Improve my NMDS graph design

Hello everyone, my level / knowledge in R is super mega beginner, and it has cost me a lot to perform my statistical analyzes. It is not for vanity, but I would like to improve my graphics. Do you know what I could do to obtain a graph similar to the one attached below? Thanks in advance.

library(readxl)
Family <- read_excel("~/RSTUDIOPICRUST/NMDS.xlsx")


#NORMALIZATION OF RAW READ TABLE LOG2
#OTUS en filas, columnas son las muestras, es al reves que para el resto de pasos
data<- Family



replicates <- as.data.frame(colnames(data)[-1])
colnames(replicates) <- "replicates"
attach(Family)
rwnames <- OTU
data <- as.matrix(data[,-1])
rownames(data) <- rwnames
data[is.na(data)] <- 0
library(DESeq2)
#> Loading required package: S4Vectors
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#> Loading required package: parallel
#> 
#> Attaching package: 'BiocGenerics'
#> The following objects are masked from 'package:parallel':
#> 
#>     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
#>     clusterExport, clusterMap, parApply, parCapply, parLapply,
#>     parLapplyLB, parRapply, parSapply, parSapplyLB
#> The following objects are masked from 'package:stats':
#> 
#>     IQR, mad, sd, var, xtabs
#> The following objects are masked from 'package:base':
#> 
#>     anyDuplicated, append, as.data.frame, basename, cbind, colMeans,
#>     colnames, colSums, dirname, do.call, duplicated, eval, evalq,
#>     Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply,
#>     lengths, Map, mapply, match, mget, order, paste, pmax, pmax.int,
#>     pmin, pmin.int, Position, rank, rbind, Reduce, rowMeans, rownames,
#>     rowSums, sapply, setdiff, sort, table, tapply, union, unique,
#>     unsplit, which, which.max, which.min
#> 
#> Attaching package: 'S4Vectors'
#> The following object is masked from 'package:base':
#> 
#>     expand.grid
#> Loading required package: IRanges
#> 
#> Attaching package: 'IRanges'
#> The following object is masked from 'package:grDevices':
#> 
#>     windows
#> Loading required package: GenomicRanges
#> Loading required package: GenomeInfoDb
#> Loading required package: SummarizedExperiment
#> Loading required package: Biobase
#> Welcome to Bioconductor
#> 
#>     Vignettes contain introductory material; view with
#>     'browseVignettes()'. To cite Bioconductor, see
#>     'citation("Biobase")', and for packages 'citation("pkgname")'.
#> Loading required package: DelayedArray
#> Loading required package: matrixStats
#> 
#> Attaching package: 'matrixStats'
#> The following objects are masked from 'package:Biobase':
#> 
#>     anyMissing, rowMedians
#> Loading required package: BiocParallel
#> 
#> Attaching package: 'DelayedArray'
#> The following objects are masked from 'package:matrixStats':
#> 
#>     colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges
#> The following objects are masked from 'package:base':
#> 
#>     aperm, apply
dds <- DESeqDataSetFromMatrix(data, replicates, ~ replicates)
#> converting counts to integer mode
cts <- counts(dds)
geoMeans <- apply(cts, 1, function(row) if (all(row == 0)) 0 else exp(mean(log(row[row != 0]))))
dds <- estimateSizeFactors(dds, geoMeans=geoMeans)
norm <- counts(dds, normalized=TRUE)
logcounts <- log2( counts(dds, normalized=TRUE) + 1 )# in case you want a log transformed normalized count
write.csv(logcounts, file = "~/RSTUDIOPICRUST/NDMS_NORMALIZADOS.csv")
#> Warning in file(file, ifelse(append, "a", "w")): no fue posible abrir el archivo
#> 'C:/Users/Osiris Díaz Torres/Documents/RSTUDIOPICRUST/NDMS_NORMALIZADOS.csv':
#> Permission denied
#> Error in file(file, ifelse(append, "a", "w")): no se puede abrir la conexión
#log counts are like variance stabilized counts (https://support.bioconductor.org/p/76712/)

#DATA ANALYSIS
#Introduce two tables, one has to have the samples in the first column and the
#species in the first line with the reads per sample.
#The second table is with environmental parameters, it has to have the samples
#in the first column and the environmental parameters in the first line.
#You can have the environmental parameters as presence/absence with 1 and 0 or 
#with numbers.
library(readxl)
Family <- read_excel("~/RSTUDIOPICRUST/NMDS_TRANSPU.xlsx")

#Family <- t(Family)
Metadata<- read.csv("~/RSTUDIOPICRUST/Metadata-final.csv", row.names=1)
attach(Family)
Family <- Family[,-1]
rownames(Family) <- SampleID
#> Warning: Setting row names on a tibble is deprecated.

