# How do I add vector labels?

Hi,

I've had assistance creating this stupidly complex NMDS plot. Because I am still learning how to use vectors, I don't know how to add the species name to their respective vectors (it should be the top 12 most abundant species). I was hoping that one of you awesome people could please help me understand how to add text to vectors. If it helps, I've attached the dput and codes below. Thanks in advance. Have a great rest of your day!

Cheers,
Terrible_Coder

DPUT

``````structure(list(c__Actinobacteria = c(25.533, 33.218, 39.028,
37.187, 36.828, 34.838, 28.097, 27.552, 29.992, 22.224, 32.853,
29.845), c__Alphaproteobacteria = c(15.81, 16.29, 19.782, 20.998,
19.288, 21.472, 23.169, 22.302, 18.005, 18.726, 15.635, 18.228
), c__Betaproteobacteria = c(13.048, 11.506, 8.502, 6.898, 7.147,
6.977, 7.033, 6.618, 2.659, 3.414, 2.991, 4.203), c__Deltaproteobacteria = c(8.608,
6.485, 3.675, 4.092, 6.175, 5.218, 7.93, 6.87, 4.883, 2.026,
3.543, 2.482), c__Gammaproteobacteria = c(5.402, 5.049, 3.326,
3.693, 5.416, 5.311, 6.582, 5.933, 3.599, 2.87, 2.701, 3.819),
c__Sphingobacteriia = c(3.468, 3.674, 3.033, 3.655, 2.499,
1.888, 1.923, 2.215, 1.89, 1.498, 1.154, 2.422), c__Acidobacteria_Gp6 = c(3.364,
2.445, 2.282, 2.601, 2.179, 2.739, 2.305, 2.756, 2.554, 2.763,
2.669, 3.454), c__Bacteria_unclassified = c(2.38, 1.869,
1.579, 1.247, 2.3, 2.108, 1.36, 1.193, 3.126, 1.885, 2.987,
2.37), c__Gemmatimonadetes = c(1.955, 1.482, 0.903, 0.881,
0.968, 0.91, 0.512, 0.615, 4.553, 3.568, 3.094, 1.909), c__Chloroflexi_unclassified = c(1.918,
2.012, 1.989, 2.041, 2.782, 3.152, 3.559, 3.14, 2.747, 1.918,
3.323, 3.736), c__Proteobacteria_unclassified = c(1.788,
1.457, 1.166, 1.046, 1.26, 1.059, 1.13, 1.313, 0.869, 0.547,
0.626, 0.31, 0.312, 0.566, 0.529, 0.494, 0.556, 1.232, 0.539,
0.904, 0.836), c__Flavobacteriia = c(1.545, 2.127, 0.92,
0.907, 0.523, 0.268, 1.042, 1.298, 0.274, 0.231, 0.098, 0.049
), c__Acidobacteria_Gp3 = c(1.264, 1.152, 0.586, 0.703, 0.911,
1.129, 0.645, 0.714, 0.749, 0.669, 0.677, 0.85), c__Cytophagia = c(1.234,
1.005, 0.925, 1.255, 0.872, 0.837, 1.151, 1.079, 1.045, 0.559,
0.906, 0.479), c__Bacteroidetes_unclassified = c(1.097, 1.104,
0.222, 0.247, 0.601, 0.385, 0.345, 0.409, 0.358, 0.231, 0.284,
0.127), c__Actinobacteria_unclassified = c(0.898, 0.956,
1.223, 1.099, 1.573, 1.617, 1.09, 0.972, 0.972, 0.669, 1.044,
0.708), c__Acidobacteria_Gp16 = c(0.868, 1.043, 1.757, 1.614,
0.716, 0.954, 0.848, 1.092, 0.904, 1.213, 1.346, 1.588),
c__Acidobacteria_Gp4 = c(0.828, 0.607, 0.381, 0.566, 1.042,
1.203, 0.666, 0.751, 3.325, 3.869, 1.634, 3.472), c__Acidobacteria_Gp17 = c(0.654,
0.357, 0.251, 0.281, 0.252, 0.342, 0.403, 0.385, 0.207, 0.374,
0.236, 0.48), c__Nitrospira = c(0.462, 0.244, 0.198, 0.208,
0.016, 0.032, 0.024, 0.042, 0.296, 0.103, 0.229, 0.211),
