What is the equivalent of sumproduct function of Excel for two vectors in R?

The sumproduct function in Excel multiply each value of two or more arrays with the corresponding values then add the sum of the result. For example, if we have 1, 2 in A1, A2 in Excel and 2, 2 in B1 and B2 then sumproduct will multiply 1*2 and 2*2 then take the summation of those two multiplications. In R, we have crossprod function for the same.
 Live Demo
x1<-rnorm(50,1.5)
x1
[1] 1.99359076  1.88748588 0.72095131 3.10185637 -0.90790685 0.51694935
[7] -0.05495242 1.49991243 2.54843446 2.79770387 -0.59328378 1.44738196
[13] 2.31391231 0.92844811 2.45329835 0.70609159 2.23518856  0.61631980
[19] 2.75137005 1.85486279 2.05531035 2.20960910 0.20037395  2.68390848
[25] 1.40389120 0.17282489 0.36633216 2.18996730 -0.10523904 -0.11863699
[31] 1.39480555 3.04977324 4.51243878 2.50746627 2.03817155  3.08036664
[37] 0.39284354 2.59858026 2.77797656 0.94338344 1.03293175  1.46812123
[43] 2.40907064 1.61772641 6.14482972 1.10566435 0.57390910  0.87722282
[49] 1.62433138 1.80906998
 Live Demo
y1<-rnorm(50,2.5)
y1
[1] 1.8783136 2.7331609 4.7371202 3.9985253 2.8102108 3.7087233 1.8897202
[8] 2.3324918 2.3859289 3.6085963 1.2847904 2.9825826 1.8944861 2.8596306
[15] 4.1415533 2.7008292 1.5199610 1.5111808 2.2279718 0.2254175 3.3921581
[22] 2.9906191 1.1403147 2.6683893 3.7104668 2.4567271 4.1465060 2.4379809
[29] 3.1013512 2.5057454 1.3899342 4.0594080 2.9544847 2.9267532 1.5415416
[36] 2.8127391 1.6027450 2.1677937 2.7763609 2.2713097 3.1494273 4.1289289
[43] 2.8311815 2.6957011 2.3890127 1.3183123 1.9293547 3.3658244 2.2184533
[50] 2.1856964
crossprod(x1,y1)
[,1]
[1,] 221.1997
 Live Demo
x2<-rpois(100,2)
x2
[1] 2 0 1 2 1 0 2 0 1 1 2 2 3 2 3 1 2 4 4 1 0 3 2 2 0 2 0 2 1 1 4 4 3 2 0 0 1
[38] 3 2 0 1 2 2 1 6 2 3 3 3 0 3 5 1 2 4 0 2 0 0 3 2 1 1 2 2 3 1 5 2 3 3 1 2 4
[75] 3 2 3 2 4 2 1 1 4 3 1 3 3 1 1 2 1 3 0 3 4 1 3 1 2 3
 Live Demo
y2<-rpois(100,5)
y2
[1] 5 6 3 4 5 3 8 7 6 4 6 7 5 7 6 10 6 6 4 2 1 1 3 4 4
[26] 7 4 6 8 6 5 4 6 7 5 4 7 6 6 3 5 5 7 6 6 5 4 5 3 2
[51] 3 4 10 5 6 3 7 5 6 4 5 3 7 2 8 3 6 2 4 3 4 9 2 6 4
[76] 3 1 6 5 4 5 5 5 3 4 7 6 4 4 5 4 2 8 7 3 5 4 6 4 7
crossprod(x2,y2)
[,1]
[1,] 954
 Live Demo
x3<-runif(50,2,5)
x3
[1] 4.066125 4.753823 4.404160 4.986185 4.123433 4.390743 2.763934 2.751122
[9] 2.188381 4.402746 3.286630 4.485267 3.004045 4.475638 4.382088 4.394354
[17] 2.014214 3.078435 2.781432 2.611495 2.672326 4.700600 4.553896 4.691448
[25] 4.441690 4.278304 4.523280 2.590837 4.312463 4.180315 4.868710 2.495915