Metadata$Month <- factor(Metadata$Month,
                         levels = c("July", "August", "September"))

#remove the rare microbes. It keeps only the microbes that are present in at least 10% of the samples
dim(Family)
#> [1]    96 10415

Family <- Family[,colMeans(Family) >=.1]

dim(Family)
#> [1]   96 8291

library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.5-6
#NMDS
#autotransform=F evita la transformaci?n de datos
#sratmax=0.999999 para que el an?lisis no se detenga antes de tiempo cuando se tienen muchos datos  
Family.mds <- metaMDS(Family, k=2, distance="bray", trymax=100, zerodist="add", autotransform=F)
#> Run 0 stress 0.1274936 
#> Run 1 stress 0.1274939 
#> ... Procrustes: rmse 9.269493e-05  max resid 0.0005469849 
#> ... Similar to previous best
#> Run 2 stress 0.1274941 
#> ... Procrustes: rmse 0.0001485437  max resid 0.0007761965 
#> ... Similar to previous best
#> Run 3 stress 0.1274912 
#> ... New best solution
#> ... Procrustes: rmse 0.000960561  max resid 0.006058439 
#> ... Similar to previous best
#> Run 4 stress 0.1631234 
#> Run 5 stress 0.1631233 
#> Run 6 stress 0.1274918 
#> ... Procrustes: rmse 0.0002420235  max resid 0.001806596 
#> ... Similar to previous best
#> Run 7 stress 0.1606099 
#> Run 8 stress 0.1703349 
#> Run 9 stress 0.1594046 
#> Run 10 stress 0.1434982 
#> Run 11 stress 0.1454402 
#> Run 12 stress 0.1437091 
#> Run 13 stress 0.1454402 
#> Run 14 stress 0.1525878 
#> Run 15 stress 0.1274927 
#> ... Procrustes: rmse 0.00048292  max resid 0.004084371 
#> ... Similar to previous best
#> Run 16 stress 0.1274944 
#> ... Procrustes: rmse 0.001087916  max resid 0.006300639 
#> ... Similar to previous best
#> Run 17 stress 0.156991 
#> Run 18 stress 0.1800571 
#> Run 19 stress 0.1274936 
#> ... Procrustes: rmse 0.0009613467  max resid 0.006058546 
#> ... Similar to previous best
#> Run 20 stress 0.1274937 
#> ... Procrustes: rmse 0.0009853107  max resid 0.006129919 
#> ... Similar to previous best
#> *** Solution reached
stressplot(Family.mds)

#add the metadata information
##THIS HAS TO BE IN THE SAME ORDER IN THE METADATA FILE AND IN THE COUNTS FILE
colvec<- c("yellowgreen","turquoise4", "tomato2")
plot(Family.mds, type="n", xlim=c(-.5,.5), ylim=c(-0.6,0.6))
pl <-ordiellipse(Family.mds, Metadata$Month, kind="se", conf=0.95, lwd=2, col="gray30", label=T)



#plot with ggplot
data.scores <- as.data.frame(scores(Family.mds))
data.scores$site <- rownames(data.scores)
data.scores$grp <- Metadata$Month
head(data.scores)
#>         NMDS1        NMDS2 site    grp
#> 1 -0.14279213  0.001943053    1   July
#> 2 -0.12419134 -0.030650515    2   July
#> 3 -0.10328531 -0.119223304    3   July
#> 4 -0.09456066  0.107006047    4 August
#> 5  0.04013923  0.030698429    5 August
#> 6  0.08413096 -0.003885208    6 August
library(ggplot2)
NMDS <-  data.frame(MDS1 = Family.mds$points[,1], MDS2 = Family.mds$points[,2],group=Metadata$Month)
#NMDS.mean <- aggregate(NMDS[,1:2],list(group=group),mean)
veganCovEllipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100) 
{
  theta <- (0:npoints) * 2 * pi/npoints
  Circle <- cbind(cos(theta), sin(theta))
  t(center + scale * t(Circle %*% chol(cov)))
}
#attach(NMDS.mean)
df_ell <- data.frame()
for(g in levels(NMDS$group)){
  df_ell <- rbind(df_ell, cbind(as.data.frame(with(NMDS[NMDS$group==g,],
                                                   veganCovEllipse(pl[[g]]$cov,pl[[g]]$center,pl[[g]]$scale)))
                                ,group=g))
}


