plot a very large data with many zeros - r

This is a small portion of a vey big data
df<- structure(list(A = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0.68906, 0, 0, 0, 0, 0, 0, 0, 0, 0.13597, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), B = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0.40001, 0, 0, 0, 0, 0.69718, 0, 0, 0, 0, 0, 0, 0,
0, 0.090752, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), C = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.84068, 0, 0, 0, 0.34713, 0, 0, 0, 0, 0.65201,
0, 0, 0.25725, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.86419, 0, 0, 0, 0.3845,
0, 0, 0, 0, 0.67091, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), E = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1083, 0.8324,
0, 0, 0, 0.38499, 0, 0, 0, 0, 0.69064, 0, 0, 0.14596, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), F = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 1.0954, 0.74426, 0, 0, 0, 0.37715, 0, 0, 0, 0, 0.68884,
0, 0, 0.20826, 0, 0.38782, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), G = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 1.0985, 0.66651, 0, 0,
0, 0, 0, 0, 0, 0, 0.68861, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1812,
0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("A", "B", "C", "D", "E",
"F", "G"), class = "data.frame", row.names = c(NA, -39L))
What I want is to show the values in a more stressed way when there are a lot of zeros in a data
How I plot it is like this
eucl_dist=dist(df,method = 'euclidean')
hie_clust=hclust(eucl_dist,method = 'complete')
my_palette <- colorRampPalette(c( "green", "yellow", "red"))(n = 1000)
heatmap.2(mydata, scale = c("none"), Colv=F, Rowv=as.dendrogram(hie_clust),
xlab = "X", ylab = "Y", key=TRUE, keysize=1.1, trace="none",
density.info=c("none"), margins=c(4, 4), col=my_palette, dendrogram="row")
But as you see, in this small example, the zero dominate my plot and when it is very large then it is impossible to see anything. also I cannot change the position of the values


You are asking a lot of questions here, I'll try to answer those I see.
Zero dominates plot
Zeros dominate you data but, what do the zeros mean? Without some insight into what the zeros actually mean its hard to prescribe one best way to deal with it.
Colormap
The colorful colormap that you chose is not the best way to describe quantitative data. I would suggest a simple white to blue (or color of your choice) so that your zeros are shown as white and get hidden with the nonzero data emphasized. Example (only changing my_palette <- colorRampPalette(c("white", "cornflowerblue"))(n = 1000)):
Changing the position of the values
I'm not certain what you mean here but the layout is fixed by the dendrogram you defined.

Related

For loop to get rowmeans of each 8 columns in a large dataframe

I have a large data.frame with 8 columns per sample with 200 samples. I need to get row-means of each 8.
rowMeans(mat[j,1:8]), rowMeans(mat[j,9:16]), rowMeans(mat[j,17:24])...
rownames are gene names.
I used the following:
for(j in 1:nrow(mat)){
for (i in 1:ncol(mat)/8) {
row_m[j, i]<- rowMeans(mat[j,c(i:i+7)])
}
}
Dataframe sample data, here I have shown 9 columns, should get the mean from first 8 (AM) and then repeat for other samples....
dput(head(deconv3[1:9], 20))
structure(list(AM.amplifying.intestine = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM43.5.epithelial.of.mammary = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.76506, 0, 0, 1406.48, 0, 196.401,
0, 1996.5, 0), AM.epithelium.of.bronchus = c(549.649, 1647.63,
0, 0, 0, 0, 0, 0, 699.868, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
AM.epithelium.of.intestine = c(0, 0, 0, 0, 0, 0, 572.85,
59.2414, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM.epithelium.of.trachea = c(0,
0, 0, 0, 199.549, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), AM.kidney.epithelial.cell = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1.32926, 0, 0, 333.592, 0), AM.medullary.thymic.epithelial.cell = c(126.847,
0, 0, 0, 0, 0, 0, 0, 0, 63.1822, 0, 0, 0, 0, 0, 0, 0, 26.0598,
0, 11.117), AM.myoepithelial.cell = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AK.amplifying.intestine = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c("A1BG",
"A2M", "NAT2", "SERPINA3", "AANAT", "ABAT", "ABCA2", "ABCA3",
"ABCB7", "ABCA4", "ABO", "ACACA", "ACADL", "ACADS", "ACADSB",
"ACAT1", "ACLY", "ACR", "ACP1", "ACRV1"), class = "data.frame")
But it does not work. I am wondering if you could help me with this. Thanks in advance!

sample_length <- 8
row_m <- matrix(nrow=dim(mat)[1], ncol = ncol(mat)/sample_length)
for (j in 1:nrow(mat)) {
for (i in seq(from = 1, to = ncol(mat), by = sample_length)) {
row_m[j, (sample_length - 1 + i)/sample_length] <- mean(as.numeric(mat[j, i:(i + (sample_length-1))]))
}
}

Try:
row_m <- do.call(cbind, lapply(1:(NCOL(mat) %/% 8 + 1), function(i){
rowMeans(d[, ((1:NCOL(mat) - 1) %/% 8 + 1) == i, drop=F])}))

