How I made the hourly~daily~monthly~yearly heatmap : https://www.r-graph-gallery.com/283-the-hourly-heatmap.html

HeatmapHrByDay.R

# https://www.r-graph-gallery.com/283-the-hourly-heatmap.html

library(ggplot2)
library(dplyr) # easier data wrangling 
library(viridis) # colour blind friendly palette, works in B&W also
library(Interpol.T) #  will generate a large dataset on initial load
library(lubridate) # for easy date manipulation
library(ggExtra) # because remembering ggplot theme options is beyond me
library(tidyr) 


data<- data(Trentino_hourly_T,package = "Interpol.T")

names(h_d_t)[1:5]<- c("stationid","date","hour","temp","flag")
df<- tbl_df(h_d_t) %>%
  filter(stationid =="T0001")

df<- df %>% mutate(year = year(date),
                  month = month(date, label=TRUE),
                  day = day(date))
  
df$date<-ymd(df$date) # not necessary for plot but 
#useful if you want to do further work with the data

#cleanup
rm(list=c("h_d_t","mo_bias","Tn","Tx",
          "Th_int_list","calibration_l",
          "calibration_shape","Tm_list"))


#create plotting df
df <-df %>% select(stationid,day,hour,month,year,temp)%>%
        fill(temp) #optional - see note below

# Re: use of fill
# This code is for demonstrating a visualisation technique
# There are 5 missing hourly values in the dataframe.

# see the original plot here (from my ggplot demo earlier this year) to see the white spaces where the missing values occcur:
# https://github.com/johnmackintosh/ggplotdemo/blob/master/temp8.png 

# I used 'fill' from  tidyr to take the prior value for each missing value and replace the NA
# This is a quick fix for the blog post only - _do not_ do this with your real world data

# Should really use either use replace_NA or complete(with fill)in tidyr 
# OR 
# Look into more specialist way of replacing these missing values -e.g. imputation.



statno <-unique(df$stationid)



######## Plotting starts here#####################
p <- ggplot(df, aes(day,hour,fill = temp))+
  geom_tile(color= "white", size=0.1) + 
  scale_fill_viridis(name = "Hrly Temps C",option = "C")
p <- p + facet_grid(year ~ month)
p <- p + scale_y_continuous(trans = "reverse", breaks = unique(df$hour))
p <- p + scale_x_continuous(breaks =c(1,10,20,31))
p <- p + theme_minimal(base_size = 8)
p <- p + labs(title = paste("Hourly Temps - Station",statno), x = "Day", y = "Hour Commencing")
p <- p + theme(legend.position = "bottom") +
  theme(plot.title = element_text(size = 14)) +
  theme(axis.text.y = element_text(size = 6)) +
  theme(strip.background = element_rect(colour = "white")) +
  theme(plot.title = element_text(hjust = 0)) +
  theme(axis.ticks = element_blank()) +
  theme(axis.text = element_text(size = 7)) +
  theme(legend.title = element_text(size = 8)) +
  theme(legend.text = element_text(size = 6)) +
  ggExtra::removeGrid() #ggExtra

# you will want to expand your plot screen before this bit!
p #awesomeness

#################################

You could try creating individual plots for each year and then use {patchwork} to align them as you need?