-5"></a> <span class="dt">size =</span> <span class="dv">1</span>) <span class="op">+</span></span>
<span id="cb24-6"><a href="#cb24-6"></a><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">position =</span> <span class="kw">position_jitter</span>(<span class="fl">0.2</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>If we want our plot to show more summary statistics of our data, than just the mean, we can use the <code>geom_boxplot()</code> geom. In the example below, we also choose to colour any outlying points (more then 1.5 x IQR from the median) as red, and to help visualise the distribution of points by adjusting their alpha (1 = totally opaque, 0 = totally transparent).</p>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a>p3 <span class="op">+</span><span class="st"> </span><span class="kw">geom_boxplot</span>(<span class="dt">fill =</span> <span class="st">"lightblue"</span>, <span class="dt">outlier.color =</span> <span class="st">"red"</span>) <span class="op">+</span><span class="st"> </span></span>
<span id="cb25-2"><a href="#cb25-2"></a><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">alpha =</span> <span class="fl">0.1</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
<p>Similar to a boxplot, you can use a geom_violin() geom to show the distribution of points for each group. The <code>draw_quantiles</code> argument takes a vector of quantiles to draw. In the example below, I used the 1st and 3rd quartiles. I also use a <code>geom_point()</code> layer to show the median for each group, just like we did with <code>geom_bar()</code>.</p>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1"></a>p3 <span class="op">+</span><span class="st"> </span><span class="kw">geom_violin</span>(<span class="dt">fill =</span> <span class="st">"lightblue"</span>, <span class="dt">draw_quantiles =</span> <span class="kw">c</span>(<span class="fl">0.25</span>, <span class="fl">0.75</span>)) <span class="op">+</span></span>
<span id="cb26-2"><a href="#cb26-2"></a><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">stat =</span> <span class="st">"summary"</span>, <span class="dt">fun =</span> <span class="st">"median"</span>)</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="factorial-bar-graphs" class="section level2">
<h2><span class="header-section-number">5.5</span> Factorial bar graphs</h2>
<p>Sometimes we might want to plot an outcome variable, across the values of two (or more) categorical variables. Let’s say we wish to know how the bill_length_mm variable varies with different combinations of the sex and species variables. For this, we can create a factorial bar graph.</p>
<p>In the example below, we map the sex variable to the x aesthetic, bill_length_mm to the y aesthetic, and species to the fill aesthetic. We add a geom_bar layer that will summarise the median of each group and, importantly, set the argument <code>position = "dodge"</code>. This means that bars for the same value of x aesthetic (sex), but a different fill aesthetic (species), will be “dodged” so that they are adjacent to each other, instead of on top of each other. Other position arguments include <code>"stack"</code> and <code>"fill"</code> (try these out for yourself).</p>
<blockquote>
<p>Note: In order for the <code>geom_point()</code> layer to be correctly dodged with the bars, it must use the argument <code>position = position_dodge(0.9)</code>.</p>
</blockquote>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1"></a><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(sex, bill_length_mm, <span class="dt">fill =</span> species)) <span class="op">+</span></span>
<span id="cb27-2"><a href="#cb27-2"></a><span class="st"> </span><span class="kw">geom_bar</span>(<span class="dt">stat =</span> <span class="st">"summary"</span>, <span class="dt">fun =</span> <span class="st">"median"</span>, <span class="dt">col =</span> <span class="st">"black"</span>, <span class="dt">position =</span> <span class="st">"dodge"</span>) <span class="op">+</span></span>
<span id="cb27-3"><a href="#cb27-3"></a><span class="st"> </span><span class="kw">geom_point</span>(<span class="dt">position =</span> <span class="kw">position_dodge</span>(<span class="fl">0.9</span>))</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="facetting" class="section level2">
<h2><span class="header-section-number">5.6</span> Facetting</h2>
<p>When we have multiple variables we wish to represent in a plot, we can create clearer, more effective visualisations by splitting the data across subplots, or <em>facets</em>. To illustrate this, let’s start by creating a ggplot of bill_length_mm vs. bill_depth_mm, and colouring by the species variable.</p>
<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb28-1"><a href="#cb28-1"></a>p4 <-<span class="st"> </span><span class="kw">ggplot</span>(penguins, <span class="kw">aes</span>(<span class="dt">x =</span> bill_length_mm, <span class="dt">y =</span> bill_depth_mm, <span class="dt">col =</span> species)) <span class="op">+</span></span>
<span id="cb28-2"><a href="#cb28-2"></a><span class="st"> </span><span class="kw">geom_point</span>()</span></code></pre></div>
<p>If we call our p4 object, we’ll get a scatter plot with all of the data in a single plot, coloured by species. But what if, to make the plot more understandable, we want to separately plot the data for each species? Rather than filtering our data and making three separate plots, we can do this by simply adding a <code>facet_wrap()</code> layer, and using a tilde (~) symbol to indicate the variable we wish to facet by.</p>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1"></a>p4 <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>species)</span></code></pre></div>