RC Bray function not accepting phyloseq otu_table as argument

I am using James Stegen et al's code here to calculate an abundance-weighted raup-crick value for my 16S dataset.
I load in my phyloseq object then extract the otu_table. I then use the otu_table as the spXsite argument in the function raup_crick_abundance(). My otu_table is available as a dput() below the text.
physeq<-qza_to_phyloseq(
features="~/Documents/qiime2-analyses/CRD/fresh_run/table.qza",
tree="~/Documents/qiime2-analyses/CRD/fresh_run/rooted-tree.qza",
"~/Documents/qiime2-analyses/CRD/fresh_run/taxonomy.qza",
metadata = "crd-metadata.txt")
otu_table <- (otu_table(physeq))
raup_crick_abundance = function(spXsite=otu_table, plot_names_in_col1=TRUE,
reps=9999, set_all_species_equal=FALSE,
as.distance.matrix=TRUE, report_similarity=FALSE){
Where the remaining code is verbatim from the github link above.
I am having a hard time understanding the error I have been receiving:
Error in sample.int(x, size, replace, prob) :
incorrect number of probabilities
Called from: sample.int(x, size, replace, prob)
Browse[1]> traceback()
1: raup_crick_abundance()
I had thought perhaps the function was looking for an equivalent number of columns and rows, but that was not the case. I searched the function sample() and think it's hoping to select values from my argument otu_table but can't find the expected range?
The sample() causing problems is on line 48 of the github where I believe the function is weighing the otu occurrences and entering the number of occurrences into a column.
##two empty null communities of size gamma:
com1<-rep(0,gamma)
com2<-rep(0,gamma)
##add observed number of species to com1, weighting by species occurrence frequencies:
com1[sample(1:gamma, sum(spXsite.inc[null.one,]), replace=FALSE, prob=occur)]<-1
com1.samp.sp = sample(which(com1>0),(sum(spXsite[null.one,])-sum(com1)),replace=TRUE,prob=abundance[which(com1>0)]);
com1.samp.sp = cbind(com1.samp.sp,1); # head(com1.samp.sp);
com1.sp.counts = as.data.frame(tapply(com1.samp.sp[,2],com1.samp.sp[,1],FUN=sum)); colnames(com1.sp.counts) = 'counts'; # head(com1.sp.counts);
com1.sp.counts$sp = as.numeric(rownames(com1.sp.counts)); # head(com1.sp.counts);
com1[com1.sp.counts$sp] = com1[com1.sp.counts$sp] + com1.sp.counts$counts; # com1;
#sum(com1) - sum(spXsite[null.one,]); ## this should be zero if everything work properly
rm('com1.samp.sp','com1.sp.counts');
Any thoughts are greatly appreciated. Thank you in advance!
> dput(otu_table)
new("otu_table", .Data = structure(c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0,
0, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 39, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 26, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 4), .Dim = c(6L, 48L), .Dimnames = list(
c("e54924083c02bd088c69537d02406eb8", "3112899fc7a2adb7f74a081a82c7cde4",
"db5d745b02355b6fed513c4953b62326", "2faf2046aa9ab2f6598df79cd52e9c7b",
"bec8db81cc8ec453136c82ede8327a9f", "601e47b8adcbd21d159f74421710e1b5"
), c("sample-10", "sample-11", "sample-12", "sample-14",
"sample-16", "sample-18", "sample-19", "sample-20", "sample-21",
"sample-22", "sample-23", "sample-24", "sample-25", "sample-26",
"sample-27", "sample-28", "sample-29", "sample-30", "sample-31",
"sample-32", "sample-33", "sample-34", "sample-35", "sample-36",
"sample-37", "sample-40", "sample-41", "sample-43", "sample-44",
"sample-45", "sample-46", "sample-50", "sample-55", "sample-56",
"sample-57", "sample-58", "sample-59", "sample-61", "sample-62",
"sample-63", "sample-64", "sample-65", "sample-67", "sample-68",
"sample-69", "sample-70", "sample-71", "sample-8"))), taxa_are_rows = TRUE)
>

How to map a two-dimensional density plot onto a photograph [duplicate]

