library("tidyverse")
# intsalibrary("dplyr")
library("plotly")
library("htmlwidgets")
library("lubridate")
library("viridis")

## The folders htmlplots and pngplots are assumed to be in the dir
## the file assumes gisaid-india and india-states are present in the dir
df <- read_tsv("gisaid-india.tsv")
states <- read_tsv("states.tsv")


## this is the update file
file <- list.files(pattern = "metadata.tsv")

## if the file exists, it updates gisaid-india file and deletes update file
if(length(file) > 0){
  
  if(file.exists(file)){
    update <- read_tsv(file)
    df <- df %>% 
      rbind(update) %>% 
      distinct()
    write_tsv(df, file = "gisaid-india.tsv")
    
    file.remove(file)
    
  }
}


## cleanup
df <- df %>% 
  mutate(mydate = parse_date_time(x = df$date,
                                  orders = c("%Y-%m-%d", "%Y-%m", "%Y"))) %>% 
  mutate(Month = paste0(year(mydate), "-", month.abb[month(mydate)]))

  ## Change state names
df["division"][df["division"] == "Andhra pradesh"] <- "Andhra Pradesh"
df["division"][df["division"] == "Chhatisgarh"] <- "Chhattisgarh"
df["division"][df["division"] == "Chandighar"] <- "Chandigarh"
df["division"][df["division"] == "Tamilnadu"] <- "Tamil Nadu"
df["division"][df["division"] == "ARUNACHAL PRADESH"] <- "Arunachal Pradesh"
df["division"][df["division"] == "Dadra & Nagar Haveli"] <- "Dadra and Nagar Haveli and Daman and Diu"
df["division"][df["division"] == "Dadra and Nagar Haveli"] <- "Dadra and Nagar Haveli and Daman and Diu"
df["division"][df["division"] == "Dadra And Nagar Haveli"] <- "Dadra and Nagar Haveli and Daman and Diu"
df["division"][df["division"] == "New Delhi"] <- "Delhi"
df["division"][df["division"] == "Jammu & kashmir"] <- "Jammu and Kashmir"
df["division"][df["division"] == "Jammu & Kashmir"] <- "Jammu and Kashmir"
df["division"][df["division"] == "Jammu and Kashmīr"] <- "Jammu and Kashmir"
df["division"][df["division"] == "Jammu"] <- "Jammu and Kashmir"


## There are around 800 unique lineages.
## Reducing by ignoring less than .25 percent in India
lin <- df %>% 
  count(pangolin_lineage, sort = TRUE) %>% 
  mutate(percent = n/sum(n)*100) %>% 
  filter(percent > 0.2)

lin <- lin[order(lin$pangolin_lineage),]

#unique_lineages <- unique(df$pangolin_lineage)
#length(unique_lineages)

## getting viridis colors for plots
#my_colors <- viridis_pal(option = "D")(nrow(lin)+1)
#my_colors <- viridis_pal(option = "D")(length(unique_lineages) + 1)
my_colors <- viridis_pal(option = "D")(100)
my_labels <- c(lin$pangolin_lineage, "Others")


## function plots interactive graph and saves html file in htmlplots
## and static png file in pngplots for given Indian 'state'

output <- function(state) {
  
  ## Subsetting given state's data
  df <- df[df$division == state,]
  
  
  ## new dataframes to calculate percentages
  newdf <- df %>%
    count(Month, pangolin_lineage, sort = TRUE) 
  
  bymonth <- df %>% 
    count(Month, sort = TRUE)
  
  comb <- merge(x = newdf, y = bymonth, by = "Month")
  comb$percent <- round(comb$n.x/comb$n.y*100, 2)
  
  my_levels <- c(paste0("2019-", month.abb),
                 paste0("2020-", month.abb),
                 paste0("2021-", month.abb),
                 paste0("2022-", month.abb),
                 paste0("2023-", month.abb),
                 paste0("2024-", month.abb))
  
  ## Subsetting statewise data by
  onepercent <- comb[comb$percent > 5,]
  onepercent$Month <- factor(onepercent$Month, my_levels)
  onepercent <- onepercent[!is.na(onepercent$Month),]

  others <- comb[comb$percent <= 5, ]
  others <- others %>% group_by(Month) %>% 
    summarise(percent = sum(percent)) %>%
    mutate(pangolin_lineage = "Others")
  
  onepercent <- onepercent %>% select(Month, percent, pangolin_lineage)
  onepercent <- rbind(others, onepercent)
  
  p <- onepercent %>% 
    transmute(Variant = pangolin_lineage, Month = Month, Percent = percent) %>% 
    ggplot() + 
    geom_col(mapping = aes(x=Month, y = Percent,
                           fill=Variant)) +
    scale_fill_manual(values = my_colors,
                      labels = my_labels) +
    ggtitle(label=paste("Variant Map for", state)) +
    theme(axis.text.x = element_text(angle = 45, hjust=1)) +
    xlab("Month") +
    ylab("Percentage") 
  
  f <- paste0("htmlplots/", states[states$name == state,]$code, ".html")
  f <- file.path(normalizePath(dirname(f)), basename(f))
  saveWidget(ggplotly(p), file=f, selfcontained = FALSE, libdir = "plotly.html")
  
  #################
  
  p <- onepercent %>% 
    transmute(Variant = pangolin_lineage, Month = Month, Percent = percent) %>% 
    ggplot() + 
    geom_col(mapping = aes(x=Month, y = Percent,
                           fill=Variant)) +
    scale_fill_manual(values = my_colors,
                      labels = my_labels) +
    theme(axis.text.x = element_text(angle = 45, hjust=1)) +
    ggtitle(label=paste("Variant Map for", state)) +
    guides(fill=guide_legend(ncol=2)) +
    xlab("Month") +
    ylab("Percentage")+
    theme(plot.title = element_text(size=22))
  
  
  f <- paste0("pngplots/", states[states$name == state,]$code, ".png")
  ggsave(f, height = 11.28, width = 13.54)
  
}

for (s in states$name){
  output(s)
}