In this topic we will explore temporal data in R in order to visualize the temporal aspect of porpoise distribution.
1. Dealing with time variables in R
As discussed previously, a variable is recognized as time in the POSIxct data type, for example by applying lubridate::parse_date_time().
A first step is to plot the value of interest against time.
ggplot(data = poddata_day, aes(x = Time, y = Dpm)) +
theme_bw() +
theme(axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
geom_point() + geom_line()
![](data:image/png;base64,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)
More relevant is plotting the time series for each zone seperately. This can be done with facet_wrap() or by making a list of plots.
ggplot(data = poddata_day, aes(x = Time, y = Dpm)) +
theme_bw() +
theme(axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
geom_point() + geom_line() + facet_wrap(~Zone, nrow = length(unique(poddata$Station)))
![](data:image/png;base64,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)
lapply(unique(poddata_day$Zone), function(x){
ggplot(data = poddata_day[poddata_day$Zone == x,], aes(x = Time, y = Dpm)) +
theme_bw() +
theme(axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
geom_point() + geom_line() + ggtitle(x)
})
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)
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)
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)
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)
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)
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)
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VLOa3MlII8+7D7CZjN2W0Pxew2VpoalVPLlLXab8y58tAsG0bBK4+G4ePGRevNvBNTZss72+zDLbYz81ZPZpDJl/AOqPWdTTEDowUsnEnjvNtT8/qEdwq+YLSAheMG79xGGwbeXGOozFOHTZhaOoLRAhZOP7xnUWOYV6lMs7Mb57WDd7p63pkdvmC0gIXTB+/bB1mh++b2ey/0bUp4Z3eFDQBeO3zBaAELpw/e5wW0jVvSq17Z3O5tgIA3sahHwGgBC6cH3vpm3udG6q2HFDZ+V1lU/dNcAgtvMjr9gtECFk4PvPXXKKZ9DZPw9iro8wEIr6UI74ACPh+A8FqK8A4q2PMBCK+lCO8IBXo+wBbC23pteLTmDW+w23CDPB9g6+B1FeEdqR58wWgBC4fwdgQU9AsQnfiC0QIWDuHtCCjwt3c68AWjBSwcwtsRUGB4O/AFowUsHMLbEVBweFvxBaMFLBzC2xHQBuBtwReMFrBwCG9HQBuBt4EvGC1g4RDejoA2BK9xyxkYLWDhLALe07aY5wpvItMvGC1g4RDejoA2CW+NLxgtYOEQ3o6ANgtviS8YLWDhEN6OgDYNb+L1+QCE11Ke4W3/7syC4U08Ph+A8FrKL7wdv6KwaHhHft1ihAivpQivu0494Ut4LUV43aWMgB4PQHibTY/SFta8pRHM4wEIb7Ppceq4jpasAV6XH5bWjFxVGoE8HoDwNpseJ2d4o6E3V6OCBq8rvoTXUtPg7cZmu+F1w5fwWmoSvD3cbDu8LvgSXkvNF168mrfU1LgIr6VmDO9g125Aa4M3mZh+Ca+l5lvzQsM7CV/Ca6mJow2d7RPeStb4El5LEV53dRpZ4kt4LWUBr6wVEOB1eXSrZuSqHiOrCAmvpcbDq/XS1gFvbr8seK3wJbyWGv8ETO1hk53Pimx9imTUuY14AmbhH/WYN7adxSMr5xFlAOFn3sk35hT+S8u8SmEfD7BFmXd00yNrXsLbppCPByC8zaa1RtcA70Jr3lLhHg9AeJtNrx3efHKp8I7Al/BaivC6a7RRmMcDEN5m04TXg1GIxwMQ3mbThNeLUc8tZ4TXUoDwDtxROHN4k+70S3gthQfv0E3m84e3C1/Ca6n5whtZ3jnQaeSsKUZtoRNeS60f3pJFwqurGTzhtdTa4Y1GwxvZ1bxRXxhjtGF4m/uK8FpqGrzdeLnCq1YeN9owf3jNvUV4LbVZeCPTdLBoqAIqb4SYLgB49f1FeC3lGd5G2ozWBG++3vzhlfuS8FoKEV7dw4RZwOv23XcQeOu9SXgtBQZvsxpoIKrD67AjYOAt9yfhtZQfeCuIWr6TZgdvox/WAa+H1AsEb+Lz8QCEt9m0JNZgpqao/Vcf1wFvPqa2GHjVGyW8loKHt6PmLS5SLKLmLeX6E6mFCG+zaRd41cwheKMOeE3H0/bZ9oKD99TxaKxs/GhL4O2reasu2wC85QGw1fAmPtIv4W023QdvjWCzw5YQXjsfV3wJb7Npr/DWpLbCq50+tw1eV3wJb7PpyfC21LxlKdFV82r0bh+8bvgS3mbT0+EtjAbgTQivfDEdX8LbbHokvI1LbJUR4bXzmYov4W027RPelpq3zNqseWtNw5fwNpv2C28SNVav4I0Ib6kp+G4O3vZhasLbDGgr4J2C78bg7bhcvwR4DSAJ71gfW3wJb7NpR3jNTlgPvMbpZ9vhtcWX8DabDgivbkJ47fANCK8B62Jr3kF4i+leeJXFVsJrg284eBuptjVGJ3hfxZl2PlMvXu7H8e6jeuFmat5yNUt4s521pfB25TVrm9GCgPdIwFtM36kWbmS0IYqmZd7thjcZmX4XBe9hnnOVzlfxvSQ5jus564a3flXDW39/h/Da+ozAd3M1r394Lw9uPCunj/Kce1SnXl/381ZxdR6LHuDd5pq30iC+QY+l3peGzwR4L/ZulZOXB3nKPV/dPDEMO2UJr3kmmQivRqxut+3wDuK7JHhfxZ88WcU7d0/qJHyxVyXjTcDbV/NGje10O2ZepV58lwRv0UeL02R7sZdn3ALi95XGPwNOPAzPfC5e+SzAnN1IbiM2OtUmi//lcwnFy4622u22UCBPJmxw0KcJ8B7GP3yaJC9XaZ1rwCuPhk61Z95GtyxPpGbilS9PtesR1f/MvBN9OrPvkjJvIVXnhoFXzh8Bb7kG4bX16cB3gfAqYP3VvH7hFfUv4bVR/+VYR02BtzceV3h9jTa0w1v3xOptqpeEdx0+LbgsCN4yy75SPTZv47wd8PZepCC8a/FpALM5eNt/c9Yl8x7Gu2mH7XgV3/d4hc0vvGLTIXiTLb/C1iYD3yXBe7Ev7mfwdW+DR3gTu8wbNdeYqMXAa+yjJcGbXD5Z1XeSebqrzHPZUIE8Bt7IXGOiFgSv0bnwIwh4e0V43YUAb1LvKMLbbFpQ1IIo4QXwyXfN5m5y2054601Z8zpJ7R3C22x6bfAW83vglcRytKFf3n5gfdT9vPmnUA8YEV59O+2GCcI7LLvvGndrNLz55xM1rlCZPhuEV0TmCm9ZJkh4o4Tw+rLxg68dvNHy4BUvCG8gH2XjA1/CK42mwsuad4KNO752Ne+M4a1HDNYBr9yS8I61ccV3JLzVUKd5S2HDBxhe0QkTC6RRVK2bJLbwauNppvU0LR1eV3zHwVtXdcuGN9HeohW8+pUM03qalg+vG76W8IJm3pIbwrteozXA64KvHbwRJrzlkaVxM6Xm7YK32qwd3moXda0xUdsB73R8bWreeryhxwcVXr0Tpi2ojaquaQu8kVbaNuBt1rzue2Bb4E2SdqjsfdqM5WhZ+6eCCa8cwRoPb9IGrzz1GFvm8JbbikuRrtoeeJNJ6XckvBE0vFXNa8CrkzYZ3sQKXq3qcNNWwTsBXyt4a0i6fTYz2mCcsafCW2JHeNfq02ljia8lvPmMPp85wytOMWLdqnrQa17Cuw4bK3ytat7ZwCuq9DqcquSZBq98qW8Z1daseZ1tLPAde3k4Er7w8LZkP3lpewy8Na0a8z3w1jvHLCsmayvhtcDXBt6WUaKmDxS8kR28Sc1uVL9nudkoeE+ZeR1tRuI7Ct6C36qCxIS3hEzQZwtveYKpU68tvOofwutuMwrfkfBWSaic0+mzMXi1gYBI3k2UmCvI2AhveJ9RNiPwXSa8xdlCH9YaD692tay35hVDEITXt80gvhbwgte8PuGVLvX22pwC2CrHE9412AzgawOvnNPpg1DzlpDp8NYruMAr2I2iGl45vEZ4/dn04rsgeLW0aLAWyblrgrda6TTSDqTpIrxJL75Lgdc8p4v/6mi8w1vXvDq8WgkzXYQ3U+euJLya0Xh4C26LmTq8CeH1bNO+M8fBm2wLvPqb7YW3qLMTZt4gNm27c2zm1T7vFqMN3xKZjII3GeywDcEr92EnvGJs2E2EV6pJ3bjLw1oeac0qc4VXhukVXh97gPDqMrEjvJoR4Q3hM91GB28G8I56zKHxtMpIzDuVfyLjMYjmExHFNtnDMSNjtvaAS7UjionTKJIrRdG2PwBzXbJ8sGakP+PUfGUIJ/PW6U9cq7DIvM1LdomeeesMa442aLFMFzNvm+qPYORow6AAy4aq1yRvyvAIb6LDm2jwJuywrc+m/BC2DN6S4UoGvLJY1Qa8CK9HH3eb/GMgvNJIH5+V9BJejz4+bNSeXxC8cqAgEmSV5W9iC28yBG/VLuHdgM2YxwOM+wzQ4K2w01KvC7wa/tW8Lnh5hW39NsM3/G4VvMbdYOKL7pbwtg4nWovwDvkM7eRFw6sh1jzdyxuE++Gt6uuE8IayKU9xPWvME94kkqB21bw6Y47wRoQ3rE3p07OnZwpvIqfqhCyWRtpYWFLCqxW95ZZml69a2gEva94ANrVP574GhjcH5FRcTGvAWyMl5pfZuAtesbrgvJgSzffAy9GG9dtInw58ceEtgCl/6KYN3roYlfM1eGu/02puvXoDXtl+jm5UVimEN6yN7tOK78LhFX4u8JbLKnhHjOMMivBa+bTs8TnCa/TIWuFt0lvBK1oQPrpzuZTwbsym6dPY57jwdtS8Bn6RzrKIrQ1efeilJQcnxlIJb1QGpG87WYTX2sfY68Dw5o0Yow0GkVoxIXpVHZnX8Ce86/BZazjafl8wvMYIdx+8LRfnCO+Gbbp8tLFOK59NwFv191vgrdmqFpXFgzkg0HYWqqea8CaEd6M23T51X8XOZ1Pw1l2zbniTkt0yZxLejfgECKft4xr2CQxvpG6MK5Gtviih9bnMBGsBrxw1i4wZSd09rPqBxfCH2MBJhNfBR5xjR/uEhbdGpwPexmCuDbxyW9FIvVwPoWyM8IaxGfJp+ewHfDYJbzIK3vqSMeHdjE+ocGYEb9WqjLj96ItaF9jCW1fQbfB62AOE180HG155R0wFr14ONGvepKxOzbdmWfMWhYrwKaYIbxibYZ9x7G4O3mQAXm20od5sHLx6S20mxoEifEbuuD4R3sA+weFNygGyslWzI9bMvNEEeDuOAB1e4TP2lNUnwhvYZ17warMt4S1r3sYqhDeMzSLgjcopO3gbZgNlQ+s7aow+JIQ3lM0S4D2txxna4W3NmS3x9e2LDhaN4Qfpw5p33TaLgVcOVg3D246WX3g9CM6I8FpqYJy3GAyrxhzGwdvKIuEN5AMWzmbgjXQla4TXuub1ITgjwmspEHhHqLSEYw6NFrBwNg1vX83bGor1aMOwqgMCjjk0WsDC2XTNK2eYl9RGd/sJbyAfsHA8wvtyP453H9WvRz2HrVRZPRizxgVDeAP5gIXjD96jONOdaoYNvNEm4WXNG9gGDt7zVXwvSY7jnc9Mw+Gmqytn+hBAMHh9++AZEd5eHeU596hOvfbwGriGqnm9++AZEd4+XR7kKfd8dfPEMBxuWhs6m9A42j7FMyK8fbo8uPFM/b3Yy/8mE2pewrs2I8Lbp4u9POMWEL+vZPmAxFxWG1FUJq/wyqOhU9rxNzXrNowcBJcw0d4ZWDhryrzScLjpvN3pdyKi7VM8I8LbJ8ea101o+xTPiPD2yW20wVFo+xTPiPD2ymmc11Fo+xTPiPD2yukKm6PQ9imeEeHtl9O9DW5C26d4RoR3QE53lTkJbZ/iGRFeSxFeHCPCaynCi2NEeC1FeHGMCK+lCC+OEeG1FOHFMSK8liK8OEaE11KEF8eI8FqK8OIYEV5LvU9RoeQb3mG6g7U0UnABoUUEFk4zHsILJLCIwMLZJLwU5VmEl5qtCC81WxFearYivNRs5Q3ew/obQUriS/HJN09Wcfy9e/kL4yb2o3K1lz+K4527J4m7vv40bW7nky+aC35eTweOydw9bRoO6WIvLlTv3Cm6PIjvwwRTyZ6gAPB+XbzLW+qF8fWh4/KdF/NvupNSOMU7PzYWnK9uVdOBYxoD74iQfPFyvhp6UwGDqWRPUAB4D2OVBY9X6mjXv7h5+evynZ+vdu5lQQ5lhEGl7001980T+QW7JG+jhjdsTMkYeMeElGtE4uzX0c7HA+EEDKZu05qg9cNb/rrOkTpwtK/Mn+/Hu/v5aof5HngV30rclBL3mTlVLarMw8aUmQ3BOyqkXEfmYWmptKnjfouAwdSyJ8gvvHn7ihJZseTK58ofKzncufvlQXEK8nJuTrR9mU9epuVSVkEc6d92DhiTUr5nvv704zSIv32azfjFp/pXWIdDKtdwDOxVfKf4qaP0U/rvJ0VARzf+az++8TR0MLXsCQoHr2JJ/5moP5yUM9ItLj/10Tkq31uSFO8vPasV0LbBGySmTNmeSU95eTmudlU9OTqk4i26nqdVLPlpJfX91zKKoxv/2KjvAwSjh2VHUDB4z1fpjMYP9BUTr+K/3/PSOZLpMvM+inefFhWErHlDxpQp3z3xP5xkVfQdNb3zSH32Rky9IWV65RpQ5p4XQ+rQvpdcHubn48aBFCAYIXuCQsF7nlXb3aCojzJ5uXItoEx4i0ycVRANeAPFlEkWdEc5vHeSZkHdH1I+6ZrrsuYv9lRAqZmKIktjzeI1RDBC9gQFgvc46yP2gNL44b5pMuGVr014Q8WUqYD38v9+87MfZdVLvavGh+QlnKKQyoIoqywVzJHJYYhgpOwJCgLv5ZN4537VZCJ+GrWqLxu/9ztN0kCZdsMbLqZM2Z5RNXTJTQu8gyEpOffuy8I7znoEObwKXAPeMMFI2RPkDu/5/q2BptNjffdpMWVkM++gGKMNnfAGi0nbPfHOP/38N18ctcM7HJJYNF3FHsqMujNvoGAyTSbIA7zZm8pN8z6sGo/TC6NPSoLMn0YtVitrJ/fTkDHOW7zXjBEDlEAxid1THEl5odmAd0RIiYcBvLzYTcpPSda8At5AweSaTJA7vBd7+UCUCiD7N+3iaE3Lc4v506jlammBeS/rHPm+wpaNNnx9kF+b+YuTeq1QMYndk3KT9gC/3m8vG8aE5OGSSTU+oDrvKRXVmIcGb6Bgck0myEPNW1xsvp+U9dTfaUm/vgaeB6ddJ69WeyKuXnuF3KYAAAC2SURBVLvp9+Vg6o/zBqqmVXB3wsckds9h2eqtJrzjQnKvMg9LRtWZJc12H5d3J0h4QwWTlD7TCPLRYTv+uLpUdL6fQqNXLK9i2bRxh1K9mjIp7xtyU3YLUnVXWXaFLT/p/H5Vghg0pnr3XGY3R939X3WaNOEdGVJjTMBS4ikiaZL78mDnF+kh+sMTwzlQMJUmEsT7ebdZnnpcmxLh3WYRXmq2IrzUbEV4KWozIrzUbEV4qdnq/wGXDY+YhbDjEwAAAABJRU5ErkJggg==)
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)
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)
![](data:image/png;base64,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)
Same can be done for different deployments.
