Seasonal data

seasonal_data$Q <- factor(seasonal_data$Q,levels = c("Q1","Q2","Q3","Q4"))

seasonal_data <- rbind(seasonal_data,c(2020,4,0,"Q2"))
seasonal_data <- rbind(seasonal_data,c(2021,4,0,"Q2"))

#seasonal_data <- filter(seasonal_data,Year > 2019 & Q == "Q2")

seasonal_data$Year <- as.numeric(seasonal_data$Year)
seasonal_data$Month <- as.numeric(seasonal_data$Month)
seasonal_data$Total <- as.numeric(seasonal_data$Total )


seasonal_data_aggregated <- seasonal_data %>%
  dplyr::filter(Year > 2015)%>%
  dplyr::group_by(Year,Q)%>%
  dplyr::summarise(Totals = sum(Total))%>%
  ggplot(aes(factor(Q), Totals))+
  geom_col(fill = rgb(0,51,160, maxColorValue = 255))+
  geom_text(aes(label = ifelse(Totals > 0, Totals,"")),vjust = -0.2, fontface = "bold")+
  #ylim(0,500)+
  theme_classic()+
  theme(
    legend.position = "top",
    legend.title = element_blank(),
    axis.title.x = element_blank(),
     axis.title.y = element_blank(),
    axis.text.x = element_text(size = 12, color = "black"),
    axis.ticks.y = element_blank(),
    axis.text.y = element_blank()
  )+
  labs(
    title = "Aggregated quarterly data for period 2016 - 2020"
  )+
  facet_wrap(~Year,ncol = 1)+
  ylim(0,500)

## `summarise()` has grouped output by 'Year'. You can override using the `.groups` argument.

seasonal_data_aggregated