From e8f5e9bcb0bd2fe19daae8c84acbbaf450fb446b Mon Sep 17 00:00:00 2001
From: "B.Anderson" <ba1e12@srv02405.soton.ac.uk>
Date: Thu, 9 Jul 2020 00:21:03 +0100
Subject: [PATCH] pdf/latex fix: > install.packages(c('tinytex', 'rmarkdown'))
> tinytex::install_tinytex()
#rtfm
---
Rmd/cleaningFeederData.Rmd | 13 ++++++++-----
1 file changed, 8 insertions(+), 5 deletions(-)
diff --git a/Rmd/cleaningFeederData.Rmd b/Rmd/cleaningFeederData.Rmd
index 032a465..4c35220 100644
--- a/Rmd/cleaningFeederData.Rmd
+++ b/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.
--
GitLab