Summary Statistics and Summary Statistics by Year:

sum1 <-  j %>% summarize(n=length(v1), mean.v1 = mean(v1), mean.v2 = mean(v2),
                 sd.v1=sd(v1), sd.v2=sd(v2)
 )
kable(sum1, digits=1)
n mean.v1 mean.v2 sd.v1 sd.v2
43 6.2 8 1.9 2.3 
sum2 <- j %>% group_by(year) %>% summarize(n=length(v1), mean.v1 = mean(v1), mean.v2 = mean(v2),
                                   sd.v1=sd(v1), sd.v2=sd(v2)
)
kable(sum2, digits=1)
year n mean.v1 mean.v2 sd.v1 sd.v2
2011 10 5.7 7.0 1.8 2.6
2012 17 6.7 8.2 2.0 2.3
2013 3 7.3 7.3 1.2 0.6
2016 13 5.7 8.7 2.1 2.3

A Scatter Plot with points colored by year:

ggplot(j, aes(x=v1, y=v2, color=as.factor(year)))+geom_jitter()

A Scatter Plot with a best fit line:

Correlation and Slope of the Best Fit Line:

r <- with(j, cor(v1, v2))
slope <- r *with(j, sd(v2)/sd(v1))
r; slope
## [1] 0.1891664
## [1] 0.2249342
lm(v2 ~ v1, data=j)
## 
## Call:
## lm(formula = v2 ~ v1, data = j)
## 
## Coefficients:
## (Intercept)           v1  
##      6.6266       0.2249