library(tidyr)
library(lubridate)
library(ggplot2)
library(scales)

# Load data
D_2km <- read.csv("/home/megha.patnaik/CIAMBELLE/OUTPUT/ring_radiance_2-50km.csv")

# Subset San Francisco (Right now)
SF_2km <- subset(D_2km, MetroShort == "San Francisco, CA")

# reshape SF2 (you could just reshame D_2km similarly? try it out)
SF_2km_long <- gather(SF_2km, key = "variable", value = "value", -city_index, -MetroShort, -cbd_lon, -cbd_lat, -coord)

#SF_2km_long <- ts(SF_2km_long)

year.month <- seq.Date(as.Date("2018-01-01"), as.Date("2022-07-01"), by = "month")
SF_centre_ts <- data.frame(year.month, SF_2km_long)
SF_centre_ts = subset(SF_centre_ts, select = c("year.month", "MetroShort", "value"))

# Normalize with March 2019 as 100 
SF_centre_ts$value2 <- (as.numeric(SF_2km_long$value)/6130)*100

# Now make a plot 


sc <- scale_x_date(
  limits = range(SF_centre_ts$year.month), date_labels = '%B %Y', date_minor_breaks = '1 month')

ggplot(SF_centre_ts, aes(year.month, value2)) +
  geom_line() + sc