<p><img 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5q3pgBaPLuSMEkyjyS1r5sRs3yYLaAtdyFOA0jXG9AaGA1eiaCeZZdkNpMjcxGDLVP2Jn4jfpJU01j0Xi+aQ0jN5H12m/huyx3gNNzMrzWgxV29Ja9G0pLCgwqgq83uoHJ5s6h5awqgLPXXA4Dabw78JF85lzlPBFA9qp2/1wKq+tCi21+pAK0XNW/lcgZAl1kAqP7pl9p+6038lXtH59pNe/PYLGreyuVMbj9o04N2RPZ60PYQkn0dSFLd/CZfmvy46EEPfYDqkbKksJskleNliWQzYdKTpGpR89YUQEVOFrHBNNQbTM1l0wY0q9O8AS2OyvGsHCL1bqaLRbV7SQ2Zvd1MalH9Vq7E94MCUJYTXc7JA1pL4JCmT+L7QQEoy4kuJwCVPllkMeCza0K7iZL1/qS6+U1kmkm/XscBo83q7vQRn0SXE4AKnywyHD7NcdM9hU+qm99EpZn0690cztQ3G/E/iSznbADNKNmTRQAo95PIcgJQ6UkSAOV+EllOACo+SWpo7C2xvhpRsm5+E5kmelCuRYa2CMmdLNKtTj1WNjzWL7PUzW9aniY77mZzm0XGNyX0TYbzGgD6v27xMfRL7GSRbnWMmXx7eHNhHASVrJvftDRNtqXaiDu35cHrZjoD0BBlvrvdIQB1xBimCUB9cgP6/BdLB1H/6gBQvwmAcpUBUHcP6psgpdXNb1qeJjsuetC+ZgroUEsInQmgOZzocgJQAMqNK+xElxOAAlBuXGEnupwAdCRAD9eiBwWgfc0fUGOyVDSvD60naXXzm1hpMuIKO9HlnDugz39en+XUHaR8duUe6wLPvICau5va+f2h+SSxbn4TJ01OXGEnupzzAbT8dToAPfrb+vQjC9DlVBrK+nXcAPSsAKp/nwNAX3z6H3/1SB+h/INf7tQXddYjaPAVnuT5oMfv7xePt5R21Uv19K19ACrptNaAPvvRo7vlyHn3anG0saMeDi42gFYvmID2L5p72uD49NoT9XB/1zD6VseeDtU9aHva/Px7UHM3fcD1nmca0IOSzIu6Xyw38aofLZ9WgNYvmID2zge9/+bv39eAnny0px5Ov9xbDqhjh1K9xBpBpermN2UC1DxWGXLF/BkB1NmDqus51V0dVNd5d0efinTuVg3ojdBrPMnzQY8rQB+/rV+dfNhs618t5YlXH3cfLBo8namqe4UNn59N0bP4cguvyaxH0GrArAENGj2VyIvmKkDrAbQ4fm/PGEU9f28YQXmfVJB5zmcEdQF6oMa7chvf9aBHFx51PehRd1OcEECbk0H7k6S2A63U9qG+1XEgWGFp96BCdfObMgGKHjQY0Bf/qjgqu81yU1/P4tU9HtpZfNhdHMjdTBWg928ai5YC6lq4HqfbYRbPA1RGjknSkT2Ctlt1NZKefsXazVQLgHotBZknAA0AtGpB612ib7YTec6KAlCvBYAGA3rQ9qBBO1BZK9r2oNJ185ti0gyJK+xElxOA5vkSBeNc+sx185sC0lxq6Z08Xxgn1R/ab1uSzNI0pQDd3t5e4qTewfqkCQHKErE6zc6k0OOZUwW0f/lRYV6W1HtbRJ4ZAN1WIp30O/hpTgVQvZkPG0aJ1QGg4Z9ElxOA2oAeVN/IEHSHJmJ1AGj4J9HlBKD0LJ6Sa10WvS+bWTS3Zaos7r30AdXhWdCDUk5z7kFTAXWOnAtbRA2kLJjFizpNCNBCfyND/CYegPKdiHKOnwxhcUMw+gg6PBzvlWNVACjfiSjn+MkQFjcEo8/iGeqvR9tp2ufN9wiVrpvf5EkzNS73JqBTBbTfiq49oA1+hXE7euuB4HNGgHpm7LMDdDCZnwmgBxsbOwdBp+kBUE4yfounnMlxl5hGAlR9N1KJU30l5+CCzpArPC1A7174nxs7gV/n1VsNABr3SesCaFmWIaB6v/pdfZKy050L6PMbO2pPU9xupkV3ewa7B20e/HzOCFD0oJZMPg1CG570bRtefLLz7MpfqwOU6lzlH/2bPlR5pObh6tLjr//4l+fvqauJd4rnv/h3x0nMYoC2q7NobyZiT43WYBbvPWa0HoA69uG3B6ZiAG2v6Xh2ub3ao3qmLkmqL0G+vKNB1hfSXXg0ZM8+1Kk28YmHOjsq7ck7NY+fCaCbzpvXhiTjt6wIUNcm3nEUtFsUBWjTK6pNeXWpR/2sviWOcRucElk1OL74tD+E2pOko+q75wGoUwDUC6izB9VXdToBVbcI6a5PKvtUvcVX1N7tn6okfrodAGV90roA6prFGz1oH9CiMK7wVENnPawuG0EFAD3ssYkelLSsCFDpHtS9m8mcxVuAqu60A1T/f+3W8xsXHZcik5cdxwBq72Li1k3CknUWL+20KkC5lihAzf2g9giqt+nl4Hr+azWcHmyo65Kf//xvlszim/NBg85Y9q0OAGU5rTegLLU3E7Ukej7oIQDlOtHlBKDygNp753PXzW9almZsXGEnupxnClC3clzyUauHaDFclF43vyk4TWbcWoNz7fnxRgfUczJ9UDg3BGMDWu0HTb5orlJ/I1947x02Q0AHVytFxBsbUN/lSEHh3BCMDihDS1cUgIZ8El1OAJrxSxQAaMgn0eUEoDm/5cOAcVHf+mZuPai/00QPqjRvQDvpkXOGs/jgcXIegM5/Ft8KgGoB0EMAmlY3vykizYEFgB6uI6AL47viazU9qHjd/KalaRKWFkv79CVzMS+ZpWkCUJ/kAbXPYcpcN79pWZqEs/uLErqnw5OaACgADbcAUFGnSQIaIO/qANDwT1p3QI9c9+vmfZ2s+Pmgzh40W938pqVpEhaTP/Sg1U+nOia3t7eHgB7pcz77p3ZEAsrVshVtL/TIWje/KTBNdtxD16XHcwI0y/1B9VGqPqDVNxeqK5PqLzrW1xzr5/WXHQd853EuQI3LksLqJmfJDajjwo8ZAeo54JkD0G6orL/ouL36uPmiuYDvm8uwidcCoGGftNaANld1dl90bHxVZ7mwfggDlKsl1QGgYZ+0JoA6e9D2+uLui46NS5NefHqrflgJoGegBzUwnRSgS85ZGu8e9VUPWo+UhXl5fMwIqu97I7eJ51jmBmglcyCdEqDUWXX5ZvHO3Uz6jt3qR/tFx829GiJ6UK4ESwBA1xRQtYFvZuvVjUTU/xefTGEWz7EA0HUFVED5rkkKt8wT0Ln2oHEWNwRjAyp7VWewZaaAijjR5ZRJpiVWP9l2DrGzAFTmunh+RQFoVkBbILdNMcO5IQCgADQ9mfUBtEIz8d5MERUFoHMFdARFH+r0SV3cAU1KGsjq53bRPs5E0ruZYm4CihE086HOdtiUnsWPIADKjCvsRJcTgAJQblxhJ7qcAFQGUPOUEM/59ADUbXGVUyqZbirkmxqFh0sGLVYSgPZPqnOPogDUZXGUUyoZg0jv5D04XDJosQKgzLjCTo5ySiUDQBsBUACaTeI9aO91SAkAaO4e9HAZn2sOqG9FLVABqMtCl1NuFl/98Dv54V1jQO1NPQB1WehyyiTTbeC9TsTwCkABqDdPAApAuXGFnehyAtAcR5I8LwCoy0KXU7gHJTA8Kz2o/0gnAHVa6HKKJkMSelZm8QCU6USXE4ACUG5cYSe6nAA0bw8aWAIAmuGqzj6J7mNJ7eszA+hov3kA6lFFYZ/F6nXfqXsXAAWgTgtdTgAKQLlxhZ3ocgJQAMqNK+xEl1O2B3U4oQcNsQBQMUA9Z4Q006PCNY13o9v7oGTQYgVAmXGFnehycuN6jme2E3jPRJ48zwmAAlBvngAUgHLjCjvR5QSgAJQbV9iJLmdKD9qCaOyiN3tQ+wkABaBOC13OhGRaFM0x03DqDaUAFIA6LXQ5ASgA5cYVdqLLCUABKDeusBNdzpRkhs2m7WRPltYV0MHJSwCU51SQeaYm05+xxxc6GbRYpQE6PP0TgPKcCjLPxGQG+5QAKABlOhVkngAUgHLjCjsVZJ4AFD0oN66wU0HmGRO3Y3J41OjsAcpeUTEnAOpWR6XjzCUACkCZTnQ5ASgA5cYVdqLLCUABKDeusBNdzqQedHjmkgPZwDQBqHi4iaS5ull8dw5TI9fJdqHhkkGLFQBlxhV2ossJQAEoN66wE11OAApAuXGFnehyJiWDHjSubkJOALSnlj2n03Z7B8aoNAGoeLiJpDkaoB18LqdtQzFpAlDxcBNJE4AmCoAy4wo70eUEoACUG1fYiS5nfA9qgNiBiR4UgHKd6HJGxzVQNAfO+EIngxYrAMqMK+xElxOA0oAev79fFI+3trbe2lcvTz7cuvaksUWuqJgTAKUsZwPQpxrM+7v1y9M7u8Xjtxtj5IqKOQFQv6XXdfYUk+YkAb3/5u/LEfT0y7369cnH+9WYqhVRtwgTAI2I66JyGaFzBLTaxJfb9a0tPYgef/CkOPlI4fpqqVGyg2K0DNBV58fSUkCP39urR9Gn1xpAlfh/2F4LRlCMoF4tnyQp6T60G0GV+HXzWgCofA9qHX1f0x60Dyh60AxOBZlnfFw3iGs2i9c4qg376VcKy9M7NzGLl3aiyymxm4mRDGHJQ1+AgvaDvrnXzJewH1TYiS4nAMWRJG5cYSe6nAAUgHLjCjvR5YyKu+06L6R6BUABKNOJLmdM3BpNm9D6FQAFoEwnupwAFIBy4wo70eUEoACUG1fYiS5nfA/a3xOKHjRwRcWcAGilIYdZCp0MWqwAKDOusBNdzuVxHVtyAKolWAIACkC9AqDMuMJOdDkBKADlxhV2ossZ14M20DYnL3VvAaAAlOlElzN2Fj88yS4oGcKSDFqsACgzrrATXU4ACkC5cYWd6HICUADKjSvsRJeTEbdrNLd7Omz+A9DIEgDQdEC7YbLPZjdfAqABKyrmBEAtAdC+IldUzAmAWgKgfUWuqJgTALXl6kGNxehBA1dUzAmA9tQSut0/224bO+rDV1TMCYDaaokc7FzCbibOioo5AVBbALSnyBUVcwKgtgBoT5ErKuYEQHtqWk+jB+2INd8TkyYAFQ83kTTHA1RJQ1mYL4b3so1KE4CKh5tImgA0UQCUGVfYiS4nAAWg3LjCTnQ5Q+OaraeyWG2oAWX7Hm6aAFQ83ETSHAVQe9ZeWBN5i8bi0DeMAlAA6rTQ5QSgAJQbV9iJLicABaDcuMJOdDnZPWhtsfeJ9pzQg3ItADTzLcCNwTUyHAAVDzeRNFcEqLUd77b0ABSAMp3ocgJQAMqNK+xElxOAAlBuXGEnupyMuB2QZg+63V7wEZIMYUkGLVYAlBlX2IkuZ3hcY8gsnEtDkiEsyaDFCoAy4wo70eUEoACUG1fYiS4nAAWg3LjCTnQ543vQwdKQZAhLMmixAqDMuMJOdDlZcbupuvNgZkAyhCUZtFgBUGZcYSe6nJy47c6kYnAIPjQZwpIMWqwAKDOusBNdTgAKQLlxhZ3ocgJQAMqNK+xElzOmBzVPsutzCkABKNOJLmdE3B6fmMUzV1TMCYC6BUBrRa6omBMAdQuAQlNRSV/7ZNtcahpWkJeoMIIy4wo70eUk47bjY2/Wvm0bGMkQlmTQYgVAmXGFnehyAlAAyo0r7ESXE4ACUG5cYSe6nHTclj6bw2LbNHCSISzJoMUKgDLjCjvR5WTG3V5yI3oACkC5TnQ5eXHrLToA1RIsAQAFoF4BUGZcYSe6nAAUgHLjCjvR5QyJa0yErB7UdUITAAWgTCe6nAFxh6fWFZ7ly5MhLMmgxQqAMuMKO9HlBKAAlBtX2IkuJwAFoNy4wk50ORk9aIcjelCtyBUVcwKglowBE7N4LcESAFAA6hUAZcYVdqLLCUABKDeusBNdTkbcQQ8akQxhSQYtVgCUGVfYiS5nTFzX5CgwGcKSDFqsACgzrrATXc6IuM7dS4HJEJZk0GIFQJlxhZ3ocgJQAMqNK+xElxOAAlBuXGEnupzxPaibUgAKQJlOdDmj43rGUQAKQJlOdDkBKADlxhV2ossJQAEoN66wE13O+LjoQSNXVMwJgJoyecxS6GTQYgVAmXGFnehyhsa1tugAVEuwBAAUgHoFQJlxhZ3ocgJQAMqNK+xElxM9KADlxhV2oss5cjKEJRm0WAFQZlxhJ7qcABSAcuMKO9HlBKAAlBtX2IkuJwAFoNy4wk50OQEoAOXGFXaiywlAASg3rrATXU4ACkC5cYWd6HICUADKjSvsRJcTgAJQblxhJ7qcABSAcuMKO9HlBKAAlBtX2IkuJwAFoNy4wk50OQEoAOXGFXaiywlAASg3rrATXU4AOuLXcb86bafxPzEpzfE+Mi3NZAHQlX0iAA0RAF3ZJwLQEAHQlX0iAA3ReIBCUIQAKDRpAVBo0gKg0KQFQKFJaxxAT+/sFsXJh1vXngS7PN7a2nprn+l0emfrzT3uJ80uzbHyTE1TROMA+nhrV1f18dvBLvd31U++09NrT5hOnWaS5lh5pqYpolEAPf673+4WJx/vF8fv7we6nH65px54TurdbKfZpTlWnqlpymgMQE+//K/yr/D4gyfFyUd7gT7ldmVri+t0/MF/qo0Sz2luaY6WZ2KaQhoD0PXlLekAAAIVSURBVMc31Wai3Fpwfonv7ak/e6bTr/TvgOc0tzRHyzMxTSGNAGi5kqfsP3mt+7vcoUm/O+5vfiZpjpdnWppSGgFQNYHc2roZ0cvc32U2d/+iaxnXNc0kzfHyTEtTSuPtZjq9c5MxG1QbltOv9nlOat6pBxiW0+zSHCvP1DRFNOX9oPydcOW739ofez/o6GmOlWdqmiLCkSRo0gKg0KQFQKFJC4BCkxYAhSYtAApNWmcc0Gev3Woeq399g0v/99+kGZIUADWehgGqTAB0LAFQ4ykAnZ7OPKCfbWxceOTexL/4ZGPj/L3y6eeXNzZ2iuL5jY1zn1/5unxxVfupZe3bP9eL9RutF1Cazjqgly88evHJRSeganlxcOGRek9xcP7e8xtXS0bP39MjaL3MjFMcbFQLrRerWa/10ZkHdMeYIdmAHim6nt/Yad6jXx80gO7Y/YF+3QQzX6xmvdZHZx3QKzWEDkAPNrSu6qXqdTkkKoe2B+23rM0P68Vq1mt9BED9gCogiw41ALoKnXVAO6aGm/hzt6z36E380XkAOqrOOqDkJKkcMktKG9SaSVI54ALQ0XTWAVW7iy56jiSp3UznjNFS7Wb6rBxG725cBKBj6YwDytZR3ZhCIwmABkv1pHrfKDSiAKgldfRH6Zxr06z2O10MfzskIQAKTVoAFJq0ACg0aQFQaNICoNCkBUChSQuAQpPW/wNXSzlh/7NtzgAAAABJRU5ErkJggg==" /><!-- --></p>
<p>By default, the plot is drawn with the facets all in a single row, but we can change this with the <code>nrow</code> argument.</p>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1"></a>p4 <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>species, <span class="dt">nrow =</span> <span class="dv">3</span>)</span></code></pre></div>
<p><img 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Bo8MMlETudIQNDggUkmcjpHAoIGD0wykdM5EhA0eGCSiZzOkcAEtiBqUIMGD0wykdM5EhA0eGCSiZzOkVAEffH2huDcHYcT/VyHCBPjLOLrr7/OnKgSpaCvvr7gfqKf6xBhYpRFfP11xdCFCPry6o77iX6uQ4SJ4Yuo1o62QxYo6KuvIWiAwKFETT6XYxYiaPbcqfdZ4uc6RJgYpaCJ9EFf/OCbcSe2YJAUPJBFUFLiCKKsQV99/Y77iX6uQ4SJUfZBiYl0ohQUg6QggX2JdDeHEkfiKGjR5u7kTfmvNzbOPxF1XNkAi68Xyia+3lQdOkVQDJKCBPYkjmjdBxLH4iZo0cd88fbOi7fP3RE3KYsblYfnn4ivorrL99eb6kOnCJq9uCT3Pk8+29ray7LT61sfPSs2tEsQ1E9icoJWwhTi5QoWo+zczHpwJG+65D4Atwn68uqGNEg6/eV+dvL5/tmtvezpj8SGdgmCTk40i2gQ1MnY2Zr420VLXtakuYWHpUDv1LeD8u31pvrQKYKqHAsX7+2dfnU/O/n5fWFsswRBpybaqkqTnw6GzjhIymu1c3caQUU/NGvvVwpBq031oXyCCvJa9OSLZ0Vlmjf5zdL3c6jfaOHkmmmrnQ2285o9tmNmRrTfRRN/6c7z15qmvf5ab6oPpab33gc9u7WbHX9Ua9kuCfz8okaYyBKoVISGGrSnRm129FSmchlH3gjoSSzWjRQ1ZW5hO0jK68tcyXrAVAySyk31oVME1d8scnp9V6432yUISkUXtNao3mqRT/bTUdCx4yx7YrluMTSvz/IqsrjNdCErby+JKlO7zSQ2VYdOEVSrgE8+28vknif6oOMQwnQEXdX2NaJaBTWuWMsYVtAKhpc0bdjvg5Z+Fs18PYrfxSieTmmMbE3Wbm9schnV281biKDqm0Webgn2qrufourEfdAxdIyRK02DTcoWR9nm6oPWhBIUbxbpCxz5P28bD9n9pH+fhbwWjzeL9AWObTutA3ajnxBUA28WcQ1kF1TZ43IzabiMfEQpKN4s0hc4evShn6a5WKxK4WO+y0IEzSgv6Pu5DhEmTu2DDiQ2lk5Jn0vQ/zUzyUgN+5tFBvBzHSJMZAtsFNTG3I2gnebemYUISsLPdYgwUQ8cW9W1Cup3LZs2vvdVJUoZpxO5oC9/OliJ+rkOESZqgVO6oo2gajdU64Wabz9RysgABDVfhwgTm9d9VvXCZEE7MiqN/Ot0QyFoFz/XIcLEInBSA1zR9kHVMXunkYegEqMFXRaFMc1iNva9mdVZUpq0pn6PON/7qRGzoH5+USNM1GrQknH1aHWWVoMqL863R5LLyEr6Naif6xBhotYHXdWrEwRtbs533k83qYycQFDzdYgw0RhoFHRQsEbQZmV8j3a4jKyJZgmGBH35k+pdTu2LlC8u3SE94AlBxwWa/XQwdLWegh4dHRkEff637xgEdbfTKqgDfq5DhImdQJtSPbK1vcw2sbm71Bs6soyTIQp6dCQbWu9+9av/+KsnxSuUf/SzneqhzqoGdX7CE+8HrbAaYnglyXyoXVD5RlKbKB89sS8aqaAvfvjkdl5z3n4ne76xI74cXqgFLVeIgi55hmW7Xc6C2sUyC2oLHdHsRyroYW7mhaK/mDfxoj+aL5aCVitEQZf8flC6oIT+oy6ocp6yq78oVmYX1NgHFc9zilkdRK/z9k7xVqTXvqkEver6jCfeD1riLuhK9tPdUPWL0p7X95wGi2JlfkFNo/i8hS/MrGrQssKsBHV+JQgzLFf09UFtQ/aRQ3CToE5FsUL9oYe/A4egh6K+y9v4tg/6/PyTtg/6vJ0Ux0XQ+s2gSx0k9QSaHfInKB3iD+3wPRkEffWvwqO8t5k39dUoXszx0Izi3WZx6L3NJJ41rp8+zsoHkT9c4MQNfYKOSuz2QScSpaA8GAZJTUN/XOt4XD4Of29POph2SVyvQ4SJBkHb+rPvv7mv08DMMgW9d/n35UQ31YRMZ9/uSwfTLonrdYgwsdsHrd0c9NN12DWZOPugzIIeNn3Q5m5oNRNTNd/N6fW6rV/49IuFekxHpc08NWhLNWlYNaPdyef7Ui1K/J11/EUNlUjoP6pPEHVPtgWZv4XYNn8NSk+MQtAOpaDH7YRMUj/Uz3UIlEgZ4bSBzTman4QXlspq1en7UliMoEUz31ajpaD3dqUjFi+ocTzvXDoIOknQw/ITGZoZmgpBm1Zd1KRnv1uL20xjBLWM3CHobKP4StBqeuXylujlZiDv5zqESqT3Qa23lmj3MdEH5RS0Dz/XIcLEIUFHJzKyEEGz4hMZpCa+Dz/XIcLEjqCW49zNTeCHjlNQ0svxfq5DuETybaaB2nPcjSsmFiIoCT/XgTmxt75zfMbX/G6mzneAoBCUnNgjjbRrQC3jm0W6MRA0hKCHGxs7h05v04OghpgRReQhVkHFZyPlOlVPcnYe6HR5wlMR9Pb5/7m64/hkkp/rwJvoNqaZIKi5Xzqk6joKmv/MXUGL++q3izcpG0+nCvry6o6407RGt5ncarXxfVCbn/3fdg0FLX5mXdBy2oZXX++8uPTX4gVK8V7lH/5b8VLlczEOF48e/+GPf3bujniaeCd7+dN/N7yJeb0F9R0IQfsEbZ7pePF287RHuSQeSaoeQX57pxC5eJDu/JOue+pLnaKJx31QQqBJRQhaC1r3FUVTXj7qUS1VU+JI0+DkyorK8dWv9CpUHSQ9Lz97Hkzi9bV/H2hNfx+0eKqzWNAFFVOEtM8n5f3UosUX1t7WHyxe69tMcQYmmTgkqGkUL/VBdUGzTHrCU1SdVbU6VINC0ACBSSaOEVQZxSuCit5pK2jx9wffvLx6wfAoMh47Dh6YZOIoQeX7oGoNWrTpeeV67g+iOj3cEM8lv/zJ3wyM4uv3gzrNL+LnOkSYmEARoxWURDOZqALebhc8MMlEswQQNILEBIq4HoKa6X3kow8/1yHCxASKuBhBy/ugblPc+bkOESYmUMTlCErAz3WIMDGBIi5PUHyIgsfAJBPNEkDQCBITKOJiBS0eO24nXTy9vtVOMuLnOkSYmEARlypoOf1iM5fI2a29eh4xCLq0xBgFLadfbKcLO/3qfj3fHQRdWmKMgpZNfDvp4skXz6ppRhY+/SKoiUHQdtJFMTdTPRUjatCFJcYraEHRD21rUAi6uMTIBK1QBUUfdMGJZkGGBH0uz9ddQ/s42d73gwod20kXz27tYhS/1MQhQbe3t7uCPi/e86m/tWOkoCbq+6CX9+vxEu6DLjRxQNDtbdnQam/5yYXiyaTqg46LZ46L5erDjh0+8xivxQcPTDJxjKBtVVl90HHz9HH9QXMOnzeHRz6CByaZOErQ+qnO9oOOpY/qzDdWX9wEpeLnOkSYmEAR5xfU2Adtni9uP+hYejTp1a++qb5A0LgCk0wcEtQ0ii/7oFVNmcmPx4+pQYt5b9DEBwhMMnGMoOWM3eKf5oOO67kaRvRBqfi5DhEmJlDEWAUVDXw9Wi8nEhF/X32NUXzUgUkmjhOUATyTFDwwyUSzBKEFxVOdIQKTTDRLEFhQPBcfJDDJRLMEMQsKQPgP8sLcTAECk0zkdI4EXuoMHphkokcF+8FtpuCBSSZyOkcCggYPTDKR0zkSEDR4YJKJnM6RgKDBA5NM5HSOBAQNHphkIqdzJCBo8MAkEzmdIwFBgwcmmcjpHAkIGjwwyURO50hA0OCBSSZyOkcCggYPTDKR0zkSowUFIAQQFEQNBAVRA0FB1EBQEDUQFEQNBAVRM1rQA7AkOJ0jAUGBC5zOkYCgwAVO50hAUOACp3MkIChwgdM5EhAUuMDpHAkIClzgdI4EBAUucDpHAoICFzidIwFB+3iwubn5lr7x4Xs35yjLvHA6RwKC9vDg4s2Dx19+oG2FoCGBoHYef3kl//fh+3cPHn28mbv68P1/2Ny8UiyXG6rtS4DTORIQ1E5bVd744OC7tw4evvvBwYM374rNN4ql+ssC4HSOBAS1I+rOgkefXDt49OlNYWb+N/+Tr4iN1Zd5SxkGTudIQFA7hZDvbr5xLW/JN/MvraBi6fEvrlVf5i5nCDidIwFB7ZR90KqmPKiERQ0aFgjaw4M3rpX/3Gg6n1X1iT5oKCBoH3kDX4/Wc00rQR9/iVF8OCAocIHTORIQFLjA6RwJCApc4HSOBAQFLnA6RwKCAhc4nSMBQYELnM6RwPygwQOTTOR0jgQEDR6YZCKncyQgaPDAJBM5nSMBQYMHJpnI6RwJCBo8MMlETudIQNDggUkmcjpHAoIGD0wykdM5EhA0eGCSiZzOkcAEtiBqUIMGD0wykdM5EhA0eGCSiZzOkYCgwQOTTOR0jgQEDR6YZCKncyQgaPDAJBM5nSMBQYMHJpnI6RwJCBo8MMlETudIQNDggUkmcjpHAoIGD0wykdM5EhA0eGCSiZzOkYCgwQOTTOR0jgQEDR6YZCKncyR6BD35bGtrL8tOr2999KzY0C5lEHRhid5FtGEX9PSX+9nJ5/tnt/aypz8SG9olgZ/rEGFiAkVcpqDHwsV7e6df3c9Ofn4/X26XBH6uQ4SJCRRxmYIK8lr05ItnRWWaN/nN0vdzgpQOLJ5eQc9u7WbHH9VatksCP7+oESYmUMSl1qCn13flerNdEvi5DhEmJlDEhQp68lk+hs/QB+UOTDIxgIpm7IKWfhbNfD2K38UofqmJ3kW0YRf06ZZgr7r7KapO3AddbmIQGU3glaTggUkmcjpHAoIGD0wykdM5EhA0eGCSiZzOkYCgwQOTTOR0jgQEDR6YZCKncyQgaPDAJBM5nSMBQYMHJpnI6RwJCBo8MMlETudIQNDggUkmcjpHAtMvgqhBDRo8MMlETudIQNDggUkmcjpHAoIGD0wykdM5EhA0eGCSiZzOkYCgwQOTTOR0jgQEDR6YZCKncyQgaPDAJBM5nSMBQYMHJpnI6RwJCBo8MMlETudIQNDggUkmcjpHAoIGD0wykdM5Er2Cikc564c7s/I5zw/xXPwiE72LaKNP0ONax+PyaeN7e9JOP9chwsQEirhQQe9d/n05j0g1383Zt/vSXj/XIcLEBIq4UEGzeqKbajqR0+t1W4/Z7UAoHAStJww7+XxfqkX9/KJGk7i9vc0bKCEntt+HK5GHlGrQ43a+G6kf6uc6xJK4vd2Y47WI0vdhSmQiJUHv7UqbIOh0HAQlartoQZtWXdSkZ79bxm2mmQWlVqyLFrSavba8JXq5Gcj7uQ7RJM7bB50m6JReQ31uGoL2MfoSqD83T4zPxBmKuD1J0Cn92uZcCFr93DwxPhN5A8X/v5uglFQI2mXkFdB/bp4Yn4msgYUAEJQABA0a6CYouReJPmiX0ZdA/bl5YnwmziEolaWO4vvwcx0iTAzQB518sx6CdvFzHSJMzLhej5QTFaa/nARBu/i5DhEmZuMEsp/S2VPnj9cUgnbxcx0iTBwnqEG6arGbVm2ZUJFC0C5+rkOEiZMElU6VlIWg7mD6RQfEyJu4pxyul1/KY+qFZoPxWMZSrweoQacE9lR62wrNBuMpbeM/tozMw7h1qEH9XIcIE0cKqiraXWW9NzD9PoAOBDVfhwgTjbeZrAMe9Ripa2muUSkFGf5WjEBQ83WIMNE0SGq29Hkhmaj7OUbQvuMhaBc/1yGexOZ/vFfQoYjmKJug7mJp31NZQR+0i5/rEE1iq8MYQTs9AK0nupL87G24rd9TXcNtpi5+rkM0ibKg5j6oaotxryaoUsRhQfV9egUKQXvxcx2iSRzoZXb7l5a9jIL27ISgXfxch3gSO620vrdV0yqoZHdXJ8lwawn6h2DtCgTt4uc6RJdos6T1c9vwCFFvD7W3anZOUYCgXfxch/kSLTYMtsGSqG7hSuXLxFIFLR47biddPL2+1U4y4uc6zJZo9cU4QJLOMXQEOg27sttVagoLFbScfrGZS+Ts1l49j1i2HEGbQHW8o/Y967pU6ZJ2K9huv4DJ0WUKWk6/2E4XdvrV/Xq+u2x5gqq1ZWuptFem3aSJutI2sxi6TEHLJr6ddPHki2fVNCPrOP3i9sBb3QqXmoVmTd0v72uWmoOVHdIBoIdBQdtJF8XcTPVUjGtXg/YGbpvac63u61SsWsW5Wqlt/2q14hspLbkGLSj6oW0NKvBzHXwm0lxojs5WsnbVLrOhK2Vb18Lqa6ZEEcpkBYIKQRPvg9Jqq/bobCXZuC33Lqvl5gQ9wPaNE2g2EhK0nXTx7NZuyqN4u6Cm7ZKgauNdW6lsWMkS698Ngk7B6T7o5f16vJTwfVCroMYdWj9RZqUvykcZQiHoFBb0SlJPBWpQVPULgs7FggS1obvVbJV2S7fX9QG76rFtRS2i7XdlLBC0i5/rEDbRUvs125Sl2s1Ondo5Qw/Udcz6fh9GAUG7+LkOQRM1CRVFuoK2y0ZBO4cZ6+WyiN0dtkMdgaBd/FyHoImdOs+4T37eRxJUf6nSJqhBOwhKAII2S9377p1AWVDtJCVAFthURFMLD0GNLFnQlean2RLp3UyymcAFSHcAAAOlSURBVCut32gy3CqovSjjgKBd/FyHeRJ7mmS5rmx2d20ynWvzM5IfmpbI6RyJ9Ra0r17S+49GQbc7WKPcG2kISmCtBe2TRlHK2md0EpToJwSlsNbTLxbW9O1T9jaembYZ9vVEATaWVoPqzbp+ur5xW+mDGpPsnU2HIvKAGrSLn+vAnGjy03LjsnuAFLi9va1nmU+nF5EFCNrFz3XwnWiu6yzaVuuUJIYixpnI6RwJCLrqqw3tGkLQMCxMUHONSNNw23Yz1BUISmBpgmoMyWYeZk18s9zcP/SYRE7nSCxb0GHZjBUoBA0HBB2STQ+EoEGBoFRBp78dHoISWLagLrLNXcQ4EjmdI7FwQecITDKR0zkSEDR4YJKJnM6RWLs3iyQwq1kCRYynjBA0PAkUMZ4yQtDwJFDEeMoIQcOTQBHjKePaCQrWCwgKogaCgqiBoCBqICiImrUSVHySkzrNbmRUH4oWcxHziygmLI6mjGsl6NOtPe3jxiKj/FC0qIsoynj80bNoyrhOgp783T/vaR/1EBfVh6LFXMSicFlEZVwjQc++/a/81179sJy4qD4ULeYi5oX7z+IzCWIp4xoJ+nRXtEvqx43FRfWhaDEXMTv5rPgNiqaM6yNoflXPIq9BC+7tRV3EqnDRlHF9BBUj5K2t3Wg6Tzbu7UVdxNN/KcyMpozrI2hWDo/VjxuLi+pD0WIuYjGKLxqjSMq4doJGcwPPRPmhaFEXURTuw/vxlHGtBAXrBwQFUQNBQdRAUBA1EBREDQQFUbNwQV/84Jv6a/lH32Hi//67dzfgBIJKi26Cil0QNBQQVFqEoPGxeEF/vbFx/om5iX/19cbGuTv54m/e3tjYybKXVzde+82lP+Qr7xTniW3N4b8pNhcHKitgGksX9O3zT159fcEoqNieHZ5/Io7JDs/deXn1ndzRc3eKGrTaJudkhxvlRmVlnp9rfVi8oDvSCEkV9Lmw6+XVnfqYYv2wFnRH7R8U63WYvDLPz7U+LF3QS5WEBkEPNwreKbaK9bxKFCc0fVC9y1r/o6zM83OtDxDULqgQMmtVg6BzsHRBW6e6Tfxr3yjHFE3883MQNChLF7R3kJRXmbmltWr1ICmvcCFoMJYuqLhddMHySpK4zfSaVFuK20y/zqvR2xsXIGgoFi4omedVxxQEAoI6I/qkxb1REBAIqiBe/RG8ZmqaxX2nC+6HAw4gKIgaCAqiBoKCqIGgIGogKIgaCAqiBoKCqPl/NP5py1A7/CcAAAAASUVORK5CYII=" /><!-- --></p>
<p>If we wish to see this relationship by both species and sex, we can just keep species mapped to the colour aesthetic and facet by sex instead.</p>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1"></a>p4 <span class="op">+</span><span class="st"> </span><span class="kw">facet_wrap</span>(<span class="op">~</span><span class="st"> </span>sex)</span></code></pre></div>
<p><img 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" /><!-- --></p>
<p>If we want to create a grid of facets, where the rows and columns correspond to different variables, we can use <code>facet_grid()</code> instead. The variable before the ~ will correspond to the rows, and the variable after the ~ will correspond to the columns.</p>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1"></a>p4 <span class="op">+</span><span class="st"> </span><span class="kw">facet_grid</span>(sex <span class="op">~</span><span class="st"> </span>year)</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="customizing-labels" class="section level2">
<h2><span class="header-section-number">5.7</span> Customizing labels</h2>
<p>If we want to share our plot with others in a report or publication, we’ll probably want it to have a nice title and labels. We can easily add a title and change the default labels by adding a <code>labs()</code> layer to our plot.</p>