ggplot() + 
  geom_path(data=df_ell, aes(x=NMDS1, y=NMDS2,colour=group), size=1, linetype=2)+
  geom_point(data=data.scores,aes(x=NMDS1,y=NMDS2,colour=grp),size=1.5) + # add the point markers
  theme(legend.title = element_blank()) +
  ylab("NMDS2")+
  xlab("NMDS1")+
  #geom_text(data=data.scores,aes(x=NMDS1,y=NMDS2,label=site),size=6,vjust=0) + 
  scale_colour_manual(values=c("July" = "yellowgreen", "August" = "turquoise4", "September" = "pink")) 

Created on 2021-06-01 by the reprex package (v0.3.0)

I add my data so that it can be reproducible, it's also synthesized. It is only important to mention that I have an error, I did not know how to apply the function row.names = 1 in the metadata file, when the data is reproduced in this way, I'm sorry and thank you very much in advance.

Family <- tibble::tribble(
            ~SampleID,     ~K00370,     ~K00371,     ~K00374,     ~K02567,     ~K02568,     ~K00368,     ~K15864,     ~K04561,     ~K02305,     ~K00376,     ~K02585,     ~K02593,     ~K02596,     ~K02597,     ~K04488,     ~K15790,     ~K15861,     ~K02588,     ~K02586,     ~K02591,     ~K00531,     ~K10944,     ~K10945,     ~K10946,     ~K10535,     ~K00362,     ~K00363,     ~K03385,     ~K00367, ~K10534,     ~K00372,     ~K00360,     ~K00366,     ~K17877,     ~K01120,
                 "1A", 12.60826933, 12.59387294, 12.58242827, 8.658177059, 8.654818552, 12.39259191, 8.875738182, 13.30169117, 13.23015428, 12.40313895, 8.986755702,   8.7956609,   8.7956609, 9.649657193, 12.80857018, 8.637907728, 9.908449129, 8.915573375, 8.892945265, 8.912764195, 3.723758308, 3.915575591, 4.002697437, 4.373425552, 1.932860333, 13.03212083, 12.97064336, 5.793988069, 5.692819382,      0L, 11.21187223, 6.080985352, 6.139827357,           0, 6.196363174,
                 "1C", 10.85111666, 10.51628714, 10.47316521, 12.59133496, 12.59100967, 10.81480582, 6.640661761, 11.34807149, 10.67816214, 10.77641355, 9.010564151, 8.823865852, 8.901467628, 9.815606527, 11.28993854, 8.819429773, 9.883052886, 8.975107452,  8.96710843, 8.971113485,           0,           0,           0,           0,           0, 13.08434153, 13.07040844, 7.840243987, 7.965980112,      0L, 11.03644689,           0, 3.600508638,           0, 7.101635393,
                 "1D", 14.86428566, 14.85699297, 14.84651048, 8.732362469, 8.732362469, 11.77211189,  9.49327795, 12.05851906, 9.958018276, 10.80103387, 7.890709098,           0,   7.8576836,           0,  14.9651165,           0,  11.8931705, 7.890709098, 7.890709098, 7.890709098,           0,           0,           0,           0,           0, 15.31434467, 15.10448672, 7.954575106, 9.369882273,      0L, 12.99900903, 9.853011196, 7.189987452,           0, 9.655793912,
                 "1E",  11.0375149, 11.02563759, 10.85857454, 9.449081993, 9.365885069,  10.4609361, 8.994861357, 11.59181604, 10.57867658, 10.86525433, 9.336468717, 8.433274173, 8.640040373, 9.346919584, 12.89570338, 8.475161923, 8.281734733,  9.52620972, 9.352116764, 9.416376674, 4.058928093, 5.116206199, 5.148427408, 5.938243369, 3.265569708, 13.06931013, 11.97984879, 9.619675763, 9.284860642,      0L,  12.1506354, 7.929788004, 8.413523389, 1.359918286, 7.101224149,
                 "1F", 10.11131442, 10.13226188,  9.59041767, 9.312694087, 9.263115646, 10.82558104, 9.910083683, 11.87462456, 11.79567509, 11.20638875, 9.924723347, 9.301216597, 9.351282699, 10.25850935, 12.04002777, 9.125348724, 7.517278994, 9.888851117, 9.828271014, 9.845008917, 6.038077222,  6.82590961,  6.22870436, 7.679112526, 5.575229252, 11.17826569, 11.18830242, 8.629959046,  6.98840685,      0L, 11.07243943, 9.572438501, 8.242273249, 1.217887296,  6.65420049,
                 "1G", 9.674460458, 9.786108569, 9.424338792, 9.459911487, 9.439267486, 12.06450569, 9.836597751, 13.16834862, 13.14486284, 12.20565036, 10.02056228,  9.51450029,  9.55344182, 10.46910632, 11.52636421, 9.265885181, 7.556454349, 9.876631962,  9.81844183, 9.847830258,  4.18206088, 5.897309418, 5.501740779, 6.617577237, 4.393054339, 10.73415185, 10.65195313, 8.307809442, 4.393054339,      0L, 10.53312916, 8.268226781, 7.197634024, 1.280563118, 5.141764139,
