How can i start this code found on github? - r

Question

I'm following this code on github and in line 51 i have a problem with option[i,]<- skew.raw why? Said: object "i" not found. Why? What should i put?
It also fails to take values as after starting the get.option function I have NA values.
# Define function for formating/retrieving options data from json obj
get.options = function(symbols, date){
options = matrix(ncol = 11, nrow = length(symbols))
colnames(options) = c('Cl_price', "call_strike",
"call_lastPrice","call_vol","call_openInt", "call_ImpVoli",
"put_strike","put_lastPrice", 'put_vol',"put_openInt", 'put_ImpVoli')
rownames(options) = symbols
for(u in 1:length(symbols)){
s = symbols[u]
d = as.numeric(as.POSIXct(date, origin = '1970-01-01', tz = 'GMT'))
json_file <- sprintf('https://query2.finance.yahoo.com/v7/finance/options/%s?
date=%d&formatted=true&crumb=UNus6VhY1bn&lang=en-US&region=US&corsDomain=finance.yahoo.com',s,d)
json_data <- suppressWarnings(fromJSON(paste(readLines(json_file), collapse = "")))
# CALLS
n = length(json_data$optionChain$result[[1]]$options[[1]]$calls)
if (n < 1) next
calls = matrix(ncol = 6, nrow = n)
for(i in 1:n) calls[,2][i] = json_data$optionChain$result[[1]]$options[[1]]$calls[[i]]$strike$raw
Cl.price = json_data$optionChain$result[[1]]$quote$regularMarketPrice
x <- which.min(abs((calls[,2]/Cl.price) -1))
calls = calls[x,]
calls[1] = Cl.price
calls[3] = json_data$optionChain$result[[1]]$options[[1]]$calls[[x]]$lastPrice$raw
calls[4] = json_data$optionChain$result[[1]]$options[[1]]$calls[[x]]$volume$raw
calls[5] = json_data$optionChain$result[[1]]$options[[1]]$calls[[x]]$openInterest$raw
calls[6] = json_data$optionChain$result[[1]]$options[[1]]$calls[[x]]$impliedVolatility$raw
# PUTS
n = length(json_data$optionChain$result[[1]]$options[[1]]$puts)
if(n < 1) next
puts = matrix(ncol = 5, nrow = n)
for(i in 1:n) puts[,1][i] = json_data$optionChain$result[[1]]$options[[1]]$puts[[i]]$strike$raw
x <- which.min(abs((puts[,1]/Cl.price) - 0.95))
puts = puts[x,]
puts[2] = json_data$optionChain$result[[1]]$options[[1]]$puts[[x]]$lastPrice$raw
puts[3] = json_data$optionChain$result[[1]]$options[[1]]$puts[[x]]$volume$raw
puts[4] = json_data$optionChain$result[[1]]$options[[1]]$puts[[x]]$openInterest$raw
puts[5] = json_data$optionChain$result[[1]]$options[[1]]$puts[[x]]$impliedVolatility$raw
options[u,] = c(calls, puts)
}
return(options)
}
# Define stocks and gather options data
date = '2017-04-21'
symbols <- c('DIS','CAT','TSLA')
daily.options = as.data.frame(get.options(symbols, date))
which(is.na(daily.options))
skew.raw = daily.options$put_ImpVoli - daily.options$call_ImpVoli # SKEW(i,t)
options[i,] <- skew.raw
write.table(options, 'DISCATTSLA', sep = ",")
options = read.table('DISCATTSLA', sep = ",")**
I’m following this code because I read the paper by Rhui Zhao but in the paper I did not talk about how to implement the skew volatility on a software and then I was able to find this code on github.