<blockquote>
<p>Note: by giving the col aesthetic a label, we are changing the label of the legend for this aesthetic.</p>
</blockquote>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1"></a>p5 <-<span class="st"> </span>p4 <span class="op">+</span><span class="st"> </span><span class="kw">labs</span>(<span class="dt">title =</span> <span class="st">"Penguin bill length and depth by species"</span>,</span>
<span id="cb33-2"><a href="#cb33-2"></a> <span class="dt">subtitle =</span> <span class="st">"Data collected on Torgersen, Biscoe, and Dream Islands"</span>,</span>
<span id="cb33-3"><a href="#cb33-3"></a> <span class="dt">x =</span> <span class="st">"Bill length (mm)"</span>, </span>
<span id="cb33-4"><a href="#cb33-4"></a> <span class="dt">y =</span> <span class="st">"Bill depth (mm)"</span>, </span>
<span id="cb33-5"><a href="#cb33-5"></a> <span class="dt">col =</span> <span class="st">"Species"</span>)</span>
<span id="cb33-6"><a href="#cb33-6"></a></span>
<span id="cb33-7"><a href="#cb33-7"></a>p5</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
<div id="customising-the-look-of-your-plot-with-themes" class="section level2">
<h2><span class="header-section-number">5.8</span> Customising the look of your plot with themes</h2>
<div id="pre-built-themes" class="section level3">
<h3><span class="header-section-number">5.8.1</span> Pre-built themes</h3>
<p>You can customize pretty much any feature of your ggplots, such as the presence of gridlines and tickmarks, font size, and the colour of the plot background. While you can change all these elements individually, a good starting place is to use one of the pre-built <em>themes</em>. Below, we reproduce p5 using the different pre-built themes, by adding the theme as a layer.</p>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1"></a>p5 <span class="op">+</span><span class="st"> </span><span class="kw">theme_bw</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1"></a>p5 <span class="op">+</span><span class="st"> </span><span class="kw">theme_classic</span>()</span></code></pre></div>
<p><img 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mvhp4KuoCmcIbGFgbqbZkOauzaETRcQNAgI2hcQNAgI2hekBU1PhV5mTq3nG2oOnxrISNPuVHPJkW3WrnPkUBbUOTLP+Og2NFnbzimlatEOMbI8ELQSELQrIGglIGhX0BI0m3RaG7I5aKZQsiaTg15mC2iHS7e0Xg5at54JQUpQK9Cl/CwhUU6UzVQmFzqaCHCCmFoEBIWgpIGgEJQ0lmerGWevdMFy5PuQssiRNgZVq5bs6rI71shBG4CrCT8L0J5tD5WZq9nOaYmCZQmJWBWiWk7cLFdJhyB4BpF4tv3ZuV5rvSgoWBoIyoGgQfSQg0JQDgQNoo9BUrmEska1lP1E+hmG5dlmwQdJu4X9e7pgN9TxzVW2aX+naBwbV/tGLJv7xcMZL6ZnNyfzagW7oU6P7SrbdAYwxT57xYLZenbU3hFMz7aHJQ4EQdNMUNCbE27M8u5Fa4ewIygErcEkBVVz5pv7X85mzNObE5Ejsp9z0cWrVTyDLBtsLc/WQdmno2A3VPPp0nyeQwP15aea+X7KLR6BB+r2Wp3W2Sx2z1mKyNPE1d0L9pP1yLGgahVPRzeLkoYOaZBUicbnmioESlnEU3K40ZeNkuZSvFhBHuNiM9XgyFz1IDz8aewuPugkZ7YgZSBo62wPZXiMLVRny1VnHK9PTqAvucolGdAgqRoQtH1iCxNB5XjJFFQPobaHYd2zQceDpAqXZ6SvAckpm1tl4LHCBamgUrkcdDiuyo6cCcq7+Afn651Tcwvv4s150lJBkGHnoCWyhEqCVrjATe8rlnLK5lYZeCxCIYxQUwpZMvnYKEgPkuJ4GSupBkx8kCRW8aBaeuLe7uJnLQ+SIGgAhJpSzEpen8mnmViGyeaUhLXWNBNbtS446+Sk23PxEDQAQk0Jp/YpzTw6vlgkPwe1VpkvKuSgzroKPNVS9H6JyQD97EjQHudBrUBXc+TtLB5cZ+PD/knQiaB9XiwCQYETKvOgEBQ46fdiEVvK4Hyx6NT6pUuyYO0Cd7xXfTBjlRtixtkhvV4skvaoVpRrMgYHcO9eZUOtcoMcs3dIr4MkCApBi+j1YhEICkGL6HeQlLYoPefp3kuvyk87G/XToVANPwnkoAP6nSB1Rb2WzNQtPxq6928ch4n3agnaN0NqebcXixQAQbthSC3v9mKRAiBoNwyp5aSebheeg7r2b09Rdw46pEwuxYBaTkvQ6ly2aijojR4eHtYKEHSkWI9flIP4lh+/2AoQdKRYni27eYBtLezZeGt9yZuVCjeCumx/KiOdnsHcPDgvdf/x0HJQw8OwkOndC1G3Kdyf4/qvZbCzBC1VMQSFoA3g/iBvvvj1X1zw5PHOoyN5z7GMoME3IHs9u/r4efRin3HMXrLF956HFGwPCEoS9we5eftiGUfO5R5/AF78YzVXgooXAfg8e6V0fPXwJfvx7DiwYGlKiGLnoAHFWshBw6YRBzTZWBv3N7GKzZzzKaG4i2f5aLwoBJUvAmr2ePbs3d98zAW9/uyM/bj95iysYGkqD8EDirUQJMNOxAzpdE19nD38CT8tybPO5RE/T7lzKgU9DL0FubCLj3nxPn91/anq69+MgaCN7DVm4h6emykjqAiYUtCg6MnwXrAsBJUBNLr66MyIohC0kb3GzIqN3OM+Xueg67sXOgddBz321ntXpxBUZqCCJA/tKwfNFGxgl7IgBw3h5p9ZoIuzzbirl6P4uFPXo/iwh4x4L1gWgj57bKxqR9D6YDQ+UrwXLHNBk16dRdLbb3ueZsoB80VjxXtXJxdUpKBySvTdM2fB3oGgY0VfzSSvVh7mI8Ah6Fihd6oz3zSfgyX9LBxiHxwcOEsFrvQduPg4QOMYJIU9vqEtQfNjYYNRsvB+jYMDhznOMuVmk9J7O48DDCCoEwhKBe3ZKslBa5/ErwMEBSY9PbjBf4VRyJb0yad0Mb/MyEGHQj+DJE8wDIyTl6nTo+liGNaPBNsz3s2HhVEICrrA8mwl/mhd+/ckQVAQSLej+ESaHHvY6vwrmxw5qF6VXmrJz46uAEFqquhU0KKwJrfn7OZY7dqz1djZ0TV0GNwn2Ofid05b7eIhaCBjEZT96a67F+pOzswNnSF3eLoeHhZ0Oh6CtsjwBHV+LHxMs+QXKTsLlRW0FPVy0JxNcrtjN5md6m35maa9R2N0+UC7en7WKVyxrPODEY9tuDk52jz4SzY5xK5VfvsXfJpozWIgu/X4u7ce7Z6zOzmOou3Pfum4iJnKxSKp8U5uqFQL+XGyeI9KdPm8xXoBtE7pqmWdH01yT8dmkdztIZbYLUnyFuTFEReZ30h39yI7/snMgx6tgm4VgaAtMhZB1Tlz1pWLWz3kknwkjvEYnFhZNka/+SIdQu1nM9394fAo8O/NQdD2GJ6gzhyU39XJF9KCstsz9f1J/KFgu8LaZfo0UWqaiVncz9VMpk2WqzkL+fa1moM2WWM+9YZIPeSgTowcNC1oFBl3eDLpZFj1R9A+BXXHu1ZH5KBtzFG8JSjLTrWg/P/7p9vDueNWZPtUJ+viuzjVmQWCjhFjHtSOoLxPj4Pr7ncsnMZDnzuPjrY//avCUfw6+PGgEBQ0TPIwUQsq00w5GaM19dlUpe0QlqAObQK+Q4gLmk/1MNplAA4bQg3vFFHfDOC2Ywg6ZVzXgwZdsQxBbSBoO5C7q9NBiGWeFNZN43OayEFbYQiCBlA6VuLhiAOhn1s+GgeCjhXHPGgHN801DgQdKwOYZgqi9GgIfg4DUoJmLw2pSUOPQvbsAM9bhpKgmWvq6hLWjxfu5dkBmULbQFAIShoICkFJQ0lQz+XJjn1CaD0HbcbP0c7er13PSiz352Rpn4vPhlJaF+A1E0DHcP7T+Q7W/JrP9LR6RUHLAkEjCJrgfAvir8awO5PkHzrm9xzzZfnHjgP+5jEErQMElTjfgg6V8g8dJ3cfqz80F/D35mh38XVz0IbI98fpZ2lpB+9njqDqrk79h46NP9UZr5Q/vDXTjqA0KBnhJjmyd31Cyf3F+g8dG7cm3XxxKn94K4agxUDQaogcVEbKyLw9vkoE5fcck+viSQBBK8Kflsj+Sf7QsXpWQ4UctCwTErRsjsj9zC+T3SLXDD8VTcOeCiZH6+JBIuz/m5PxjOKHS37UzW6Ra0YwmG8Bw7PlbLbHlA96NBME9QNBG0J7trx7cXMyO3JM/RcUBA4gaEMknvEhF78dqShtTRVsAkqT7w2RzSvV4oRy0AYwR/FK0M5H8bRODzWJERURICsBQVsFgtYFgrYKBK1Ly4IGiud+eu0AKHTuQHuJTLMK7V4sUiE0DiqahkRFtQ/G6pVod6IegkLQmkDQOkDQ1mn5VGcF2fKKkPTWK1sq6dSGdtyOQTOYc/GDiqycTKxsL3iOOCxD0