lapply(unique(poddata_day$Deployment_fk), function(x){
ggplot(data = poddata_day[poddata_day$Deployment_fk == x,], aes(x = Time, y = Dpm)) +
theme_bw() +
theme(axis.text = element_text(size = 16),
axis.title.x = element_blank()) +
geom_point() + geom_line() + ggtitle(x)
})
2. Autocorrelation
R provides a very easy function to investigate the autocorrelation in your data: acf.
poddata_day <- arrange(poddata_day, Zone, Time)
acf(poddata_day$Dpm)
![](data:image/png;base64,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)
Again, we are interested in the autocorrelation of each Zone seperately.
![](data:image/png;base64,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)
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAGwCAMAAAB8TkaXAAAAolBMVEUAAAAAADoAAGYAAP8AOjoAOmYAOpAAZmYAZpAAZrY6AAA6ADo6AGY6OgA6OpA6ZmY6ZpA6ZrY6kLY6kNtmAABmADpmAGZmOgBmkLZmkNtmtttmtv+QOgCQOjqQZgCQZjqQttuQ2/+2ZgC2kDq2kGa227a22/+2/9u2///bkDrbkGbbtmbbtpDbtrbb25Db////tmb/25D/27b//7b//9v////jkNmyAAAACXBIWXMAAA7DAAAOwwHHb6hkAAAUmUlEQVR4nO2dDXvixhVGhdvd1rTpx9qbTQNpapKmpl+Ggv7/XysjgRdsIe4dzZXmSuc8T2KMR3M1L2eF0MdQlL2yeywq7l8fPYRHi7LcFHfP+2X11MeXuvHx1+r3Tf1wUX5tsyqK2dO6fv7uuWpSLbk+PlP1u52HPwVWoVbV6r7+rSr+prt+00iCJtL6+UMoxwFXjc8fPJx1tzlFGzIM/130dWpXxXnqoU57fVyZw+PwxNeVKc9erRQUabqRchjId1+K2afq0fc/FrO/lGdJl/sfD0P708up9f7nb4ri199Vv//7S1F8+Ft48tRm+6W4+6X857z48HMVx6Gfh2qxf80PDX459ruqXC0ra48vXGi1/2leFX/bnT80kR7lDVk8nEVwenCU99hdFe3fH2dPJ3kv+jrJO/v08rWHWt5DxNVG5r58K+/Zq5WCnuX9yinHdGznLrec6UgfaeaMRt7DP/fTNnaqIG9fGMj7IdG7kVeQF8ALyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7hFKO/qNIUbQDbI5F0dZxlcTXxCJcgKkby7z8cZ7La/n/iMSpATyAtuEe42HHcXTrsPFz1MmJSvRDNDj3BIbocjy/A4pXXTB7YJH6/oQ177ErmSTF7THtyCvJYgrynIa0lqec8/sL3bNZlezoYjJt1+t7zEa1mCdGNaiHsgXssSpBvTQtwD8VqWIN2YFuIeiNeyBOnGtCjPv+2w4evfiLeXEqQb06Ki5Yoc4u2lBOnGtKjYL69eDUm8vZQg3ZgW4h6I17IE6ca0EPdAvJGEL0j/x7L522dJt1MLcQ/EG8lqUW7CF6+uG+wl3U4txD0Qbxzhaunqiummq6VJt1MLcQ/EG0cl77fPyPsG5DWF3QZLkNcUPrBZgrymcKjMEkt5ueIUeU1hy2tK4hFzqf8FyGsKW15LkNcU5LUEeU1JNeKWmeDepzuZmJHXlFTHeVtmgkPeTi3EPUwm1VfSnWGrkJ1hm0zMyGsK8lqSSt7d40O5nXMO6A2pdhvaZoK7/mDsJJI33EdRbRY4+35BqhFLZoJriHnkgSeSN7yxVd5y3dMFgxwqQ15FiwP7H57KDfK+A3ktSbXPu5k97ZeL5tswkbeXEsgb06Kinrlh9tTWw8izbAB5LeFQmSnIawnympKLvONMPrW8XLR3AfJawpbXFOS1BHlNMRzx9fc15L1s8Xp2/Sqqi/Ymg/Dc+s10JSWQ90qLKt5VyxcL6y7amwwKedvSFZVA3istbsWrvO5pMiCvJchrSnbyjuolSLbboLpobzIgryVSedum7a9QXbQ3GYTy3kxXUgJ5Y1qIexhVciKyO847qpcAeU3JWN4RvBhiedfhHW3TeNWYuEbR9OSokY5SlO664fME8gparOu9se084mMF8t7iRrrKLwpD3ssWr4fCNo3/+oU1kLeZm+nWVrPlfYNW3qbjuOIayNuMIN2wbUbeNwjlfb29pznAK0u2XRI5guwEyEYpSXf3eI+8b5Du8x53x9Yxn9iQ9xaidDcF8l4iP1RWXTZ2/TtaJTWQ9yop00VeRQtxD8hrWQJ5b7TYpDmBKSo8BnSjvJmu7CYrpbx+XwmVvKvmaxfEPSBvG8nSRd53LfbLqOtGSuSVkDJd5H3TYjs/fNatvoWxQw3kvcLNdCNnRkfe8L/6MHpLvGHLEaIVX4zuNzINmpMULenGzoyOvNX/D3K2bhtCrCFi5L1AOMob6UZPLo28pwerlr2yOt7N3TPyXiAf5e10