diff --git a/Rmd/cleaningFeederData.Rmd b/Rmd/cleaningFeederData.Rmd
index aa31ad5ec002c798e509b654822e30c77169eca0..032a46503dfc3cffc86b096f41b1198d70b97694 100644
--- a/Rmd/cleaningFeederData.Rmd
+++ b/Rmd/cleaningFeederData.Rmd
@@ -9,7 +9,7 @@ author: '`r params$authors`'
 date: 'Last run at: `r Sys.time()`'
 output:
   bookdown::html_document2:
-    self_contained: TRUE
+    self_contained: no
     fig_caption: yes
     code_folding: hide
     number_sections: yes
@@ -305,13 +305,17 @@ aggDT[, propExpected := sumOK/(uniqueN(feederDT$feeder_ID)*24*4)] # we expect 25
 summary(aggDT)
 
 message("How many days have 100%?")
-nrow(aggDT[propExpected == 1])
+n <- nrow(aggDT[propExpected == 1])
+n
 ```
 
+So, there are `r n` days with 100% data...
+
 If we plot the mean then we will see which days get closest to having a full dataset.
 
 ```{r bestDaysMean, fig.width=8}
 ggplot2::ggplot(aggDT, aes(x = rDate, colour = season, y = meanOK)) + geom_point()
+
 ```
 
 Re-plot by the % of expected if we assume we _should_ have 25 feeders * 24 hours * 4 per hour (will be the same shape):
@@ -319,6 +323,27 @@ Re-plot by the % of expected if we assume we _should_ have 25 feeders * 24 hours
 ```{r bestDaysProp, fig.width=8}
 ggplot2::ggplot(aggDT, aes(x = rDate, colour = season, y = 100*propExpected)) + geom_point() +
   labs(y = "%")
+
+aggDT[, rDoW := lubridate::wday(rDate, lab = TRUE)]
+h <- head(aggDT[season == "Spring"][order(-propExpected)])
+kableExtra::kable(h, caption = "Best Spring days overall", 
+                  digits = 3) %>%
+  kable_styling()
+
+h <- head(aggDT[season == "Summer"][order(-propExpected)])
+kableExtra::kable(h, caption = "Best Summer days overall", 
+                  digits = 3) %>%
+  kable_styling()
+
+h <- head(aggDT[season == "Autumn"][order(-propExpected)])
+kableExtra::kable(h, caption = "Best Autumn days overall",
+                  digits = 3) %>%
+  kable_styling()
+
+h <- head(aggDT[season == "Winter"][order(-propExpected)])
+kableExtra::kable(h, caption = "Best Winter days overall", 
+                  digits = 3) %>%
+  kable_styling()
 ```
 
 This also tells us that there is some reason why we get fluctations in the number of data points per hour after 2003.
diff --git a/Rmd/cleaningFeederData_allData.log b/Rmd/cleaningFeederData_allData.log
index 8b7564885cbe3188d26fd17986f41690f91ac17e..318599028f02c106bb96cf5612c33b613d85db70 100644
--- a/Rmd/cleaningFeederData_allData.log
+++ b/Rmd/cleaningFeederData_allData.log
@@ -1,4 +1,4 @@
-This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013) (format=pdflatex 2020.4.15)  8 JUL 2020 22:59
+This is pdfTeX, Version 3.1415926-2.5-1.40.14 (TeX Live 2013) (format=pdflatex 2020.4.15)  9 JUL 2020 00:11
 entering extended mode
  restricted \write18 enabled.
  %&-line parsing enabled.
diff --git a/_drakeCleanFeeders.R b/_drakeCleanFeeders.R
index 9e0fbd70f41d78079d0bc2a59b3c8e8e379ca6dd..1cfc5b2a176b0234cad9da3b1292bfcd6a141282 100644
--- a/_drakeCleanFeeders.R
+++ b/_drakeCleanFeeders.R
@@ -54,7 +54,7 @@ addSeason <- function(dt,dateVar,h){
 }
 
 
-getData <- function(f,update){
+getData <- function(f,updateData){
   # gets the data
   dt <- data.table::fread(f)
   dt[, rDateTime := lubridate::as_datetime(Time)] # the dateTime is now called Time!!!
@@ -120,7 +120,7 @@ saveData <- function(dt, which){
   }
 }
 
-makeReport <- function(f,version, type = "html"){
+makeReport <- function(f,version, type = "html", updateReport){
   # default = html
   message("Rendering ", f, ".Rmd (version: ", version, ") to ", type)
   if(type == "html"){
@@ -149,14 +149,14 @@ makeReport <- function(f,version, type = "html"){
 
 # Set the drake plan ----
 my_plan <- drake::drake_plan(
-  origData = getData(dFile, update), # returns data as data.table. If you edit 'update' in any way it will reload - drake is watching you!
+  origData = getData(dFile, updateData), # returns data as data.table. If you edit 'update' in any way it will reload - drake is watching you!
   uniqData = makeUniq(origData), # remove duplicates
   wideData = toWide(uniqData),
   saveLong = saveData(uniqData, "L"), # doesn't actually return anything
   saveWide = saveData(wideData, "W"), # doesn't actually return anything
   # pdf output fails
-  #pdfOut = makeReport(rmdFile, version, "pdf"), # pdf - must be some way to do this without re-running the whole thing
-  htmlOut = makeReport(rmdFile, version, "html") # html output
+  pdfOut = makeReport(rmdFile, version, "pdf", updateReport), # pdf - must be some way to do this without re-running the whole thing
+  htmlOut = makeReport(rmdFile, version, "html", updateReport) # html output
 )
 
 # see https://books.ropensci.org/drake/projects.html#usage
diff --git a/docs/cleaningFeederData_allData.html b/docs/cleaningFeederData_allData.html
index 33850f81b9338ea46e5ef51291d40c75818e2ed9..6b6c96862139d78a9278a2cf7eec1190202e5897 100644
--- a/docs/cleaningFeederData_allData.html
+++ b/docs/cleaningFeederData_allData.html
@@ -181,7 +181,7 @@ summary {
 <h1 class="title toc-ignore">Testing electricity substation/feeder data</h1>
 <h3 class="subtitle">Outliers and missing data...</h3>
 <h4 class="author">Ben Anderson &amp; Ellis Ridett</h4>
-<h4 class="date">Last run at: 2020-07-08 23:36:27</h4>
+<h4 class="date">Last run at: 2020-07-09 00:01:07</h4>
 
 </div>
 
@@ -2296,8 +2296,10 @@ summary(aggDT)</code></pre>
 ##  Max.   :77.00   Max.   :6130   Max.   :0.8186432  
 ## </code></pre>
 <pre class="r"><code>message(&quot;How many days have 100%?&quot;)
-nrow(aggDT[propExpected == 1])</code></pre>
+n &lt;- nrow(aggDT[propExpected == 1])
+n</code></pre>
 <pre><code>## [1] 0</code></pre>
+<p>So, there are 0 days with 100% data...</p>
 <p>If we plot the mean then we will see which days get closest to having a full dataset.</p>
 <pre class="r"><code>ggplot2::ggplot(aggDT, aes(x = rDate, colour = season, y = meanOK)) + geom_point()</code></pre>
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width="768" /></p>
@@ -2305,6 +2307,787 @@ nrow(aggDT[propExpected == 1])</code></pre>
 <pre class="r"><code>ggplot2::ggplot(aggDT, aes(x = rDate, colour = season, y = 100*propExpected)) + geom_point() +
   labs(y = &quot;%&quot;)</code></pre>
 <p><img 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wkfVtnhIysjfFhlh4+sjPBhlR0+MuAUUdIB0JNPPnnXXXcFg8E77rjj3HOPlXR5/PHHv/nNb37xi1/8/Oc/f/rpp4tIIBAYHh6Ox+MZt8diMRHJutjB4/G0tWU52sm2bcvKsp9oPqlaLkce17v1a3VJN1q/n3J90nKXpSMLpgZAWe031mfGPBn/jkxeR9PrTX3ovNd+8NI6Lcc6oNHuTdbvs6Y/ozQxjVcetK68NaO9N7Sj4IRdkshxZdJ8IsbMvx7CnVI6X05KKU3TRMS27YlFtVCydF13HMe2M5fgoQSN/c+D0vkjjzxG/z7kL8Nyoev66C/481UWRv988WGVhbFvDvl+48Qa+1sLKEElHQD9+Mc/FpGPfvSjY+mPiFx22WWDg4N33333T37yk3/5l38Rkdra2uHh4UgkknH7aEttbe3UkU877bRf/vKXU9sHBgbmfc2gow9XObYuSo7o77TE1Ww9omTi3/iT4o81Ayteat5czLCve96W2eRMHm9KzrMrEP3D8177x20rV4T9Uwcc7V5tv5b/ud7wzuHubmfyslUnXeAkMhFpsPYV7CMiuwPRt0nutUJ52SkZ6BkukTJAPp9vNAyNxWKJRK7wCyWkpqYmlUpFozkDUJSOyspKt9stIqFQiB97Sp+u6zU1NZFIJJUqXA4Pc260pKNlWfP+O7T5wev1BgIBPqyy4HK5qqqqRCSZTE79SQozNrU0LVA6SvcUsJGRkd7eXhGZmP6MGt38tWfPntH/dzdac2FqrZ/Rf3uyBkCnLCuixQ65xrKYEe0MW9x5+reGm6qTVcWMPKI1iUzed6UKl87ZXxH70IZNf37O6wk9y49MSiy3U+AbCOVYWujYLj+VTBp7dro3vtiyv/D39KtSTxpO4W593uTEl9PLcpQovSTSHwAAAADAKa50AyCfzze6KHHq/001TVNE/H7/6JL7pUuXisjmzZkLVUZbFi9efBJmWy4GX/aNZRhKrMXm9/WcO6FG+6hr9r+10LFeIiLHwpQZbR9+rTp0wJ+5g09ElNjFRC6OaYttu599ouLbX/Pdf6/nsUcaH9tk2AXWXgad/osSdxccfM3IpCqh03p/rmpLsQIUAAAAAFACSjcAcrvdHR0dIvLcc89lXBptWbZs2ejLK6+8Uin18ssvJ5PjizVCodDWrVvdbvcll1xysqZcBmKHx9f7VNubPU5f/v7m4uXLQkvfuedKVSiHaTJ3T2lzMn/p5MxzKswsSYktLlMKntKiLF+l75c/8zz3lEqnxoY/u3dtoRvlosSPLkj83/wZU2s8+xlMxSzsCa5IFu4EAAAAAMDsK90ASERuvvlmEfne97732GOPjVYmM03zgQce+PnPf65p2k033TTaraWl5fTTTx8aGrrzzjtHlwslk8kvfvGLqVTqrW99KydeT2THxz/xSrtwpWRzydL4jTevSZ53ftc5GZcyEpBF5qYpd6vMX+ZeP1OXnrITzRERGdHX5J9hVC3y7Ntm7NmZ8cgrDl6kOwWX3ziXx7/zp+E/OSv5oOZMr2xHwaVARqVVfXaWZU0AAAAAAJx8JV0E+uKLL967d+/999//9a9//Vvf+lZNTU1/f79lWbquf+QjHznttNPGev7FX/zFpz71qaeffnrjxo0LFizYt29fKpVqbW39yEc+MofzL0Xa+FlchowU7O555nGVTIrIWmvlc60bJ17KSEA8kqN03OTDv3IFJx57ShapRER6tKtrrI2apHNMUHUZ1y595Z6pF3RHu3n7O3562gNO3sU6mhgrjaRhPbFJXZ+1w1H/TOolGxV227tDJVL+GQAAAACAkl4BJCIf/vCHv/zlL1944YWNjY3Dw8Pt7e2XXnrpnXfeecMNN0zs1tLS8o1vfOPqq6/2er179uyprq5+17ve9W//9m/BYMENRKcWIzB+4JctroL91fFddY2xutWDyyZeysg2au1OlfU83eKq5uQ6ijep6g8aH8oxVXVEv8H01qmR4az3Lg0t+KM33lOR+0SwOv/qPzjr4Q+vf2XV8p/kiomebMgsLl6QI9JwWcRVxUlAAAAAAIBSUdIrgEatXLnyf/2v/1WwW21t7cc//vGTMJ+yVrEsmew99qEnVLPIluLvffveK4Y9oa6K3tGXGcGO3x5eaL16wMjcKVakmG5Vmtm/Goe1M5KuT7ZYv6q0tx0/sV5FtSVHtesi2vLGth2SeytbR6jlExv/9ECw87XL/ZF0t22bXletx6iqcDe2Vp7XXnWRUpqIbEkkcgVVm6pGXqgbestATfHvRWnibcu1ZAkAAAAAgDlQBgEQTqDac+KDzx0rijSsndNs/a74k829lvtDW296rm3jK81bYkaW6jaXx79zd+Dblirii8rJDJBerRm5tK8uV/e4tO4zbtUl6XH6lVhJVW+K3xFRSirbBvMEQKMWRRfULf87Uao/uv3g0GOR5FHLTsXTg6Ydd+kVItKTzr1aR8k/rN319U1rVocChd/X8XfnpDTxsgIIAAAAAFAqCIBOLWZIV+rYfqu4aunTL26wnir+dsPR39q54eLO9QO+oa31O59pe2Xi1VZz+1mpB1/x3Fh4oCnLbf59+YE8AdBof0s8MdU2sc0I2m5roODTbK83ZvY/uusT+wYfmdjuNaovXHzH2uYPjlj5wpqQYX703C03H2q94WhTRyznhrJxjoR3uWvOpQI0AAAAAKBUEACdKpL9es8jwdSAMXHFzxH93ZqTrrOfn9ZQSlR9vPacntOfbX3FmRzl9GkLM3tPWeyTYXHUf11X48pwxaA7VZuachZY/pk4omLRgt1iQfnZ5ncMx/dltCfM4Ud3/3U8PTBgFgit0sr5ycIjP1l4pNI0btu78MbO5vz9UwP8yQIAAAAAlBB+TD0lxA66u35ZObXQsiPaIeOWQWd9vfW83z5oqKgthssJFbMvLJgKnDa4fFvd7omNUX3KKp7c6Y/mqL/Ys/CWw21qxodl6Y64Cpey/n3Lk1PTnzHPH/zyvupri3xgyDDTRUw33umy4kr3cQoYAAAAAKAklPopYHjzrLjW9WAw1zFbIhJRyw4YH3zD/ffd2tUuZ6T4qkDX7L+k1rVgYovupESKGcARkdt3L37/oTeR/oi46yy7vjF/n4SR3O59OU+