Plot error and size too large (Very large!!!)

Hello to all,
Hi I'm new here and I'm trying to learn how to use this program with a not quite simple case. (I'm a beginner in general with programming).

I'm having some problems with RStudio because I'm working with a really big file:

It is a file of 49966 rows (the first row is the name and follow rows my element name and bit ex. 1 0 1 0 0) and 1024 columns (the first column is the name, then a series of 1 and 0). I have everything saved on an excel file. I would also like to specify that the name is also a number!
example:

NAME
123 1 0 1 1 1 1 1 1
352 0 0 0 0 1 1 0 0

first problem why only 256? I have 1024 variables

str(mydata)
'data.frame': 49965 obs. of 256 variables:
NAME : int 3000300 3000350 3007493 3013793 3013798 3013808 3013817 3013821 3013825 3013838 ... X : int 0 0 0 0 0 0 0 0 0 1 ...
X.1 : int 0 0 0 0 0 0 0 0 0 0 ... X.2 : int 0 0 0 0 0 0 0 0 0 0 ...
X.3 : int 0 1 0 0 0 0 1 0 0 0 ... X.4 : int 0 0 0 0 0 0 0 0 0 0 ...
X.5 : int 0 0 0 0 0 0 0 0 0 0 ... X.6 : int 0 0 0 0 0 0 0 0 0 0 ...
X.7 : int 0 0 0 0 0 0 0 0 0 0 ... X.8 : int 0 0 0 0 0 0 0 0 0 0 ...
X.9 : int 0 0 0 0 0 0 0 0 0 0 ... X.10 : int 0 0 0 0 0 0 0 0 0 0 ...
X.11 : int 0 0 0 0 0 0 0 0 0 0 ... X.12 : int 0 0 0 0 0 0 0 0 0 0 ...
X.13 : int 0 0 0 0 0 0 0 0 0 0 ... X.14 : int 1 0 0 0 0 0 0 0 0 0 ...
X.15 : int 0 0 0 0 0 0 0 0 0 0 ... X.16 : int 0 0 0 0 0 0 0 0 0 0 ...
X.17 : int 0 0 0 0 0 0 0 0 0 0 ... X.18 : int 0 0 0 0 0 0 0 0 0 0 ...
X.19 : int 0 0 0 0 0 0 0 0 0 0 ... X.20 : int 0 0 0 0 0 0 0 0 0 0 ...
X.21 : int 0 0 0 0 0 0 0 0 0 0 ... X.22 : int 0 0 1 0 0 0 0 0 0 0 ...
X.23 : int 0 0 0 0 0 0 0 0 0 0 ... X.24 : int 0 0 0 0 0 0 0 0 0 0 ...
X.25 : int 0 0 0 0 0 0 0 0 0 0 ... X.26 : int 0 0 0 0 0 0 0 0 0 0 ...
X.27 : int 0 0 0 0 0 0 0 0 0 0 ... X.28 : int 0 0 0 0 0 0 0 0 0 0 ...
X.29 : int 0 0 0 0 0 0 0 0 0 0 ... X.30 : int 0 0 0 0 0 0 0 0 0 0 ...
X.31 : int 0 0 0 0 0 0 0 0 0 0 ... X.32 : int 1 0 1 1 0 1 0 0 0 1 ...
X.33 : int 0 0 0 0 0 0 0 0 0 0 ... X.34 : int 0 0 0 0 0 0 0 0 0 0 ...
X.35 : int 0 0 0 0 0 0 0 0 0 0 ... X.36 : int 0 0 0 0 0 0 0 0 0 0 ...
X.37 : int 0 0 0 0 0 0 0 0 0 0 ... X.38 : int 0 0 0 0 0 0 0 0 0 0 ...
X.39 : int 0 0 0 0 0 0 0 0 0 0 ... X.40 : int 0 0 0 0 0 0 0 0 0 0 ...
X.41 : int 0 1 0 0 0 0 0 0 0 0 ... X.42 : int 0 0 0 0 0 0 0 0 0 0 ...
X.43 : int 0 0 0 0 0 0 0 0 0 0 ... X.44 : int 0 0 0 0 0 0 0 0 0 0 ...
X.45 : int 0 0 0 0 0 0 0 0 0 0 ... X.46 : int 0 0 0 0 0 0 0 0 0 0 ...
X.47 : int 0 0 0 0 0 0 0 0 0 0 ... X.48 : int 0 0 0 0 0 0 0 0 0 0 ...
X.49 : int 0 0 0 0 0 0 0 0 0 0 ... X.50 : int 0 0 0 0 0 0 0 0 0 0 ...
X.51 : int 0 0 0 0 0 0 0 0 0 0 ... X.52 : int 0 0 0 0 0 0 0 0 0 0 ...
X.53 : int 0 0 0 0 0 0 0 0 0 0 ... X.54 : int 0 0 0 0 0 0 0 0 0 0 ...
X.55 : int 0 0 0 0 0 0 0 0 0 0 ... X.56 : int 0 0 0 0 0 0 0 0 1 0 ...
X.57 : int 0 0 0 0 0 0 0 0 0 0 ... X.58 : int 0 0 0 0 0 0 0 0 0 0 ...
X.59 : int 0 0 0 0 0 0 0 0 0 0 ... X.60 : int 0 0 1 0 0 0 0 0 0 0 ...
X.61 : int 0 0 0 0 0 0 0 0 0 0 ... X.62 : int 0 0 0 0 0 0 0 0 0 0 ...
X.63 : int 1 0 0 0 0 0 0 0 1 1 ... X.64 : int 0 0 0 0 0 0 0 0 0 0 ...
X.65 : int 0 0 0 0 0 0 0 0 0 0 ... X.66 : int 0 0 0 0 0 0 0 0 0 0 ...
X.67 : int 0 0 0 0 0 0 0 0 0 0 ... X.68 : int 1 0 0 0 0 0 0 0 0 0 ...
X.69 : int 0 0 0 0 0 0 0 0 0 0 ... X.70 : int 0 0 0 0 0 0 0 0 0 0 ...
X.71 : int 0 0 0 0 0 0 0 0 0 0 ... X.72 : int 0 0 0 0 0 0 0 0 0 0 ...
X.73 : int 0 0 0 0 0 0 0 0 0 0 ... X.74 : int 0 0 0 0 0 0 0 0 0 0 ...
X.75 : int 0 0 0 0 0 0 0 0 0 0 ... X.76 : int 0 0 0 0 0 0 0 0 0 0 ...
X.77 : int 0 0 0 0 0 0 0 0 0 0 ... X.78 : int 0 0 0 0 0 0 0 0 0 0 ...
X.79 : int 1 0 1 1 1 1 1 1 1 0 ... X.80 : int 0 0 0 0 0 0 0 0 0 0 ...
X.81 : int 0 0 0 0 0 0 0 0 0 0 ... X.82 : int 0 0 0 0 0 0 0 0 0 0 ...
X.83 : int 0 0 0 0 0 0 0 0 0 0 ... X.84 : int 0 0 0 0 0 0 0 0 0 0 ...
X.85 : int 0 0 0 1 0 0 0 1 0 0 ... X.86 : int 0 0 0 0 0 0 0 0 0 0 ...
X.87 : int 0 0 0 0 0 0 0 0 0 0 ... X.88 : int 0 0 0 0 0 0 0 0 0 0 ...
X.89 : int 0 0 0 0 0 0 0 0 0 0 ... X.90 : int 0 0 0 0 0 0 0 0 0 0 ...
X.91 : int 0 0 0 0 0 0 0 0 0 0 ... X.92 : int 0 0 0 0 0 0 0 0 0 0 ...
X.93 : int 0 0 0 0 1 0 0 1 0 0 ... X.94 : int 0 0 0 0 0 0 0 0 0 0 ...
X.95 : int 0 0 0 0 0 0 0 0 0 0 ... X.96 : int 0 0 0 0 0 0 0 0 0 0 ...
$ X.97 : int 0 0 0 0 0 0 0 0 0 0 ...
[list output truncated]