c__Bacilli = c(0.423, 0.362, 2.233, 2.468, 0.363, 0.463,
0.942, 1.303, 0.505, 0.562, 0.764, 0.683), c__Acidobacteria_Gp7 = c(0.421,
0.238, 0.122, 0.182, 0.328, 0.422, 0.327, 0.237, 1.964, 1.014,
1.364, 0.475), c__Saccharibacteria_genera_incertae_sedis = c(0.412,
0.498, 0.444, 0.54, 0.503, 0.564, 0.821, 1.001, 0.664, 0.61,
0.451, 0.717), c__Firmicutes_unclassified = c(0.382, 0.301,
0.256, 0.247, 0.174, 0.212, 0.188, 0.145, 0.471, 0.352, 0.389,
0.432), c__Anaerolineae = c(0.364, 0.467, 0.137, 0.225, 0.537,
0.442, 0.509, 0.422, 0.399, 0.179, 0.279, 0.194), c__Planctomycetia = c(0.305,
0.221, 0.325, 0.265, 0.217, 0.31, 0.239, 0.245, 0.264, 0.339,
0.517, 0.346), c__Acidobacteria_Gp10 = c(0.288, 0.267, 0.193,
0.22, 0.326, 0.593, 0.436, 0.589, 0.141, 0.225, 0.355, 0.566
), c__Acidobacteria_Gp5 = c(0.246, 0.149, 0.146, 0.168, 0.014,
0.02, 0.048, 0.028, 0.117, 0.079, 0.105, 0.155), c__Subdivision3 = c(0.236,
0.084, 0.056, 0.053, 0.059, 0.049, 0.033, 0.064, 0.109, 0.059,
0.092, 0.066), c__Acidobacteria_unclassified = c(0.222, 0.13,
0.09, 0.109, 0.092, 0.124, 0.07, 0.075, 0.173, 0.059, 0.169,
0.086), c__Thermoleophilia = c(0.207, 0.242, 0.383, 0.354,
0.642, 0.61, 0.412, 0.445, 0.328, 0.328, 0.417, 0.311), c__Spartobacteria = c(0.2,
0.132, 0.168, 0.185, 0.086, 0.083, 0.076, 0.057, 0.189, 0.315,
0.263, 0.268), c__Clostridia = c(0.181, 0.141, 0.264, 0.242,
0.322, 0.578, 0.512, 0.582, 0.117, 0.126, 0.14, 0.148), c__Bacteroidia = c(0.179,
0.252, 0.259, 0.149, 0.211, 0.229, 0.176, 0.184, 0.237, 0.197,
0.222, 0.175), c__Fibrobacteres_unclassified = c(0.14, 0.074,
0.007, 0.009, 0.072, 0.044, 0.136, 0.079, 0.117, 0.018, 0.167,
0.065), c__Acidobacteria_Gp22 = c(0.139, 0.074, 0.01, 0.007,
0, 0.005, 0, 0.007, 0, 0.002, 0.002, 0.001), c__Chloroflexia = c(0.133,
0.183, 0.342, 0.319, 0.271, 0.278, 0.618, 0.468, 0.161, 0.172,
0.401, 0.49), c__Rubrobacteria = c(0.131, 0.17, 0.198, 0.168,
0.359, 0.422, 0.267, 0.265, 0.481, 0.41, 0.588, 0.429), c__Oligoflexia = c(0.131,
0.105, 0.102, 0.08, 0.103, 0.066, 0.064, 0.096, 0.121, 0.149,
0.151, 0.052), c__Thermomicrobia = c(0.124, 0.179, 0.271,
0.227, 0.388, 0.522, 0.445, 0.46, 0.189, 0.184, 0.275, 0.26
), c__Candidatus_Latescibacteria_unclassified = c(0.113,
0.059, 0.017, 0.005, 0.004, 0.017, 0.015, 0.009, 0, 0.011,
0.007, 0.018), c__Caldilineae = c(0.102, 0.118, 0.176, 0.19,
0.146, 0.163, 0.17, 0.121, 0.147, 0.192, 0.183, 0.225), c__Opitutae = c(0.102,
0.05, 0.01, 0.007, 0.059, 0.024, 0.03, 0.048, 0.087, 0.303,
0.043, 0.021), c__Verrucomicrobiae = c(0.096, 0.114, 0.078,
0.085, 0.062, 0.061, 0.036, 0.07, 0.111, 0.033, 0.085, 0.027
), c__Latescibacteria_genera_incertae_sedis = c(0.085, 0.04,
0.01, 0.004, 0.012, 0.015, 0.033, 0.015, 0.012, 0.016, 0.011,
0.018), c__Acidobacteria_Gp1 = c(0.079, 0.057, 0.071, 0.099,