HncaGZK5r2ebW70kVnJgZ0Y78otpKFHcEGgAAAAAAs4wVQPPf8MtexyqcRLickRbrVwW79foHXmp+bX/V4Yg76hZ/Y+U5nnRzd/gVx7FFpNHa16MvL+Lod7UyHLips6WY+ecRXJ40Fy8Vn1/iscxrx7eeHQ32WpKvxM8Wd+7lP9neyMu1wyJTdrpNeXpqQB94pqLxykiBngAAAAAAzD4CoPkvtMtbTLdqe5MmBda2vNj82qMLn7XV6FnsEpfIwYEnRaS18rzVTe9LmEONVsv/FxaRwqV/ru1qeDNrf0Ypw3Fc7vilV/l+88sp1479N7SiLfPSZCP6hByq0LRFZEcw8krN8LlD1XlnJiIS3uGtvziqedgIBgAAAACYY2wBm//sWFEbkfxOZ/4OW+t3/W7R02Ppz0RHQy/u6X/o3Pa/3ND8zmNNhZ65Mlz0qeq5xY+6RMRcuy55xdtE1zMvK5U6e4O25oL8gzgTC1krKWYD2z+t2d3tzblvbHxkSxLdhUsUAQAAAAAw21gBNP85RezIEhFNEnmu2sp+dMGzeTocHHr04NBjO/T1Rc6qwpyS10yfFTuWYKbO3mAuWeba9Ip++KAWj9luj93all53TqhW7zr6X6KU5K6BZEvGTAr/dvV6Un9y7paHnl1fcBGTGaUMEAAAAABg7hEAzXNOWhVZ09mU4OQ7JyUhh4NdYXeBcjY7e++PNZ1b5MQG3ekie+ahT9hdZVfXJi+7euLVrd0/fvLlz5p2vmBLRAb1BTmvZd8R5oioc4aqitnCprP/CwAAAABQAtgCNs/Z+cofTxLRlk96PTn4GPANFRxhOLGn2Sg2Uny1ZqTYmeXmbsyZIm3v/emju/+6cPqjdZgq9y6t7Mt3lIjcOFjg9LHRjp4ms3A3AAAAAABmGQHQPKd7HM1d1CKUEe30lKrNddXUCidJppVYX+Hzazm+qCbP4qHWnqhedDqVjdKdiqXZq1YnzdBT++4oZpCDxlkzebTImemKgt10wzECWUomAQAAAABwkhEAzXdK/AsLnO01yhbXAf2PbMm+HKYyWbhmc9Db7lXqz+pqcszkeALkiIgMutP/vHrPm0lHas6L5dpgtW/g4US68JIlETlknJGldeqox1uakp73dLZ8Zs9SI1q4hpGVVo5JDSAAAAAAwNyjBtB850jFslRkj6eYSkBRbclu1yeWBP6va+hoxqVFoXbd0axsR4CN96m5QkT+prHulVj8mWgsYxqijkchx//7WFN/2GX+7c4lHTHfeE8lgWVJV4019JI/z7OUy6ldH891tS/6ep57xwxrLTvcl06Y4fgcpjxP3Lb2l7sX3XikWXeUiBS5eCm63xVYXlQABwAAAADA7CEAmr8cGdniG3jRZ8ensc4rptrD7/1o8Nn7XNs2jzVaynqh5dX8Nwa97aub/kBEPErdu7D9X/v6/2tgOGIfD4zGIpXJZZVfrh1+31teXTdStSpU8W5/1dkNbn9H2qi0xJHILk96OPsqGyXS8f7hPJNJmeEC71NERH7v/4u08k6d1VS6o760ZdVbBnIsbsot3kUABAAAAACYewRA85NjS9cDVbGDucsb5+CutVxVVvqMs8YCIEtZ9656aF/VoTx3eY3qd6y+29B8IuKI49bUZ5sabm+oeykWfzAUfiocP5A+HoJMyVlsJZuqhzdVjxg10Uvbmse6LfjD4SP3VyaOZL4Fze103Dzsqs63BMfvbij4TuOqapfr4lyzynDDkaYZpD8iIjZbwAAAAAAAc48AaH7q+mVV7NC00x8RqTs/JiJWW4fV0qZ3HRGRF1o35U9/FtZcdtHif9w78Ovf7fr4YGyX49iV3oWLa69a0/qx7w6oR8IFDo8XkdEA5qfDoc81NdQYx1b9KN1pv2kkNWAMveRN9LocWxkBq/rM+PiCGsfROw+5dmxTw4OiaXZVteOrEHHE7VlYveLlQo/s1pfZohdc+zPqPUdaingXWRgVb6rQNQAAAAAAJwQB0Dw0ssU7k/RHSe2GWMWypIiIUonr3uW/5wdOIvpi82v579OU54E3PhBOdI5PIHHgNz1P/WX04i5tSfHPTzvOC7H4tZWTqk2768yma7NESHrnIe8Dv9Ci2bd6rVBO45lNvZ6ePI8Lac0iRaU/AdNYEslXkCgPjoEHAAAAAJQCTgGbbxxb+h4vfGLXVLXnxmvXh7X+Xr2nS0XCdm1d7I9u7V7mj7ly1loedWDodxPTn4hW9/8CX/1e5femlf6M6jWLikuMfbv99/4oV/ojIpqj3rXzCq/pyTPIkN5a5Kyq0zPMSY1Ky9eWntm9AAAAAACcQKwAmm9CW7wzuMuQcFPf/Z67tqjjxXqc6urUGecMXXSWbC9wr+OM73KKqaofBb45qLfPYA4iEtAKJ5IqmfA+8AuxC5wg3xir+8jWmx9a+vvDwa6sHXQnXeT+r7Axk1U8SpfGKyKq8GHxAAAAAADMOgKg+Sa8K9+yl6y8Ttdy85vGrtDERjU87Hnq0crNPbJ6GkM94f/ojNMfETndVzi9MrZvHUup8ni9fsdvFz8V15PHG5TbCJze8pHdvf8TSh4SEbckCqc/joiSEZfZ6Uu0x6eRrGleu+nqiH8By38AAAAAACWBAGi+MRPT29anSXqp+Z+GE8p6tT5arTuapQostxmVFu8W1zXTevpEZ/m8Kzzugt3cG18qMA3d+va6H4XcGZWDnJQZ3nj4TrcR1JTLdtI+ezjLCqCMFiUiEtC0Jxf0fWBnR56H+hellCZ2Umk+29+RDq5Kam6n4HsBAAAAAODkIACabwyvbco09h3VWC+7nYFcV32mZ/nQ4h21e4sZqstYaapprz8a5VXqS61NxfTUhnLOVkRs5XzjrO8njESuDikzrJSuRDVbe7KsAMq2Jug91ZV3rJHhFeoAACAASURBVPQfGTET3dn/vHhb0i3vCLHbCwAAAABQsigCPd+Mn5JenCp7q4iIjC1XyVy3csWhC315qimr8cgkriqn9egxNaL/OLG89amGo/dVHX2gcuB5f2ooe5qiwmFxnOPzzLLE5veLnsqT/oxyHMsRp9Lunnola/93VQaV7rTcMBJYluX3NrA82XoD6Q8AAAAAoKSxAmi+qVqb6H+q4tiLIoocu2VQRCb0y7yhJlF1y/YbfrHiNyOezFO3GoNre8Nbx176nJzHcuWxdiT4+a0rWxLjo8f2u4de9lefFa+7MKomR5TG7rGS1Nnf2CtNW7O2T+VxYlPasozpVuqigF9EdK/T/PZQoseI7nWP5lPuWiuwNOVp5KB3AAAAAECpIwCab5TLCSxPRnZ7RIo64sopYhVYa7Tpts1/uLltz87TJZQ8rIlRH1izsuHd2/vumRgANVu7NDHt7F9U2bOoZZGKb7y2xm9OWT/jyPCrPsdUDZdNKuXj2rYlzzwj7qgtRZUrEhEltiFJUwrsWbujqXHiS2+T6W0i8QEAAAAAlBkCoHmo+drwwaNGOlrUrqSkavA7nQW7uWzjnP6zV675m4mNzx/654kv3U4saA+MaFlL+WTPoj65c0mW9Oe4kde9gVUJX8t44KKFRvJMsjPHie+5LE89t919Wf4+KafYRAkAAAAAgJJFDaB5yLGVt8MsZvmPiJhSU+Swdk1tRoua/PVjKndU1YrkqqWTqS3uPXM4b9kgR8JvTDp83dHypVpm0ct/Ri1JFzhQTES+3jewZsfeNTv2Xr/v0H8MDEVt8iAAAAAAQPkhAJpv0iF937dqwzs8xaQwbeZ9DfZjRY5srlid0VLjXzLxZVjVm8olUtTWMxFZFQ4U7JPsmbRIzamtldz5kq1NL50xVOGC2SHb7jXNXtN8IRb/+67eS/Yc2J6cXpltAAAAAADmHAHQvOJYcvBH1Y5dVABTZz/XaD9R7MguI33GWRmNq5pvmNb0MgTShTepWclJ78VcukJy50tpLT2tCWiONa3+InIwlb5p/+F+c9o3AgAAAAAwhwiA5pW+R4NiFbf8RpwW6zdjvy7YW6VN10vPZTSuan53S9XZYy+DTr/hTGN1zJC7cF7jqpg0t/TqtZJ7F1ham1555jr70LT6j+o1za/2DczgRgAAAAAA5goB0LwS2eUusmeFc8jljBVULiozcr/yokpPimw0pd941g8N7dhBWoaTWmK+WOQERGRLddhWBbInb/ukJzoVgcTlV+fqXJH2Ff90EUmJP0trEVvn7h8O2cXVOQIAAAAAoBQQAM0rdrHLf8TtTHsNizLT+uGDGY0vHfiWaSfHXl4S/54uxe7DGnalH2nuy/dE3alam8hoTJ+1PnHVdY4+tg5oPInpiLQW+ehRu1wXZntq4RsHLKvf4jB4AAAAAEDZIACaP6Z5XnmxUdGke0LDE1+mzMhrh384saXJ2vPO6Bd0p9hw5M5lBw7547muNlwWNSqzVNtJn3lu9Lbbk5dcaS5eZjW1OsHgaHt1orIpVl/ko0VkWG8pvnOGhMMSIAAAAABA2TAKd0GZUJqIKvYI9qRqmMkzjElfMEeGXzKtzBU6umPaKm+w6IynT8Pu9EfPef1vdy69rK9u4m4wI2DXXxIJLMtZUcjxV6Q2XCAbLhh96dr6mvv5p7XhoXftueY/z7jHKe53QeUPcZxsKZkjosRQqtHgzw4AAAAAoGzwQ+y8orudjGOzcompNkt5dSczvsnPqp20viaWytzAFVW1DwY+4+RfWaZERKp1/T3VwRHLNkTshYNe267s8pkRTXOJp8H0daRU4SPCxqXXnplee6Y2PFgRGrks2v54/5dyRTua5rpkyf95au8dlpOssY9mXp4Y+mT9jVQiIm/x+7xq/HK80xU75DbDmjIcT4MZWJ7SfdNbjgUAAAAAwKwiAJpXfG2pyL5jJZmzrl+ZQIXVympnc/GD25VVdkvbxBa3Eczos8lzfVIqxl/nmMTpXs9/dLQu90wuWd0wvTQqywyra6W69nT5G0/fskd3/3XKCk++rlqC51y/9h6fUXN684df7rwr3Pdy5sFmxW2Mu72hdvQXZkjveTgY75r056j/aaf2vFjNOfEZbbMDAAAAAODEIwCaRxxJ9LnGXhUMH4a09dV20QGQUsnLrxE1adTmyjOVqIn7rQ64zp58V5aRbm+o+0xjvTab4ciKhnctqr1yV9/9R0deSlkjhu5tq7x4ddNNhnbsmDCltA0df7WhQ/YcOvqrUDj/aBk+1Vh/SaBCRMyw1vmzKjOSudzJMdXAsxV2Uqu7MHpC3g4AAAAAAG8SAdD8YUY1MzyNqt4hbZUplYaECnfV9cTl15jLV2U0B72tSxqu3Nv3u7GWiFZXcLAmQ5/V9GeUWw+sbf7g2uYP5u92V1tzv2m+GMtSiPpcn2/QsvalxusQLXG7/7654frKwOjLvqcqpqY/Y4Y2+gLLkp4mDgsDAAAAAMw9AqB5YtA0Nw5Yi6dziy3uw8bNi83v5SwcrZQTrDIXLU6d8xa7PnvR6JUtN0wMgNxOrOBzo3YJ1ccJ6tr/LO744eDwj4ZGtieSIqIrdabPe2tt9burK0XkjUTyvpHwtkQy7TiNhn44lepKmy0uw4prseO77bJzZGSrt7EpcnLeCAAAAAAAeRAAlbeHQ5HvDg4/G4mZ4tSl3A9JW+F7JhjWzjhgfKjD+mlmNWilHLdHJRMqNGzs3S1KS513oV1VPXWEnpEtE182W3uO6qflf+g/9/RHbfszTQ2zsgzIcfTOg8aRTkklHY/Xal9gtbZn7FzLYCj1p3U1f1pXE7XtQdOqNwzf8RVK25Op2zq7diSSY51/LvKF3v6/bqi7LdLiFAqykt2uAj0AAAAAADgpCIDKleU4t3Z2PTgyXr9mwJ1ylKOcfGHH1KLMI9rpNfbGKuf1yf0clTwWCaloxLV5o/HG64l33GguW5kxYChxeOLL05MPv+p+Z77nidgiX+sbfDQSSznOgGnW6Pq5ft+Ha6vP8nnzzLwY2tEjvkce0PonnU1mNTYnr73BamwqeHuFplW4x7d07Umm3rX/0KBpZXRL2c6/9PQ3RIIXSGX+Ac04VaABAAAAACVhGiVjUDrSjnPtvkMT0x8RWTdcmT/9kWxFmTvMe6rs17N0zbgxnfI+8HO9rzejXVeTTvLqMLesS/063/NGObIlntiRSPaZ1q5k6p6hkWv2HryjuzfX2e2FpxePGzve8P/07oz0R0T03m7fPT/Qu6ec+F7IZ7t7p6Y/Y+5PDBccQfeW0GY3AAAAAMCpjBVAZelPDx/dFM88NP323dMqASQiUuHsr7U3FtlZWZb76cfi737fxMa6iuUZ3a6LfkVzrE2e6/OOldngiHy7f8ivtE831Rc5HxFRyaTrpWfdb7yuQiP5uqVT3l//T/TDt4lWbOJ5OJV+IpzvDK+tlRFHicoRWY2ufPK1UgEaAAAAAFASWAFUfh4JR34dylJaeFHUN92haopOf0YZB/aqCadiichprTdl9NEl/fbYl85J3j/dyYjIXQODh1PpIjtrI8P+u//D88Iz+dOfY50H+o2D+4ufyaZEIv9ypGF3+tXmoVxXlYgoqVybGdIBAAAAADAnCIDKz991Ze5yGuW2p/1p+uzu6d1gWVpo0tan1upzVjVmZkAi4nHyLZ/JJWU7D4bChfuJiGX5fnGPNlJ4H9YYvfNg8Z2Hc2/+GvPdlQeNQM5NXjXrY55GVgABAAAAAEoCAVD5OTi6BseZenr7tEsOK1XscptxUw5xv2L51xbXXp3RGLAHpj2yiIhsn3DkVh6uN17XBvqnNbKKTiOTqtX1gn20oNV+84i3NfP3ULmc+oujdW+JTWt6AAAAAADMHmoAlZnX46P5iDP1aHNbHG2aGVBaqqb3eKXsYOYthua9fs3/3dP/wOtdP+oOb0xbUZ+r9q3B4G8Lr6HJIu4UVQna2L2j+DFHK/I43mmcMrahwqeJ5K/h/Ba/3wha7e8dSRx1RQ+6zLCuuRx3vRlcntIo/wwAAAAAKCUEQGVmy7HT2bMEPVGXVZWa3gca0lZV268V399qbXd8WSoNKVHL629YXn+DiDiOrZQmIr8/0PlEZNobwVpdRb0FbXAay39Gf7Pslrbib2k0jLdXBh7MVmtplEupD9Ycy8K8remp64AAAAAAACgdbAErM3rug967PNMuOTyknZtWNcX2Vip54aVF9Dr2RfW11qYi0xyR8e1slwYqippLqqidYuPDVwTMJZkHluX3zy1N7W5Xrqt3NDcs87inNSAAAAAAAHOFAKjMrPMf38c0aaeUIyJ+q3DZmgy2uPcbf2yJp3BXpZKXXmUtnMZJ8+1u12+XLnx7ZbCobWlKROQMn/fSiqICIEdN580qlbjqOseVM83JqtllPLS445IpgVSNod/Z3nJbXdHBGQAAAAAAc40tYGVmtcdtKGU6zuRNYMpla+3xadS4GRNVC3e6PtVu3lfpvDGeKmmaOI6MluNRymrrSF54qbVg0XQHbzKMHy5oPZw2n49Ge9JWtaEvcLk+cbQ761nvDYb+3Y5WrbgqRo7fL5HQlNYse+Mcry95zTvM5aumO3kRaXO5fr6ofXM88Xgk2mVafqVO93muCQYqNJJTAAAAAEA5IQAqP++sDN43kpl91KZcWu7dYZI9GzkmqRr2uj4aaBxo37BTpdN2RYXd0ia2ow0NOJbt1NRmrftTvA6X0VE9Xjr690sX3tHd94vhkHm83rMm8s6q4D81NzYXt2VMWaYTrJTeKWfYT36HVmNT+oyzzdPOcDxFLHHKbZ3Pu843k3ANAAAAAIASQQBUfj7RWDs1AIrpBc7cKrCwRknVxYbZvmJim9XUMoPpFVSr6//e1vz5poaN8cSAZdXo2jk+X71R1JYulUq5n3ncteVVlS5cdDl5zfVWc+ubni8AAAAAAGWPAKj8LHdPqT3sSNhljrjSVel8ZW6Ux1GO2KnMLEhpUvfWiK/9pJ5jVWvoVwWLKvczRsVj/p/erfX3FdPZ6lhI+gMAAAAAwChKmZQfXalARg0aJSLidgp8molgquP9w8FVSeU6tvdK6eJfmGq9abh63bRPEDv5vL95oMj0xwkEE9e9a7bnAwAAAABAuWAFUFk60+d5Jhqf2LIg5vOZBXZRqajuqrKargk3WmKGdMcRV9AeC4NKnN7Xa+zdVbifUuaylYkrrnWCwdmfFAAAAAAA5YEAqCz9Y0vTlXsOTGy5qL/wqeTuxLGPW+niqilQM6jU6Pt3F+xjLl6WvPrtdmVVwZ4AAAAAAJxS2AJWltZ5PR+omRRz1KXe1EFXpU8Lhwv2cVwu0h8AAAAAAKYiACpXX21tqtbH93yFDLPgLUovj91eWTl64WPClJGvBjYAAAAAAKcstoCVK12piwL+h0aOrYvZE4gUvMVVaWcf6sA+1643tIF+sW2nutZctiK9fJVo0wsH4+mBo6GX4uk+tx5sCp5V5V00rdsLsuvqT0ifEy41qIe2+OJHXOmI0r2Ot9msXJM4yUeqAQAAAACQHwFQGbs2WPHQSFgcESWv1oSSmu2x86U2VWdkHvWl4nHvr+4z9u8dbzraabyxxdXYlLjhvXZ1bTHTSJjDT+393K7+X1j2eOrRXn3RZUu/VOtfMY33k5e5bKVjPKzM3MGKpqVXrj5RjyvS8CbfwLMVzvF6SnZC0sN6eIcneFqi8fKIKrxoCQAAAACAk4EtYOUqZTvHNn0pEZG4bv1oUWee/kbQrlw7OQCyLP9990xKf47Te3v89/5YxaIFpxFJ9tz72jXbe386Mf0Rkc7hZ3666aqu0MsFRyiS4/Onzr84T4fU2RvsmroT9bhihN/w9j81nv5MuRQ4mZMBAAAAACAPAqCy9HQktmH3/r/q7J7Y+MOFnY829mftr/vt1neNZNQAcm3eqB09kusRKjTiefbJgjP59esfH47vy3opbcd+s/PPTDtz2dGMpc67MLX+fFEqy7PWnpm85MoT9aBiOKbqe9qfp8PI695UPyvsAAAAAAAlgR9Qy8/j4egtBzunrjuxNedza3e+1DX8gYNtC2K+0ca4bjWsTjdcENN9mQWAXNu25H+QsX2rXH6N5K6+PBTbu73rf/KMEE507uq7/7SmW0ZfvhxLfG9w6PlobNiyK3Vtvc/74drqtwYq8k9jnFLJS69KL1/l3vSK0XlQYlHx+sy2jvS6c6xFS4od5ASJHXTZibz5qSPhXZ66+sLFuQEAAAAAmG0EQGUm4Th/1tmVmf44xzaCOUoeaO15oLWnKeFpSLpjumVXpV9enS0ccRy9tztL+wQqmdCGh/JUVt7f/1jBCR8afvK0plsckf/d3fet/sGxNUgx234wHXkwFHl/TdW/tTbp2db1ZGW3dSTaOorsPHtinYVPHEv28ucLAAAAAFAS+AG1zPz3UGjYmrL6Z0p40uNN9niTIlKj9KcjsYsDmZuVlGmKnf1QsIkqvv9tx+uxFi9Pnn/x1CQokiwQIYlINNktIt/oG/xm/2DWDvcMjVRq2j+1NBYcqqSk+gr/2TFDbLEEAAAAAJQEfkAtM/89PDKt/kOmddOBw/eNhDLaHZfLcbmz3zOpUpCjEglj++sVP/yO+5UXMjq6jMK7t1y6v9+0vtqbvTjRqP8aHN6XShUcqqQ4ZhFLlvjjBQAAAAAoDfyEWmYOpXOfg56DLXL7ke6j6cxiNNaCRdlvyJps2Lbn8d+6tm6e2NYQWFPw6fUVax4ORxKOk6eP5TgPjIQLDlVStCk1labSA9lOCAMAAAAA4KQjACozdr4gJae47fxgcDijMXXOedMdx/PEb5U5nkAtqrs06G3Nd4NSKxrevTtZeHXP7uS0g6255a4tHO5UdJTZmwIAAAAAzFcEQGWmwch5Jld+T0djGS3WwsWp9edPaxAVj+sHxg99NzTP+kW35YmklKi0FU7mXf4zKlFEQaKS4l9UONUqpg8AAAAAACcBAVCZuTIYmMltjnRP2QImIslLrkxcea3j8xU/kt7fN/Zr006+uP+beWrhOI79xN7PtBQRWrW5y6weub897W3Jt8AnsDzprmMLGAAAAACgJBAAlZkP1lYZ2Yv05KUkoGf7rJVKn7U+etvt8Rv/QLSihnXS46ta9vT+Oprsyd+/N7LlXNdAwWEvDxSuJ11alDRfFzYqs0c8nkaz4YrISZ4RAAAAAAC5EACVmQUu1x3NDTO4ca3Xk+uSY7jMZSvtYFUx4zjByrFfHxx8uphbGs0dV+dduLTe77uk7AIgESNgd7x/uHpdQrnG97jpHqdmfaz9vSO6Z0blmgAAAAAAmAVltu8GIvLn9TUeJf+7pz82nbo5762uzN/BXLrC/epLBUZRylq8bOxVweU/o9JW9M725nfuO7QrWzXodrfrP9tbpr+oqSToHqf+0kjdxdFkv24nle5z3HWmIlYFAAAAAJQYflQtS39cV7NpxZL/09xQ5Od3fWXgikJLbFIbLnBc7vx90itPs6uqx156XEUtGgq4W+p0/ZGlC2+tq/ZN2GjmVuoPa6oeXbKw3e0qZpySpXTH22T6F6Q9DaQ/AAAAAIBSxAqgclVr6G+rDP59d5+IiCN56gK9u6ryG+3NBQd0gpWJG27y3v/fyspSLlpErIbG5FXXTWxpDq4rOKyu3C1VG0QkoGn/3NJ0R3PjlniiN23WGcY6n8evkZcAAAAAADDr+PG7jG1NJI79Knf6U6Hp3+lo8aqitliZi5fFPvRn5uJlMrm/o+upc86Lf+CPHe+k88JWt7zboxc4leyMlj92af6xl16lNvh976gKnl/hI/0BAAAAAODkYAVQGQtbx2sA5V4BFLWt6/cdemjJgiLHtOvq4ze9X6XTWtcRrbdLub1WdbXd0u64suzS8rsbrlrzpYe2/EWu0Wr9Ky9c/PdFPhoAAAAAAMwSAqAy1jQWykxMf6aEQS/G4t8bGPqTupriR3ZcLmvBImvBooI9z1n4Z5Fo+Kl9n7Od9OQrakndNdet/p6uCtQVAgAAAAAAs40AqIyd6/O6lUo5k44br0673t7VeOZIZXXKFTLM16tDv2rp/UrvwLQCoGlZ1/ony+rf/kbPTw8OPR5Ndbt0f2PgrPMW/HXQ0z5LTwQAAAAAANNCAFSuYrZ9/0io3eXalxo/W/3arsa/3bnEZ+ljLRcM1Hxkf8e/LzsQWWkHZq3mToW7eX3H7es7bp+l8QEAAAAAwJtBAFSWnopE/7yzu9ecdFzXVT31n9u+XDmZnd229te7ljxV3Xvd+SdvhgAAAAAAoHRwDFP5eSkWf//BIxnpj8/SP7FzydT0Z8ziV+utWObHbSeVnSrqgDAAAAAAAFC+WAFUZhyRvznSnZxY98cRUXLBQE1NOstBXWNcphbe7a5elxARM6QPvuyL7nVbcU1EdL8dWJaqPjfmCtp5RgAAAAAAAGWKAGiSyspKpUp6RczL4ej2ZGpSkxIRWRmqKHzzsL+62juyWx3+hW4lx5utmDayxRvZ5V36XqtySe5FRFNomqaUqq6uLv4WzBXteAUov9/v9XrndjIohqZpHo/H5coX7KJE6PqxymuVlZWOM42/RTEnRv+hr6io8Pv9cz0XFEvTNL7fKAuj32/wYZWFsZ963G43HxlwiiAAmsSyrLmeQgGvhEKZTY6Ikgqr8EeZjjuRHmvPf3scM8tVKyF779VX3pp0Vxf704vL5VJKmWa24VBiDMMY/Z7MsqzS/zqHiOi6bts2f77KwmgaLiKmaRIAlT6llK7rlmXZNutey4BhHPsOh78Py4Ku65qm8WGVBU3TRv8HhuM4fGQn0NjfWkAJ4qtzkmg0WuLfu/fFYplNSkRkyJWWY1lQTo5m7vqBO2v6M8pKSefj0nhVpMjJBAIBwzAikWL7Yw75fL7Rf42SyWQikZjr6aAwl8uVTqej0ehcTwSFVVZWut1uEYnFYgSspU/XdY/Hk0gkUqlU4d6Ya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width="768" /></p>
+<pre class="r"><code>aggDT[, rDoW := lubridate::wday(rDate, lab = TRUE)]
+h &lt;- head(aggDT[season == &quot;Spring&quot;][order(-propExpected)])
+kableExtra::kable(h, caption = &quot;Best Spring days overall&quot;, 
+                  digits = 3) %&gt;%
+  kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+Best Spring days overall
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+rDate
+</th>
+<th style="text-align:left;">
+season
+</th>
+<th style="text-align:right;">
+meanOK
+</th>
+<th style="text-align:right;">
+minOk
+</th>
+<th style="text-align:right;">
+maxOk
+</th>
+<th style="text-align:right;">
+sumOK
+</th>
+<th style="text-align:right;">
+propExpected
+</th>
+<th style="text-align:left;">
+rDoW
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+2002-04-14
+</td>
+<td style="text-align:left;">
+Spring
+</td>
+<td style="text-align:right;">
+63.490
+</td>
+<td style="text-align:right;">
+60
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6095
+</td>
+<td style="text-align:right;">
+0.814
+</td>
+<td style="text-align:left;">
+Sun
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-03-20
+</td>
+<td style="text-align:left;">
+Spring
+</td>
+<td style="text-align:right;">
+63.458
+</td>
+<td style="text-align:right;">
+61
+</td>
+<td style="text-align:right;">
+67
+</td>
+<td style="text-align:right;">
+6092
+</td>
+<td style="text-align:right;">
+0.814
+</td>
+<td style="text-align:left;">
+Thu
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2002-03-21
+</td>
+<td style="text-align:left;">
+Spring
+</td>
+<td style="text-align:right;">
+63.385
+</td>
+<td style="text-align:right;">
+62
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6085
+</td>
+<td style="text-align:right;">
+0.813
+</td>
+<td style="text-align:left;">
+Thu
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-03-14
+</td>
+<td style="text-align:left;">
+Spring
+</td>
+<td style="text-align:right;">
+63.385
+</td>
+<td style="text-align:right;">
+61
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6085
+</td>
+<td style="text-align:right;">
+0.813
+</td>
+<td style="text-align:left;">
+Fri
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-03-16
+</td>
+<td style="text-align:left;">
+Spring
+</td>
+<td style="text-align:right;">
+63.375
+</td>
+<td style="text-align:right;">
+60
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6084
+</td>
+<td style="text-align:right;">
+0.812
+</td>
+<td style="text-align:left;">
+Sun
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-03-17
+</td>
+<td style="text-align:left;">
+Spring
+</td>
+<td style="text-align:right;">
+63.375
+</td>
+<td style="text-align:right;">
+61
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6084
+</td>
+<td style="text-align:right;">
+0.812
+</td>
+<td style="text-align:left;">
+Mon
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>h &lt;- head(aggDT[season == &quot;Summer&quot;][order(-propExpected)])
+kableExtra::kable(h, caption = &quot;Best Summer days overall&quot;, 
+                  digits = 3) %&gt;%
+  kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+Best Summer days overall
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+rDate
+</th>
+<th style="text-align:left;">
+season
+</th>
+<th style="text-align:right;">
+meanOK
+</th>
+<th style="text-align:right;">
+minOk
+</th>
+<th style="text-align:right;">
+maxOk
+</th>
+<th style="text-align:right;">
+sumOK
+</th>
+<th style="text-align:right;">
+propExpected
+</th>
+<th style="text-align:left;">
+rDoW
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+2003-08-22
+</td>
+<td style="text-align:left;">
+Summer
+</td>
+<td style="text-align:right;">
+63.854
+</td>
+<td style="text-align:right;">
+56
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6130
+</td>
+<td style="text-align:right;">
+0.819
+</td>
+<td style="text-align:left;">
+Fri
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-08-30
+</td>
+<td style="text-align:left;">
+Summer
+</td>
+<td style="text-align:right;">
+63.844
+</td>
+<td style="text-align:right;">
+60
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6129
+</td>
+<td style="text-align:right;">
+0.819
+</td>
+<td style="text-align:left;">
+Sat
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-08-31
+</td>
+<td style="text-align:left;">
+Summer
+</td>
+<td style="text-align:right;">
+63.812
+</td>
+<td style="text-align:right;">
+59
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6126
+</td>
+<td style="text-align:right;">
+0.818
+</td>
+<td style="text-align:left;">
+Sun
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-08-23
+</td>
+<td style="text-align:left;">
+Summer
+</td>
+<td style="text-align:right;">
+63.677
+</td>
+<td style="text-align:right;">
+56
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6113
+</td>
+<td style="text-align:right;">
+0.816
+</td>
+<td style="text-align:left;">
+Sat
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-08-25
+</td>
+<td style="text-align:left;">
+Summer
+</td>
+<td style="text-align:right;">
+63.677
+</td>
+<td style="text-align:right;">
+55
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6113
+</td>
+<td style="text-align:right;">
+0.816
+</td>
+<td style="text-align:left;">
+Mon
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-08-26
+</td>
+<td style="text-align:left;">
+Summer
+</td>
+<td style="text-align:right;">
+63.656
+</td>
+<td style="text-align:right;">
+58
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6111
+</td>
+<td style="text-align:right;">
+0.816
+</td>
+<td style="text-align:left;">
+Tue
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>h &lt;- head(aggDT[season == &quot;Autumn&quot;][order(-propExpected)])
+kableExtra::kable(h, caption = &quot;Best Autumn days overall&quot;,
+                  digits = 3) %&gt;%
+  kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+Best Autumn days overall
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+rDate
+</th>
+<th style="text-align:left;">
+season
+</th>
+<th style="text-align:right;">
+meanOK
+</th>
+<th style="text-align:right;">
+minOk
+</th>
+<th style="text-align:right;">
+maxOk
+</th>
+<th style="text-align:right;">
+sumOK
+</th>
+<th style="text-align:right;">
+propExpected
+</th>
+<th style="text-align:left;">
+rDoW
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+2003-09-02
+</td>
+<td style="text-align:left;">
+Autumn
+</td>
+<td style="text-align:right;">
+63.823
+</td>
+<td style="text-align:right;">
+57
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6127
+</td>
+<td style="text-align:right;">
+0.818
+</td>
+<td style="text-align:left;">
+Tue
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-09-01
+</td>
+<td style="text-align:left;">
+Autumn
+</td>
+<td style="text-align:right;">
+63.771
+</td>
+<td style="text-align:right;">
+56