head(mydata)
NAME X X.1 X.2 X.3 X.4 X.5 X.6 X.7 X.8 X.9 X.10 X.11 X.12 X.13 X.14 X.15
1 3000300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0
2 3000350 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
3 3007493 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
X.16 X.17 X.18 X.19 X.20 X.21 X.22 X.23 X.24 X.25 X.26 X.27 X.28 X.29 X.30
1 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
3 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
X.31 X.32 X.33 X.34 X.35 X.36 X.37 X.38 X.39 X.40 X.41 X.42 X.43 X.44 X.45
1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0
X.46 X.47 X.48 X.49 X.50 X.51 X.52 X.53 X.54 X.55 X.56 X.57 X.58 X.59 X.60
1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
X.61 X.62 X.63 X.64 X.65 X.66 X.67 X.68 X.69 X.70 X.71 X.72 X.73 X.74 X.75
1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0
2 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
X.76 X.77 X.78 X.79 X.80 X.81 X.82 X.83 X.84 X.85 X.86 X.87 X.88 X.89 X.90
1 0 0 0 1 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
3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
X.91 X.92 X.93 X.94 X.95 X.96 X.97 X.98 X.99 X.100 X.101 X.102 X.103 X.104
1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
X.105 X.106 X.107 X.108 X.109 X.110 X.111 X.112 X.113 X.114 X.115 X.116 X.117
1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.118 X.119 X.120 X.121 X.122 X.123 X.124 X.125 X.126 X.127 X.128 X.129 X.130
1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.131 X.132 X.133 X.134 X.135 X.136 X.137 X.138 X.139 X.140 X.141 X.142 X.143
1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.144 X.145 X.146 X.147 X.148 X.149 X.150 X.151 X.152 X.153 X.154 X.155 X.156
1 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 1 0 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.157 X.158 X.159 X.160 X.161 X.162 X.163 X.164 X.165 X.166 X.167 X.168 X.169
1 0 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 1 0 0 0 0 1 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.170 X.171 X.172 X.173 X.174 X.175 X.176 X.177 X.178 X.179 X.180 X.181 X.182
1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.183 X.184 X.185 X.186 X.187 X.188 X.189 X.190 X.191 X.192 X.193 X.194 X.195
1 1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.196 X.197 X.198 X.199 X.200 X.201 X.202 X.203 X.204 X.205 X.206 X.207 X.208
1 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 1
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.209 X.210 X.211 X.212 X.213 X.214 X.215 X.216 X.217 X.218 X.219 X.220 X.221
1 0 0 0 0 1 0 0 0 0 0 0 0 0
2 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
X.222 X.223 X.224 X.225 X.226 X.227 X.228 X.229 X.230 X.231 X.232 X.233 X.234
1 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
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.235 X.236 X.237 X.238 X.239 X.240 X.241 X.242 X.243 X.244 X.245 X.246 X.247
1 0 1 0 0 0 0 0 0 0 0 0 0 0
2 0 0 0 0 1 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0 0 0 0 0
X.248 X.249 X.250 X.251 X.252 X.253 X.254
1 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0
[ reached 'max' / getOption("max.print") -- omitted 3 rows ]