0.012, 0.022, 0.018, 0.007, 0.431, 0.362, 0.346, 0.552),
c__Thermodesulfobacteria_unclassified = c(0.068, 0.057, 0.115,
0.103, 0.008, 0.01, 0.015, 0.007, 0.01, 0.003, 0.002, 0.013
), c__Phycisphaerae = c(0.065, 0.044, 0.024, 0.02, 0.051,
0.039, 0.039, 0.028, 0.066, 0.092, 0.034, 0.018), c__Chlorobi_unclassified = c(0.052,
0.025, 0.002, 0.012, 0.029, 0.046, 0.033, 0.04, 0.05, 0.02,
0.046, 0.025), c__Acidimicrobiia = c(0.05, 0.065, 0.073,
0.038, 0.043, 0.037, 0.015, 0.015, 0.01, 0.011, 0.016, 0.014
), c__Acidobacteria_Gp11 = c(0.046, 0.032, 0.02, 0.016, 0.006,
0, 0.015, 0.007, 0.008, 0.007, 0.011, 0.018), c__Verrucomicrobia_unclassified = c(0.042,
0.023, 0.007, 0.02, 0.027, 0.022, 0.012, 0.02, 0.024, 0.016,
0.037, 0.015), c__Elusimicrobia = c(0.041, 0.015, 0.012,
0.001, 0.002, 0.002, 0, 0.007, 0, 0.002, 0.005, 0.001), c__Cyanobacteria_unclassified = c(0.035,
0.023, 0.017, 0.014, 0.031, 0.039, 0.015, 0.039, 1.306, 0.349,
0.49, 0.077), c__Spirochaetia = c(0.035, 0.021, 0.002, 0.001,
0, 0.002, 0.024, 0.039, 0, 0, 0, 0), c__Chloroplast = c(0.031,
0.019, 1.042, 1.341, 0.174, 0.081, 2.581, 4.578, 4.553, 1.565,
9.808, 8.067), c__Parcubacteria_genera_incertae_sedis = c(0.03,
0.013, 0, 0, 0.01, 0.015, 0.042, 0.037, 0.032, 0.059, 0.053,
0.011), c__Dehalococcoidia = c(0.03, 0.019, 0.027, 0.022,
0.004, 0.007, 0.003, 0.013, 0, 0.018, 0.009, 0.008), c__Chlamydiia = c(0.028,
0.017, 0.046, 0.05, 0.014, 0.007, 0.021, 0.022, 0.07, 0.074,
0.08, 0.152), c__Acidobacteria_Gp2 = c(0.028, 0.011, 0.007,
0.015, 0, 0, 0, 0, 0.02, 0.03, 0.034, 0.024), c__Chitinophagia = c(0.028,
0.019, 0.002, 0.009, 0.004, 0, 0.006, 0.011, 0.048, 0.002,
0.027, 0.008), c__Planctomycetes_unclassified = c(0.022,
0.002, 0.005, 0, 0.006, 0.007, 0.006, 0.004, 0, 0.007, 0,
0), c__Elusimicrobia_unclassified = c(0.018, 0.006, 0, 0,
0.002, 0, 0.015, 0.009, 0, 0, 0, 0.004), c__Ktedonobacteria = c(0.017,
0.011, 0.039, 0.026, 0, 0, 0, 0, 0.026, 0.113, 0.064, 0.025
), c__Cyanobacteria = c(0.013, 0.006, 0.012, 0.018, 0.115,
0.071, 0.033, 0.072, 1.274, 21.049, 1.119, 0.622), c__Armatimonadia = c(0.013,
0.017, 0.007, 0.023, 0.008, 0.024, 0.012, 0.006, 0.064, 0.03,
0.037, 0.028), c__Mollicutes = c(0.013, 0.008, 0.002, 0.004,
0.006, 0.005, 0.018, 0.033, 0.006, 0.003, 0.025, 0.046),
c__Chthonomonadetes = c(0.011, 0.008, 0.012, 0.005, 0.002,
0.007, 0.003, 0.002, 0.028, 0.015, 0.018, 0.02), c__BRC1_genera_incertae_sedis = c(0.011,
0.008, 0.002, 0.003, 0.008, 0.015, 0.006, 0.013, 0.018, 0.026,
0.008, 0, 0.003, 0.008, 0.007, 0.006, 0, 0.008, 0.013, 0.009,
0.01), c__Hydrogenophilalia = c(0.011, 0.013, 0, 0, 0, 0.005,
0.003, 0, 0, 0, 0, 0), c__Saprospiria = c(0.009, 0.008, 0.002,
0.001, 0.006, 0.005, 0.006, 0.006, 0.429, 0.067, 0.117, 0.011
), c__Candidatus_Saccharibacteria_unclassified = c(0.009,
0.013, 0.012, 0.012, 0.021, 0.037, 0.021, 0.015, 0.008, 0.026,