NaAoE0AQVsDgjZB74IGazc0Px2J4YHCvbnJQ42GvgUdXmCshWHoiKNek0DQToGgZYGgnQJBy9K3oINILbMmlXdLT4pWzUGn6XPvgg6AbKwrH/3qx8uJRlwIWgwE7REIWgwE7REIGkCDOWijrZgCEDSHRnTQleQ5XvkwU9EVgrpppEO1RuzOLKHyYSbT4UNQNxCUCBDUDQQlAgTNoeD792/OzsVXyUG9x5iInxC0Gv4A1kx4m0yQ9AJBKwFBuwKCVgKCdkXDgg7h0o9GsNzJiNSMWfAzalrQiV08J0GoaxEIWh8I2iIQtD4QtEWQgxYQMuEJP9sDo3g/XQzXgQcI6geC9gwE9QNBewaCFhDmZxlPD/Ag2xJA0Or4LlYqLIXgGwYErQ4E7QAIWh0I2gEQVFJFF9/lnvmV6xwUghYDQQWtBjSrcvUCITQICCqAoESBoAIIShQIKik1ygndwTlNWmXudLpA0LIURr5qg3vgBoKWBYJ2itezq4+fR9GL/f39956zl9ef7j98GVRwzEDQTvF59oqL+exYvrx9chy9eD+k4ChJXFPz7I7tB+a2A2M4ZFcRPH8KfJ49e/c3cQS9/eZMvr7+/LmIqUUFR4kdDbOx8SAhvUtmASG2BIVdfNyv7+/zIHr1ycvo+jOm65sxEBSCdkGhoFcfncko+uqhErSw4AiBoP1QPEhi8DxUR9DCgmPEVsmXg6Z3QQ5agxKCTjsHNYFW3VEoKOvYb79lWt4+eTzlUbwGHXOHBM2DvnumxkuYB40gaKfgTFJ5IGiHQNAAHCP2Rq8uAflA0GKcETM8jCLg1gGCFgNBewSCFgNBe2RCgpbTxDnlXq06+FmD6QhaLpAh7BEBgjaxN2gNCNrE3qA1piNojRxUv8zUkb0ipNLRQB4TErQOMqBm4mr2mrr0BlAPCBoEBO0LCBoEBO0LCBr25C9tqLEmsnNQqx7jBVStAQQtCnbZ4HmQEFQPgmkdICgEJQ0EhaCkgaDZ9DG72fiRLGaLIAdtAQjK8BmaEyrD4i6oCwRlQFCyQFAGBCULBOX4EsqcbTmJqNqhSN/KTZ0YENRNWATM2auoMMJrOBDUDQQlAgR1A0GJMGlBTU0cI6GCEqlM01eZt5qgEpNlyoKaflWImHaRelERMTUPCJpdDisBQTsBgmaXw0pA0E6YsqCl0kZzrwPXDUqZCoomQ3ObAgwmLWhFSkRbeFcXCFoeCNohELQ8ELRDIGgFgvNV+FkbCMpoQiTI2AoQNGpmkgfhsh0gaARBKQNBIwhKGQjKOEju2/TeO+dVEH62AgRVFEwLYdaoHyCoAoKSBIIqIChJIGiCU8AkO/XnoDC3LSColwNN4W5dtWlaQFAvELRvIKgXCNo34xa0cF4zrAaVg9arClRh1IIGjMrD68nfG362CAQNrQeC9gIEDa0HgvbCqAWtkoPmrvTnoJWaB4oZt6DlQTQkBgS1gaDEgKA2EJQYUxO0UL9gPyFyJ0xM0OYCJEJtN0DQ3msCPiBo7zUBHxMTNOS5tLVqAg0zNUGdIBrSBYJGEJQyEDSCoJSBoAzvNSWdtgSkgKBeEFv7BoJ6gaB9A0G9QNC+gaB+4GfPQNDSwNkugaBlQa/fKRC0LBC0UyBoWSBop0xcUFwlQp1pC4poSB4ICkFJA0EhKGmmLSjySfJMXFBAHQgKSANBAWkgKCANBAWkgaCANBAUkAaCAtJAUEAaCApIA0EBaSAoIA0EBaSBoIA01QUlypt9N6CYATQx08YmnSvnWW9Hbok3+25AMQNoIp02QtDuGUAT6bQRgnbPAJpIp42jExSMCwgKSANBAWkgKCANBAWkGZWgt0+Oo+j60/2HL/tuSQ4v9vf333tOuonxh7j/7hmdj3FUgr7YP+aSvni/75bk8OyY/Uu6iayNrx6+JNPGMQl69Q//eBxdf/48uvr4ed9tcXL7zRn7QbmJvHERoTaOSNDbb/49/rW/+uRldP3ZWd+NcRJ3m/v7tJsYN+7fWBdPpo0jEvTFY9Yvxd0TjU/WwdVHZyyKUm5idPVz/htEpo3jETT+VG+JR1DOs2PSTZSNI9PG8QjKRsj7+4/JJE95PDsm3cTrf+JmkmnjeASNxPD49sljGsNPB6zfvP32OeUm8lE874yItHF0gpKZwHMRR3lKc4xO4sa995xOG0clKBgfEBSQBoIC0kBQQBoICkgDQQFpJi3ozQm/5/soijb3T8V/bLX84eT109Tmm5Oj/ANsf5pfEwhi4oLuxf+uZ1yxIEHZJnvzau47wvruRRMNnTAQVP5bUdCCGOmNryAACMr/zXTxrPffPY8Xv1rwHGB7ONv56sF38Yu9zf0vRV7AWMUhUuy0t+F7Wi8QQmsDQblDaUFvTuZcvs0i3rjaPd8e7sWO7p7zCCrXJTWIFTOx1nrhz2dBABMXlA+SeKS0BV0zubaHR5vFkX69UoIeJeLFu0RyhVxrvZA7gOpMXFAWQbeHcaRMCboSz3Tb42vYa9ZTbx6cJzmoJWiyQtWh/5EJLqgKBI37eB4ZbUFl6ghB+waCOgVd74jUUZnGu/j1bnlB0cXXBII6R/E3J3HIjC1VpqlBEvPNFDRa7vkFxSCpJhMXVGSajjNJbNOOES3ZNNOXcRhdzuaWeHIKIFfQFaaZ6jFpQUvimtPERH3LQNAgWE7K50Yz4FRnu0DQMNi8k1NFXCzSLhAUkAaCAtJAUEAaCApIA0EBaSAoIA0EBaT5f5J0qrjVO421AAAAAElFTkSuQmCC" /><!-- --></p>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1"></a>p5 <span class="op">+</span><span class="st"> </span><span class="kw">theme_test</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb37-1"><a href="#cb37-1"></a>p5 <span class="op">+</span><span class="st"> </span><span class="kw">theme_dark</span>()</span></code></pre></div>
<p><img 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x00SUMA6qvtO5OUpCEA9VU+gDpOd9IE1DcHbWx2y0E9r3kecBTiZI6kbAB1XTBKFFA/R9fVqJqj710j/Y5SnMyRBEAnNwSgFAHQyQ0BKEXZADpdDtr6oKiAjsHkCBty0K4YOsHSDwk4tkN1zCYyhTvM4i2K0w8JOAJQmyMncyQB0LYAqM2RkzmStgBQbp64c9AOg0uznYdPAGpRnH7wUCviJTeL70bJZc92fwHQruL0g4cAKAC1KU4/eAiAAlCb4vSDEPUaY1IOqi7KJ9iz56D6vcvpd0cB0K7i9MNJ8F0aw9/VpRaX4bjakXIkcnoFVgDaVZx+OAGgALQhABpmOC4AGqRNA2phpbWJiOvQd3VZ3RjqY+g7p+nLQXsckYM2tGFAx6MZNaAOfFd+sVkb8s264znyCYCWAqCsjnwCoKUAKKsjnwCo6od+ZGoPEDkhZKIjOSilcS1DtnX1iI5sAqD2fjBqhjv34JfcmaTtcORkjiQAOrlhlo6czJEEQCc3zNKRkzmSkgK0AWGTyBatro4OGiN/GpzC0lEA2lWEfnAMk8PFqN/V6IdOAmjghB6AdhWhHwAoo2OgAKilHwAoo2OgAKitHxwnQshBXRzDBEDt/dArF3ZlmY5juyZ1tT72l8+xWg9Au4rTD31yGf3LMm3Hdk3y+c7IXz7L+U4A2lWcfugTAHV3ZBEAtfdDnwCouyOL5gVoPxIBOWjPlmV793gOOkws25dfgbhsbuzcQ0cnFoB2RTjYgaDl37N9psvW7uAL+ri+fIPi0rbR9spZALQrwsECUCEA6iUACkBdHDmZIymBHNR5iXNk02X9ymYJnf7Hu3m6ifWi3nOavhx04JWrAGhXbP3gPkF3KaNKXhoxNLH+GQyzblxuRxAAdWhi/TMA6LQCoA5NrH8GAJ1Wmwe0LwVsbB4vo2Hs5KAcTax/YPi6+rKVZVociR8CQLuK0w9GxMBavWzP4oMV4ctvRmFLTKaGaQDaVZx+MAKgFEeehg04cjJHEgAdEwAV4mSOJG9Al7FVADb4Xm3Tm12KB6gAJpqbflfbyvtxDOJkjqRkI2hb1pBomajHue2Y7yk1J50IqhXwGYigXcXph34B0CHHwBaNO3IyRxIAHRMAFeJkjqSkAW0vhV52Tq13CbVfDxowYyoN2+z4sVTWaq+DhnmenABQm+L0Q0Nt9uwnh1pbrFczhczprU30i3aqFs4kEQRAvZoIQKcSAPVqIgCdSmkB2k06Gzu6OWinUnV2v5ODWh4+Zhh2b6JSWA4a6tMWAO0qRj80Al2LT9cIaL0+6tJqpl6MmEf98plWBwBoVzH6AYCGOzIJgNr6AYCGOzIpTUDPF1L7LhUDDr7GQ28O2prXuPBZlqlfYdq065oF5KAhUo4STeSgIzKAXh9pMs8XO6ejFf2PvY5eX896TLtVlWVnS6BiffmcJ6fmAOj1nz41Wxtv7PI/dgAq/wWgTtpADgpA5b8A1EmbmCT156DWMjTbZWdLoOLmoKyOjEoT0Ks9OUm6NTq+r6e/munEkbceR1tdN37dm+hK3DadSRLz6qEZy9W98enMoOqA3jzeda8Ypx+G5DZi2x1tdR0zAOcmOo/ZWwTouQhmq8VxCILDqgN6fUT4oDj9MCQAukFHOwQ3jyUxZ3cuAhAcVjOCAlBXQ4tmCaheM7+69/FiITi9eVzmiOLnbjnE600yg6QG20YOunLKPjcFqGcOell/ngPdr2vYi2E/n2qP+mE76LAZ08aG+JU+rXO1d+upSBFlmnh+50L8FCNyAajeJNPRqz0ioTlNkrwcg9ea2oYey0Oqiq5pOejANacNzuLFLGlXgVcgKGNcQaaeHNU33XcPf3ZATcAGoP2GALSt6yMVHgsK9dlyPRgX26sT6GcS5QBAE58k+TkCUBbHQRYKCitA1XypDqiZQl0fuQ3PNU08SWpfnuHQs40rPk56M0e9uScHHVUvIO456Ki5Yw5K/4BNAaoGcgGoHOLvP13tnNb3yCG+vk5KCoJCzRyUkCV4HXbnArfxnjVly1c9EbHa7BlB+0PY5HNuj2i6sQh6JuATsyAzSSriZYGknjDJSVK5SQZV8sJ9c4hfRJ4kAVAHx5wALa/Q3FfLTCLDFGtKJbWNZSaxaTVy1mkUUJK8DhuAOjhmBahW8CnNPk18sUh/DtrAqP7GIwe1eo1waqBoFZx+WT2fHNRoGkA3uA7aCHQhM2/rLR8Uz3bBHM/78DuO4TAJoJu8WASApu3IDp6rUlkHBaBpO7KD56rNXizShNI5Xxw6ta5y0G4BZ+Ydc9C73ovry0Y9juuWZwLo5BeLtDnyjnK1bUua07h6vvy7d70JbdRjufNjLoBOPUkCoAB0TBu9WASAZg5oT+UgIlva7CSpTVF7zdNeymxqlDc5qL2Op5Z9f2kr4xx0/DOTBHTjV9SbuFePgP3R0FaePZgse/9OjDegHM0KcXRoeZKAxr9YZKQfAKinZgJo/ItFRvoBgHpqJoCSROsSx35wzkFt5Rs5KJ96c1Dv7HHjgOaag5JE7BLHfvDWpSY0BqBwTADQ6R4eNtQP3gKgcR1JEIRjadR4/KKaxEd+/OJQP3gLgMZ1JEHAAaZWY4g/m+YBtgP9MK5aQtrITXtz0MEVUYfl0hxx4nckQVDtv/6einRmBfPq/lPS/ce55aC1Kb19dk+6ot7lhFOOOPE7jkHQ6Mdq/+qvVLBrAEriDIACUBfHEQiaHal33/z4Z39+IZPH2w+O1T3HKoI634A8COirD56vXxwIPRJvxct3ngPQUOXo6AXo1TcvzorIebYvH4BX/Djf1YCWbwIB/VLj+OW7L8WPZ49sbePoBxdQTFc0eqVdbYoc1G0B1P1O+2AlCuh5QeauXBIqhniRjxYvS0DVmzBAn739iw8koK8/eiJ+vPn8SSRAL62oOchSzfOuzn51v3y3U0gJ3SfK4DgCqDUHFbcbFwO5zDrPjuV5yp1TBeiR6y3Io0N8oRffle9ef6jH+rcKLRmlAPWsGFyEKokeU6lcNAaojeZihJdkqghaBkwFqFP07ADavmC5BFQF0PWr95/UoijnLyoiaKA2H0Fthc/FzL0Y400OurpzYXLQldNjbwfv6iwBVRloqSoPZe0Hb4pCc1AHIQcV8gD05p9EoCuyzWKoV7P4YlA3s3i3h4wMXrBcAvrsYW1THEDDVWGIM0lRHD0A5dHgBcsS0GpUF5H0zc83vMzUIxOCAWgUxyQA7dzVKQEtU1C1JPp2NZGP0w++AqCRHTcPqL5aOc9HgAPQyI4kCJjYlErvVGf/pGZoukPMQUcn4oeHhycdQ2sd2mXLonS9ieZzQjQTQNUkye3xDXH6we0GD5qjRaP3axweVuQYQ2sd2o0f5eKo9XNCBECd2xbYDwDUS7MA9LzKQZ1O4sfpBwDqpVkAOumDGwamNI45aPvkU9txGGbkoERHEgRBRLa0mUlSBRclgnaLta9sqjkGn+GslOOcm9+RBIEvUzY1AZXDvFsYDTp8AJqbIwmCUCjragB6Xv7Ruvj3JAHQ3BxJEIRjaTTtLL6CpicHFZv7r2yy5KBmk3EsX3Hx2WriRI/6IqamANS5bYPqore07e8h1LLZsmnJFzstTfR+3E2vo1XUyf1MAF3LP1oXcYgHoF1Hq7YFUPGnu+5c6Ds5Ozd0utzhaXt4mNPpeJ+jBqBdR6vyA7TRLXq3nNOcyYuUrdWpgJLkddhdnJq7LtvPpW3srhfpzTSXrRLB0k0MeqCd1bFf1OX7pmPI2qquSwS02TFqb/nYhpvHx1f3/0IsDolrlb/5E7lMtBIxUNx6/KtvPLj1VNzJcby+/tOfWi5iTuVikdZ8pzdU6he9cXI5WsKviWx4OgBKPr/UcAw5O1XV5QC0uqfjaq+626N8JW5JUrcg7x1LkOWNdHcuuvOfzjro8bnTrSIANNixX9sCqD5nLoby8lYP9Uo9Eqf2GJwCWTFHv/lxO4Q2n8105/dHx45/b86zB1oCoDblB6g1B5V3dVoBFbdnmvuT5EPBbpXUnrVPE7WWmQTFm7maqU5Tg9WeF330Rc5BOR37RSVs4zmotXAtB20Dul7X7vAU0KmwOhxBNwmoPd55REFcUR/F0QfQxiy+AajITg2g8v97p9dHu5ZbkZunOsUQP8Wpzm4/ANC0Hb0Ara+DNiOoHNOL4HrrVyKcFlOf2w+Or7/3l6Oz+JXz40EB6Lwc/QAlqXqYaEOpLDP1ZIyNpU+iY68pTa5fvluCKnI8AGpV4oD2ixZG644sE3nHL99tCiVnyQCUoAxuOwago5oDoELqelCnK5bj9INNAHRUMwE0ibs6LXKhrCrjmoM6r2kiBxUiQRCOpVEOgDrIxErWgEcwJChHRxIE4VgabeaWj95+8BUAjexIgiAcSyPLOugEN8319oOvAGhkRxIEoVDWlcEyk5PsOeiA2HNQd+XoSILAlymbkgK0e2lIoGPgo5C14UABn+tHAChBKQHauaYu1NFtHB8pZf178bRP6DhyC4A6ty2kHwCorwCoc9tC+gGA+gqAOrctqB/6L0+2lHFxjJ6Del3D3MEp+AliqQK6sj0rkfbnZNM+F98NpaPBddLL7fwusm87hj+DcfOANo6g4lNe89leVvcElKo4/dASAPVzDBcR0OYhqL3lX40RdyapP3Qs7zmWr9UfO3b4m8cAdEwAVMgHUBMq1R86ru4+1n9ozuHvzaU9xIfmoBxaDuWIVj7HoN3CHNQOqL6r0/yh49qf6iw2qh9ugFIVpx8SdFxSI9xoWM3goFly0Or+YvOHjmu3Jt38+FT9AKCBhgDUbxZf5qAqUq7rt8f7RFB5z3FyQ3wCjgBUyAfQ8mmJ4p/qDx3rZzV45KBUxemHBB0Hc1CrJJ/9dbp71Bb/VDRRQOXfd1ez9fJBIuL/m8fbM4tPwdHPsD/qdveoLQGT+VQBZVAN0LPFYl8g7/RoJgA6LADKJAPo2Z2Lm8eLY8vSP6Vtgf2QoCMAFSJBwEKmUgWonHLJ25HG0tZSS05dXl6y+iUgcfem+VF/WdvUWzY52SGYElB5x1wJ6OSzeK4nJRpHRgUZ1qKieZnBsJFgBAWgMQy3HNAJBECjGgLQUEUG1PHMuf3ptV6aFtDRac2h4TJ4tbNf8wA0wsUiHtcehUbTSQF1mXjrMuFz9X7NAVCqnA4TgALQQAHQEEMAGl2RT3V6XL3ZV8WR24Ry0FbSaQhlbF2pJbvpXACl9kO/XCNrOrP4TqzUG/jjHX9YBqD2fugXAKV8VLAAqL0f+gVAKR8VLACq+8F5SpRkDjqoDjSHWvbd/kIO2hVTP3CeRCodOc3YDWuEckY9zOK7YuoHAMohANoVUz8AUA4B0K64+oGZz4nuSaKzZRZFfXPQgfIAtKs4/ZCgo+2uTnr0q9fgvkYfgNoUpx8SdASgQpzMkQRAxw0BKADlU+I5qHL0EXJQkuL0QzqOFQ4hhoapOl3L2hbvmXyjIgDtKk4/JONoBtQAw8aMvRVByy3ea03NigC0qzj9kIwjAG2KkzmSAKhdALQpTuZIAqA9GslBh7nqrsX75KCDn4EcdERx+iFBR6vhcOQbiYuOTSREVwDaVZx+SNARgApxMkcSAPUyBKBTiRlQ6qUfuQLaTAE7IA2T5dpE9/kTAO3Keljki+eyBbQu6lQ8g4MGoD39EC4AGsWRkzmSAGi4IQCNKOSgI3JZ8CQutqd/0NsLaGg/JOcYNF3vUfIHbXHkZI4kADosAFqKkzmSAOiwAGgpTuZIAqAjcuOTwukh/4NsAWhXcfohQcd+w56LlUakynpfx2QTAO0qTj8k6AhAhTiZIwmA+hsC0AkEQJV6cRkwtF/uOWZuclAAOi4AWqo/oDE0sWGu33CGUADaVZx+2JgjAB125GSOJABaCoAOO3IyRxIAVXLPQUe56qam1qtHkYO6CIBSDUcj3+jkPoODBqA9/ZCgIwAV4mSOpEFAX33wfL1+cXBw8M5z8fb1hwfvvtT74vRDgo4AVCg2h70aAvRLCeazR+rtm88erV98V++M0w8JOmrDijW9zt4qd6iew9B9Vm2z5kn9Tx4w5aHzBPTZ278oIuibz5+o969/9LyMqVJx+iFBR2XYjIbd2HhYqV2k88LvDJRDGxmVA6DlEF+M6wcHMoi++uHL9euPBK5vFVrOTJKlnnfVlvrm6nXnhSnVtUlVk8Bo0yigr95/oqLol+9qQIXi/KIm6IgIKhSfxB6NT5KEZB5qIqhQnH5I0LGTg1re6S1tiO0vkIMSRAB03jloXYFYZXDQGQEqBvY3PxdYvvns4Zxn8UahA3MGB50RoGId9O0ner6EddATADqpcCaJbghAJxQAdTC0zNgpiWi7ZAYHDUB7+iFBR9vfSTqhhNFOyQwOGoD29EOCjgBUiJM5kgDouCEABaB8GrwJk+BTlbbkoEQ75KABmg+gtLm3KZ0jTvyOnMyRBEDHSueIE78jJ3MkAdCx0jnixO/IyRxJ8wE0IAc1bzse3StCxj8NgBI0Iw0zsqYAAAcBSURBVEBDDFVA7UTh7jV17R1TNBGAWhSnHxJ0BKBCnMyRBECdDAHopgRAx5LTZR3NQ3MXXO2lMmn41N5gHTRAAHRset8NnoeVnHxwJilEABSAujhyMkcSAAWgLo6czJEEQLvpY8vQTINqFfpum7O9QQ4aIAAqNERoT6h0i7t8TRwUAO0qTj9syBGAjjlyMkcSABUCoGOOnMyRBEClBhLKZc++nkRUFxjKarme11Bz5BYAtfdDMo6GsCHDHg5HoutyZD9dALSrOP2QjCMAbYqTOZIAqF0AtClO5kiaNaB1TCwzIYthe6mzZ7VzmD9bDhpGLADtKk4/TOpY56s/qNUN20Q2mXRlzNLEwJgKQLuK0w+TOgJQd0dO5kgCoN3X/YYAdHLNGVC3tNGSgx7ablDqGPQuhtqaiBy0R7MG1NPQLdwdHvYRmsFBA9CefkjQEYAKcTJHEgClGwLQCQVAPQzdEkZSDhomANpVnH7YkOMQca6G7tOcRA6a5MjJHEkA9GRkzHY0DFtmChQA7SpOP2zGEYCOOnIyRxIAPQGgDo6czJEEQIUOq/s2u4wtTZlBBJGDRhEA1epbFloO7/ZQQgft7MjJHEkAVAuADjlyMkcSANUCoEOOnMyRBEArWQGsstPhHJREbkoH7erIyRxJAHRQh0ajxZxNUz9omyMncyQB0EEBUCVO5kgCoIMCoEqczJHkDegyBxXYDO0b2NtwkCUHSrtZZS1O5kja6gg6Mq1xC3vLWhwdsPJsIo8QQbuK0w+8jgCUzZGTOZIAqIMhAAWgbLLc42ZXD1adjUu1ccCLuH4PQAnabkDp6mKbXBM34sjJHEkAtCkAanfkZI4kANoUALU7cjJH0twAHU0XrTmol1OvAChBMwOUOuHuN/RwGnEMEADtKk4/xHYEoJ6OnMyRBEA9DQHoNJoZoD2Z4xBryEGFOJkjaW6AWjUYDdNo4qYdOZkjCYCeAFAHR07mSAKgJwDUwZGTOZIAqNDgNSUBjbErkYMmOXIyRxIAHVTAXL1fqR+0zZGTOZIA6KAAqBIncyQB0EEBUCVO5kgCoMNCDlqKkzmSACjZMJjZDA4agPb0Q4KObcPwUT+DgwagPf2QoCMAFeJkjiQASjUEoJNq5oA6sIYcVIiTOZLmDahLNMwRJ35HTuZIAqAA1MWRkzmSACgAdXHkZI6keQPqlYMGK0dHTuZImjmgmzDM0pGTOZIA6OSGWTpyMkcSAJ3cMEtHTuZIAqCTG2bpyMkcSQB0csMsHTmZIwmATm6YpSMncyQB0MkNs3TkZI4kADq5YZaOnMyRBEAnN8zSkZM5kgDo5IZZOnIyRxIAndwwS0dO5kgCoJMbZunIyRxJAHRywywdOZkjCYBObpilIydzJHkDmqre2nQDxpVBE9NpIwCdXhk0MZ02AtDplUET02kjAJ1eGTQxnTZuHaDQdgmAQkkLgEJJC4BCSQuAQklrqwB989mj9fr1hwfvvtx0S3r04uDg4J3nSTex6MSDt5+k041bBeiLg0cS0hff3XRLevTskfg36SaKNn757stk2rhNgL76+394tH79o+frVx8833RbrHrz+RPxI+UmysatE2rjFgH65vN/L37tX/3w5fr1R0823RirimHz4CDtJhaN+zcxxCfTxi0C9MVDMS4Vw1MaPWvRq/efiCiachPXr34gf4OSaeP2AFr06pvEI6jUs0dJN1E1Lpk2bg+gYoZ8cPAwmeSpT88eJd3E1/8oyUymjdsD6LqcHr/57GEa00+LxLj55ufPU26inMXLwSiRNm4doMks4NlURPmU1hitKhr3zvN02rhVgELbJwAKJS0ACiUtAAolLQAKJS0ACiWtWQN683ghdLxeX907Lf8Tm9UPq776orX75vFx/wdcf6/fCXLSzAHdL/5dLSRiToCKXc3d57tDn7C6c8HR0BkLgKp/PQEdiZGD8RVyEACV/3aGeDH633pavPxkT+YA10eLnU/u/6p4s3917+MyLxA6L0JkWWj/SpZsvEEIDRYAlQy1Ab15vCvhu9ordp7fenp9tF8weuupjKBqW+VQbliUWxtvhvNZyEEzB1ROkmSkbAK6EnBdHx1f7R2b9+ca0OMKvKLIWm1QWxtvVAHIXzMHVETQ66MiUrYAPZfkiuH8tHwvRuqr+0+rHLQBaLVBe5h/VIIL+QqAFmO8jIxNQFXqCEA3LQBqBXS1U6aOmjQ5xK9u0QHFEB8oAGqdxd88LkJmQakmTU+SBG91QNdn+8OAYpIUqJkDWmaaljNJYtdOLVqKZaaPizB6tthtgKeWAHoBPccyU5hmDShRtjVNLNRHFgB1kshJ5dpoRzjVGVcA1E1i3cmKIi4WiSsACiUtAAolLQAKJS0ACiUtAAolLQAKJS0ACiWt/wdN7YnzYOgyJwAAAABJRU5ErkJggg==" /><!-- --></p>