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6122
+</td>
+<td style="text-align:right;">
+0.818
+</td>
+<td style="text-align:left;">
+Mon
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-09-07
+</td>
+<td style="text-align:left;">
+Autumn
+</td>
+<td style="text-align:right;">
+63.740
+</td>
+<td style="text-align:right;">
+57
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6119
+</td>
+<td style="text-align:right;">
+0.817
+</td>
+<td style="text-align:left;">
+Sun
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-09-03
+</td>
+<td style="text-align:left;">
+Autumn
+</td>
+<td style="text-align:right;">
+63.667
+</td>
+<td style="text-align:right;">
+57
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6112
+</td>
+<td style="text-align:right;">
+0.816
+</td>
+<td style="text-align:left;">
+Wed
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-09-04
+</td>
+<td style="text-align:left;">
+Autumn
+</td>
+<td style="text-align:right;">
+63.615
+</td>
+<td style="text-align:right;">
+57
+</td>
+<td style="text-align:right;">
+66
+</td>
+<td style="text-align:right;">
+6107
+</td>
+<td style="text-align:right;">
+0.816
+</td>
+<td style="text-align:left;">
+Thu
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-09-06
+</td>
+<td style="text-align:left;">
+Autumn
+</td>
+<td style="text-align:right;">
+63.552
+</td>
+<td style="text-align:right;">
+57
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6101
+</td>
+<td style="text-align:right;">
+0.815
+</td>
+<td style="text-align:left;">
+Sat
+</td>
+</tr>
+</tbody>
+</table>
+<pre class="r"><code>h &lt;- head(aggDT[season == &quot;Winter&quot;][order(-propExpected)])
+kableExtra::kable(h, caption = &quot;Best Winter days overall&quot;, 
+                  digits = 3) %&gt;%
+  kable_styling()</code></pre>
+<table class="table" style="margin-left: auto; margin-right: auto;">
+<caption>
+Best Winter days overall
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;">
+rDate
+</th>
+<th style="text-align:left;">
+season
+</th>
+<th style="text-align:right;">
+meanOK
+</th>
+<th style="text-align:right;">
+minOk
+</th>
+<th style="text-align:right;">
+maxOk
+</th>
+<th style="text-align:right;">
+sumOK
+</th>
+<th style="text-align:right;">
+propExpected
+</th>
+<th style="text-align:left;">
+rDoW
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+2002-02-28
+</td>
+<td style="text-align:left;">
+Winter
+</td>
+<td style="text-align:right;">
+63.292
+</td>
+<td style="text-align:right;">
+60
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6076
+</td>
+<td style="text-align:right;">
+0.811
+</td>
+<td style="text-align:left;">
+Thu
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2002-02-25
+</td>
+<td style="text-align:left;">
+Winter
+</td>
+<td style="text-align:right;">
+63.125
+</td>
+<td style="text-align:right;">
+61
+</td>
+<td style="text-align:right;">
+65
+</td>
+<td style="text-align:right;">
+6060
+</td>
+<td style="text-align:right;">
+0.809
+</td>
+<td style="text-align:left;">
+Mon
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2002-12-11
+</td>
+<td style="text-align:left;">
+Winter
+</td>
+<td style="text-align:right;">
+62.979
+</td>
+<td style="text-align:right;">
+61
+</td>
+<td style="text-align:right;">
+64
+</td>
+<td style="text-align:right;">
+6046
+</td>
+<td style="text-align:right;">
+0.807
+</td>
+<td style="text-align:left;">
+Wed
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2002-12-01
+</td>
+<td style="text-align:left;">
+Winter
+</td>
+<td style="text-align:right;">
+62.917
+</td>
+<td style="text-align:right;">
+61
+</td>
+<td style="text-align:right;">
+64
+</td>
+<td style="text-align:right;">
+6040
+</td>
+<td style="text-align:right;">
+0.807
+</td>
+<td style="text-align:left;">
+Sun
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-01-01
+</td>
+<td style="text-align:left;">
+Winter
+</td>
+<td style="text-align:right;">
+62.906
+</td>
+<td style="text-align:right;">
+60
+</td>
+<td style="text-align:right;">
+64
+</td>
+<td style="text-align:right;">
+6039
+</td>
+<td style="text-align:right;">
+0.806
+</td>
+<td style="text-align:left;">
+Wed
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+2003-01-03
+</td>
+<td style="text-align:left;">
+Winter
+</td>
+<td style="text-align:right;">
+62.906
+</td>
+<td style="text-align:right;">
+60
+</td>
+<td style="text-align:right;">
+64
+</td>
+<td style="text-align:right;">
+6039
+</td>
+<td style="text-align:right;">
+0.806
+</td>
+<td style="text-align:left;">
+Fri
+</td>
+</tr>
+</tbody>
+</table>
 <p>This also tells us that there is some reason why we get fluctations in the number of data points per hour after 2003.</p>
 </div>
 <div id="summary" class="section level1">
@@ -2313,7 +3096,7 @@ nrow(aggDT[propExpected == 1])</code></pre>
 </div>
 <div id="runtime" class="section level1">
 <h1>Runtime</h1>
-<p>Analysis completed in 218.01 seconds ( 3.63 minutes) using <a href="https://cran.r-project.org/package=knitr">knitr</a> in <a href="http://www.rstudio.com">RStudio</a> with R version 3.6.0 (2019-04-26) running on x86_64-redhat-linux-gnu.</p>
+<p>Analysis completed in 221.84 seconds ( 3.7 minutes) using <a href="https://cran.r-project.org/package=knitr">knitr</a> in <a href="http://www.rstudio.com">RStudio</a> with R version 3.6.0 (2019-04-26) running on x86_64-redhat-linux-gnu.</p>
 </div>
 <div id="r-environment" class="section level1">
 <h1>R environment</h1>
@@ -2360,18 +3143,19 @@ nrow(aggDT[propExpected == 1])</code></pre>
 ##  [1] storr_1.2.1       progress_1.2.2    tidyselect_1.1.0  xfun_0.14        
 ##  [5] repr_1.1.0        purrr_0.3.4       colorspace_1.4-0  vctrs_0.3.1      
 ##  [9] generics_0.0.2    viridisLite_0.3.0 htmltools_0.3.6   yaml_2.2.0       
-## [13] base64enc_0.1-3   rlang_0.4.6       pillar_1.4.4      txtq_0.2.0       
-## [17] glue_1.4.1        withr_2.1.2       lifecycle_0.2.0   stringr_1.4.0    
-## [21] munsell_0.5.0     gtable_0.2.0      rvest_0.3.5       evaluate_0.14    
-## [25] labeling_0.3      knitr_1.28        parallel_3.6.0    fansi_0.4.0      
-## [29] highr_0.7         Rcpp_1.0.1        readr_1.3.1       scales_1.0.0     
-## [33] backports_1.1.3   filelock_1.0.2    webshot_0.5.2     jsonlite_1.6     
-## [37] digest_0.6.25     stringi_1.2.4     dplyr_1.0.0       grid_3.6.0       
-## [41] rprojroot_1.3-2   cli_2.0.2         tools_3.6.0       magrittr_1.5     
-## [45] base64url_1.4     tibble_3.0.1      crayon_1.3.4      pkgconfig_2.0.2  
-## [49] ellipsis_0.3.1    xml2_1.3.2        prettyunits_1.0.2 httr_1.4.1       
-## [53] assertthat_0.2.0  rmarkdown_2.2     rstudioapi_0.11   R6_2.3.0         
-## [57] igraph_1.2.2      compiler_3.6.0</code></pre>
+## [13] base64enc_0.1-3   rlang_0.4.6       R.oo_1.22.0       pillar_1.4.4     
+## [17] txtq_0.2.0        glue_1.4.1        withr_2.1.2       R.utils_2.7.0    
+## [21] lifecycle_0.2.0   stringr_1.4.0     munsell_0.5.0     gtable_0.2.0     
+## [25] rvest_0.3.5       R.methodsS3_1.7.1 codetools_0.2-16  evaluate_0.14    
+## [29] labeling_0.3      knitr_1.28        parallel_3.6.0    fansi_0.4.0      
+## [33] highr_0.7         Rcpp_1.0.1        readr_1.3.1       scales_1.0.0     
+## [37] backports_1.1.3   filelock_1.0.2    webshot_0.5.2     jsonlite_1.6     
+## [41] digest_0.6.25     stringi_1.2.4     dplyr_1.0.0       grid_3.6.0       
+## [45] rprojroot_1.3-2   cli_2.0.2         tools_3.6.0       magrittr_1.5     
+## [49] base64url_1.4     tibble_3.0.1      crayon_1.3.4      pkgconfig_2.0.2  
+## [53] ellipsis_0.3.1    xml2_1.3.2        prettyunits_1.0.2 httr_1.4.1       
+## [57] assertthat_0.2.0  rmarkdown_2.2     rstudioapi_0.11   R6_2.3.0         
+## [61] igraph_1.2.2      compiler_3.6.0</code></pre>
 </div>
 </div>
 <div id="the-raw-data-cleaning-code" class="section level1">
diff --git a/docs/cleaningFeederData_allData.tex b/docs/cleaningFeederData_allData.tex
index 6db441e85ad06170a8b9aff0165dec4d94a824df..8528c853b0f3d1118c085a7e51b0fa13f91dc66d 100644
--- a/docs/cleaningFeederData_allData.tex
+++ b/docs/cleaningFeederData_allData.tex
@@ -110,12 +110,26 @@
   \apptocmd{\@title}{\par {\large #1 \par}}{}{}
 }
 \makeatother
+\usepackage{booktabs}
+\usepackage{longtable}
+\usepackage{array}
+\usepackage{multirow}
+\usepackage{wrapfig}
+\usepackage{float}
+\usepackage{colortbl}
+\usepackage{pdflscape}
+\usepackage{tabu}
+\usepackage{threeparttable}
+\usepackage{threeparttablex}
+\usepackage[normalem]{ulem}
+\usepackage{makecell}
+\usepackage{xcolor}
 
 \title{Testing electricity substation/feeder data}
 \providecommand{\subtitle}[1]{}
 \subtitle{Outliers and missing data\ldots{}}
 \author{Ben Anderson \& Ellis Ridett}
-\date{Last run at: 2020-07-08 22:56:02}
+\date{Last run at: 2020-07-09 00:07:52}
 
 \begin{document}
 \maketitle
@@ -139,1041 +153,267 @@
 \end{Shaded}
 
 \begin{verbatim}
-## kableExtra 
-##       TRUE
-\end{verbatim}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{# Parameters ----}
-\CommentTok{#dFile <- "~/Dropbox/Ben_IOW_SS.csv" # edit for your set up}
-
-
-\CommentTok{# Functions ----}
-\CommentTok{# put more general ones that could be useful to everyone in /R so they are built into the package.}
-
-\CommentTok{# put functions relevant to this analysis here}
-\end{Highlighting}
-\end{Shaded}
-
-\section{Intro}\label{intro}
-
-We have some electricity substation feeder data that has been cleaned to
-give mean kW per 15 minutes.