This question already has an answer here:
R: add alpha-value to png-image
(1 answer)
Closed 2 years ago.
I have a two-dimensional density plot which I need to overlay onto a photograph. I can read-in the picture with the package rtiff:
install.packages("rtiff")
library(rtiff)
x <- readTiff('F01_screenshot.tiff')
plot(x)
The data for the density plot is a very large dataframe called eet2 (see reproducible data below); the code for the density plot is this:
library(ggplot2)
ggplot(eet2, aes(x=X, y= Y)) +
geom_point() + stat_density2d()
I can easily produce both plots separately but don't know how to overlay the density plot onto the photograph in such a way that the density plot is scaled down proportionately to fit the picture. Any help is much appreciated!
Reproducible data:
eet2 <- dput(eet1[1:1000, 2:3])
structure(list(X = c(0, 177.378, 27.289, 0, 852.719, 0, 852.813,
71.068, 0, 144.875, 0, 140.385, 21.598, 0, 170.325, 0, 136.746,
21.038, 0, 146.279, 0, 141.86, 11.822, 0, 146.078, 0, 137.681,
21.182, 0, 148.867, 0, 132.886, 20.444, 0, 146.129, 0, 80.251,
6.688, 0, 141.08, 0, 149.789, 23.044, 0, 74.097, 0, 141.182,
21.72, 0, 83.075, 0, 81.192, 6.766, 0, 849.784, 0, 153.96, 23.686,
0, 78.374, 0, 142.782, 21.967, 0, 72.929, 0, 142.922, 11.91,
0, 83.639, 0, 143.912, 22.14, 0, 134.809, 0, 142.826, 21.973,
0, 132.7, 0, 85.876, 7.156, 0, 138.935, 0, 80.951, 12.454, 0,
144.385, 0, 141.716, 21.802, 0, 76.768, 0, 74.406, 6.2, 0, 134.444,
0, 155.341, 23.899, 0, 189.336, 0, 224.517, 34.541, 0, 207.46,
0, 216.122, 18.01, 0, 204.552, 0, 208.524, 32.081, 0, 207.513,
0, 203.162, 31.256, 0, 197.578, 0, 204.362, 17.03, 0, 195.223,
0, 956.396, 147.138, 0, 201.969, 0, 224.989, 34.614, 0, 214.064,
0, 140.374, 11.698, 0, 140.235, 0, 958.501, 147.462, 0, 141.898,
0, 143.337, 22.052, 0, 955.901, 0, 954.965, 79.58, 0, 131.323,
0, 223.214, 34.341, 0, 952.203, 0, 143.102, 22.016, 0, 935.525,
0, 918.238, 76.52, 0, 123.337, 0, 127.93, 19.682, 0, 915.755,
0, 128.336, 19.744, 0, 913.158, 0, 120.076, 10.006, 0, 911.196,
0, 124.387, 19.136, 0, 911.631, 0, 911.389, 140.214, 0, 910.433,
0, 119.375, 9.948, 0, 115.898, 0, 910.073, 140.011, 0, 915.461,
0, 965.025, 148.465, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1125.565,
0, 1074.389, 165.291, 0, 1061.611, 0, 1051.23, 161.728, 0, 1044.3,
0, 269.733, 22.478, 0, 245.869, 0, 206.934, 234.525, 245.609,
250.874, 251.576, 260.164, 262.974, 263.161, 269.25, 262.693,
261.818, 265.634, 266.479, 266.592, 266.39, 262.751, 262.508,
265.093, 262.666, 262.343, 1059.856, 1075.848, 1077.98, 1068.255,
1065.442, 1065.255, 1071.821, 1072.292, 1072.355, 1073.788, 1072.572,
1072.41, 1074.295, 1074.487, 1074.499, 1078.434, 1080.074, 1080.293,
1081.104, 1080.014, 1079.869, 1080.261, 1080.181, 1080.175, 1078.568,
1079.422, 1079.536, 1078.84, 1077.459, 1077.275, 1077.805, 1076.787,
1076.719, 1077.199, 1076.045, 1075.891, 1077.362, 1076.663, 1076.569,
1077.942, 1078.842, 1078.902, 1078.713, 1080.023, 1080.198, 1080.764,
1081.553, 1081.659, 1081.946, 1082.445, 1082.479, 1083.987, 1084.113,
1084.13, 1084.741, 1085.809, 1085.952, 1085.031, 1084.708, 1084.686,
1085.901, 1085.266, 1085.181, 1085.513, 1085.159, 1085.112, 1088.024,
1088.29, 1088.308, 1088.889, 1088.327, 1088.252, 1069.543, 1065.069,
1064.472, 1067.409, 1068.075, 1068.119, 1068.377, 1070.019, 1070.238,
1072.384, 1071.454, 1071.33, 1072.31, 1069.704, 1069.53, 1068.856,
1073.986, 1074.67, 1075.824, 1077.42, 1077.633, 1080.095, 1082.62,
1082.788, 1087.112, 1090.026, 1090.414, 1094.654, 1098.678, 1099.215,
1103.801, 1097.674, 1097.266, 1110.129, 1105.4, 1104.769, 1115.638,
1115.922, 1115.96, 1115.612, 1115.278, 1115.256, 1114.682, 1112.96,
1112.73, 1063.573, 948.249, 932.873, 844.512, 834.23, 833.545,
865.203, 867.402, 867.695, 838.499, 864.3, 867.74, 837.215, 864.187,
865.985, 0, 760.653, 862.073, 861.199, 860.757, 860.699, 0, 861.681,
71.807, 0, 851.287, 855.415, 855.965, 859.554, 864.523, 865.185,
868.109, 873.22, 873.561, 845.881, 882.877, 887.81, 893.391,
899.782, 900.634, 908.492, 56.781, 0, 0, 0, 0, 0, 0, 0, 319.09,
0, 0, 0, 0, 0, 0, 0, 0, 0, 696.905, 0, 704.008, 58.667, 0, 0,
0, 0, 677.276, 673.676, 673.195, 673.45, 671.209, 671.06, 672.474,
674.07, 674.283, 674.896, 656.33, 653.855, 678.654, 681.884,
682.099, 684.933, 715.507, 719.584, 735.659, 1290.244, 1364.188,
1362.632, 1366.322, 1366.568, 1371.973, 1364.1, 1363.051, 1361.383,
812.325, 739.117, 734.716, 1322.199, 1361.365, 730.422, 731.132,
731.227, 729.084, 724.164, 723.507, 726.199, 725.939, 725.922,
725.437, 1278.917, 1352.714, 726.791, 1281.431, 1355.383, 1366.558,
764.435, 724.294, 725.46, 724.544, 724.422, 724.178, 1288.932,
1364.232, 723.902, 680.495, 677.601, 724.172, 722.856, 722.681,
735.236, 743.706, 744.835, 554.779, 735.387, 747.427, 724.914,
720.25, 719.628, 721.122, 742.971, 745.884, 742.171, 737.947,
737.665, 729.207, 808.032, 818.542, 905.216, 106.496, 0, 1169.534,
0, 1195.852, 99.654, 0, 1236.396, 0, 0, 0, 0, 1190.473, 183.15,
0, 1143.686, 1132.754, 1132.025, 1123.856, 1103.6, 1100.9, 1083.716,
1079.191, 1078.588, 1083.785, 1070.395, 1069.503, 1066.885, 1065.121,
1064.885, 1064.401, 1065.285, 1065.403, 1075.009, 1076.036, 1076.105,
1086.308, 1075.821, 1074.423, 1073.282, 126.268, 0, 1092.281,
0, 1019.684, 1087.663, 286.389, 33.693, 0, 1117.064, 0, 1114.254,
171.424, 0, 1111.518, 0, 1110.347, 92.529, 0, 1077.293, 0, 1107.754,
170.424, 0, 1107.136, 0, 1076.571, 165.626, 0, 1067.475, 0, 1110.71,
92.559, 0, 274.852, 0, 280.654, 43.177, 0, 280.278, 271.773,
270.639, 0, 1060.434, 88.369, 0, 1054.964, 1047.876, 1046.931,
1052.977, 1064.93, 1066.524, 1061.181, 1069.339, 1069.883, 275.888,
970.662, 1063.299, 268.308, 974.232, 1068.355, 1044.757, 1065.286,
1066.655, 1076.68, 1085.54, 1086.721, 1088.216, 1083.409, 1082.768,
1087.272, 1087.283, 1087.283, 1086.689, 1087.19, 1087.257, 1089.988,
1087.837, 1087.55, 272.953, 1040.007, 1091.144, 1076.712, 1089.387,
1091.077, 1103.104, -360.56, -555.715, 1072.081, 1073.131, 1073.201,
1073.04, 1089.257, 1091.419, 1072.888, 1073.111, 1073.141, 1104.182,
1075.399, 1073.48, 1103.711, 1076.133, 1072.456, 1104.247, 1105.181,
1105.305, 1072.744, 1104.02, 1106.105, 1074.673, 1102.759, 1106.503,
1076.082, 1104.401, 1108.177, 1078.025, 1109.772, 1111.888, 288.949,
292.278, 292.722, 1112.981, 1123.139, 1124.493, 1127.996, 1134.943,
1135.406, 1136.071, 1141.749, 1142.506, 1148.754, 1150.764, 1151.032,
1156.093, 1154.597, 1154.497, 1150.481, 1124.218, 1120.716, 1108.646,
1095.181, 1093.385, 1113.175, 1089.783, 1088.224, 1719.795, -286.164,
-553.625, 1112.49, 1099.064, 1097.274, 1106.601, 1105.705, 1105.645,
1100.75, 1110.143, 1111.395, 1114.044, 1108.662, 1107.944, 1115.615,
1114.492, 1114.417, 296.606, 1011.559, 1106.886, 213.15, 735.037,
804.622, 643.816, 590.868, 587.338, 588.443, 591.022, 591.366,
595.437, 598.575, 598.994, -70.23, 562.977, 605.191, 608.988,
14.755, -64.476, -86.326, 490.189, 567.057, 573.015, 572.841,
572.83, 576.607, 578.243, 578.461, 579.714, 1343.595, 1445.446,
585.277, 588.153, 588.345, 588.809, 588.843, 588.848, 592.898,
591.447, 591.254, 592.538, 589.623, 589.429, 590.233, 587.901,
587.59, 589.276, 603.526, 605.426, 691.889, 810.649, 818.566,
889.948, 866.466, 863.335, 164.367, 164.895, 164.966, 158.056,
851.73, 897.975, 164.51, 806.786, 892.423, 894.069, 892.465,
892.251, 153.408, 842.909, 888.876, 151.935, 802.889, 889.683,
154.817, 146.446, 145.329, 888.428, 193.529, 147.202, 885.088,
224.817, 136.78, 147.255, 802.581, 889.958, 888.64, 889.975,
890.064, 1575.971, 968.922, 887.982, 889.232, 860.991, 857.226,
872.007, 893.987, 895.452, 859.373, 892.292, 896.681, 859.84,
859.942, 859.955, 898.274, 860.41, 857.885, 879.17, 862.76, 860.572,
856.434, 854.683, 854.45, 851.949, 174.027, 128.832, 848.35,
847.791, 847.716, 847.847, 849.008, 849.163, 133.198, 806.744,
851.647, 146.291, 773.622, 857.267, 860.622, 834.937, 831.512,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 623.517, 0, 631.712, 97.187,
0, 631.526, 0, 641.912, 98.756, 0, 648.161, 0, 654.673, 54.556,
0, 659.733, 0, 603.775, 684.278, 696.548, 689.181, 688.198, 687.25,
682.216, 681.881, 711.089, 713.482, 713.802, 713.969, 715.124,
715.278, 711.369, 709.299, 709.162, 679.428, 675.016, 674.428,
675.948, 673.724, 673.427, 675.195, 678.8, 679.041, 681.486,