S+R9g+pow+bq5+FjvJuP/0XeczSjvJ5u9MzosX9zgvI473Z+ZfNwjHctu2jv8rG70OTohnY13c4zoyNvG/vl0V7kPaeHoSFvpxbiHpDXskRCeZ28IshrCvJaklpe8RSnDuRNsF7Iawlb3usgb+b0Ie+vAm9/FleeH9nPzuHdxFTevC1OJq92ilO2vInoR97YgbQt1/C3lua61uIWAfUUp8ibiJ7llY1I1Fy0v9LtfUG0tvpZIn3Ja7Hh6Uj6i9GRt6O8OYmct7zvSgwmb9tTsfIm2iMX7jZopzhF3kQMJm9Gha8izF87xSnyJiIjh9zKK+kBeQ3IyCHk7QlBIeRF3mh5TV955I37G/JePs5JXsEqJqiSkowcQt6UlVsYjbyik9XF6fdCd5L7/XJFTsvdDGdE8go6z1TeXm59Z8vb1sPo5L25homG0M+t78h7viSkkbenu4eR16QHycY5/g0/jWGN9ZG3l8JXQV4ZhvL2fus78ibswYO8jT2m6rrnW9+RN2EPV7u7Le8gJJdXVQt53zeNph95DSuqQd5+C19Flr9olsjeGKu8jfdmTxj5C9GKaJbI3hirvK21pkcieWWzRPbG0C8o8vZCUnlvzRLZG0O/oMnllZwenh4pdxvKW7NE9kY2L2iiFRGdHp4e6T6wCWaJ7I1sXtA0KyI7wzY9HB4qy7LiFZDXknHKmw2pdhskp4enR2p5m7YNFnOAefmpCu86LaeHcxhltumy5e0Ah8ossdxtkJ8IGS3Ia0kyeTkS2QTyWpLsOG/bkcgJo3oxohh6hENyOxxJgq0Hc3RdZbPkMEXjURbVNXfaOfL2WjQep36Zdq46Pdx4JDKmagZLIq//zoVNW45ExlTNYEnk9d95whcCeS1x6pdp54qmr3u+3bvKYEnk9d858vZaNB6nfpl2jry9Fo3HqV+mnU/4DA54B3nBLcgLbkFecAvygluQF9yCvOAW5AW3IC+4BXnBLcgLbkkl737ZNI+ZhO28KNqucb++YLitI65utWhE4bDIostgI1EWVI1MGaMmOmVcx+bytU8l7+q+8e5MAZu4xcrdY0gkqm69qL7w7nFRbmZPHQYbibKgZmTKGDXRKeM6NZevfSJ5wxVnrTe4XWfVenvGVdbFHw/1ourWi0YU3hw2CPvlosNg49AWVIxMGaMqOmVcx+aKtU8k7/aYgJ79DzFLleV/XkLNqLr1opGFD9uH+MFGoiyoGZkyRnV0yrgOzRWdDy7v7vM3xSxKhGh5T6sbVXj98SV3eXUjU8aojE4Z16G5ovPB5d3+7qncfRvzHtxR3rjCYYbi3OXVjSxCXnkBZVxVc/naDy5vIFL7bvJGFV6HbULu8gbkzSPkFRdQxrU+bXGF6zL8B7ayi7yRdWPlrWeGz/4DW6mVV1FAE50yrq/z7vcrb/zRo014p/hD9HHeuLph0YjC4R0tkPmhMt3IlDEqolPGdWyuWPtU8u4eY4/br4vIJavU4+pWi+oL13NlLjoMNhJlQdXIlDEqolPGdWouX3tOD4NbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS0e5e11wo/J4Shd5IVLHKWLvHCJo3Q9y7spiuK+mgb+t8vFwOs0Hhyl61je4zTw4ce6yDRehzhK17G8gd3npzAbfIgY0uAoXc/yhq+NmT2tj19lAGlwlK5jeasptDPfNjjEUbqO5Q0Tua4y3ytziKN0Xco7DxO5PuyXRfHp8I52+PU3fU6yP3IcpetR3vf0+g0RkyPbdP3LG3bONv1OVD4hsk7Xv7xhGvi4L3IDATmnOwJ5YaogL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtyAtuQV5wC/KCW5AX3IK84JY85V0Xs+/L8n8/FnfPu8fi/mX/08Wj/bJ4CDdXfV/+q37+7pfDInfP6+LD8/6vxceXw1OLMOdL9cdF1RwCu8dwT2UR/jd72s6Pof5vWZw/ek322GDxNu9wdfr75/sfTZ7y7ldFzZ/raYcOLM4frcLl/XWbhxBjxf2pQZ34V3nL8CIMPaQ8COLtl0cDq1jWp8zOHp2SPclbB/g175O8l8/3T57yluW/vxz8/NMvp4cf/nbxaPvlsLHd/zQvZn+pYvzuSzH7dHj+n/Piw9/rjcu5vId874ccTD5U4q0PabzKW2X2c3Hx6JTsq7xVgF/zfpX34vn+yVVegJsgL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtyAtuQV5wC/KCW5AX3IK84BbkBbcgL7gFecEtyAtuQV5wC/KCW5AX3NJd3mLCJHgBSPcqt8PpHm/nHtzSh7z2JXIFeU1BXkuQ1xTktcRS3ne7JtPL2XDEpNvvlpd4LUuQbkwLcQ/Ea1mCdGNaiHsgXssSpBvTQtwD8VqWIN2YFuIeiNeyBOnGtBD3QLyWJUg3poW4B+K1LEG6MS0C4YvV/7EsGr/Bl3i7QrqNpJJ3tSg34XuS1w35Em9XSLeRRPLuPj9V/5Xb3z9f74F44yDdZlLK++0z8b4hobyk+w52G0xht8ESPrCZwgc2SzhUZgqHyixBXlOQ15LU8p5/pOCK09QjJt0L2PKawpbXEuQ1BXktSSbvqn4Pe2jrgXhjId0mkh3n/fhS/+RI5DmpjvOSbhMJz7BVcA7ognRn2CpI94KB5J1K0APJO5F4k+02HN/QTm9wjT0gbyTKdKcSb7IPbGvVR4qJpJtsnLp0pxLvQIfKJpLuUIfKJhIv8pqCvJYgrynIawnymoK8liCvKchrCfKagryWIK8pyGsJ8pqCvJZYyttyufRUNDYcXdvF6OMO9ZXBt7zjzpktryVCeV8vDelUA3mbSZ3uyEN9RSPvqunUuqYG8jaTOt2Rh/oK8pqCvJYgrynIawnymjK4vKOOVyrv4+nAzN37a/nFNZC3mdTpTiVeDpWZknR064b7KJC3Y4uviOOdisZpxtS6ZRbLO758xfKuQ26b2ZUDktp4kfeS9nTL7TzsEHfc8o4vX6m861rKOsUGlPEi7wW30q1bIO8btGfYNo0BBlTx3pB3NDkrz7BdT/fw3naPvG/Qyts0c8ARTbzIe44k3YPZRSp5xxKvUN798viG1qznEXm8yHuOLN1bJZD3Wovj7tj66mcKSY0IeZ3nLFz9xOmOPNRX5IfKqokKm74UQV6jk7wuExevtCxd6eTSxvLm8lKkPkkhjBd505cwljfDvTqlvBvD08PImypd5G1osWqeLUvcA/K20ZaucnJp5H3TYr9svW5EFy/yvqE9Xe3k0sh70WI7Lz6+VF8h2owy3k7y5pKdAPHRhtZ0O8zPK3/yFn7lreNLFy/ynpM63Th5b6yrX3nD21rKbQPyXnAj3Q6TS0fJ27jWjuUtq73alr2y2JnRkbemNd34yaWN5R32pVAdbdi0fB6OnRkdeU+0pSspYSKvxUe/ZKjkDZ8sMjjOO1J506WLvPIW4h6Q17JEPvL29QIhrykTkneAD3TIa8rU5bUdv295s9cYeS1BXlOQ1xLkNQV5LbGU1+p6XuStuk5yMTrySntAXssSyBvTQtwD8lqWQN6YFuIekNeyBPLGtBD3gLwG/Cpw9Wdh+/fiRv1bf+/482Y4PuTNVGO2vE0PU9HHllf4b7Poto3I82fn8G6CvJ1aiHtgy2tZAnljWoh7QN5YzO4eRt5Aj3cPT09eu7uHkbcc6u5hqcaDqZ2mcN83YE5M3oFuwMx+u4y8TQ9TMTl5+/U41W7DYHcPG/fUjWS7DYPcPTwVeYe7e3gS8g5z93AnefvQuIcayNuphbgH5LUsgbwxLcQ9IG9HGmc/njCJ8yfeS1ThRdHvTnxium3De93y5kryTbki3s5kn66MW8NA3ivkL6/k/OVkSC/vOOLNVF7R+cvJkFzekcSbp7yyU0CTIbW844u3aaWRNwuQN4bhdhsk5y+nR7LdhsnG28sHNsn5y+mR7APbZONt3b/gUJklHCqzAHl7oQ95c7gRcrw3YOYwymzTZcvbAba8lrDbYAryWoK8piCvJb3IO2E6h0e6LdwOJ2HOfSySaxFzotZpjAslXL5bV8grJnOlkNdxEXMyVwp5HRcxJ3OlkNdxEXMyVwp5HRcxJ3OlkNdxEXMyVwp5HRcxJ3OlvMsL0C/IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnBLKnn3y+Lu/TxmrWznRdEwfVTLAmGmNF2hahFFodB0ETUaU6LWR51vGRWxPuQyVc6p5F3dN85/2sZG2X73GAaqKlQvIi+0e1yUm9lTzGhMiVofbb5lVMT6kMtkOSeSN8wj2TSFZBurppnPrrMu/ngooCpUL6IotDlsO/bLRcxoLIlbH2W+ZVTEESGXyXJOJO/2OGgF+x9Uzcv/vIQiqkL1IspCh61CxGhMiVofbb5lVMSRIZdJch5O3t3nb4qZaomIZOvmqkLrjy+jkFefbxkVcVTIZZKch5N3+7uncvet9oNBhLy6Quu7iJfPmKj10edbdpBXXS1FzsPJG9AtEimvqtA6bD5GIW9Ab3ysvNpqSXIe8ANbGSOvspA213V15GYcH9jKSHnV1SLkTZPzcIfKNuF94w/q47y6QmERRaHw5hcYw6Eyfb5lVMTqkMtkOaeSd/eoPty8LpSLVMnqClWLyAvVXze3iBmNKVHro863jIpYHXKZLGdOD4NbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS0e5c1pPpDx4Shd5IVLHKWLvHCJo3Q9y7spiuK+miL+t8vFwOs0Hhyl61je49zw4ce6yDRehzhK17G8gd3npzBFfIgY0uAoXc/yhq+UmT2t6+83GHidxoOjdB3LW03Gnfm2wSGO0nUsb5gSdpX5XplDHKXrUt55mN31Yb8sik+Hd7TDr7/JaA5+7zhK16O878npCyTGR7bp+pc37JxtsprHfExkna5/ecOE8urvGwMpOac7AnlhqiAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFtyAvuAV5wS3IC25BXnAL8oJbkBfcgrzgFuQFt/wf1NGxNcpDiDQAAAAASUVORK5CYII=)
par(mfrow=c(2,2)) # see four plots at a time
lapply(unique(poddata_day$Zone), function(x) {
data <- acf(poddata_day[poddata_day$Zone == x,]$Dpm, plot = F)
plot(data, main = x)
})
[[1]]
NULL
[[2]]
NULL
[[3]]
NULL
[[4]]
NULL
[[5]]
NULL
[[6]]
NULL
[[7]]
NULL
[[8]]
NULL
[[9]]
NULL
[[10]]
NULL
![](data:image/png;base64,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)
We can also take these values and combine them in one plot.