-
-There seem to be some NA kW values and a lot of missing time stamps. We
-want to select the `best' (i.e most complete) days within a
-day-of-the-week/season/year sampling frame. If we can't do that we may
-have to resort to seasonal mean kW profiles by hour \& day of the
-week\ldots{}
-
-Code used to generate this report:
-\url{https://git.soton.ac.uk/ba1e12/spatialec/-/blob/master/isleOfWight/cleaningFeederData.Rmd}
-
-\section{Data prep}\label{data-prep}
-
-\subsection{Load data}\label{load-data}
-
-Loaded data from
-/mnt/SERG\_data/Ellis\_IOW/Cleaned\_SS\_Amps/amps\_all\_substations.csv.gz\ldots{}
-(using drake)
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{origDataDT <-}\StringTok{ }\NormalTok{drake}\OperatorTok{::}\KeywordTok{readd}\NormalTok{(origData) }\CommentTok{# readd the drake object}
-\KeywordTok{head}\NormalTok{(origDataDT)}
-\end{Highlighting}
-\end{Shaded}
-
-\begin{verbatim}
-##                    Time region sub_region           rDateTime    rTime
-## 1: 2003-01-13T10:30:00Z   ARRN       ARRN 2003-01-13 10:30:00 10:30:00
-## 2: 2003-01-13T10:45:00Z   ARRN       ARRN 2003-01-13 10:45:00 10:45:00
-## 3: 2003-01-13T11:15:00Z   ARRN       ARRN 2003-01-13 11:15:00 11:15:00
-## 4: 2003-01-13T11:30:00Z   ARRN       ARRN 2003-01-13 11:30:00 11:30:00
-## 5: 2003-01-13T11:45:00Z   ARRN       ARRN 2003-01-13 11:45:00 11:45:00
-## 6: 2003-01-13T12:15:00Z   ARRN       ARRN 2003-01-13 12:15:00 12:15:00
-##         rDate rYear rDoW         kW feeder_ID season
-## 1: 2003-01-13  2003  Mon  2.0000000 ARRN_ARRN Winter
-## 2: 2003-01-13  2003  Mon 18.2500000 ARRN_ARRN Winter
-## 3: 2003-01-13  2003  Mon  0.6666667 ARRN_ARRN Winter
-## 4: 2003-01-13  2003  Mon 28.5000000 ARRN_ARRN Winter
-## 5: 2003-01-13  2003  Mon 19.5555556 ARRN_ARRN Winter
-## 6: 2003-01-13  2003  Mon 12.8000000 ARRN_ARRN Winter
-\end{verbatim}
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\NormalTok{uniqDataDT <-}\StringTok{ }\NormalTok{drake}\OperatorTok{::}\KeywordTok{readd}\NormalTok{(uniqData) }\CommentTok{# readd the drake object}
-\end{Highlighting}
-\end{Shaded}
-
-Check data prep worked OK.
-
-\begin{Shaded}
-\begin{Highlighting}[]
-\CommentTok{# check}
-\NormalTok{t <-}\StringTok{ }\NormalTok{origDataDT[, .(}\DataTypeTok{nObs =}\NormalTok{ .N,}
-                  \DataTypeTok{firstDate =} \KeywordTok{min}\NormalTok{(rDateTime),}
-                  \DataTypeTok{lastDate =} \KeywordTok{max}\NormalTok{(rDateTime),}
-                  \DataTypeTok{meankW =} \KeywordTok{mean}\NormalTok{(kW, }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{)}
-\NormalTok{), keyby =}\StringTok{ }\NormalTok{.(region, feeder_ID)]}
-
-\NormalTok{kableExtra}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(t, }\DataTypeTok{digits =} \DecValTok{2}\NormalTok{,}
-                  \DataTypeTok{caption =} \StringTok{"Counts per feeder (long table)"}\NormalTok{) }\OperatorTok{%>%}
-\StringTok{  }\KeywordTok{kable_styling}\NormalTok{()}
-\end{Highlighting}
-\end{Shaded}
-
-Counts per feeder (long table)
-
-region
-
-feeder\_ID
-
-nObs
-
-firstDate
-
-lastDate
-
-meankW
-
-ARRN
-
-ARRN\_ARRN
-
-94909
-
-2003-01-13 10:30:00
-
-2017-10-25 22:15:00
-
-151.74
-
-BINS
-
-BINS\_C1T0
-
-218480
-
-2001-09-21 07:30:00
-
-2017-10-13 23:45:00
-
-94.70
-
-BINS
-
-BINS\_C2T0
-
-208447
-
-2001-09-21 07:30:00
-
-2017-10-13 23:45:00
-
-93.91
-
-BINS
-
-BINS\_E1L5
-
-414980
-
-2001-09-21 07:30:00
-
-2017-10-13 23:15:00
-
-94.31
-
-BINS
-
-BINS\_E2L5
-
-115260
-
-2001-10-10 12:00:00
-
-2017-10-04 20:45:00
-
-20.58
-
-BINS
-
-BINS\_E3L5
-
-337064
-
-2001-10-10 12:00:00
-
-2017-10-14 23:30:00
-
-59.67
-
-FFPV
-
-FFPV\_FFPV
-
-32278
-
-2014-09-25 09:15:00
-
-2017-10-11 16:15:00
-
-36.14
-
-FRES
-
-FRES\_E1L5
-
-452480
-
-2001-10-10 12:00:00
-
-2017-10-13 23:45:00
-
-53.08
-
-FRES
-
-FRES\_E1T0
-
-188186
-
-2001-09-11 15:15:00
-
-2017-09-01 19:00:00
-
-128.98
-
-FRES
-
-FRES\_E2L5
-
-178744
-
-2001-10-10 12:00:00
-
-2017-10-13 23:30:00
-
-25.64
-
-FRES
-
-FRES\_E2T0
-
-164910
-
-2001-09-11 15:15:00
-
-2017-10-12 23:45:00
-
-122.44
-
-FRES
-
-FRES\_E3L5
-
-463006
-
-2001-10-10 12:00:00
-
-2017-10-13 23:00:00
-
-50.65
-
-FRES
-
-FRES\_E4L5
-
-15752
-
-2010-07-30 17:00:00
-
-2017-09-18 19:45:00
-
-60.89
-
-FRES
-
-FRES\_E6L5
-
-317352
-
-2001-09-11 15:15:00
-
-2017-10-13 23:00:00
-
-85.32
-
-NEWP
-
-NEWP\_E11L5
-
-367422
-
-2005-01-20 10:00:00
-
-2017-09-28 23:15:00
-
-72.32
-
-NEWP
-
-NEWP\_E13L5
-
-252979
-
-2010-01-01 00:15:00
-
-2017-09-28 23:45:00
-
-126.20
-
-NEWP
-
-NEWP\_E15L5
-
-295094
-
-2008-01-07 12:00:00
-
-2017-10-10 23:45:00
-
-76.95
-
-NEWP
-
-NEWP\_E17L5
-
-63422
-
-2011-03-10 12:45:00
-
-2017-10-11 23:30:00
-
-11.44
-
-NEWP
-
-NEWP\_E19L5
-
-126299
-
-2011-03-14 09:45:00
-
-2017-10-11 23:45:00
-
-18.38
-
-NEWP
-
-NEWP\_E1L5
-
-318151
-
-2001-10-10 12:15:00
-
-2017-09-26 23:45:00
-
-45.66
-
-NEWP
-
-NEWP\_E1T0
-
-101494
-
-2001-09-11 15:30:00
-
-2017-09-18 19:45:00
-
-475.07
-
-NEWP
-
-NEWP\_E2L5
-
-67835
-
-2001-09-11 15:30:00
-
-2017-09-26 22:45:00
-
-58.44
-
-NEWP
-
-NEWP\_E2T0
-
-399812
-
-2001-10-10 12:15:00
-
-2017-09-27 12:00:00
-
-426.55
-
-NEWP
-
-NEWP\_E3L5
-
-480643
-
-2001-10-10 12:15:00
-
-2017-09-26 23:45:00
-
-73.64
-
-NEWP
-
-NEWP\_E3T0
-
-246265
-
-2005-08-03 11:15:00
-
-2017-09-26 23:45:00
-
-383.05
-
-NEWP
-
-NEWP\_E4L5
-
-191514
-
-NA
-
-NA
-
-105.57
-
-NEWP
-
-NEWP\_E5L5
-
-448392
-
-2001-09-11 15:15:00
-
-2017-09-27 23:45:00
-
-42.46
-
-NEWP
-
-NEWP\_E6L5
-
-434217
-
-2001-09-11 15:30:00
-
-2017-09-27 23:45:00
-
-69.91
-
-NEWP
-
-NEWP\_E7L5
-
-306799
-
-2001-10-10 12:15:00
-
-2017-09-27 23:15:00
-
-71.96
-
-NEWP
-
-NEWP\_E8L5
-
-537871
-
-2001-10-10 12:15:00
-
-2017-09-27 23:30:00
-
-139.40
-
-NEWP
-
-NEWP\_E9L5
-
-363063
-
-2002-12-19 22:30:00
-
-2017-09-28 23:45:00
-
-101.30
-
-RYDE
-
-RYDE\_E1L5
-
-356616
-
-2001-09-21 09:30:00
-
-2017-10-11 23:45:00
-
-70.48
-
-RYDE
-
-RYDE\_E1T0 \&E1S0
-
-251062
-
-2001-10-10 12:15:00
-
-2017-10-11 23:30:00
-
-336.55
-
-RYDE
-
-RYDE\_E2L5
-
-297293
-
-NA
-
-NA
-
-71.14
-
-RYDE
-
-RYDE\_E2T0
-
-238332
-
-2001-10-10 12:15:00
-
-2017-10-11 23:45:00
-
-351.26
-
-RYDE
-
-RYDE\_E3L5
-
-304293
-
-2001-09-21 09:30:00
-
-2017-10-11 23:45:00
-
-85.22
-
-RYDE
-
-RYDE\_E4L5
-
-519366
-
-NA
-
-NA
-
-70.23
-
-RYDE
-
-RYDE\_E5L5
-
-362368
-
-2001-09-21 09:30:00
-
-2017-10-12 23:15:00
-
-82.05
-
-RYDE
-
-RYDE\_E6L5
-
-442859
-
-2001-09-21 09:30:00
-
-2017-10-12 23:45:00
-
-96.24
-
-RYDE
-
-RYDE\_E7L5
-
-324195
-
-2001-09-21 09:30:00
-
-2017-10-12 22:45:00
-
-69.86
-
-RYDE
-
-RYDE\_E8L5
-
-275373
-
-2001-10-10 12:15:00
-
-2017-10-12 23:15:00
-
-57.04
-
-RYDE
-
-RYDE\_E9L5
-
-267617
-
-2001-09-25 17:00:00
-
-2017-10-12 23:30:00
-
-59.20
-
-SADO
-
-SADO\_E1L5
-
-212775
-
-2001-09-21 13:30:00
-
-2017-10-25 23:15:00
-
-50.98
-
-SADO
-
-SADO\_E1T0
-
-421960
-
-2001-09-21 13:30:00
-
-2017-10-25 23:45:00
-
-230.66
-
-SADO
-
-SADO\_E2L5
-
-178715
-
-2001-09-21 13:30:00
-
-2017-10-25 23:15:00
-
-39.74
-
-SADO
-
-SADO\_E2T0
-
-412191
-
-2001-10-10 12:15:00
-
-2017-10-25 23:30:00
-
-173.51
-
-SADO
-
-SADO\_E3L5
-
-272831
-
-2001-09-21 13:30:00
-
-2017-10-25 23:15:00
-
-64.61
-
-SADO
-
-SADO\_E4L5
-
-479020
-
-2001-09-21 13:30:00
-
-2017-10-25 23:45:00
-
-58.38
-
-SADO
-
-SADO\_E5L5
-
-343918
-
-2001-09-21 13:30:00
-
-2017-10-25 23:45:00
-
-82.67
-
-SADO
-
-SADO\_E6L5
-
-239227
-
-2001-09-21 13:30:00
-
-2017-10-25 23:30:00
-
-56.34
-
-SADO
-
-SADO\_E8L5
-
-282455
-
-2004-08-16 17:45:00
-
-2017-10-25 23:30:00
-
-89.57
-
-SHAL
-
-SHAL\_C3L5
-
-163204
-
-2001-10-10 12:45:00
-
-2017-10-15 23:15:00
-
-38.22
-
-SHAL
-
-SHAL\_C4L5
-
-187940
-
-2001-09-11 15:30:00
-
-2017-10-15 23:45:00
-
-38.77
-
-SHAL
-
-SHAL\_C5L5
-
-29417
-
-2015-12-03 15:00:00
-
-2017-10-15 23:45:00
-
-36.35
-
-SHAL
-
-SHAL\_E1L5
-
-465913
-
-2001-10-10 12:15:00
-
-2017-10-14 23:30:00
-
-70.65
-
-SHAL
-
-SHAL\_E1T0
-
-181132
-
-2001-10-10 12:15:00
-
-2017-10-14 23:15:00
-
-101.23
-
-SHAL
-
-SHAL\_E2L5
-
-290286
-
-2001-10-10 12:15:00
-
-2017-10-15 23:00:00
-
-47.09
-
-SHAL
-
-SHAL\_E2T0
-
-174129
-
-2001-10-10 12:30:00
-
-2017-10-14 22:45:00
-
-107.44
-
-SHAL
-
-SHAL\_E3L5
-
-258805
-
-2010-03-11 07:00:00
-
-2017-10-15 23:45:00
-
-33.26
-
-SHAL
-
-SHAL\_E4L5
-
-322135
-
-2001-09-11 15:30:00
-
-2017-10-16 12:30:00
-
-54.03
-
-SHAN
-
-SHAN\_E1L5
-
-288894
-
-2001-09-21 14:15:00
-
-2017-10-24 23:15:00
-
-63.52
-
-SHAN
-
-SHAN\_E1T0
-
-330691
-
-2001-10-10 12:15:00
-
-2017-10-24 23:45:00
-
-226.58
-
-SHAN
-
-SHAN\_E2L5
-
-321760
-
-2001-09-21 14:15:00
-
-2017-10-25 23:15:00
-
-72.63
-
-SHAN
-
-SHAN\_E2T0
-
-315053
-
-2001-10-10 12:15:00
-
-2017-10-24 23:45:00
-
-186.69
-
-SHAN
-
-SHAN\_E3L5
-
-105606
-
-2001-09-21 14:15:00
-
-2017-10-25 23:15:00
-
-26.30
-
-SHAN
-
-SHAN\_E4L5
-
-216626
-
-2001-09-21 14:15:00
-
-2017-10-25 23:30:00
-
-33.63
-
-SHAN
-
-SHAN\_E5L5
-
-254742
-
-2001-09-21 14:15:00
-
-2017-10-25 23:15:00
-
-48.50
-
-SHAN
-
-SHAN\_E6L5
-
-363107
-
-2001-09-21 14:15:00
-
-2017-10-25 23:15:00
-
-68.69
-
-SHAN
-
-SHAN\_E7L5
-
-384165
-
-2001-09-21 14:15:00
-
-2017-10-25 23:15:00
-
-66.12
-
-SHAN
-
-SHAN\_E8L5
-
-146605
-
-2002-02-05 17:30:00
-
-2017-10-25 23:15:00
-
-25.28
-
-VENT
-
-VENT\_E1L5
-
-203617
-
-2001-09-11 15:45:00
-
-2017-10-15 23:15:00
-
-33.24
-
-VENT
-
-VENT\_E1T0
-
-240745
-
-2001-09-11 15:30:00
-
-2017-10-15 23:15:00
-
-191.42
-
-VENT
-
-VENT\_E2L5
-
-402307
-
-2001-09-27 14:00:00
-
-2017-10-16 23:45:00
-
-46.68
-
-VENT
-
-VENT\_E2T0
-
-208020
-
-2001-09-11 15:45:00
-
-2017-10-15 23:30:00
-
-115.47
-
-VENT
-
-VENT\_E3L5
-
-493337
-
-2001-09-11 15:45:00
+## Loading required package: kableExtra
+\end{verbatim}
 
-2017-10-16 23:45:00
+\begin{verbatim}
+## kableExtra 
+##       TRUE
+\end{verbatim}
 
-83.59
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{# Parameters ----}
+\CommentTok{#dFile <- "~/Dropbox/Ben_IOW_SS.csv" # edit for your set up}
 
-VENT
 
-VENT\_E4L5
+\CommentTok{# Functions ----}
+\CommentTok{# put more general ones that could be useful to everyone in /R so they are built into the package.}
 
-387037
+\CommentTok{# put functions relevant to this analysis here}
+\end{Highlighting}
+\end{Shaded}
 
-2001-09-11 15:45:00
+\section{Intro}\label{intro}
 
-2017-10-16 23:30:00
+We have some electricity substation feeder data that has been cleaned to
+give mean kW per 15 minutes.