686.341, 686.988, 696.445, 694.867, 694.657, 703.31, 705.003,
705.116, 0, 711.882, 109.52, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1269.549, 1438.822, 0, 720.784, 60.065, 0, 721.678, 0, 713.535,
109.775, 0, 709.244, 0, 681.534, 104.851, 0, 693.092, 0, 681.394,
56.783, 0, 679.538, 0, 680.866, 104.749, 0, 675.661, 0, 668.345,
102.822, 0, 0, 0, 0, 0, -69.364, -10.671, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 677.983, 0), Y = c(0, -560.914,
-86.294, 0, 12.57, 0, 10.336, 0.861, 0, -569.851, 0, -582.641,
-89.637, 0, -513.461, 0, -582.556, -89.624, 0, -567.846, 0, -574.869,
-47.906, 0, -567.25, 0, -571.902, -87.985, 0, -554.095, 0, -574.651,
-88.408, 0, -557.127, 0, -365.662, -30.472, 0, -565.123, 0, -564.378,
-86.827, 0, -380.282, 0, -587.338, -90.36, 0, -373.494, 0, -394.186,
-32.849, 0, -15.305, 0, -586.833, -90.282, 0, -391.571, 0, -603.611,
-92.863, 0, -392.659, 0, -595.738, -49.645, 0, -369.805, 0, -585.56,
-90.086, 0, -607.653, 0, -598.77, -92.118, 0, -603.821, 0, -362.726,
-30.227, 0, -590.569, 0, -365.806, -56.278, 0, -584.274, 0, -594.203,
-91.416, 0, -378.246, 0, -385.426, -32.119, 0, -585.678, 0, -569.332,
-87.589, 0, -572.327, 0, -566.971, -87.226, 0, -586.06, 0, -584.482,
-48.707, 0, -593.944, 0, -583.298, -89.738, 0, -580.776, 0, -590.48,
-90.843, 0, -600.616, 0, -598.535, -49.878, 0, -602.826, 0, -29.44,
-4.529, 0, -618.062, 0, -592.995, -91.23, 0, -609.041, 0, -411.878,
-34.323, 0, -413.262, 0, -52.608, -8.094, 0, -411.569, 0, -415.928,
-63.989, 0, -48.63, 0, -47.12, -3.927, 0, -415.299, 0, -599.916,
-92.295, 0, -44.314, 0, -411.139, -63.252, 0, -46.124, 0, -48.509,
-4.042, 0, -407.485, 0, -413.499, -63.615, 0, -55.391, 0, -415.922,
-63.988, 0, -57.45, 0, -422.827, -35.236, 0, -54.607, 0, -414.86,
-63.825, 0, -51.45, 0, -48.986, -7.536, 0, -45.942, 0, -413.839,
-34.487, 0, -411.075, 0, -38.956, -5.993, 0, -14.525, 0, 35.454,
5.455, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 279.172, 0, 315.145,
48.484, 0, 313.523, 0, 314.854, 48.439, 0, 314.166, 0, -89.369,
-7.447, 0, -125.57, 0, -138.883, -157.401, -162.487, -158.14,
-157.561, -171.662, -160.906, -160.189, -166.883, -171.154, -171.724,
-187.379, -175.901, -174.37, -188.734, -185.215, -184.98, -189.818,
-189.088, -188.991, 283.22, 302.83, 305.444, 270.066, 289.663,
290.969, 275.119, 276.953, 277.198, 260.347, 261.677, 261.855,
246.632, 250.916, 251.202, 238.808, 246.008, 246.968, 240.285,
253.356, 255.099, 251.122, 262.086, 262.817, 258.677, 268.433,
269.734, 264.812, 273.771, 274.966, 268.696, 276.112, 276.606,
268.26, 275.135, 276.051, 265.545, 272.265, 273.161, 264.077,
268.116, 268.385, 261.015, 266.217, 266.911, 259.724, 265.892,
266.714, 259.602, 265.93, 266.352, 258.209, 265.537, 266.514,
259.411, 265.687, 266.523, 259.761, 266.978, 267.459, 260.97,
268.01, 268.949, 262.4, 271.867, 273.13, 264.803, 273.569, 274.153,
266.979, 275.25, 276.353, 268.073, 274.926, 275.84, 269.625,
279.424, 280.077, 271.785, 281.741, 283.068, 276.083, 284.162,
285.239, 279.865, 291.741, 292.533, 291.52, 302.124, 303.538,
300.685, 313.141, 314.802, 313.594, 327.048, 327.945, 325.593,
339.796, 341.69, 341.58, 355.702, 357.585, 356.602, 381.738,
383.413, 370.869, 394.752, 397.936, 391.659, 409.376, 411.739,
415.584, 433.281, 434.461, 437.478, 454.368, 456.62, 445.173,
427.865, 425.558, 383.004, 370.373, 369.531, 382.343, 399.016,
401.239, 413.831, 422.157, 423.267, 432.015, 448.845, 449.967,
0, 425.956, 482.75, 487.1, 501.178, 503.055, 0, 508.065, 42.339,
0, 492.242, 494.125, 494.376, 484.667, 484.368, 484.328, 467.742,
464.267, 464.035, 468.697, 433.854, 429.208, 400.85, 393.727,
392.777, 376.735, 23.546, 0, 0, 0, 0, 0, 0, 0, 885.955, 0, 0,
0, 0, 0, 0, 0, 0, 0, 500.323, 0, 481.941, 40.162, 0, 0, 0, 0,
530.014, 516.432, 514.621, 494.618, 478.341, 477.256, 456.114,
458.922, 459.297, 456.514, 425.5, 421.365, 462.707, 472.408,
473.055, 467.768, 454.51, 452.742, 441.741, 201.307, 169.249,
167.631, 163.795, 163.539, 160.509, 161.775, 161.943, 161.298,
397.997, 429.557, 419.546, 177.979, 161.875, 418.022, 423.34,
424.049, 413.55, 419.645, 420.457, 407.472, 415.496, 416.031,
407.006, 148.509, 114.042, 409.442, 164.128, 131.419, 109.849,
407.293, 427.122, 418.844, 426.465, 427.481, 417.779, 161.317,
127.123, 409.12, 367.918, 365.171, 395.673, 396.298, 396.381,
388.306, 392.859, 393.466, 473.545, 400.213, 395.325, 366.771,
370.934, 371.489, 339.469, 387.501, 393.905, 386.022, 390.883,
391.207, 378.721, 394.352, 396.436, 384.581, 45.245, 0, 380.042,
0, 375.485, 31.29, 0, 335.187, 0, 0, 0, 0, 323.048, 49.7, 0,
359.169, 361.068, 361.195, 348.111, 362.176, 364.051, 383.932,
392.518, 393.663, 349.699, 392.668, 395.532, 391.36, 396.741,
397.458, 387.906, 391.383, 391.846, 371.849, 378.048, 378.461,
318.908, 357.694, 362.865, 342.973, 40.35, 0, 288.149, 0, 259.291,
276.577, -102.466, -12.055, 0, 241.977, 0, 234.288, 36.044, 0,
247.849, 0, 244.967, 20.414, 0, 290.17, 0, 258.996, 39.846, 0,
269.854, 0, 259.002, 39.847, 0, 297.18, 0, 266.569, 22.214, 0,
-87.029, 0, -89.617, -13.787, 0, -95.348, -79.043, -76.869, 0,
246.968, 20.581, 0, 272.257, 289.812, 292.152, 271.933, 287.652,
289.748, 276.443, 284.481, 285.017, -116.323, 230.711, 276.983,
-141.084, 229.411, 278.81, 314.961, 280.304, 277.994, 277.686,
282.62, 283.278, 287.306, 282.107, 281.414, 290.288, 291.936,
292.045, 288.296, 302.772, 304.702, 292.15, 302.242, 303.588,
-136.669, 270.271, 297.4, 274.154, 301.517, 305.165, 290.369,
-426.184, -521.724, 321.366, 329.046, 329.558, 321.7, 306.849,
304.869, 321.009, 327.981, 328.91, 290.339, 328.378, 330.914,
289.022, 325.172, 329.992, 288.447, 295.19, 296.089, 322.57,
298.091, 296.459, 321.394, 300.861, 298.123, 320.881, 300.679,
297.985, 319.966, 296.464, 294.897, -164.893, -160.079, -159.437,
278.924, 281.357, 281.682, 269.259, 274.752, 275.118, 267.165,
265.143, 264.873, 258.509, 264.614, 265.429, 256.744, 266.259,
266.893, 263.361, 301.473, 306.555, 262.416, 313.245, 320.022,
281.712, 320.535, 323.123, -194.846, -521.034, -564.526, 274.367,
278.267, 278.787, 264.133, 268.555, 268.85, 256.818, 266.194,
267.444, 260.084, 265.714, 266.464, 262.669, 272.063, 272.689,
-175.62, 225.568, 279.06, -159.431, 216.327, 266.428, 245.597,
254.043, 254.606, 244.851, 255.798, 257.258, 248.152, 256.508,
257.622, -319.948, 219.787, 255.769, 243.947, -235.689, -299.64,
-334.288, 164.071, 230.518, 224.188, 233.409, 234.024, 228.544,
236.363, 237.405, 235.462, -116.121, -162.999, 240.889, 251.796,
252.524, 246.8, 258.72, 260.309, 257.726, 267.828, 269.175, 265.087,
277.995, 278.856, 273.876, 287.09, 288.852, 280.858, 292.608,
294.174, 246.387, 213.675, 211.494, 201.469, 209.375, 210.429,
-217.38, -218.456, -218.6, -232.09, 196.612, 225.193, -248.699,
166.3, 221.633, 213.54, 219.095, 219.836, -261.438, 192.469,
222.729, -265.48, 164.234, 221.529, -254.99, -254.199, -254.094,
209.197, -218.172, -246.663, 207.52, -196.823, -250.735, -261.477,
163.4, 220.05, 210.838, 222.305, 223.069, -196.463, 176.753,
226.515, 217.024, 249.012, 253.277, 203.798, 226.753, 228.283,
241.225, 228.683, 227.011, 235.665, 243.606, 244.664, 212.621,
240.812, 242.692, 207.285, 207.473, 207.498, 194.745, 202.346,
203.359, 191.408, -221.759, -249.304, 186.022, 192.304, 193.141,
182.101, 191.751, 193.037, -260.225, 162.901, 191.109, -252.884,
141.317, 193.877, 186.754, 220.72, 225.249, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 304.742, 0, 316.744, 48.73, 0, 334.833, 0,
352.419, 54.218, 0, 368.749, 0, 378.178, 31.515, 0, 378.548,
0, 339.912, 385.233, 406.951, 373.432, 368.963, 411.64, 362.786,
359.529, 358.897, 353.738, 353.051, 336.054, 340.344, 340.915,
335.944, 355.461, 356.762, 345.061, 372.321, 375.955, 376.513,
404.13, 407.812, 416.338, 426.28, 426.943, 432.67, 429.483, 429.058,
426.368, 425.226, 425.074, 416.298, 411.677, 411.369, 0, 345.327,
53.127, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 183.69, 208.182, 0,
261.561, 21.797, 0, 298.1, 0, 281.941, 43.375, 0, 314.226, 0,
304.413, 46.833, 0, 374.171, 0, 368.525, 30.71, 0, 359.118, 0,
367.905, 56.601, 0, 287.494, 0, 285.213, 43.879, 0, 0, 0, 0,
0, 735.244, 113.115, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 304.619, 0)), row.names = 31537:32536, class = "data.frame")

