### pdf/latex fix:

```> install.packages(c('tinytex', 'rmarkdown'))
> tinytex::install_tinytex()

#rtfm```
parent a6db3391
 ... ... @@ -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. ... ...
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!