lacf <- lapply(unique(poddata_day$Zone), function(x){
o <- filter(poddata_day, Zone == x)
oa <- acf(o$Dpm)
dfo <- data.frame(acf = oa$acf[,,1], lag = oa$lag[,,1], Zone = x)
})
library(plyr) # Now we use the package plyr to make a dataframe out of our list
dacf <- ldply(lacf, rbind)
ggplot(dacf) + geom_path(aes(x=lag, y=acf, group=Zone))
![](data:image/png;base64,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)
3. Smoothing
The goal of smoothing is generally to aid visual interpretation of a time series. Let’s try on two subsets of our data.
test1 <- filter(poddata_day, Deployment_fk == 2578)
test2 <- filter(poddata_day, Deployment_fk == 2585)
Applying geom_smooth() plots a loess smoother (weighted regression) on the series.
ggplot(data = test1, aes(x = Time, y = Dpm)) +
geom_point() + geom_line() + geom_smooth() + ggtitle(2578)
![](data:image/png;base64,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)
ggplot(data = test2, aes(x = Time, y = Dpm)) +
geom_point() + geom_line() + geom_smooth() + ggtitle(2585)
![](data:image/png;base64,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)
Another way to smooth data, is to calculate a moving average.
acf(test1$Dpm) # First, we check the autocorrelation to choose our window size.
![](data:image/png;base64,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)
library(pastecs)
Loading required package: boot
Attaching package: <U+393C><U+3E31>pastecs<U+393C><U+3E32>
The following objects are masked from <U+393C><U+3E31>package:dplyr<U+393C><U+3E32>:
first, last
movavg <- decaverage(test1$Dpm, order = 3) # By choosing an order of 3, a window size of 7 will be obtained (3 values to the left, 3 to the right).
movavg # movavg is a list
Call:
decaverage(x = test1$Dpm, order = 3)
Components
[1] "filtered" "residuals"
plot(movavg)
![](data:image/png;base64,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)
Now in ggplot:
test1$decavg <- data.frame(movavg$series)$filtered
ggplot(test1, aes(x = Time, y = Dpm)) + geom_point() + geom_line() +
geom_path(aes(y = decavg), size= 1.2, colour = "red")
![](data:image/png;base64,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)
movavg <- decaverage(test1$Dpm, order = 3, times = 5)
test1$decavg <- data.frame(movavg$series)$filtered
ggplot(test1, aes(x = Time, y = Dpm)) + geom_point() + geom_line() +
geom_path(aes(y = decavg), size= 1.2, colour = "red")
![](data:image/png;base64,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)
Now, we can do the same for the other subset.
acf(test2$Dpm)
![](data:image/png;base64,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)
movavg <- decaverage(test2$Dpm, order = 1, times = 5)
test2$decavg <- data.frame(movavg$series)$filtered
ggplot(test2, aes(x = Time, y = Dpm)) + geom_point() + geom_line() +
geom_path(aes(y = decavg), size= 1.2, colour = "red")
![](data:image/png;base64,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)
Discussion: which smoothing technique is preferred? Is smoothing necessary?
---
title: "C-POD data workshop: Time series"
author: "VLIZ - Flanders Marine Institute"
date: "October 5-6, 2017"
output: html_notebook
---

In this topic we will explore temporal data in R in order to visualize the temporal aspect of porpoise distribution.

## 1. Dealing with time variables in R
As discussed previously, a variable is recognized as time in the POSIxct data type, for example by applying **lubridate::parse_date_time()**.

A first step is to plot the value of interest against time.
```{r}
ggplot(data = poddata_day, aes(x = Time, y = Dpm)) + 
  theme_bw() +
  theme(axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  geom_point() + geom_line()
```
```{r}
ggplot(data = poddata_day, aes(x = Time, y = Dpm)) + 
  theme_bw() +
  theme(axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  geom_point() + geom_line()
```

More relevant is plotting the time series for each zone seperately. This can be done with **facet_wrap()** or by making a list of plots.