This should give you a way to get the image you want. However, I don't have your original TIFF, and therefore the alignment won't be correct here since I had to use a cut-and-pasted version of the png in your question.
Anyway, the method I would use is:
Convert the image to a raster
Convert the raster to a grid::rasterGrob
Plot the rasterGrob as your first layer in ggplot using annotation_custom
Plot your other layers as normal.
Here's an example:
library(ggplot2)
library(rtiff)
library(grid)
x <- readTiff('F01_screenshot.tiff')
pic <- as.raster(array(c(x#red, x#green, x#blue), c(x#size, 3)))
picgrob <- rasterGrob(pic)
ggplot(eet2, aes(x=X, y= Y)) +
annotation_custom(picgrob) +
geom_point() +
stat_density2d() +
coord_equal()
You may need to scale your Y axis data to make it match the aspect ratio of the picture.
As an example, if we assume max(eet2$Y) is the top edge of the image, and min(eet2$Y) the bottom edge, and also assume that min(eet2$X) is the left edge and max(eet2$X) the right edge (as you have suggested is the case in your comments), we can marry the picture to the data like this:
pic_ratio <- dim(pic)[2]/dim(pic)[1]
data_ratio <- diff(range(eet2$X)) / diff(range(eet2$Y))
eet2$Y <- eet2$Y * data_ratio / pic_ratio
ggplot(eet2, aes(x=X, y= Y)) +
annotation_custom(picgrob) +
geom_point() +
stat_density2d() +
coord_equal(xlim = range(eet2$X), ylim = range(eet2$Y))
If this alignment is not correct, then we need extra calibration information not present in the data (i.e. what value of eet2$Y should represent the top and bottom of the image, and what value of eet2$X represents the left and right edges.