0.014, 0.02), c__Acidobacteria_Gp18 = c(0.009, 0.008, 0.005,
0, 0.004, 0.002, 0.009, 0.002, 0.004, 0.002, 0.007, 0.007
), c__Epsilonproteobacteria = c(0.009, 0.002, 0.005, 0.005,
0.012, 0.007, 0, 0.002, 0, 0.002, 0, 0.003), c__Acidobacteria_Gp25 = c(0.007,
0.011, 0.005, 0.022, 0, 0.002, 0, 0.011, 0.008, 0.03, 0.021,
0.054), c__Erysipelotrichia = c(0.007, 0.004, 0.002, 0.005,
0.004, 0.007, 0.009, 0.024, 0.004, 0.003, 0.007, 0.035),
c__Acidobacteria_Gp15 = c(0.007, 0.011, 0.002, 0.003, 0,
0.002, 0, 0.002, 0.008, 0.005, 0, 0.003), c__Thermotogae = c(0.007,
0.002, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), c__Armatimonadetes_gp4 = c(0.006,
0.008, 0.017, 0.019, 0.014, 0.017, 0.009, 0.024, 0.07, 0.074,
0.048, 0.048), c__WPS.1_genera_incertae_sedis = c(0.006,
0.004, 0.024, 0.011, 0.008, 0.005, 0.009, 0.006, 0.024, 0.033,
0.018, 0.023), c__Armatimonadetes_gp5 = c(0.004, 0.008, 0.015,
0.022, 0.041, 0.037, 0.018, 0.022, 0.024, 0.021, 0.016, 0.018
), c__Negativicutes = c(0.004, 0, 0.005, 0, 0.002, 0.059,
0, 0.018, 0.002, 0.002, 0, 0.004), c__Acidobacteriia = c(0.004,
0.006, 0.002, 0.001, 0.002, 0.005, 0.003, 0.002, 0, 0.007,
0.005, 0.004), c__Deinococci = c(0.004, 0, 0.002, 0.003,
0.002, 0.002, 0, 0.002, 0.002, 0.008, 0, 0.003), c__Acidobacteria_Gp9 = c(0.004,
0.002, 0.005, 0.003, 0, 0, 0, 0, 0, 0, 0, 0), c__Ignavibacteria = c(0.002,
0, 0.002, 0.003, 0.004, 0.012, 0.003, 0, 0, 0, 0.002, 0),
c__Holophagae = c(0.002, 0, 0, 0, 0.004, 0.002, 0, 0, 0.002,
0.002, 0.007, 0.004), c__Fimbriimonadia = c(0.002, 0.002,
0, 0, 0.002, 0.002, 0, 0, 0.004, 0.007, 0, 0.001), c__Candidatus_Gracilibacteria_unclassified = c(0.002,
0, 0.005, 0.003, 0.002, 0, 0.003, 0, 0, 0, 0, 0.001), c__Candidatus_Microgenomates_unclassified = c(0.002,
0, 0.005, 0, 0, 0, 0, 0, 0, 0.002, 0, 0), c__Candidatus_Melainabacteria_unclassified = c(0.002,
0.002, 0, 0, 0, 0, 0, 0, 0, 0.002, 0, 0), c__Candidatus_Tectomicrobia_unclassified = c(0,
0, 0, 0, 0.014, 0.029, 0.024, 0.017, 0, 0, 0, 0), c__unclassified = c(0,
0, 0.002, 0, 0.008, 0.002, 0, 0, 0, 0.005, 0, 0.01), c__candidate_division_WWE3_unclassified = c(0,
0.002, 0, 0.003, 0, 0.002, 0.012, 0.002, 0, 0, 0, 0.003),
c__Microgenomates_genera_incertae_sedis = c(0, 0.002, 0,
0.001, 0, 0.005, 0.003, 0.002, 0.002, 0.003, 0, 0.001), c__Longimicrobia = c(0,
0, 0, 0, 0.006, 0, 0, 0, 0.008, 0, 0.002, 0.001), c__Fusobacteriia = c(0,
0, 0, 0, 0, 0, 0.003, 0.004, 0, 0, 0, 0.001), c__WPS.2_genera_incertae_sedis = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0.003, 0, 0.003), c__Candidatus_Hydrogenedens = c(0,
0, 0, 0, 0, 0.002, 0, 0.002, 0, 0.002, 0, 0)), class = "data.frame", row.names = c("D15B",
"D610B", "D15F", "D610F", "HR15B", "HR610B", "HR15F", "HR610F",
"C15B", "C610B", "C15F", "C610F"))
``````

CODES

``````> Class.dat <- structure(list(...