 
-40.86
+There seem to be some NA kW values and a lot of missing time stamps. We
+want to select the `best' (i.e most complete) days within a
+day-of-the-week/season/year sampling frame. If we can't do that we may
+have to resort to seasonal mean kW profiles by hour \& day of the
+week\ldots{}
 
-VENT
+Code used to generate this report:
+\url{https://git.soton.ac.uk/ba1e12/spatialec/-/blob/master/isleOfWight/cleaningFeederData.Rmd}
 
-VENT\_E5L5
+\section{Data prep}\label{data-prep}
 
-481677
+\subsection{Load data}\label{load-data}
 
-2001-09-27 14:00:00
+Loaded data from
+/mnt/SERG\_data/Ellis\_IOW/Cleaned\_SS\_Amps/amps\_all\_substations.csv.gz\ldots{}
+(using drake)
 
-2017-10-16 23:45:00
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{origDataDT <-}\StringTok{ }\NormalTok{drake}\OperatorTok{::}\KeywordTok{readd}\NormalTok{(origData) }\CommentTok{# readd the drake object}
 
-88.43
+\NormalTok{uniqDataDT <-}\StringTok{ }\NormalTok{drake}\OperatorTok{::}\KeywordTok{readd}\NormalTok{(uniqData) }\CommentTok{# readd the drake object}
 
-VENT
+\NormalTok{kableExtra}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(}\KeywordTok{head}\NormalTok{(origDataDT), }\DataTypeTok{digits =} \DecValTok{2}\NormalTok{,}
+                  \DataTypeTok{caption =} \StringTok{"Counts per feeder (long table)"}\NormalTok{) }\OperatorTok{%>%}
+\StringTok{  }\KeywordTok{kable_styling}\NormalTok{()}
+\end{Highlighting}
+\end{Shaded}
 
-VENT\_E6L5
+\begin{table}
+
+\caption{\label{tab:loadData}Counts per feeder (long table)}
+\centering
+\begin{tabular}[t]{l|l|l|l|l|l|r|l|r|l|l}
+\hline
+Time & region & sub\_region & rDateTime & rTime & rDate & rYear & rDoW & kW & feeder\_ID & season\\
+\hline
+2003-01-13T10:30:00Z & ARRN & ARRN & 2003-01-13 10:30:00 & 10:30:00 & 2003-01-13 & 2003 & Mon & 2.00 & ARRN\_ARRN & Winter\\
+\hline
+2003-01-13T10:45:00Z & ARRN & ARRN & 2003-01-13 10:45:00 & 10:45:00 & 2003-01-13 & 2003 & Mon & 18.25 & ARRN\_ARRN & Winter\\
+\hline
+2003-01-13T11:15:00Z & ARRN & ARRN & 2003-01-13 11:15:00 & 11:15:00 & 2003-01-13 & 2003 & Mon & 0.67 & ARRN\_ARRN & Winter\\
+\hline
+2003-01-13T11:30:00Z & ARRN & ARRN & 2003-01-13 11:30:00 & 11:30:00 & 2003-01-13 & 2003 & Mon & 28.50 & ARRN\_ARRN & Winter\\
+\hline
+2003-01-13T11:45:00Z & ARRN & ARRN & 2003-01-13 11:45:00 & 11:45:00 & 2003-01-13 & 2003 & Mon & 19.56 & ARRN\_ARRN & Winter\\
+\hline
+2003-01-13T12:15:00Z & ARRN & ARRN & 2003-01-13 12:15:00 & 12:15:00 & 2003-01-13 & 2003 & Mon & 12.80 & ARRN\_ARRN & Winter\\
+\hline
+\end{tabular}
+\end{table}
 
-6631
+Check data prep worked OK.
 
-2001-09-27 14:00:00
+\begin{Shaded}
+\begin{Highlighting}[]
+\CommentTok{# check}
+\NormalTok{t <-}\StringTok{ }\NormalTok{origDataDT[, .(}\DataTypeTok{nObs =}\NormalTok{ .N,}
+                  \DataTypeTok{firstDate =} \KeywordTok{min}\NormalTok{(rDateTime, }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{),}
+                  \DataTypeTok{lastDate =} \KeywordTok{max}\NormalTok{(rDateTime, }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{),}
+                  \DataTypeTok{meankW =} \KeywordTok{mean}\NormalTok{(kW, }\DataTypeTok{na.rm =} \OtherTok{TRUE}\NormalTok{)}
+\NormalTok{), keyby =}\StringTok{ }\NormalTok{.(region, feeder_ID)]}
 
-2017-10-24 20:15:00
+\NormalTok{kableExtra}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(t, }\DataTypeTok{digits =} \DecValTok{2}\NormalTok{,}
+                  \DataTypeTok{caption =} \StringTok{"Counts per feeder (long table)"}\NormalTok{) }\OperatorTok{%>%}
+\StringTok{  }\KeywordTok{kable_styling}\NormalTok{()}
+\end{Highlighting}
+\end{Shaded}
 
-3.95
+\begin{table}
+
+\caption{\label{tab:dataPrep}Counts per feeder (long table)}
+\centering
+\begin{tabular}[t]{l|l|r|l|l|r}
+\hline
+region & feeder\_ID & nObs & firstDate & lastDate & meankW\\
+\hline
+ARRN & ARRN\_ARRN & 94909 & 2003-01-13 10:30:00 & 2017-10-25 22:15:00 & 151.74\\
+\hline
+BINS & BINS\_C1T0 & 218480 & 2001-09-21 07:30:00 & 2017-10-13 23:45:00 & 94.70\\
+\hline
+BINS & BINS\_C2T0 & 208447 & 2001-09-21 07:30:00 & 2017-10-13 23:45:00 & 93.91\\
+\hline
+BINS & BINS\_E1L5 & 414980 & 2001-09-21 07:30:00 & 2017-10-13 23:15:00 & 94.31\\
+\hline
+BINS & BINS\_E2L5 & 115260 & 2001-10-10 12:00:00 & 2017-10-04 20:45:00 & 20.58\\
+\hline
+BINS & BINS\_E3L5 & 337064 & 2001-10-10 12:00:00 & 2017-10-14 23:30:00 & 59.67\\
+\hline
+FFPV & FFPV\_FFPV & 32278 & 2014-09-25 09:15:00 & 2017-10-11 16:15:00 & 36.14\\
+\hline
+FRES & FRES\_E1L5 & 452480 & 2001-10-10 12:00:00 & 2017-10-13 23:45:00 & 53.08\\
+\hline
+FRES & FRES\_E1T0 & 188186 & 2001-09-11 15:15:00 & 2017-09-01 19:00:00 & 128.98\\
+\hline
+FRES & FRES\_E2L5 & 178744 & 2001-10-10 12:00:00 & 2017-10-13 23:30:00 & 25.64\\
+\hline
+FRES & FRES\_E2T0 & 164910 & 2001-09-11 15:15:00 & 2017-10-12 23:45:00 & 122.44\\
+\hline
+FRES & FRES\_E3L5 & 463006 & 2001-10-10 12:00:00 & 2017-10-13 23:00:00 & 50.65\\
+\hline
+FRES & FRES\_E4L5 & 15752 & 2010-07-30 17:00:00 & 2017-09-18 19:45:00 & 60.89\\
+\hline
+FRES & FRES\_E6L5 & 317352 & 2001-09-11 15:15:00 & 2017-10-13 23:00:00 & 85.32\\
+\hline
+NEWP & NEWP\_E11L5 & 367422 & 2005-01-20 10:00:00 & 2017-09-28 23:15:00 & 72.32\\
+\hline
+NEWP & NEWP\_E13L5 & 252979 & 2010-01-01 00:15:00 & 2017-09-28 23:45:00 & 126.20\\
+\hline
+NEWP & NEWP\_E15L5 & 295094 & 2008-01-07 12:00:00 & 2017-10-10 23:45:00 & 76.95\\
+\hline
+NEWP & NEWP\_E17L5 & 63422 & 2011-03-10 12:45:00 & 2017-10-11 23:30:00 & 11.44\\
+\hline
+NEWP & NEWP\_E19L5 & 126299 & 2011-03-14 09:45:00 & 2017-10-11 23:45:00 & 18.38\\
+\hline
+NEWP & NEWP\_E1L5 & 318151 & 2001-10-10 12:15:00 & 2017-09-26 23:45:00 & 45.66\\
+\hline
+NEWP & NEWP\_E1T0 & 101494 & 2001-09-11 15:30:00 & 2017-09-18 19:45:00 & 475.07\\
+\hline
+NEWP & NEWP\_E2L5 & 67835 & 2001-09-11 15:30:00 & 2017-09-26 22:45:00 & 58.44\\
+\hline
+NEWP & NEWP\_E2T0 & 399812 & 2001-10-10 12:15:00 & 2017-09-27 12:00:00 & 426.55\\
+\hline
+NEWP & NEWP\_E3L5 & 480643 & 2001-10-10 12:15:00 & 2017-09-26 23:45:00 & 73.64\\
+\hline
+NEWP & NEWP\_E3T0 & 246265 & 2005-08-03 11:15:00 & 2017-09-26 23:45:00 & 383.05\\
+\hline
+NEWP & NEWP\_E4L5 & 191514 & 2001-09-11 15:15:00 & 2020-12-31 07:15:00 & 105.57\\
+\hline
+NEWP & NEWP\_E5L5 & 448392 & 2001-09-11 15:15:00 & 2017-09-27 23:45:00 & 42.46\\
+\hline
+NEWP & NEWP\_E6L5 & 434217 & 2001-09-11 15:30:00 & 2017-09-27 23:45:00 & 69.91\\
+\hline
+NEWP & NEWP\_E7L5 & 306799 & 2001-10-10 12:15:00 & 2017-09-27 23:15:00 & 71.96\\
+\hline
+NEWP & NEWP\_E8L5 & 537871 & 2001-10-10 12:15:00 & 2017-09-27 23:30:00 & 139.40\\
+\hline
+NEWP & NEWP\_E9L5 & 363063 & 2002-12-19 22:30:00 & 2017-09-28 23:45:00 & 101.30\\
+\hline
+RYDE & RYDE\_E1L5 & 356616 & 2001-09-21 09:30:00 & 2017-10-11 23:45:00 & 70.48\\
+\hline
+RYDE & RYDE\_E1T0 \&E1S0 & 251062 & 2001-10-10 12:15:00 & 2017-10-11 23:30:00 & 336.55\\
+\hline
+RYDE & RYDE\_E2L5 & 297293 & 2001-09-21 09:30:00 & 2017-10-11 23:45:00 & 71.14\\
+\hline
+RYDE & RYDE\_E2T0 & 238332 & 2001-10-10 12:15:00 & 2017-10-11 23:45:00 & 351.26\\
+\hline
+RYDE & RYDE\_E3L5 & 304293 & 2001-09-21 09:30:00 & 2017-10-11 23:45:00 & 85.22\\
+\hline
+RYDE & RYDE\_E4L5 & 519366 & 2001-12-20 15:30:00 & 2017-10-12 23:45:00 & 70.23\\
+\hline
+RYDE & RYDE\_E5L5 & 362368 & 2001-09-21 09:30:00 & 2017-10-12 23:15:00 & 82.05\\
+\hline
+RYDE & RYDE\_E6L5 & 442859 & 2001-09-21 09:30:00 & 2017-10-12 23:45:00 & 96.24\\
+\hline
+RYDE & RYDE\_E7L5 & 324195 & 2001-09-21 09:30:00 & 2017-10-12 22:45:00 & 69.86\\
+\hline
+RYDE & RYDE\_E8L5 & 275373 & 2001-10-10 12:15:00 & 2017-10-12 23:15:00 & 57.04\\
+\hline
+RYDE & RYDE\_E9L5 & 267617 & 2001-09-25 17:00:00 & 2017-10-12 23:30:00 & 59.20\\
+\hline
+SADO & SADO\_E1L5 & 212775 & 2001-09-21 13:30:00 & 2017-10-25 23:15:00 & 50.98\\
+\hline
+SADO & SADO\_E1T0 & 421960 & 2001-09-21 13:30:00 & 2017-10-25 23:45:00 & 230.66\\
+\hline
+SADO & SADO\_E2L5 & 178715 & 2001-09-21 13:30:00 & 2017-10-25 23:15:00 & 39.74\\
+\hline
+SADO & SADO\_E2T0 & 412191 & 2001-10-10 12:15:00 & 2017-10-25 23:30:00 & 173.51\\
+\hline
+SADO & SADO\_E3L5 & 272831 & 2001-09-21 13:30:00 & 2017-10-25 23:15:00 & 64.61\\
+\hline
+SADO & SADO\_E4L5 & 479020 & 2001-09-21 13:30:00 & 2017-10-25 23:45:00 & 58.38\\
+\hline
+SADO & SADO\_E5L5 & 343918 & 2001-09-21 13:30:00 & 2017-10-25 23:45:00 & 82.67\\
+\hline
+SADO & SADO\_E6L5 & 239227 & 2001-09-21 13:30:00 & 2017-10-25 23:30:00 & 56.34\\
+\hline
+SADO & SADO\_E8L5 & 282455 & 2004-08-16 17:45:00 & 2017-10-25 23:30:00 & 89.57\\
+\hline
+SHAL & SHAL\_C3L5 & 163204 & 2001-10-10 12:45:00 & 2017-10-15 23:15:00 & 38.22\\
+\hline
+SHAL & SHAL\_C4L5 & 187940 & 2001-09-11 15:30:00 & 2017-10-15 23:45:00 & 38.77\\
+\hline
+SHAL & SHAL\_C5L5 & 29417 & 2015-12-03 15:00:00 & 2017-10-15 23:45:00 & 36.35\\
+\hline
+SHAL & SHAL\_E1L5 & 465913 & 2001-10-10 12:15:00 & 2017-10-14 23:30:00 & 70.65\\
+\hline
+SHAL & SHAL\_E1T0 & 181132 & 2001-10-10 12:15:00 & 2017-10-14 23:15:00 & 101.23\\
+\hline
+SHAL & SHAL\_E2L5 & 290286 & 2001-10-10 12:15:00 & 2017-10-15 23:00:00 & 47.09\\
+\hline
+SHAL & SHAL\_E2T0 & 174129 & 2001-10-10 12:30:00 & 2017-10-14 22:45:00 & 107.44\\
+\hline
+SHAL & SHAL\_E3L5 & 258805 & 2010-03-11 07:00:00 & 2017-10-15 23:45:00 & 33.26\\
+\hline
+SHAL & SHAL\_E4L5 & 322135 & 2001-09-11 15:30:00 & 2017-10-16 12:30:00 & 54.03\\
+\hline
+SHAN & SHAN\_E1L5 & 288894 & 2001-09-21 14:15:00 & 2017-10-24 23:15:00 & 63.52\\
+\hline
+SHAN & SHAN\_E1T0 & 330691 & 2001-10-10 12:15:00 & 2017-10-24 23:45:00 & 226.58\\
+\hline
+SHAN & SHAN\_E2L5 & 321760 & 2001-09-21 14:15:00 & 2017-10-25 23:15:00 & 72.63\\
+\hline
+SHAN & SHAN\_E2T0 & 315053 & 2001-10-10 12:15:00 & 2017-10-24 23:45:00 & 186.69\\
+\hline
+SHAN & SHAN\_E3L5 & 105606 & 2001-09-21 14:15:00 & 2017-10-25 23:15:00 & 26.30\\
+\hline
+SHAN & SHAN\_E4L5 & 216626 & 2001-09-21 14:15:00 & 2017-10-25 23:30:00 & 33.63\\
+\hline
+SHAN & SHAN\_E5L5 & 254742 & 2001-09-21 14:15:00 & 2017-10-25 23:15:00 & 48.50\\
+\hline
+SHAN & SHAN\_E6L5 & 363107 & 2001-09-21 14:15:00 & 2017-10-25 23:15:00 & 68.69\\
+\hline
+SHAN & SHAN\_E7L5 & 384165 & 2001-09-21 14:15:00 & 2017-10-25 23:15:00 & 66.12\\
+\hline
+SHAN & SHAN\_E8L5 & 146605 & 2002-02-05 17:30:00 & 2017-10-25 23:15:00 & 25.28\\
+\hline
+VENT & VENT\_E1L5 & 203617 & 2001-09-11 15:45:00 & 2017-10-15 23:15:00 & 33.24\\
+\hline
+VENT & VENT\_E1T0 & 240745 & 2001-09-11 15:30:00 & 2017-10-15 23:15:00 & 191.42\\
+\hline
+VENT & VENT\_E2L5 & 402307 & 2001-09-27 14:00:00 & 2017-10-16 23:45:00 & 46.68\\
+\hline
+VENT & VENT\_E2T0 & 208020 & 2001-09-11 15:45:00 & 2017-10-15 23:30:00 & 115.47\\
+\hline
+VENT & VENT\_E3L5 & 493337 & 2001-09-11 15:45:00 & 2017-10-16 23:45:00 & 83.59\\
+\hline
+VENT & VENT\_E4L5 & 387037 & 2001-09-11 15:45:00 & 2017-10-16 23:30:00 & 40.86\\
+\hline
+VENT & VENT\_E5L5 & 481677 & 2001-09-27 14:00:00 & 2017-10-16 23:45:00 & 88.43\\
+\hline
+VENT & VENT\_E6L5 & 6631 & 2001-09-27 14:00:00 & 2017-10-24 20:15:00 & 3.95\\
+\hline
+\end{tabular}
+\end{table}
 
 Do a duplicate check by feeder\_ID, dateTime \& kW. In theory there
 should not be any.