I am dealing with a DTM and I want to do k-means, heirarchical, and k-medoids clustering. Am I suppose to normalize the DTM first?

The data, AllBooks has 590 observations of 8266 variables. Here is the code I have:
AllBooks = read_csv("AllBooks_baseline_DTM_Unlabelled.csv")
dtms = as.matrix(AllBooks)
dtms_freq = as.matrix(rowSums(dtms) / 8266)
dtms_freq1 = dtms_freq[order(dtms_freq),]
sd = sd(dtms_freq)
mean = mean(dtms_freq)
This tells me that my mean is: 0.01242767
and my std. dev. is: 0.01305608
So since my standard deviation is low this means the data has low variability in terms of size of documents. So I do not need to normalize the DTM? And by normalize I mean using the scale function in R which subtracts the mean of the data and divides by the standard deviation.
In other words my big questions is: When am I suppose to standardize data (specifically a Document Term Matrix) for clustering purposes?
Here is a little output of data:
dput(head(AllBooks,10))
budding = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), enjoyer = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), needs = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), sittest = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), eclipsed = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), engagement = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
exuberant = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), abandons = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), well = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), cheerfulness = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
hatest = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), state = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0), stained = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), production = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), whitened = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), revered = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), developed = c(0, 0, 0, 2, 0, 0, 0, 0, 0, 0),
regarded = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), enactments = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), aromatical = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0), admireth = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0
), foothold = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), shots = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), turner = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), inversion = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
lifeless = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), postponement = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), stout = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), taketh = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), kettle = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), erred = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0), thinkest = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), modern = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), reigned = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0), sparingly = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
visual = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0), thoughts = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0), illumines = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0), attire = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
explains = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -10L))
You can view full data from link: https://www.dropbox.com/s/p9v1y6oxith1prh/AllBooks_baseline_DTM_Unlabelled.csv?dl=0

