nMDS in RStudio v 1.3.1093

I just loaded this version of RStudio, and now I can't get my incidence data to plot using the metaMDS command in the vegan package. I can run the metaMDS to get the stress values in .1093 and I can still create the plot using the same data in version 1.3.1073. I have tried configuring the pch values to what is suggested in the help manual, but nothing is working. To troubleshoot, I was able to create a scatterplot using the cars example data. After configuring the point colors and shapes, my command line is <plot(namenmds$points, col=co[name_2$x],pch=shape[name_2$x],cex=1.2)> This always works in .1073 after running metaMDS. I'm at a loss, please help.

Without data, it's not possible to see what variables are being plotted. Try working from this

suppressPackageStartupMessages({
  library(vegan)
})

## The recommended way of running NMDS (Minchin 1987)
##
data(dune)
# Global NMDS using monoMDS
sol <- metaMDS(dune)
#> Run 0 stress 0.1192678 
#> Run 1 stress 0.1922241 
#> Run 2 stress 0.1183186 
#> ... New best solution
#> ... Procrustes: rmse 0.02027044  max resid 0.06496245 
#> Run 3 stress 0.1192678 
#> Run 4 stress 0.1922241 
#> Run 5 stress 0.192224 
#> Run 6 stress 0.2075713 
#> Run 7 stress 0.1192679 
#> Run 8 stress 0.1183186 
#> ... Procrustes: rmse 3.656377e-06  max resid 7.680827e-06 
#> ... Similar to previous best
#> Run 9 stress 0.1183186 
#> ... Procrustes: rmse 1.743913e-06  max resid 5.607072e-06 
#> ... Similar to previous best
#> Run 10 stress 0.1183186 
#> ... Procrustes: rmse 5.908868e-06  max resid 1.872976e-05 
#> ... Similar to previous best
#> Run 11 stress 0.1192678 
#> Run 12 stress 0.1192678 
#> Run 13 stress 0.1886532 
#> Run 14 stress 0.1192678 
#> Run 15 stress 0.1809577 
#> Run 16 stress 0.1192679 
#> Run 17 stress 0.1808911 
#> Run 18 stress 0.1183186 
#> ... Procrustes: rmse 8.668069e-06  max resid 2.714218e-05 
#> ... Similar to previous best
#> Run 19 stress 0.1808911 
#> Run 20 stress 0.1183186 
#> ... Procrustes: rmse 2.455886e-06  max resid 8.038281e-06 
#> ... Similar to previous best
#> *** Solution reached
sol
#> 
#> Call:
#> metaMDS(comm = dune) 
#> 
#> global Multidimensional Scaling using monoMDS
#> 
#> Data:     dune 
#> Distance: bray 
#> 
#> Dimensions: 2 
#> Stress:     0.1183186 
#> Stress type 1, weak ties
#> Two convergent solutions found after 20 tries
#> Scaling: centring, PC rotation, halfchange scaling 
#> Species: expanded scores based on 'dune'
plot(sol, type="t")

## Start from previous best solution
sol <- metaMDS(dune, previous.best = sol)
#> Starting from 2-dimensional configuration
#> Run 0 stress 0.1183186 
#> Run 1 stress 0.1192678 
#> Run 2 stress 0.1183186 
#> ... Procrustes: rmse 8.397218e-06  max resid 2.160064e-05 
#> ... Similar to previous best
#> Run 3 stress 0.1192678 
#> Run 4 stress 0.1192678 
#> Run 5 stress 0.1183186 
#> ... New best solution
#> ... Procrustes: rmse 2.69204e-06  max resid 8.154677e-06 
#> ... Similar to previous best
#> Run 6 stress 0.1192679 
#> Run 7 stress 0.1183186 
#> ... Procrustes: rmse 6.199676e-06  max resid 2.021768e-05 
#> ... Similar to previous best
#> Run 8 stress 0.1183186 
#> ... Procrustes: rmse 3.444356e-06  max resid 1.167095e-05 
#> ... Similar to previous best
#> Run 9 stress 0.1192678 
#> Run 10 stress 0.1192678 
#> Run 11 stress 0.1192678 
#> Run 12 stress 0.1183186 
#> ... Procrustes: rmse 3.667454e-06  max resid 1.242743e-05 
#> ... Similar to previous best
#> Run 13 stress 0.1192678 
#> Run 14 stress 0.1809578 
#> Run 15 stress 0.1192678 
#> Run 16 stress 0.2075713 
#> Run 17 stress 0.1922241 
#> Run 18 stress 0.1192678 
#> Run 19 stress 0.1192679 
#> Run 20 stress 0.1183186 
#> ... Procrustes: rmse 1.418263e-06  max resid 2.796378e-06 
#> ... Similar to previous best
#> *** Solution reached
## Use Arrhenius exponent 'z' as a binary dissimilarity measure
sol <- metaMDS(dune, distfun = betadiver, distance = "z")
#> Run 0 stress 0.1067169 
#> Run 1 stress 0.1073148 
#> Run 2 stress 0.1736237 
#> Run 3 stress 0.1814422 
#> Run 4 stress 0.107471 
#> Run 5 stress 0.1067169 
#> ... New best solution
#> ... Procrustes: rmse 2.836292e-06  max resid 7.464074e-06 
#> ... Similar to previous best
#> Run 6 stress 0.2131677 
#> Run 7 stress 0.1067169 
#> ... Procrustes: rmse 2.666581e-06  max resid 6.15736e-06 
#> ... Similar to previous best
#> Run 8 stress 0.1073148 
#> Run 9 stress 0.1073148 
#> Run 10 stress 0.107471 
#> Run 11 stress 0.1067169 
#> ... Procrustes: rmse 3.798976e-06  max resid 1.251695e-05 
#> ... Similar to previous best
#> Run 12 stress 0.186847 
#> Run 13 stress 0.1067169 
#> ... Procrustes: rmse 8.847931e-06  max resid 2.444281e-05 
#> ... Similar to previous best
#> Run 14 stress 0.1067169 
#> ... Procrustes: rmse 4.341968e-06  max resid 1.047293e-05 
#> ... Similar to previous best
#> Run 15 stress 0.107471 
#> Run 16 stress 0.1067169 
#> ... Procrustes: rmse 4.059188e-06  max resid 9.843856e-06 
#> ... Similar to previous best
#> Run 17 stress 0.1824921 
#> Run 18 stress 0.1073148 
#> Run 19 stress 0.1073148 
#> Run 20 stress 0.1644742 
#> *** Solution reached
sol
#> 
#> Call:
#> metaMDS(comm = dune, distance = "z", distfun = betadiver) 
#> 
#> global Multidimensional Scaling using monoMDS
#> 
#> Data:     dune 
#> Distance: beta.z 
#> 
#> Dimensions: 2 
#> Stress:     0.1067169 
#> Stress type 1, weak ties
#> Two convergent solutions found after 20 tries
#> Scaling: centring, PC rotation, halfchange scaling 
#> Species: expanded scores based on 'dune'

plot(sol$points, col=sol$species[,1],pch=16,cex=4)

Created on 2021-01-15 by the reprex package (v0.3.0.9001)

Sure. It's encouraged. See the reprex FAQ. It doesn't have to be all your data, can be a standard dataset or made-up data. Whatever illustrates the issue.

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Thanks, I'll give it a try. I'm new to this forum, so I'm inferring from your comment that I can upload my data sheet here (?). Thanks.