> require(vegan)
>  require(tidyverse)
> require(ggplot2)
>#Create Dataframe
> x <- data.frame(tax=names(Class.dat), nsites=apply(Class.dat, 2, function(x){length(which(x>0))}))
> ##Class.dat is the array; if array is matrix, the margin "1" indicates rows and "2" indicates columns; "max" is the funtion to be applied
> ###"\$" operator is used to extract or subset a specific part of a data object
> x\$max <- apply(Class.dat, 2, max)
> x <- x[order(x\$nsites),]
> rownames(x) <- NULL
> x
#Shows little evidence of zero inflation (large % 0s)
> y <- data.frame(site=rownames(Class.dat), ntax=apply(Class.dat, 1, function(x){length(which(x>0))}))
> y\$pzero <- (ncol(Class.dat)-y\$ntax)/ncol(Class.dat)
> y\$shan <- diversity(Class.dat)
> y\$simp <- diversity(Class.dat, index="simpson")
>
> #Do the sites differ in terms of single variables?
> ##no
> y\$x1 <- substr(y\$site, 1, 1)
> kruskal.test(ntax~x1, data=y)
> kruskal.test(shan~x1, data=y)
> kruskal.test(simp~x1, data=y)
#Bray-Curtis dissimilarity
> d1 <- vegdist(Class.dat)
>
> #Bray-Curtis Dissimilarity (Log+1-Transformed Abundances)
> ##log+1 problematic because many values < 1
> d2 <- vegdist(log1p(Class.dat))
>
> #Bray-Curtis on logit scale to avoid issues with log+1
> logit_class <- as.matrix(Class.dat+1)/100
> d3 <- qlogis(logit_class)
> d3 <- d3+abs(min(d3))
> d3 <- vegdist(d3)
> #Create NMDS Plot
> #Ordination using NMDS (non-metric multidimensional scaling)
> m1 <- metaMDS(d1)
> m2 <- metaMDS(d2)
> m3 <- metaMDS(d3)
#Fit vectors of classes to NMDS space
> e1 <- envfit(m3, Class.dat)
> exy <- data.frame(tax=names(Class.dat), x=e1\$vectors\$arrows[,1], y=e1\$vectors\$arrows[,2], pval=e1\$vectors\$pvals, r=e1\$vectors\$r)
> rownames(exy) <- NULL
> #Vector correlations with ordination space:
> exy <- exy[order(-exy\$r),]
> #Create Dataframe
> mdat <- data.frame(m3\$points)
> mdat\$site <- substr(rownames(mdat), 1, 1)
> mdat\$col <- ifelse(mdat\$site == "D", "red", ifelse(mdat\$site == "H", "blue", "green"))
> #See if there are group differences in NMDS space
> ##Use MRPP to test location
> mrpp(mdat[,1:2], mdat\$site)
#There are differences
> ##A: 0.5378 <- clean separation of groups
> ##Incorporate distance equation: r=sqrt[(x^2)+(y^2)]
> mdat\$rad <- sqrt((mdat\$MDS1^2) + (mdat\$MDS2^2))
> #Max is 0.1653488
> #Adding arrows to plot below will require some scaling
> exy\$x2 <- 0.17 * exy\$r * exy\$x
> exy\$y2 <- 0.17 * exy\$r * exy\$y
> #Set up some graphical parameters for plotting labels
> exy\$adj <- ifelse(exy\$x < 0, 1, 0)
> #Plot shows this:
> plot(mdat[,1], mdat[,2], pch=16, col=mdat\$col,xlab="NMDS1", ylab="NMDS2", xlim=c(-0.2, 0.2), ylim=c(-0.2, 0.2), main= "NMDS Plot of Bray-Curtis Dissimilarity")
> legend("bottomright", legend=c("D", "HR", "C"), pch=16, col=c("red", "blue", "green"))
> segments(0,0,exy\$x2[1:12], exy\$y2[1:12], col="red")
``````

Hi @Terrible_Coder,
Check out the `text()` function for manually adding labels to a base-R plot (like yours).
See `help(text)`

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