You have a sparse dataset, where most of it is dominated by zeros, hence standard deviation is very low. You can scale it if some of your non-zero counts are extremely large, eg some are 100s while others are 1s and 2s.
It might not be such a good idea to use kmeans on sparse data, because it is unlikely you can find meaningful centers. There might be a few options available, check this link on dimension reduction.There are also graph based approaches, such as this used in biology.
Below is a simplistic way to clust and visualize:
x = read.csv("AllBooks_baseline_DTM_Unlabelled.csv")
# remove singleton columns
x = x[rowMeans(x)>0,colSums(x>0)>1]
Treat it as binary and hierachical on a binary distance:
hc=hclust(dist(x,method="binary"),method="ward.D")
clus = cutree(hc,5)
Calculate PCA and visualize:
library(Rtsne)
library(ggplo2)
pca = prcomp(x,scale=TRUE,center=TRUE)
TS = Rtsne(pca$x[,1:30])
ggplot(data.frame(Dim1=TS$Y[,1],Dim2=TS$Y[,2],C=factor(clus)),
aes(x=Dim1,y=Dim2,col=C))+geom_point()
Cluster 5 seems to be very different, and they differ in these words:
names(tail(sort(colMeans(x[clus==5,]) - colMeans(x[clus!=5,])),10))
[1] "wisdom" "thee" "lord" "things" "god" "hath" "thou" "man"
[9] "thy" "shall"

