what does ky_data.MAGA[,6] look like? Can you create fake data of the same dimension and run rwf? For example, I don't know anything about your data so I created a 10x60 matrix full of random values then used the same logic you used, and it ran:
library(forecast)
m <- matrix(data=rnorm(60),nrow=10,ncol=6)
m[,6]
#> [1] -0.9322692 0.3841381 0.4401088 -0.3403564 -0.6407844 0.6065468
#> [7] -1.1301758 1.4890970 -1.0470580 -0.4042256
rwf(m[,6],h=41,drift=T,level=c(90,95),fan = FALSE,lambda = NULL)
#> Point Forecast Lo 90 Hi 90 Lo 95 Hi 95
#> 11 -0.345554131 -3.117825 2.426716 -3.648918 2.957810
#> 12 -0.286882624 -4.379882 3.806117 -5.163992 4.590227
#> 13 -0.228211118 -5.443715 4.987292 -6.442867 5.986445
#> 14 -0.169539612 -6.417098 6.078019 -7.613965 7.274886
#> 15 -0.110868106 -7.338881 7.117145 -8.723577 8.501841
#> 16 -0.052196600 -8.227650 8.123257 -9.793850 9.689457
#> 17 0.006474907 -9.093722 9.106672 -10.837078 10.850028
#> 18 0.065146413 -9.943390 10.073683 -11.860760 11.991053
#> 19 0.123817919 -10.780754 11.028390 -12.869781 13.117417
#> 20 0.182489425 -11.608620 11.973599 -13.867484 14.232463
#> 21 0.241160931 -12.428981 12.911303 -14.856244 15.338566
#> 22 0.299832437 -13.243299 13.842964 -15.837804 16.437469
#> 23 0.358503944 -14.052673 14.769681 -16.813472 17.530480
#> 24 0.417175450 -14.857944 15.692295 -17.784253 18.618603
#> 25 0.475846956 -15.659773 16.611467 -18.750930 19.702624
#> 26 0.534518462 -16.458682 17.527719 -19.714129 20.783166
#> 27 0.593189968 -17.255092 18.441472 -20.674349 21.860729
#> 28 0.651861475 -18.049345 19.353068 -21.632001 22.935724
#> 29 0.710532981 -18.841725 20.262791 -22.587419 24.008485
#> 30 0.769204487 -19.632465 21.170874 -23.540884 25.079293
#> 31 0.827875993 -20.421762 22.077514 -24.492630 26.148382
#> 32 0.886547499 -21.209783 22.982878 -25.442854 27.215949
#> 33 0.945219006 -21.996668 23.887106 -26.391725 28.282163
#> 34 1.003890512 -22.782538 24.790319 -27.339388 29.347169
#> 35 1.062562018 -23.567499 25.692623 -28.285966 30.411090
#> 36 1.121233524 -24.351640 26.594107 -29.231567 31.474034
#> 37 1.179905030 -25.135040 27.494850 -30.176286 32.536096
#> 38 1.238576537 -25.917769 28.394922 -31.120205 33.597358
#> 39 1.297248043 -26.699886 29.294382 -32.063394 34.657891
#> 40 1.355919549 -27.481445 30.193284 -33.005920 35.717759
#> 41 1.414591055 -28.262494 31.091676 -33.947836 36.777019
#> 42 1.473262561 -29.043074 31.989599 -34.889195 37.835720
#> 43 1.531934067 -29.823224 32.887092 -35.830041 38.893909
#> 44 1.590605574 -30.602976 33.784187 -36.770413 39.951624
#> 45 1.649277080 -31.382362 34.680916 -37.710348 41.008903
#> 46 1.707948586 -32.161408 35.577305 -38.649879 42.065776
#> 47 1.766620092 -32.940139 36.473379 -39.589034 43.122275
#> 48 1.825291598 -33.718578 37.369161 -40.527841 44.178424
#> 49 1.883963105 -34.496744 38.264670 -41.466323 45.234249
#> 50 1.942634611 -35.274655 39.159925 -42.404502 46.289771
#> 51 2.001306117 -36.052330 40.054942 -43.342398 47.345010