This one is interesting

plot(((comb$rm1t+comb$lm1t+comb$nm1t)/3),(comb$rv5time+comb$ra5time))

> cor.test(((comb$rm1t+comb$lm1t+comb$nm1t)/3),(comb$rv5time+comb$ra5time))

        Pearson's product-moment correlation

data:  ((comb$rm1t + comb$lm1t + comb$nm1t)/3) and (comb$rv5time + comb$ra5time)
t = 5.6931, df = 60, p-value = 3.973e-07
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4019090 0.7334968
sample estimates:
     cor 
0.592227 

Quite reasonably so, the more data points you have, the more powerful the correlation:

cor.test(((comb$rm1t+comb$lm1t+comb$nm1t+comb$rm3t+comb$lm3t+comb$nm3t)),(comb$ra5time+comb$rv5time))

        Pearson's product-moment correlation

data:  ((comb$rm1t + comb$lm1t + comb$nm1t + comb$rm3t + comb$lm3t +  and (comb$ra5time + comb$rv5time)    comb$nm3t)) and (comb$ra5time + comb$rv5time)
t = 6.1225, df = 57, p-value = 9.054e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4456685 0.7628949
sample estimates:
      cor 
0.6298656 

Interestingly, this is stronger for ra than combined:

> cor.test(((comb$rm1t+comb$lm1t+comb$nm1t+comb$rm3t+comb$lm3t+comb$nm3t)),(comb$ra5time))

        Pearson's product-moment correlation

data:  ((comb$rm1t + comb$lm1t + comb$nm1t + comb$rm3t + comb$lm3t +  and (comb$ra5time)    comb$nm3t)) and (comb$ra5time)
t = 6.6186, df = 58, p-value = 1.278e-08
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.4823875 0.7799717
sample estimates:
      cor 
0.6559632