Find the smallest distance between the profiles

I would like to find the smallest distance between the profiles stored in a data frame. I am interested especially in one row in comparison to the rest of the rows stored in the data frame.
That's a data frame:
structure(list(`10` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `34` = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 393090, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6718400,
0, 311350, 0), `59` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2164949.7,
4834137.6, 0, 0, 0, 1187816.7, 0, 0, 0, 0, 0, 0, 1340912.5, 0
), `84` = c(0, 0, 0, 0, 0, 0, 0, 0, 8607100, 0, 0, 17586713.2,
22629743.6, 0, 0, 0, 2808791.7, 0, 0, 4026222.5, 0, 0, 0, 1981900,
0), `110` = c(2296000, 0, 0, 0, 0, 2140221.7, 0, 0, 5809230.6,
0, 0, 37134898.5, 3861828.7, 2553100, 0, 12075845.8, 0, 0, 1272950,
8695273, 0, 0, 2657180, 2710080, 0), `134` = c(0, 0, 0, 1176150,
0, 1329596.7, 1471000, 0, 6511934, 6511934, 0, 18709227.3, 0,
1041211.2, 0, 6544176.9, 0, 0, 2412651.7, 7724956.9, 2878418.3,
0, 8620131.7, 2386972.8, 0), `165` = c(0, 1226610, 0, 1345098.7,
2083771.9, 0, 1808231.4, 0, 0, 10742997.7, 0, 13060798.9, 0,
538340, 538340, 2791649.5, 0, 0, 6217622, 1316097.1, 4716931.8,
0, 6615816.9, 1510532, 0), `199` = c(0, 1571525, 0, 1903038.3,
1676700, 0, 888832.2, 0, 0, 9084418.6, 0, 11189460.1, 0, 0, 1807662.5,
2564275, 0, 0, 18080359.7, 0, 0, 0, 2397710.2, 1717949.2, 0),
`234` = c(0, 1314900, 2482696, 1325684, 0, 0, 0, 0, 0, 7321432.7,
0, 9843409.2, 0, 0, 1073341.7, 2762775, 0, 0, 9335312.8,
0, 0, 0, 1950788.2, 1509100, 0), `257` = c(0, 1568700, 14604298.7,
940162.2, 0, 0, 0, 0, 0, 4779505.9, 0, 9691692.4, 0, 0, 735290,
2650165, 0, 2311383.7, 5193383.4, 0, 0, 0, 1341998.7, 1225325.6,
0), `362` = c(0, 0, 4190740.5, 288800, 0, 0, 0, 0, 0, 4846634.8,
0, 9574498.7, 0, 0, 0, 1425600, 0, 8339312.1, 3877892.5,
0, 0, 0, 1752866.7, 0, 0), `433` = c(0, 0, 773280, 0, 0,
0, 0, 0, 0, 3926582.8, 3926582.8, 5962586.5, 0, 0, 0, 1041400,
0, 1972909.3, 1895439.4, 0, 0, 0, 963891.2, 0, 1109800),
`506` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9332272, 0, 0, 0,
0, 0, 0, 2219100, 0, 0, 0, 0, 0, 0, 0), `581` = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 4371537.1, 0, 0, 0, 0, 0, 0, 2428800,
0, 0, 0, 0, 0, 0, 0), `652` = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1689871.4, 0, 0, 0, 0, 0, 0, 988399.7, 0, 0, 0, 0, 0,
0, 0), `733` = c(0, 0, 0, 0, 0, 0, 0, 1250100, 0, 0, 1754205.3,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `818` = c(0, 0,
0, 0, 0, 0, 0, 517340, 0, 0, 1149227.6, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), `896` = c(0, 0, 0, 0, 0, 0, 0, 579846.7,
0, 0, 985931.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
`972` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 858255.5, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `1039` = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 848993.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0)), .Names = c("10", "34", "59", "84", "110", "134",
"165", "199", "234", "257", "362", "433", "506", "581", "652",
"733", "818", "896", "972", "1039"), row.names = c("Mark_1",
"Mark_2", "Alex_1", "Katrin_1", "Georg_1", "Martin_1",
"Tim_1", "Tom_1", "Mike_1", "Mike_2", "Mike_3",
"Hare_1", "Dea_1", "Monty_1", "Monty_2", "Niko_1",
"Lee_1", "Marq_1", "Otto_1", "Priaq_1", "Surkta_1",
"Norsa_1", "Norsa_2", "Quer_1", "Quer_2"), class = "data.frame")
So the row named Katrin_1 is the one which is interesting for me. I would like to find which rows have the smallest euclidean distance to Katrin_1. Let say 3-5 rows.

Let's get rid of Katrin_1 column with df[!rownames(df) %in% "Katrin_1", ], subtract df["Katrin_1", ] from each of the remaining rows with sweep, find Euclidean distances by squaring the resulting matrix element-wise and using rowSums, use which.min to get the final result:
names(which.min(rowSums(sweep(df[!rownames(df) %in% "Katrin_1", ], 2, as.numeric(df["Katrin_1", ]), `-`)^2)))
# [1] "Mark_2"
This should be much more efficient than using dist as dist would compute all possible distances, while we need need only a few.

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