Example stock beta using a modified script from Daniel Khosravi: http://danialk.github.io/blog/2015/12/19/calculating-stocks-beta-using-r/

thanksKhosravi.R

#  http://danialk.github.io/blog/2015/12/19/calculating-stocks-beta-using-r/
# https://finance.yahoo.com/quote/NFLX/history?period1=1358668800&period2=1491379200&interval=1d&filter=history&frequency=1d
# https://finance.yahoo.com/quote/%5EGSPC/history?period1=1358668800&period2=1491379200&interval=1d&filter=history&frequency=1d
nflx <- read.csv('nflx_2013-01-20_2017-04-05.csv'
                 , sep = ','
                 , header = T
                 , stringsAsFactors =  F)

spx <- read.csv('gspc_2013-01-20_2017-04-05.csv'
                 , sep = ','
                 , header = T
                 , stringsAsFactors =  F)

# Making sure R knows the Date column is Date
nflx$Date <- as.Date(nflx$Date)
spx$Date <- as.Date(spx$Date)
range <- nflx$Date == spx$Date

# Extract daily closing prices
nflxPrices <- nflx$Close[range]
spxPrices <- spx$Close[range]

# Calculating the daily return, e.g (x2-x1)/x1
nflxReturns <- ( nflxPrices[1:(length(nflxPrices) - 1)] - nflxPrices[2:length(nflxPrices)] ) / nflxPrices[2:length(nflxPrices)]
spxReturns <- ( spxPrices[1:(length(spxPrices) - 1)] - spxPrices[2:length(spxPrices)] ) / spxPrices[2:length(spxPrices)]

# y ~ x is our formula
fit <- lm(nflxReturns ~ spxReturns)
result <- summary(fit)
# summary gives us a lot of useful information, but we're mostly in beta value !!
beta <- result$coefficients[2,1]
print(beta)

# Call (from 2013-01-20 to 2017-04-05)
# lm(formula = nflxReturns ~ spxReturns)

# Residuals:
#     Min       1Q   Median       3Q      Max 
# -0.86066 -0.01247 -0.00150  0.01025  0.42093 

# Coefficients:
#            Estimate Std. Error t value Pr(>|t|)    
# (Intercept) 0.001292   0.001232   1.048    0.295    
# spxReturns  1.308468   0.155087   8.437   <2e-16 ***
# ---
# Signif. codes:  
# 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Residual standard error: 0.04 on 1056 degrees of freedom
# Multiple R-squared:  0.06315,	Adjusted R-squared:  0.06226 
# F-statistic: 71.18 on 1 and 1056 DF,  p-value: < 2.2e-16

# NFLX BETA ~ 1.308468

# Call (from 2012-01-01 to 2017-04-05)
# lm(formula = nflxReturns ~ spxReturns)

# Residuals:
#     Min       1Q   Median       3Q      Max 
# -0.86050 -0.01334 -0.00160  0.01107  0.42114 

# Coefficients:
#            Estimate Std. Error t value Pr(>|t|)    
# (Intercept) 0.001085   0.001111   0.977    0.329    
# spxReturns  1.245764   0.139271   8.945   <2e-16 ***

# NFLX BETA ~ 1.2457