<div class="sourceCode" id="cb38"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb38-1"><a href="#cb38-1"></a>p5 <span class="op">+</span><span class="st"> </span><span class="kw">theme_minimal</span>()</span></code></pre></div>
<p><img 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" /><!-- --></p>
<div class="sourceCode" id="cb39"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb39-1"><a href="#cb39-1"></a>p5 <span class="op">+</span><span class="st"> </span><span class="kw">theme_void</span>()</span></code></pre></div>
<p><img src="data:image/png;base64,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" /><!-- --></p>
</div>
<div id="customising-themes" class="section level3">
<h3><span class="header-section-number">5.8.2</span> Customising themes</h3>
<p>Once you’ve found a theme that most closely resembles the design you’re looking for, you can further customize plot elements using the <code>theme()</code> function. If we call <code>?theme()</code>, we can see there is a long list of plot components that you can edit. A web search engine is your friend here, searching online for the change you wish to make will usually lead you to the correct theme element.</p>
<div class="sourceCode" id="cb40"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb40-1"><a href="#cb40-1"></a>?theme</span></code></pre></div>
<p>Most theme elements are customised using a particular <em>theme function</em>. For example:</p>
<ul>
<li>line elements are customized using <code>element_line()</code></li>
<li>rectangle elements are customized using <code>element_rect()</code></li>
<li>text elements are customized using <code>element_text()</code></li>
<li><code>element_blank()</code> is used to remove a particular element</li>
</ul>
<p>The settings for the theme element we’re adjusting go inside the theme function. In the example below, we first remove the minor gridlines for the x and y axes, change the colour of the background, set a black border around the legend, and manually adjust the legend’s position.</p>
<blockquote>
<p>Note: the <code>legend.position</code> theme element doesn’t use a theme function, but instead takes a vector of length 2, where the first element is the distance along the horizontal, and the second element is the distance along the vertical plane.</p>
</blockquote>
<div class="sourceCode" id="cb41"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb41-1"><a href="#cb41-1"></a>p5 <span class="op">+</span></span>
<span id="cb41-2"><a href="#cb41-2"></a><span class="st"> </span><span class="kw">theme_bw</span>() <span class="op">+</span></span>
<span id="cb41-3"><a href="#cb41-3"></a><span class="st"> </span><span class="kw">theme</span>(<span class="dt">panel.grid.minor.x =</span> <span class="kw">element_blank</span>(),</span>
<span id="cb41-4"><a href="#cb41-4"></a> <span class="dt">panel.grid.minor.y =</span> <span class="kw">element_blank</span>(),</span>
<span id="cb41-5"><a href="#cb41-5"></a> <span class="dt">panel.background =</span> <span class="kw">element_rect</span>(<span class="dt">fill =</span> <span class="st">"aliceblue"</span>),</span>
<span id="cb41-6"><a href="#cb41-6"></a> <span class="dt">legend.background =</span> <span class="kw">element_rect</span>(<span class="dt">colour =</span> <span class="st">"black"</span>),</span>
<span id="cb41-7"><a href="#cb41-7"></a> <span class="dt">legend.position =</span> <span class="kw">c</span>(<span class="fl">0.91</span>, <span class="fl">0.17</span>))</span></code></pre></div>
<p><img 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" /><!-- --></p>
</div>
</div>
<div id="saving-plots" class="section level2">
<h2><span class="header-section-number">5.9</span> Saving plots</h2>
<p>Once you have created a plot to be proud of, you can save it as an image (.eps, .ps, .tex (pictex), .pdf, .jpeg, .tiff, .png, .bmp, .svg or .wmf) using the <code>ggsave()</code> function. This will save <em>the last ggplot you plotted in R</em>. The first argument specifies the file name (including the file extension of the type you wish to save as), and arguments that control the size of the plot. It’s usual to experiment with increasing and decreasing the dimensions of the image until you find a sizing that works. For example, you’ll find as you make the image larger, the text will be smaller, relative to the overall size.</p>
<div class="sourceCode" id="cb42"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb42-1"><a href="#cb42-1"></a><span class="kw">ggsave</span>(<span class="st">"plots/Graph 1.pdf"</span>, <span class="dt">width =</span> <span class="dv">6</span>, <span class="dt">height =</span> <span class="dv">6</span>, <span class="dt">units =</span> <span class="st">"in"</span>)</span></code></pre></div>
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