@@ -1189,7 +429,7 @@ should not be any.
 \NormalTok{pc <-}\StringTok{ }\DecValTok{100}\OperatorTok{*}\NormalTok{((}\KeywordTok{nrow}\NormalTok{(origDataDT) }\OperatorTok{-}\StringTok{ }\KeywordTok{nrow}\NormalTok{(uniqDataDT))}\OperatorTok{/}\KeywordTok{nrow}\NormalTok{(origDataDT))}
 \KeywordTok{message}\NormalTok{(}\StringTok{"That's "}\NormalTok{, }\KeywordTok{round}\NormalTok{(pc,}\DecValTok{2}\NormalTok{), }\StringTok{"%"}\NormalTok{)}
 
-\NormalTok{feederDT <-}\StringTok{ }\NormalTok{uniqDataDT }\CommentTok{# use dt with no duplicates}
+\NormalTok{feederDT <-}\StringTok{ }\NormalTok{uniqDataDT[}\OperatorTok{!}\KeywordTok{is.na}\NormalTok{(rDateTime)] }\CommentTok{# use dt with no duplicates}
 \NormalTok{origDataDT <-}\StringTok{ }\OtherTok{NULL} \CommentTok{# save memory}
 \end{Highlighting}
 \end{Shaded}
@@ -1325,18 +565,18 @@ and then seeing if the dateTimes are contiguous.
 
 \begin{verbatim}
 ##    rDateTime                      rTime              rDate           
-##  Min.   :2001-09-11 15:15:00   Length:549530     Min.   :2001-09-11  
+##  Min.   :2001-09-11 15:15:00   Length:549529     Min.   :2001-09-11  
 ##  1st Qu.:2006-02-13 01:30:00   Class1:hms        1st Qu.:2006-02-13  
 ##  Median :2010-01-20 06:00:00   Class2:difftime   Median :2010-01-20  
 ##  Mean   :2010-01-05 16:51:28   Mode  :numeric    Mean   :2010-01-05  
 ##  3rd Qu.:2013-12-22 21:30:00                     3rd Qu.:2013-12-22  
 ##  Max.   :2020-12-31 07:15:00                     Max.   :2020-12-31  
-##  NA's   :1                                       NA's   :1           
+##                                                                      
 ##     season          nFeeders         meankW          dtDiff        
-##  Spring:137919   Min.   : 1.00   Min.   :  0.00   Length:549530    
-##  Summer:132245   1st Qu.:31.00   1st Qu.: 80.40   Class :difftime  
+##  Spring:137919   Min.   : 1.00   Min.   :  0.00   Length:549529    
+##  Summer:132245   1st Qu.:31.00   1st Qu.: 80.41   Class :difftime  
 ##  Autumn:141490   Median :39.00   Median : 96.95   Mode  :numeric   
-##  Winter:137876   Mean   :39.72   Mean   : 97.00                    
+##  Winter:137875   Mean   :39.72   Mean   : 97.00                    
 ##                  3rd Qu.:47.00   3rd Qu.:113.00                    
 ##                  Max.   :77.00   Max.   :439.56                    
 ##                                  NA's   :1
@@ -1443,95 +683,38 @@ infer how many feeders are reporting:
 \begin{Shaded}
 \begin{Highlighting}[]
 \NormalTok{wDT <-}\StringTok{ }\NormalTok{drake}\OperatorTok{::}\KeywordTok{readd}\NormalTok{(wideData) }\CommentTok{# back from the drake}
-\KeywordTok{head}\NormalTok{(wDT)}
+\KeywordTok{names}\NormalTok{(wDT)}
 \end{Highlighting}
 \end{Shaded}
 
 \begin{verbatim}
-##              rDateTime ARRN_ARRN BINS_C1T0 BINS_C2T0 BINS_E1L5 BINS_E2L5
-## 1: 2001-09-11 15:15:00        NA        NA        NA        NA        NA
-## 2: 2001-09-11 15:30:00        NA        NA        NA        NA        NA
-## 3: 2001-09-11 15:45:00        NA        NA        NA        NA        NA
-## 4: 2001-09-21 07:30:00        NA         0         0         0        NA
-## 5: 2001-09-21 08:00:00        NA         0         0         0        NA
-## 6: 2001-09-21 08:30:00        NA        NA        NA        NA        NA
-##    BINS_E3L5 FFPV_FFPV FRES_E1L5 FRES_E1T0 FRES_E2L5 FRES_E2T0 FRES_E3L5
-## 1:        NA        NA        NA         0        NA         0        NA
-## 2:        NA        NA        NA        NA        NA        NA        NA
-## 3:        NA        NA        NA        NA        NA        NA        NA
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA         0        NA        NA        NA
-##    FRES_E4L5 FRES_E6L5 NEWP_E11L5 NEWP_E13L5 NEWP_E15L5 NEWP_E17L5 NEWP_E19L5
-## 1:        NA         0         NA         NA         NA         NA         NA
-## 2:        NA        NA         NA         NA         NA         NA         NA
-## 3:        NA        NA         NA         NA         NA         NA         NA
-## 4:        NA        NA         NA         NA         NA         NA         NA
-## 5:        NA        NA         NA         NA         NA         NA         NA
-## 6:        NA         0         NA         NA         NA         NA         NA
-##    NEWP_E1L5 NEWP_E1T0 NEWP_E2L5 NEWP_E2T0 NEWP_E3L5 NEWP_E3T0 NEWP_E4L5
-## 1:        NA        NA        NA        NA        NA        NA         0
-## 2:        NA         0         0        NA        NA        NA        NA
-## 3:        NA        NA        NA        NA        NA        NA        NA
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA        NA        NA        NA        NA
-##    NEWP_E5L5 NEWP_E6L5 NEWP_E7L5 NEWP_E8L5 NEWP_E9L5 RYDE_E1L5 RYDE_E1T0 &E1S0
-## 1:         0        NA        NA        NA        NA        NA              NA
-## 2:        NA         0        NA        NA        NA        NA              NA
-## 3:        NA        NA        NA        NA        NA        NA              NA
-## 4:        NA        NA        NA        NA        NA        NA              NA
-## 5:        NA        NA        NA        NA        NA        NA              NA
-## 6:        NA        NA        NA        NA        NA        NA              NA
-##    RYDE_E2L5 RYDE_E2T0 RYDE_E3L5 RYDE_E4L5 RYDE_E5L5 RYDE_E6L5 RYDE_E7L5
-## 1:        NA        NA        NA        NA        NA        NA        NA
-## 2:        NA        NA        NA        NA        NA        NA        NA
-## 3:        NA        NA        NA        NA        NA        NA        NA
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA        NA        NA        NA        NA
-##    RYDE_E8L5 RYDE_E9L5 SADO_E1L5 SADO_E1T0 SADO_E2L5 SADO_E2T0 SADO_E3L5
-## 1:        NA        NA        NA        NA        NA        NA        NA
-## 2:        NA        NA        NA        NA        NA        NA        NA
-## 3:        NA        NA        NA        NA        NA        NA        NA
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA        NA        NA        NA        NA
-##    SADO_E4L5 SADO_E5L5 SADO_E6L5 SADO_E8L5 SHAL_C3L5 SHAL_C4L5 SHAL_C5L5
-## 1:        NA        NA        NA        NA        NA        NA        NA
-## 2:        NA        NA        NA        NA        NA         0        NA
-## 3:        NA        NA        NA        NA        NA        NA        NA
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA        NA        NA        NA        NA
-##    SHAL_E1L5 SHAL_E1T0 SHAL_E2L5 SHAL_E2T0 SHAL_E3L5 SHAL_E4L5 SHAN_E1L5
-## 1:        NA        NA        NA        NA        NA        NA        NA
-## 2:        NA        NA        NA        NA        NA         0        NA
-## 3:        NA        NA        NA        NA        NA        NA        NA
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA        NA        NA        NA        NA
-##    SHAN_E1T0 SHAN_E2L5 SHAN_E2T0 SHAN_E3L5 SHAN_E4L5 SHAN_E5L5 SHAN_E6L5
-## 1:        NA        NA        NA        NA        NA        NA        NA
-## 2:        NA        NA        NA        NA        NA        NA        NA
-## 3:        NA        NA        NA        NA        NA        NA        NA
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA        NA        NA        NA        NA
-##    SHAN_E7L5 SHAN_E8L5 VENT_E1L5 VENT_E1T0 VENT_E2L5 VENT_E2T0 VENT_E3L5
-## 1:        NA        NA        NA        NA        NA        NA        NA
-## 2:        NA        NA        NA         0        NA        NA        NA
-## 3:        NA        NA         0         0        NA         0         0
-## 4:        NA        NA        NA        NA        NA        NA        NA
-## 5:        NA        NA        NA        NA        NA        NA        NA
-## 6:        NA        NA        NA        NA        NA        NA        NA
-##    VENT_E4L5 VENT_E5L5 VENT_E6L5 nNA nFeedersReporting
-## 1:        NA        NA        NA  73                 5
-## 2:        NA        NA        NA  72                 6
-## 3:         0        NA        NA  73                 5
-## 4:        NA        NA        NA  75                 3
-## 5:        NA        NA        NA  75                 3
-## 6:        NA        NA        NA  76                 2
+##  [1] "rDateTime"         "ARRN_ARRN"         "BINS_C1T0"        
+##  [4] "BINS_C2T0"         "BINS_E1L5"         "BINS_E2L5"        
+##  [7] "BINS_E3L5"         "FFPV_FFPV"         "FRES_E1L5"        
+## [10] "FRES_E1T0"         "FRES_E2L5"         "FRES_E2T0"        
+## [13] "FRES_E3L5"         "FRES_E4L5"         "FRES_E6L5"        
+## [16] "NEWP_E11L5"        "NEWP_E13L5"        "NEWP_E15L5"       
+## [19] "NEWP_E17L5"        "NEWP_E19L5"        "NEWP_E1L5"        
+## [22] "NEWP_E1T0"         "NEWP_E2L5"         "NEWP_E2T0"        
+## [25] "NEWP_E3L5"         "NEWP_E3T0"         "NEWP_E4L5"        
+## [28] "NEWP_E5L5"         "NEWP_E6L5"         "NEWP_E7L5"        
+## [31] "NEWP_E8L5"         "NEWP_E9L5"         "RYDE_E1L5"        
+## [34] "RYDE_E1T0 &E1S0"   "RYDE_E2L5"         "RYDE_E2T0"        
+## [37] "RYDE_E3L5"         "RYDE_E4L5"         "RYDE_E5L5"        
+## [40] "RYDE_E6L5"         "RYDE_E7L5"         "RYDE_E8L5"        
+## [43] "RYDE_E9L5"         "SADO_E1L5"         "SADO_E1T0"        
+## [46] "SADO_E2L5"         "SADO_E2T0"         "SADO_E3L5"        
+## [49] "SADO_E4L5"         "SADO_E5L5"         "SADO_E6L5"        
+## [52] "SADO_E8L5"         "SHAL_C3L5"         "SHAL_C4L5"        
+## [55] "SHAL_C5L5"         "SHAL_E1L5"         "SHAL_E1T0"        
+## [58] "SHAL_E2L5"         "SHAL_E2T0"         "SHAL_E3L5"        
+## [61] "SHAL_E4L5"         "SHAN_E1L5"         "SHAN_E1T0"        
+## [64] "SHAN_E2L5"         "SHAN_E2T0"         "SHAN_E3L5"        
+## [67] "SHAN_E4L5"         "SHAN_E5L5"         "SHAN_E6L5"        
+## [70] "SHAN_E7L5"         "SHAN_E8L5"         "VENT_E1L5"        
+## [73] "VENT_E1T0"         "VENT_E2L5"         "VENT_E2T0"        
+## [76] "VENT_E3L5"         "VENT_E4L5"         "VENT_E5L5"        
+## [79] "VENT_E6L5"         "nNA"               "nFeedersReporting"
 \end{verbatim}
 
 If we take the mean of the number of feeders reporting per day (date)
@@ -1581,7 +764,8 @@ then a value of 25 will indicate a day when \emph{all} feeders have
 \begin{Shaded}
 \begin{Highlighting}[]
 \KeywordTok{message}\NormalTok{(}\StringTok{"How many days have 100%?"}\NormalTok{)}
-\KeywordTok{nrow}\NormalTok{(aggDT[propExpected }\OperatorTok{==}\StringTok{ }\DecValTok{1}\NormalTok{])}
+\NormalTok{n <-}\StringTok{ }\KeywordTok{nrow}\NormalTok{(aggDT[propExpected }\OperatorTok{==}\StringTok{ }\DecValTok{1}\NormalTok{])}
+\NormalTok{n}
 \end{Highlighting}
 \end{Shaded}
 
@@ -1589,6 +773,8 @@ then a value of 25 will indicate a day when \emph{all} feeders have
 ## [1] 0
 \end{verbatim}
 
+So, there are 0 days with 100\% data\ldots{}
+
 If we plot the mean then we will see which days get closest to having a
 full dataset.