                 "1H", 11.13164554, 11.13753508, 11.11412997, 8.358429761, 8.344239451,  11.7697941, 9.037626526,  12.7256916, 12.66836574, 11.82740417, 9.264479094, 8.511405023, 8.529434909, 9.392145317, 12.02810675, 6.605407729, 5.448841874, 9.051418179, 9.017327612, 9.030047864, 1.503665225, 3.640694665, 3.530423688, 4.130980108, 2.482604369, 11.46338527, 11.43281794, 9.512149513, 8.187288626,      0L,  9.81303876, 1.503665225, 8.556064165,           0, 7.538684939,
                 "2A", 9.936729753, 9.966825855, 9.678799275, 9.666943625, 9.646804153, 11.76982895, 9.896682653, 12.89677964, 12.84537157, 12.04014767, 10.21692639, 9.800123586, 9.825089342, 10.75721758, 11.77712663, 9.656429448,  7.82089312, 10.02742472, 10.00650343,  10.0038668, 3.822442496, 5.525944895, 5.207639494, 6.229847407, 4.026238555,  11.2124103, 11.23571212, 8.323228785, 6.339071682,      0L, 11.07256764, 9.015404785, 7.458923089, 0.342907156, 7.521356687,
                 "2B", 10.03797367, 10.48972259, 9.908996955, 10.35381509, 10.34327074, 11.74553691, 9.607964823, 12.96965286,  12.9996672, 12.35458675, 10.39612571, 10.07446524, 10.09537891, 11.05425242,  11.7826571, 9.984103388, 5.872607476, 10.42554338, 10.37827263,  10.3854404, 5.378641163, 6.027246822, 5.852057903, 6.650157365, 4.326170316, 11.16095852, 10.75929562, 9.481557201,  5.83121139,      0L, 10.70768899, 6.432136121, 7.657833318, 0.500112485, 4.803797853,
                 "2C",  10.1021309, 10.11534801, 9.919125231, 9.392338518, 9.370456601, 12.92440641, 9.619586736, 14.00078982, 13.97347911, 13.03827193, 9.867984033, 9.423371076, 9.454905899, 10.33964174, 11.73698176, 9.214061506, 7.741414612, 9.670220649, 9.642084891, 9.661236713, 3.148574103, 5.735104701, 5.750246569, 6.221490572, 3.678261287, 11.03231667, 11.01674834,  8.28437339, 6.839467423,      0L, 10.82760851, 8.602771221,  7.89236426, 0.643380395, 7.538328575,
                 "2D", 11.05615199, 11.07379325, 11.00635667, 9.007346856, 8.964430518, 10.56420436, 9.114538677, 11.64500702,  11.3385747, 11.26183102, 9.305824713, 8.747095943,  8.79687069, 9.679905969, 12.46847754,  8.49429229, 7.052864854, 9.237267286, 9.139121597, 9.196856236, 4.586244598, 6.314516801,  6.35705229, 6.869137642, 4.267408634, 11.75645523, 11.65888686, 7.994514985, 6.859307282,      0L, 10.81365271, 7.976439244, 7.651392497,           0, 6.907802637,
                 "2E", 11.13555413, 11.13267537, 11.09302763, 10.63177977, 10.62242889, 11.93663405, 9.197744277, 12.78255337, 12.52296656, 11.73013096, 7.584362739, 5.920828681, 6.805243723, 6.502936043, 12.38686885, 6.146354359, 9.230425941, 7.326674674, 7.231475903, 7.303457403, 1.546963788,           0,           0,           0, 0.714286675, 12.83914921,   12.616306, 7.743782277, 9.445863761,      0L, 11.83403931, 7.255870968, 6.666550302,           0, 9.260877781,
                 "2F",  10.5798746, 10.58467245, 10.51474914, 8.903874863, 8.849099905, 12.39306664, 9.682786329, 13.33395894, 13.25018442, 12.46898421, 8.911532812, 8.573074632, 8.648333823, 9.554157178, 12.65582839, 6.367690307, 4.860095264, 9.018234003, 8.930501712, 8.989578633,  2.27022436, 4.419172626, 4.419172626,  4.92206419, 2.608488015, 11.80943425, 11.70424366, 9.984532993, 8.911532812,      0L, 11.07102804, 4.860095264, 8.110230974,           0, 8.523962638,
                 "2H", 10.13751737, 10.13408134, 9.804426603,  7.69264147, 7.548657741, 12.29649025, 8.918164107, 13.29192095, 13.20868481, 12.25807231,  9.07691191, 7.906448711, 8.111380944, 8.756247661, 12.31519584, 7.534826767, 7.047646656, 9.328118647, 9.136509849,  9.22960214, 4.650531707, 6.998105354, 6.809945662, 7.714278939, 5.332710084, 11.96522201, 10.85078306, 9.577010383, 7.076574887,      0L,   11.405516, 7.823550376, 9.283324739, 1.478384964, 6.715012564
            )

