covidmortality-vax.R

# Pacman, version 0.4.1 is used to organize the packages used in R.

library (pacman)

p_load (readxl, tseries)

# If you are using additional packages, or feel like it, you can use `conflict_scout()` command from *conflicted* package to check conflicts betweeen packages.

# I've seperated countries before importing to R. After importing the .csv file, we are filling N/A data with 0s.

> uk <- read.csv("C:/Users/'username'/Desktop/'filename'.csv", header = T)

> 'filename'[is.na('filename')] = 0

> 'filenamecases' <- 'filename' %>%
    select("date", "ncases")
    
# To prevent errors, we are converting the date column as dates.

'filenamecases'$date <- as.Date('filenamecases')

head('filenamecases')

# Since we have mentioned new deaths follows new cases with a lag, for various reasons, and the time gap changes. So we are using different lag.

> lag0 <- lag('filenamecases', n=7)

> lag1 <- lag('filenamecases', n=9)

> lag2 <- lag('filenamecases', n=12)

# Converting these to time series (only converted one for this example)

> 'filename_ts1' <- ts(lag1[, 2], start = c(1, 1), end = c(515, 1), frequency = 1)

> 'filename_ts1'

# For comparison to deaths, we are extracting the new deaths data, and converting it to time series as well.

> 'filenamedeaths' <- 'filename' %>%
    select("date", "ndeaths")

> 'filename_ts2' <- ts('filenamedeaths'[, 2], start = c(1, 1), end = c(515, 1), frequency = 1)      

> 'filename_ts2'

# Since we used lag on new cases data, the length of it (as a vector) changed, but for comparison we need the original length.

> 'filename_ts1' <- pmax('filename_ts1', 0)

# When comparing the numbers, we need them to be scaled, so we can see clearer on plotted graphic/chart

> c1 = range('filename_ts1', na.rm=T)

> c1

> c2 = range('filename_ts2', na.rm=T)

> c2

# We are starting to plot, and want 3 different variables to be presented together.

>plot('filename_ts1', ylim=c1, ylab="Cases")

> par(new=T)

> plot('filename_ts2', ylab=NA, axes=F, col="red", ylim=c(0, 2000))

> axis(4, las=0, ylab="Deaths", tck=0.01)

> mtext("Deaths", side=4, col="red")

> par(new=T)

> plot('filename$tvaxp', ylab=NA, axes=F, col="green", ylim=c(0, 100))

> mtext("% Vaccination", side=3, col="green", ylim=c(0, 100))

# Thank you for your interest.