 
@@ -1612,6 +798,135 @@ feeders * 24 hours * 4 per hour (will be the same shape):
 
 \includegraphics{/home/ba1e12/git.Soton/ba1e12/datacleaning/docs/cleaningFeederData_allData_files/figure-latex/bestDaysProp-1.pdf}
 
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{aggDT[, rDoW }\OperatorTok{:}\ErrorTok{=}\StringTok{ }\NormalTok{lubridate}\OperatorTok{::}\KeywordTok{wday}\NormalTok{(rDate, }\DataTypeTok{lab =} \OtherTok{TRUE}\NormalTok{)]}
+\NormalTok{h <-}\StringTok{ }\KeywordTok{head}\NormalTok{(aggDT[season }\OperatorTok{==}\StringTok{ "Spring"}\NormalTok{][}\KeywordTok{order}\NormalTok{(}\OperatorTok{-}\NormalTok{propExpected)])}
+\NormalTok{kableExtra}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(h, }\DataTypeTok{caption =} \StringTok{"Best Spring days overall"}\NormalTok{, }
+                  \DataTypeTok{digits =} \DecValTok{3}\NormalTok{) }\OperatorTok{%>%}
+\StringTok{  }\KeywordTok{kable_styling}\NormalTok{()}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{table}
+
+\caption{\label{tab:bestDaysProp}Best Spring days overall}
+\centering
+\begin{tabular}[t]{l|l|r|r|r|r|r|l}
+\hline
+rDate & season & meanOK & minOk & maxOk & sumOK & propExpected & rDoW\\
+\hline
+2002-04-14 & Spring & 63.490 & 60 & 65 & 6095 & 0.814 & Sun\\
+\hline
+2003-03-20 & Spring & 63.458 & 61 & 67 & 6092 & 0.814 & Thu\\
+\hline
+2002-03-21 & Spring & 63.385 & 62 & 65 & 6085 & 0.813 & Thu\\
+\hline
+2003-03-14 & Spring & 63.385 & 61 & 65 & 6085 & 0.813 & Fri\\
+\hline
+2003-03-16 & Spring & 63.375 & 60 & 65 & 6084 & 0.812 & Sun\\
+\hline
+2003-03-17 & Spring & 63.375 & 61 & 65 & 6084 & 0.812 & Mon\\
+\hline
+\end{tabular}
+\end{table}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{h <-}\StringTok{ }\KeywordTok{head}\NormalTok{(aggDT[season }\OperatorTok{==}\StringTok{ "Summer"}\NormalTok{][}\KeywordTok{order}\NormalTok{(}\OperatorTok{-}\NormalTok{propExpected)])}
+\NormalTok{kableExtra}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(h, }\DataTypeTok{caption =} \StringTok{"Best Summer days overall"}\NormalTok{, }
+                  \DataTypeTok{digits =} \DecValTok{3}\NormalTok{) }\OperatorTok{%>%}
+\StringTok{  }\KeywordTok{kable_styling}\NormalTok{()}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{table}
+
+\caption{\label{tab:bestDaysProp}Best Summer days overall}
+\centering
+\begin{tabular}[t]{l|l|r|r|r|r|r|l}
+\hline
+rDate & season & meanOK & minOk & maxOk & sumOK & propExpected & rDoW\\
+\hline
+2003-08-22 & Summer & 63.854 & 56 & 65 & 6130 & 0.819 & Fri\\
+\hline
+2003-08-30 & Summer & 63.844 & 60 & 66 & 6129 & 0.819 & Sat\\
+\hline
+2003-08-31 & Summer & 63.812 & 59 & 66 & 6126 & 0.818 & Sun\\
+\hline
+2003-08-23 & Summer & 63.677 & 56 & 66 & 6113 & 0.816 & Sat\\
+\hline
+2003-08-25 & Summer & 63.677 & 55 & 66 & 6113 & 0.816 & Mon\\
+\hline
+2003-08-26 & Summer & 63.656 & 58 & 66 & 6111 & 0.816 & Tue\\
+\hline
+\end{tabular}
+\end{table}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{h <-}\StringTok{ }\KeywordTok{head}\NormalTok{(aggDT[season }\OperatorTok{==}\StringTok{ "Autumn"}\NormalTok{][}\KeywordTok{order}\NormalTok{(}\OperatorTok{-}\NormalTok{propExpected)])}
+\NormalTok{kableExtra}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(h, }\DataTypeTok{caption =} \StringTok{"Best Autumn days overall"}\NormalTok{,}
+                  \DataTypeTok{digits =} \DecValTok{3}\NormalTok{) }\OperatorTok{%>%}
+\StringTok{  }\KeywordTok{kable_styling}\NormalTok{()}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{table}
+
+\caption{\label{tab:bestDaysProp}Best Autumn days overall}
+\centering
+\begin{tabular}[t]{l|l|r|r|r|r|r|l}
+\hline
+rDate & season & meanOK & minOk & maxOk & sumOK & propExpected & rDoW\\
+\hline
+2003-09-02 & Autumn & 63.823 & 57 & 66 & 6127 & 0.818 & Tue\\
+\hline
+2003-09-01 & Autumn & 63.771 & 56 & 65 & 6122 & 0.818 & Mon\\
+\hline
+2003-09-07 & Autumn & 63.740 & 57 & 66 & 6119 & 0.817 & Sun\\
+\hline
+2003-09-03 & Autumn & 63.667 & 57 & 65 & 6112 & 0.816 & Wed\\
+\hline
+2003-09-04 & Autumn & 63.615 & 57 & 66 & 6107 & 0.816 & Thu\\
+\hline
+2003-09-06 & Autumn & 63.552 & 57 & 65 & 6101 & 0.815 & Sat\\
+\hline
+\end{tabular}
+\end{table}
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\NormalTok{h <-}\StringTok{ }\KeywordTok{head}\NormalTok{(aggDT[season }\OperatorTok{==}\StringTok{ "Winter"}\NormalTok{][}\KeywordTok{order}\NormalTok{(}\OperatorTok{-}\NormalTok{propExpected)])}
+\NormalTok{kableExtra}\OperatorTok{::}\KeywordTok{kable}\NormalTok{(h, }\DataTypeTok{caption =} \StringTok{"Best Winter days overall"}\NormalTok{, }
+                  \DataTypeTok{digits =} \DecValTok{3}\NormalTok{) }\OperatorTok{%>%}
+\StringTok{  }\KeywordTok{kable_styling}\NormalTok{()}
+\end{Highlighting}
+\end{Shaded}
+
+\begin{table}
+
+\caption{\label{tab:bestDaysProp}Best Winter days overall}
+\centering
+\begin{tabular}[t]{l|l|r|r|r|r|r|l}
+\hline
+rDate & season & meanOK & minOk & maxOk & sumOK & propExpected & rDoW\\
+\hline
+2002-02-28 & Winter & 63.292 & 60 & 65 & 6076 & 0.811 & Thu\\
+\hline
+2002-02-25 & Winter & 63.125 & 61 & 65 & 6060 & 0.809 & Mon\\
+\hline
+2002-12-11 & Winter & 62.979 & 61 & 64 & 6046 & 0.807 & Wed\\
+\hline
+2002-12-01 & Winter & 62.917 & 61 & 64 & 6040 & 0.807 & Sun\\
+\hline
+2003-01-01 & Winter & 62.906 & 60 & 64 & 6039 & 0.806 & Wed\\
+\hline
+2003-01-03 & Winter & 62.906 & 60 & 64 & 6039 & 0.806 & Fri\\
+\hline
+\end{tabular}
+\end{table}
+
 This also tells us that there is some reason why we get fluctations in
 the number of data points per hour after 2003.
 
@@ -1621,7 +936,7 @@ So there are no days with 100\% data. We need a different approach.
 
 \section{Runtime}\label{runtime}
 
-Analysis completed in 211.44 seconds ( 3.52 minutes) using
+Analysis completed in 221.35 seconds ( 3.69 minutes) using
 \href{https://cran.r-project.org/package=knitr}{knitr} in
 \href{http://www.rstudio.com}{RStudio} with R version 3.6.0 (2019-04-26)
 running on x86\_64-redhat-linux-gnu.
@@ -1686,14 +1001,14 @@ running on x86\_64-redhat-linux-gnu.
 ## [17] glue_1.4.1        withr_2.1.2       lifecycle_0.2.0   stringr_1.4.0    
 ## [21] munsell_0.5.0     gtable_0.2.0      rvest_0.3.5       evaluate_0.14    
 ## [25] labeling_0.3      knitr_1.28        parallel_3.6.0    fansi_0.4.0      
-## [29] highr_0.7         Rcpp_1.0.1        readr_1.3.1       scales_1.0.0     
-## [33] backports_1.1.3   filelock_1.0.2    webshot_0.5.2     jsonlite_1.6     
-## [37] digest_0.6.25     stringi_1.2.4     dplyr_1.0.0       grid_3.6.0       
-## [41] rprojroot_1.3-2   cli_2.0.2         tools_3.6.0       magrittr_1.5     
-## [45] base64url_1.4     tibble_3.0.1      crayon_1.3.4      pkgconfig_2.0.2  
-## [49] ellipsis_0.3.1    xml2_1.3.2        prettyunits_1.0.2 httr_1.4.1       
-## [53] assertthat_0.2.0  rmarkdown_2.2     rstudioapi_0.11   R6_2.3.0         
-## [57] igraph_1.2.2      compiler_3.6.0
+## [29] Rcpp_1.0.1        readr_1.3.1       scales_1.0.0      backports_1.1.3  
+## [33] filelock_1.0.2    webshot_0.5.2     jsonlite_1.6      digest_0.6.25    
+## [37] stringi_1.2.4     dplyr_1.0.0       grid_3.6.0        rprojroot_1.3-2  
+## [41] cli_2.0.2         tools_3.6.0       magrittr_1.5      base64url_1.4    
+## [45] tibble_3.0.1      crayon_1.3.4      pkgconfig_2.0.2   ellipsis_0.3.1   
+## [49] xml2_1.3.2        prettyunits_1.0.2 httr_1.4.1        assertthat_0.2.0 
+## [53] rmarkdown_2.2     rstudioapi_0.11   R6_2.3.0          igraph_1.2.2     
+## [57] compiler_3.6.0
 \end{verbatim}
 
 \section{The raw data cleaning code}\label{the-raw-data-cleaning-code}
diff --git a/make_cleanFeeders.R b/make_cleanFeeders.R
index e36214e073466d8a1d21e55353f523f3306c628c..a8fcfc3af540d6d270532c9154aecd51a4662f6a 100644
--- a/make_cleanFeeders.R
+++ b/make_cleanFeeders.R
@@ -3,7 +3,9 @@
 
 # Set up ----
 startTime <- proc.time()
-update <- "yes" # edit this in any way (at all) to get drake to re-load the data
+updateData <- "yes" # edit this in any way (at all) to get drake to re-load the data
+updateReport <- "yes" # edit this to force re-render of .Rmd
+
 library(drake)
 # use r_make to run the plan inside a clean R session so nothing gets contaminated
 drake::r_make(source = "_drakeCleanFeeders.R") # where we keep the drake plan etc