[33] 4.608362 2.661993 4.313882 2.302652 4.491288 2.926825 2.148216 4.759026
[41] 2.396023 3.255598 4.739009 3.542550 2.817052 2.977642 3.878959 4.867458
[49] 2.231442 4.950959
 Live Demo
y3<-runif(50,2,10)
y3
[1] 7.845753 4.834397 4.995095 6.552002 4.471001 9.203054 6.124359 6.622755
[9] 3.877650 7.280489 7.774599 2.747195 9.551918 9.056671 6.434740 7.348743
[17] 4.792461 9.166887 8.958969 7.335471 8.918295 5.435485 2.191420 2.603769
[25] 6.093804 8.087972 8.973729 4.278366 9.924628 3.864139 9.443934 3.410300
[33] 7.659189 5.001502 4.241095 9.632373 8.544051 7.156040 4.299191 8.890373
[41] 7.686046 8.845440 7.641526 3.969289 3.926754 3.330756 9.740170 4.345852
[49] 8.746772 5.178304
crossprod(x3,y3)
[,1]
[1,] 1214.499
 Live Demo
x4<-sample(1:10,100,replace=TRUE)
x4
[1] 2 4 10 7 6 6 1 7 7 5 10 1 7 2 3 4 4 3 4 10 6 8 5 1 8
[26] 9 1 8 3 1 7 6 5 8 8 8 2 7 6 6 9 3 8 2 9 7 9 10 6 7
[51] 1 10 1 5 6 1 5 8 9 1 1 5 1 10 1 2 7 1 8 3 5 7 1 10 1
[76] 2 9 10 4 9 3 7 10 8 7 6 4 10 5 2 5 6 5 9 2 3 6 5 1 5
 Live Demo
y4<-sample(1:50,100,replace=TRUE)
y4
[1] 17 37 38 31 6 13 24 38 46 35 14 44 37 40 12 15 10 31 29 33 9 47 49 40 34
[26] 40 9 1 21 30 43 27 31 16 29 2 21 9 45 42 44 6 14 42 9 12 10 42 39 11
[51] 50 31 2 12 47 3 41 19 45 21 42 20 26 13 24 41 38 6 30 22 45 44 22 17 32
[76] 50 43 3 47 34 28 18 12 11 3 33 27 26 35 5 17 36 22 44 33 19 24 32 44 13
crossprod(x4,y4)
[,1]
[1,] 14325
 Live Demo
x5<-runif(50,-5,-2)
x5
[1] -2.864014 -3.920292 -2.697349 -3.547204 -3.203409 -4.408416 -4.046039
[8] -2.307824 -2.026316 -2.471796 -2.572194 -3.986755 -4.363628 -3.635759
[15] -2.082331 -4.251178 -4.681768 -3.034604 -3.418636 -3.981439 -2.494370
[22] -4.450702 -3.123108 -2.693205 -4.557498 -3.379469 -2.610095 -4.637020
[29] -3.876702 -4.587460 -3.603890 -2.903007 -4.404766 -2.155481 -3.569201
[36] -2.292755 -3.353520 -3.583467 -4.595250 -2.439884 -3.363896 -2.014880
[43] -4.517038 -4.252916 -3.634848 -2.152265 -3.678823 -2.939615 -2.467037
[50] -4.190786
 Live Demo
y5<-runif(50,-3,-2)
y5
[1] -2.915089 -2.658054 -2.713909 -2.852047 -2.730487 -2.856746 -2.276689
[8] -2.369880 -2.197784 -2.566198 -2.856807 -2.027856 -2.516569 -2.975913
[15] -2.684511 -2.309696 -2.635481 -2.306488 -2.903054 -2.646775 -2.825036
[22] -2.049512 -2.873639 -2.753928 -2.289014 -2.981482 -2.144614 -2.532343
[29] -2.639120 -2.292755 -2.441756 -2.450644 -2.729074 -2.352912 -2.022934
[36] -2.920817 -2.479174 -2.324256 -2.309613 -2.697988 -2.990217 -2.537785
[43] -2.087993 -2.075586 -2.267299 -2.999600 -2.562218 -2.137688 -2.881335
[50] -2.986426
crossprod(x5,y5)
[,1]
[1,] 430.8493