  
Metadata<- data.frame (tibble::tribble(
  ~SampleID, ~SamplingPoint,         ~Depth,      ~Month,
  "1A",         "CEA1",        "80 cm",      "July",
  "1C",         "CEA4", "Interstitial",      "July",
  "1D",         "CEA1",        "80 cm",    "August",
  "1E",         "CEA3",        "80 cm",    "August",
  "1F",         "CEA5",        "80 cm",    "August",
  "1G",         "CEA2",        "80 cm", "September",
  "1H",         "CEA4",        "80 cm", "September",
  "2A",         "CEA1",        "80 cm",      "July",
  "2B",         "CEA2", "Interstitial",      "July",
  "2C",         "CEA4", "Interstitial",      "July",
  "2D",         "CEA1",        "80 cm",    "August",
  "2E",         "CEA3",        "80 cm",    "August",
  "2F",         "CEA5",        "80 cm",    "August",
  "2H",         "CEA4",        "80 cm", "September")
  
)


attach(Family)
Family <- Family[,-1]
rownames(Family) <- SampleID
#> Warning: Setting row names on a tibble is deprecated.

Metadata$Month <- factor(Metadata$Month,
                         levels = c("July", "August", "September"))


#remove the rare microbes. It keeps only the microbes that are present in at least 10% of the samples
dim(Family)
#> [1] 14 35

Family <- Family[,colMeans(Family) >=.1]

