How to use sf package with dtplyr lazy_dt() data to create geom_sf plot in r?

Question

(Post has been reframed to get more clarity & also updated all links)

Recently got to know about dtplyr package so was trying to use them to make my existing code work faster by using lazy_dt() instead of just dplyr.

But I am getting error when I run them:

step1: data:

library(tidyverse)
library(sf)
library(lubridate)
library(scales)
library(glue)

library(dtplyr)
library(data.table)

vaccination_data <- readRDS(url("https://raw.githubusercontent.com/johnsnow09/covid19-df_stack-code/main/vaccination_data.rds")) 

(UPDATED correct Shape file) Step1.2: Download shape file from: https://github.com/johnsnow09/covid19-df_stack-code/blob/main/in_countries.shp

step2: convert to lazy_dt()

# creating new dataset to keep difference in both datasets
vaccination_data2 <- lazy_dt(vaccination_data)

Step3: plotting of lazy_dt() i.e vaccination_data2 doesn't work where as plotting of usual dataframe / tibble i.e vaccination_data works

# using lazy_dt data
vaccination_data2 %>% 
            filter(date > ymd("2020-12-31")) %>% 
            mutate(month_col = lubridate::month(date, label = TRUE)) %>% 
            
            arrange(date) %>% 
            group_by(Country.Region, iso_code, month_col) %>% 
            summarise(total_vaccinations = last(total_vaccinations), 
                      .groups = "drop") %>% 
            ungroup() %>% 
                      
            full_join(sf::read_sf("in_countries.shp") %>% 
                          st_as_sf(),
                      by = c("iso_code" = "iso_a3")) %>% 
            dplyr::select(Country.Region,iso_code,month_col,total_vaccinations,geometry) %>% 
            filter(!is.na(total_vaccinations)) %>% 
            as.tibble() %>% 
            st_as_sf() %>% st_transform(crs = 4326) %>% 
  
  
  ggplot() +
            geom_sf(aes(fill = total_vaccinations), color = "white") +           
            scale_fill_gradient2_tableau(
                palette = "Sunset-Sunrise Diverging",
                na.value = "lightgrey",
                guide = "colourbar",
                labels = unit_format(unit = "M", scale = 1e-6)
            ) +
            
            theme_map() +
            facet_wrap(~month_col) +
            theme(strip.background = element_blank(),
                  strip.text = element_text(face = "bold", size = 14),
                  legend.position = "right")

Gives error: Error in CPL_transform(x, crs, aoi, pipeline, reverse, desired_accuracy, : Not compatible with STRSXP: [type=NULL].

Below code with vaccination_data works

# using usual data frame / tibble
vaccination_data %>% 
            filter(date > ymd("2020-12-31")) %>% 
            mutate(month_col = lubridate::month(date, label = TRUE)) %>% 
            
            arrange(date) %>% 
            group_by(Country.Region, iso_code, month_col) %>% 
            summarise(total_vaccinations = last(total_vaccinations), 
                      .groups = "drop") %>% 
            ungroup() %>% 
                      
            full_join(sf::read_sf("in_countries.shp") %>% 
                          st_as_sf(),
                      by = c("iso_code" = "iso_a3")) %>% 
            dplyr::select(Country.Region,iso_code,month_col,total_vaccinations,geometry) %>% 
            filter(!is.na(total_vaccinations)) %>% 
            as.tibble() %>% 
            st_as_sf() %>% st_transform(crs = 4326) %>% 
  
  
  ggplot() +
            geom_sf(aes(fill = total_vaccinations), color = "white") +           
            scale_fill_gradient2_tableau(
                palette = "Sunset-Sunrise Diverging",
                na.value = "lightgrey",
                guide = "colourbar",
                labels = unit_format(unit = "M", scale = 1e-6)
            ) +
            
            theme_map() +
            facet_wrap(~month_col) +
            theme(strip.background = element_blank(),
                  strip.text = element_text(face = "bold", size = 14),
                  legend.position = "right")