merge a few edits

Rmd/cleaningFeederData.Rmd

@@ -314,14 +314,19 @@ 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()
+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):
+Re-plot by the % of expected if we assume we _should_ have n feeders * 24 hours * 4 per hour (will be the same shape). This also tells us that there is some reason why we get fluctations in the number of data points per hour after 2003.
+
+For fun we then print 4 tables of the 'best' days per season.
 
 ```{r bestDaysProp, fig.width=8}
-ggplot2::ggplot(aggDT, aes(x = rDate, colour = season, y = 100*propExpected)) + geom_point() +
+ggplot2::ggplot(aggDT, aes(x = rDate, colour = season, 
+                           y = 100*propExpected)) +
+  geom_point() +
   labs(y = "%")
 
 aggDT[, rDoW := lubridate::wday(rDate, lab = TRUE)]
@@ -346,8 +351,6 @@ kableExtra::kable(h, caption = "Best Winter days overall",
   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.
-
 # Summary
 
 So there are no days with 100% data. We need a different approach.