dim(Family)
#> [1] 14 34

library(vegan)
#> Loading required package: permute
#> Loading required package: lattice
#> This is vegan 2.5-6
#NMDS
#autotransform=F evita la transformaci?n de datos
#sratmax=0.999999 para que el an?lisis no se detenga antes de tiempo cuando se tienen muchos datos  
Family.mds <- metaMDS(Family, k=2, distance="bray", trymax=100, zerodist="add", autotransform=F)
#> Run 0 stress 9.969131e-05 
#> Run 1 stress 9.654804e-05 
#> ... New best solution
#> ... Procrustes: rmse 0.04793172  max resid 0.1307909 
#> Run 2 stress 9.928457e-05 
#> ... Procrustes: rmse 0.07320599  max resid 0.219269 
#> Run 3 stress 9.843455e-05 
#> ... Procrustes: rmse 0.07156589  max resid 0.2137064 
#> Run 4 stress 8.916943e-05 
#> ... New best solution
#> ... Procrustes: rmse 0.01976362  max resid 0.05322951 
#> Run 5 stress 9.765706e-05 
#> ... Procrustes: rmse 0.0552266  max resid 0.1693111 
#> Run 6 stress 9.116397e-05 
#> ... Procrustes: rmse 0.05286723  max resid 0.1613987 
#> Run 7 stress 9.727769e-05 
#> ... Procrustes: rmse 0.05528396  max resid 0.169486 
#> Run 8 stress 9.562458e-05 
#> ... Procrustes: rmse 0.01910763  max resid 0.0546893 
#> Run 9 stress 9.496489e-05 
#> ... Procrustes: rmse 0.05522583  max resid 0.1693072 
#> Run 10 stress 9.169977e-05 
#> ... Procrustes: rmse 0.08777776  max resid 0.201423 
#> Run 11 stress 9.440087e-05 
#> ... Procrustes: rmse 0.08776635  max resid 0.2013933 
#> Run 12 stress 9.266926e-05 
#> ... Procrustes: rmse 0.03070879  max resid 0.08994358 
#> Run 13 stress 9.785632e-05 
#> ... Procrustes: rmse 0.02474976  max resid 0.0644543 
#> Run 14 stress 9.767342e-05 
#> ... Procrustes: rmse 0.05434868  max resid 0.1663609 
#> Run 15 stress 9.866632e-05 
#> ... Procrustes: rmse 0.009986105  max resid 0.02805647 
#> Run 16 stress 8.944125e-05 
#> ... Procrustes: rmse 0.005120099  max resid 0.01392708 
#> Run 17 stress 9.614291e-05 
#> ... Procrustes: rmse 0.04602012  max resid 0.1387744 
#> Run 18 stress 9.988593e-05 
#> ... Procrustes: rmse 0.05475556  max resid 0.1677288 
#> Run 19 stress 9.727364e-05 
#> ... Procrustes: rmse 0.04896145  max resid 0.1484392 
#> Run 20 stress 9.447012e-05 
#> ... Procrustes: rmse 0.01861441  max resid 0.05321458 
#> Run 21 stress 9.553666e-05 
#> ... Procrustes: rmse 0.0877595  max resid 0.2013674 
#> Run 22 stress 9.61222e-05 
#> ... Procrustes: rmse 0.08782066  max resid 0.2015415 
#> Run 23 stress 9.733919e-05 
#> ... Procrustes: rmse 0.05430509  max resid 0.1662195 
#> Run 24 stress 9.191404e-05 
#> ... Procrustes: rmse 0.05521235  max resid 0.1692595 
#> Run 25 stress 9.95102e-05 
#> ... Procrustes: rmse 0.05518633  max resid 0.1691812 
#> Run 26 stress 9.679203e-05 
#> ... Procrustes: rmse 0.05521129  max resid 0.1692602 
#> Run 27 stress 9.950208e-05 
#> ... Procrustes: rmse 0.05074253  max resid 0.1543305 
#> Run 28 stress 8.953321e-05 
#> ... Procrustes: rmse 0.05185096  max resid 0.1580135 
#> Run 29 stress 9.721993e-05 
#> ... Procrustes: rmse 0.01022478  max resid 0.02749834 
#> Run 30 stress 9.369102e-05 
#> ... Procrustes: rmse 0.05492537  max resid 0.168296 
#> Run 31 stress 8.603495e-05 
#> ... New best solution
#> ... Procrustes: rmse 0.08775786  max resid 0.2013639 
#> Run 32 stress 9.385069e-05 
#> ... Procrustes: rmse 0.1367656  max resid 0.3853064 
#> Run 33 stress 9.949237e-05 
#> ... Procrustes: rmse 0.1363297  max resid 0.3837648 
#> Run 34 stress 9.65593e-05 
#> ... Procrustes: rmse 0.0001028226  max resid 0.0002124013 
#> ... Similar to previous best
#> *** Solution reached
#> Warning in metaMDS(Family, k = 2, distance = "bray", trymax = 100, zerodist =
#> "add", : stress is (nearly) zero: you may have insufficient data
stressplot(Family.mds)

