A very quick and surface-level analysis of wines using the wines.com API

wines.R

library(rjson)
apikey = 'REDACTED' # please get one from wines.com
url <- paste('http://services.wine.com/api/beta2/service.svc/json/catalog?filter=categories(614)&offset=0&size=100&apikey=',apikey,sep="")
document <- fromJSON(file=url, method='C')
# I am pulling down light and crispy white wine data above

ds <- matrix(0, 100, 3)
for (i in 1:length(document$Products$List)) {
  ds[i,]  <- c(as.numeric(document$Products$List[[i]]$PriceRetail),
               as.numeric(document$Products$List[[i]]$Ratings$HighestScore),
               as.numeric(document$Products$List[[i]]$Vintage))
}

colnames(ds) <- c("price", "rating", "vintage")
ds <- as.data.frame(ds)
ds <- ds[which(ds$rating != 0),]

lm1 <- lm(price ~ rating, data=ds, na.action = na.omit)
summary(lm1)

# Call:
#   lm(formula = price ~ rating, data = ds, na.action = na.omit)
# 
# Residuals:
#   Min      1Q  Median      3Q     Max 
# -27.580  -4.516  -2.241   2.585  80.686 
# 
# Coefficients:
#   Estimate Std. Error t value Pr(>|t|)    
# (Intercept) -455.690    103.979  -4.383 4.70e-05 ***
#   rating         5.266      1.139   4.624 2.01e-05 ***
#   ---
#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 14.71 on 61 degrees of freedom
# Multiple R-squared:  0.2595,	Adjusted R-squared:  0.2474 
# F-statistic: 21.38 on 1 and 61 DF,  p-value: 2.007e-05

# A one unit increase in rating amount to a $5 increase in price
plot(ds$rating, ds$price)
abline(lm1)

lm2 <- lm(rating ~ vintage, data=ds, na.action = na.omit)
summary(lm2)
# Call:
#   lm(formula = rating ~ vintage, data = ds, na.action = na.omit)
# 
# Residuals:
#   Min      1Q  Median      3Q     Max 
# -2.4987 -1.0194 -0.4987  0.9806  4.0220 
# 
# Coefficients:
#   Estimate Std. Error t value Pr(>|t|)  
# (Intercept) 1056.7887   529.4167   1.996   0.0504 .
# vintage       -0.4793     0.2628  -1.824   0.0731 .
# ---
#   Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# 
# Residual standard error: 1.611 on 61 degrees of freedom
# Multiple R-squared:  0.0517,	Adjusted R-squared:  0.03616 
# F-statistic: 3.326 on 1 and 61 DF,  p-value: 0.0731

# A one unit increase in vintage year amounts to losing a half rating point