gistfile1.txt

inds <- seq(as.Date(min(day$date_interval)), 
            as.Date(max(day$date_interval)), 
            by = "day")
Eggs<-day%>%
  filter(location_identification==1)%>%
  mutate(egg_count = zoo::na.aggregate(egg_count))%>%
  dplyr::select(egg_count)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
Feed<-day%>%
  filter(location_identification==1)%>%
  mutate(feed_provided = zoo::na.aggregate(feed_provided))%>%
  dplyr::select(feed_provided)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
Water<-day%>%
  filter(location_identification==1)%>%
  mutate(water_provided = zoo::na.aggregate(water_provided))%>%
  dplyr::select(water_provided)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
AnimalACT<-day%>%
  filter(location_identification==1)%>%
  mutate(animals_actual = zoo::na.aggregate(animals_actual))%>%
  dplyr::select(animals_actual)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
TempMIN<-day%>%
  filter(location_identification==1)%>%
  mutate(t_min_out = zoo::na.aggregate(t_min_out))%>%
  dplyr::select(t_min_out)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
TempMAX<-day%>%
  filter(location_identification==1)%>%
  mutate(t_max_out = zoo::na.aggregate(t_max_out))%>%
  dplyr::select(t_max_out)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
TempACT<-day%>%
  filter(location_identification==1)%>%
  mutate(t_act_out = zoo::na.aggregate(t_act_out))%>%
  dplyr::select(t_act_out)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
RH<-day%>%
  filter(location_identification==1)%>%
  mutate(rh_act_in = zoo::na.spline(rh_act_in))%>%
  dplyr::select(t_act_out)%>%
  ts(.,
     start=c(2021,9,4),
     frequency=365.25)
dfy  <-ts.union(Eggs, Water, Feed)
dfexo<-ts.union(TempMIN, TempMAX, TempACT, AnimalACT)

autoplot(dfy, facets=TRUE) +
  xlab("Date") + ylab("") +
  ggtitle("Stals")+theme_bw()
ggAcf(dfy)+theme_bw()
ggPacf(dfy)+theme_bw()

tsdat_egg = dfy[,"Eggs"] %>% ts(., frequency=7) 
tsdat_egg_stl = stl(tsdat_egg, s.window = "periodic")
autoplot(tsdat_egg_stl)+theme_bw()

tsdat_feed = dfy[,"Feed"] %>% ts(., frequency=7) 
tsdat_feed_stl = stl(tsdat_feed, s.window = "periodic")
autoplot(tsdat_feed_stl)+theme_bw()

tsdat_avgweight = dfy[,"Water"] %>% ts(., frequency=7) 
tsdat_avgweight_stl = stl(tsdat_avgweight, s.window = "periodic")
autoplot(tsdat_avgweight_stl)+theme_bw()

Eggs_ts_linear <- tslm(ts(tsdat_egg)~trend)  
Eggs_ts_p5     <- tslm(ts(tsdat_egg)~trend + I(trend^2) + I(trend^3) + I(trend^4) + I(trend^5) ) # polynomial
Eggs_ts_ma5    <- ma(ts(tsdat_egg), order=5) 
Eggs_ts_trends <- data.frame(cbind(Data=as.data.frame(tsdat_egg),  
                                   Linear_trend = fitted(Eggs_ts_linear),
                                   Poly_trend   = fitted(Eggs_ts_p5),
                                   Moving_avg5  = Eggs_ts_ma5))
Eggs_ts_trends <- tibble::rownames_to_column(Eggs_ts_trends, "VALUE")
ggplot(Eggs_ts_trends,aes(x=as.numeric(VALUE))) + 
  geom_line(aes(y=x ,              color="original"),size=1) + 
  geom_line(aes(y=Linear_trend ,   color="linear"),size=1) + 
  geom_line(aes(y=Poly_trend,      color = "O(5) poly"), size=1) + 
  geom_line(aes(y=Moving_avg5,     color ="ma21"), size=1)  + 
  scale_color_manual(values = c('original'= 'grey', 
                                'linear' = 'darkblue',
                                'O(5) poly' = 'red', 
                                'ma21'= 'orange')) + 
  labs(color = 'Trend fit') +  ylab("Eggs") + 
  ggtitle("Different trend fits for Eggs") +theme_bw()