#add the metadata information
##THIS HAS TO BE IN THE SAME ORDER IN THE METADATA FILE AND IN THE COUNTS FILE
colvec<- c("yellowgreen","turquoise4", "tomato2")
plot(Family.mds, type="n", xlim=c(-.5,.5), ylim=c(-0.6,0.6))
pl <-ordiellipse(Family.mds, Metadata$Month, kind="se", conf=0.95, lwd=2, col="gray30", label=T)



#plot with ggplot
data.scores <- as.data.frame(scores(Family.mds))
data.scores$site <- rownames(data.scores)
data.scores$grp <- Metadata$Month
head(data.scores)
#>         NMDS1        NMDS2 site       grp
#> 1 -0.03379469 -0.003350833    1      July
#> 2  0.14117940 -0.044950983    2      July
#> 3  0.08974835  0.127384866    3    August
#> 4 -0.03380353 -0.003408347    4    August
#> 5 -0.03384985 -0.003424872    5    August
#> 6 -0.03385658 -0.003408284    6 September
library(ggplot2)
NMDS <-  data.frame(MDS1 = Family.mds$points[,1], MDS2 = Family.mds$points[,2],group=Metadata$Month)
#NMDS.mean <- aggregate(NMDS[,1:2],list(group=group),mean)
veganCovEllipse<-function (cov, center = c(0, 0), scale = 1, npoints = 100) 
{
  theta <- (0:npoints) * 2 * pi/npoints
  Circle <- cbind(cos(theta), sin(theta))
  t(center + scale * t(Circle %*% chol(cov)))
}
#attach(NMDS.mean)
df_ell <- data.frame()
for(g in levels(NMDS$group)){
  df_ell <- rbind(df_ell, cbind(as.data.frame(with(NMDS[NMDS$group==g,],
                                                   veganCovEllipse(pl[[g]]$cov,pl[[g]]$center,pl[[g]]$scale)))
                                ,group=g))
}


ggplot() + 
  geom_path(data=df_ell, aes(x=NMDS1, y=NMDS2,colour=group), size=1, linetype=1)+
  geom_point(data=data.scores,aes(x=NMDS1,y=NMDS2,colour=grp),size=1.5) + # add the point markers
  theme_light()+
  ylab("NMDS2")+
  xlab("NMDS1")+
  #geom_text(data=data.scores,aes(x=NMDS1,y=NMDS2,label=site),size=6,vjust=0) + 
  scale_colour_manual(values=c("July" = "yellowgreen", "August" = "turquoise4", "September" = "pink")) 

Created on 2021-06-02 by the reprex package (v0.3.0)

The following bit of code picks up where your reprex ends, and uses some ggplot options to get your final plot closer to your target plot at the top of your original post.

Overall, I think a good spot to get better at customizing your plot is to familiarize yourself with your plotting options. If you're working with ggplot2, the package has a really good documentation website at Create Elegant Data Visualisations Using the Grammar of Graphics • ggplot2.

If you're looking for a nice introduction to how ggplot2 works, the Data Visualization section of R4DS is a really nice first step;
https://r4ds.had.co.nz/data-visualisation.html


library(dplyr)
data.scores <- data.scores %>% 
  rename(group = grp) %>% 
  mutate(group = as.character(group)) %>% 
  as_tibble()

df_ell <- df_ell %>% 
  mutate(group = as.character(group)) %>% 
  as_tibble()

manual_colour_vals <- c("July" = "yellowgreen", "August" = "turquoise4", "September" = "pink")

ggplot() + 
  geom_path(
    data=df_ell, 
    aes(
      x=NMDS1, 
      y=NMDS2,
      colour=group), 
    size=3, 
    linetype=1,
    alpha = 0.6)+
  
  geom_polygon(
    data=df_ell, 
    aes(
      x=NMDS1, 
      y=NMDS2,
      fill = group),
    alpha = 0.2)+
  geom_point(
    data=data.scores,
    aes(
      x=NMDS1,
      y=NMDS2,
      colour=group,
      shape = group),
    size=3.5,
    alpha = 0.6) +
  theme_light()+
  ylab("NMDS2")+
  xlab("NMDS1")+
  scale_colour_manual(values=manual_colour_vals) +
  scale_fill_manual(values=manual_colour_vals) 

1 Like

Thank you :d SO MUCH

Hi Curtis, sorry for texting you around here, but do you know why I can't make a new post?

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