merge a few edits

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.