```{r}
ggplot(data = poddata_day, aes(x = Time, y = Dpm)) + 
  theme_bw() +
  theme(axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  geom_point() + geom_line() + facet_wrap(~Zone, nrow = length(unique(poddata$Station)))
```
```{r}
ggplot(data = poddata_day, aes(x = Time, y = Dpm)) + 
  theme_bw() +
  theme(axis.text = element_text(size = 16),
        axis.title.x = element_blank()) +
  geom_point() + geom_line() + facet_wrap(~Zone, nrow = length(unique(poddata$Station)))
```

```{r}
lapply(unique(poddata_day$Zone), function(x){
  ggplot(data = poddata_day[poddata_day$Zone == x,], aes(x = Time, y = Dpm)) + 
    theme_bw() +
    theme(axis.text = element_text(size = 16),
          axis.title.x = element_blank()) +
    geom_point() + geom_line() + ggtitle(x)
})
```

Same can be done for different deployments.
```{r}
lapply(unique(poddata_day$Deployment_fk), function(x){
  ggplot(data = poddata_day[poddata_day$Deployment_fk == x,], aes(x = Time, y = Dpm)) + 
    theme_bw() +
    theme(axis.text = element_text(size = 16),
          axis.title.x = element_blank()) +
    geom_point() + geom_line() + ggtitle(x)
})
```

## 2. Autocorrelation
R provides a very easy function to investigate the autocorrelation in your data: acf.
```{r}
poddata_day <- arrange(poddata_day, Zone, Time)
acf(poddata_day$Dpm)
```
```{r}
acf(poddata_day$Dpm)
```

Again, we are interested in the autocorrelation of each Zone seperately.
```{r}
par(mfrow=c(2,2)) # see four plots at a time
lapply(unique(poddata_day$Zone), function(x) {
  data <- acf(poddata_day[poddata_day$Zone == x,]$Dpm, plot = F)
  plot(data, main = x)
})
```

We can also take these values and combine them in one plot.
```{r}
lacf <- lapply(unique(poddata_day$Zone), function(x){
  o <- filter(poddata_day, Zone == x)
  oa <- acf(o$Dpm)
  dfo <- data.frame(acf = oa$acf[,,1], lag = oa$lag[,,1], Zone = x)
})
library(plyr) # Now we use the package plyr to make a dataframe out of our list
dacf <- ldply(lacf, rbind)
```

```{r}
ggplot(dacf) + geom_path(aes(x=lag, y=acf, group=Zone))
```
```{r}
ggplot(dacf) + geom_path(aes(x=lag, y=acf, group=Zone))
```

## 3. Smoothing
The goal of smoothing is generally to aid visual interpretation of a time series. Let's try on two subsets of our data.
```{r}
test1 <- filter(poddata_day, Deployment_fk == 2578)
test2 <- filter(poddata_day, Deployment_fk == 2585)
```

Applying geom_smooth() plots a loess smoother (weighted regression) on the series.
```{r}
ggplot(data = test1, aes(x = Time, y = Dpm)) + 
  geom_point() + geom_line() + geom_smooth() + ggtitle(2578)
```
```{r}
ggplot(data = test1, aes(x = Time, y = Dpm)) + 
  geom_point() + geom_line() + geom_smooth() + ggtitle(2578)
```
```{r}
ggplot(data = test2, aes(x = Time, y = Dpm)) + 
  geom_point() + geom_line() + geom_smooth() + ggtitle(2585)
```
```{r}
ggplot(data = test2, aes(x = Time, y = Dpm)) + 
  geom_point() + geom_line() + geom_smooth() + ggtitle(2585)
```

Another way to smooth data, is to calculate a moving average. 
```{r}
acf(test1$Dpm) # First, we check the autocorrelation to choose our window size.
```
```{r}
acf(test1$Dpm) # First, we check the autocorrelation to choose our window size.
```

```{r}
library(pastecs)
movavg <- decaverage(test1$Dpm, order = 3) # By choosing an order of 3, a window size of 7 will be obtained (3 values to the left, 3 to the right).
movavg # movavg is a list
```
```{r}
plot(movavg)
```
```{r}
plot(movavg)
```

Now in **ggplot**:
```{r}
test1$decavg <- data.frame(movavg$series)$filtered
```

```{r}
ggplot(test1, aes(x = Time, y = Dpm)) + geom_point() + geom_line() + 
  geom_path(aes(y = decavg), size= 1.2, colour = "red")
```
```{r}
ggplot(test1, aes(x = Time, y = Dpm)) + geom_point() + geom_line() + 
  geom_path(aes(y = decavg), size= 1.2, colour = "red")
```

```{r}
movavg <- decaverage(test1$Dpm, order = 3, times = 5)
test1$decavg <- data.frame(movavg$series)$filtered
```

```{r}
ggplot(test1, aes(x = Time, y = Dpm)) + geom_point() + geom_line() + 
  geom_path(aes(y = decavg), size= 1.2, colour = "red")
```
```{r}
ggplot(test1, aes(x = Time, y = Dpm)) + geom_point() + geom_line() + 
  geom_path(aes(y = decavg), size= 1.2, colour = "red")
```

Now, we can do the same for the other subset.
```{r}
acf(test2$Dpm)
```
```{r}
acf(test2$Dpm)
```

```{r}
movavg <- decaverage(test2$Dpm, order = 1, times = 5)
test2$decavg <- data.frame(movavg$series)$filtered
```

```{r}
ggplot(test2, aes(x = Time, y = Dpm)) + geom_point() + geom_line() + 
  geom_path(aes(y = decavg), size= 1.2, colour = "red")
```
```{r}
ggplot(test2, aes(x = Time, y = Dpm)) + geom_point() + geom_line() + 
  geom_path(aes(y = decavg), size= 1.2, colour = "red")
```

Discussion: which smoothing technique is preferred? Is smoothing necessary?