But I don't care if you don't show it to me. The problem is here:

pairs(mydata)
Error in plot.new() : figure margins too large

Is it possible to solve it in some way? Use another system to view such a large file? Or save it directly?

Thank you all for those who will help me!

I suggest you save the data from Excel as a CSV file and see if that reads in correctly. If not, it is much easier to debug a CSV file, since you can examine the contents with a plain text editor.

A pairs plot of 1024 variables will have ~ 1 million plots. That does not seem like a useful thing to look at. What are you trying to accomplish?

1 Like

Hello,
Thanks for the reply. I managed to get the file as you said and it reads it correctly.

What I would like to do is hierarchical clustering. Do you think it's possible?

mydata <- read.csv("C:/Users/HP/Desktop/DB5vir.csv")
str(mydata)
'data.frame': 49964 obs. of 1024 variables:
ï..3000300: int 3000350 3007493 3013793 3013798 3013808 3013817 3013821 3013825 3013838 3013839 ... X0 : int 0 0 0 0 0 0 0 0 1 1 ...
X0.1 : int 0 0 0 0 0 0 0 0 0 0 ... X0.2 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.3 : int 1 0 0 0 0 1 0 0 0 0 ... X0.4 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.5 : int 0 0 0 0 0 0 0 0 0 0 ... X0.6 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.7 : int 0 0 0 0 0 0 0 0 0 0 ... X0.8 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.9 : int 0 0 0 0 0 0 0 0 0 0 ... X0.10 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.11 : int 0 0 0 0 0 0 0 0 0 0 ... X0.12 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.13 : int 0 0 0 0 0 0 0 0 0 0 ... X1 : int 0 0 0 0 0 0 0 0 0 1 ...
X0.14 : int 0 0 0 0 0 0 0 0 0 0 ... X0.15 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.16 : int 0 0 0 0 0 0 0 0 0 0 ... X0.17 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.18 : int 0 0 0 0 0 0 0 0 0 0 ... X0.19 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.20 : int 0 0 0 0 0 0 0 0 0 0 ... X0.21 : int 0 1 0 0 0 0 0 0 0 0 ...
X0.22 : int 0 0 0 0 0 0 0 0 0 0 ... X0.23 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.24 : int 0 0 0 0 0 0 0 0 0 0 ... X0.25 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.26 : int 0 0 0 0 0 0 0 0 0 0 ... X0.27 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.28 : int 0 0 0 0 0 0 0 0 0 0 ... X0.29 : int 0 0 0 0 0 0 0 0 0 1 ...
X0.30 : int 0 0 0 0 0 0 0 0 0 0 ... X1.1 : int 0 1 1 0 1 0 0 0 1 1 ...
X0.31 : int 0 0 0 0 0 0 0 0 0 0 ... X0.32 : int 0 0 0 0 0 0 0 0 0 1 ...
X0.33 : int 0 0 0 0 0 0 0 0 0 0 ... X0.34 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.35 : int 0 0 0 0 0 0 0 0 0 0 ... X0.36 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.37 : int 0 0 0 0 0 0 0 0 0 0 ... X0.38 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.39 : int 1 0 0 0 0 0 0 0 0 0 ... X0.40 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.41 : int 0 0 0 0 0 0 0 0 0 0 ... X0.42 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.43 : int 0 0 0 0 0 0 0 0 0 0 ... X0.44 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.45 : int 0 0 0 0 0 0 0 0 0 0 ... X0.46 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.47 : int 0 0 0 0 0 0 0 0 0 0 ... X0.48 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.49 : int 0 0 0 0 0 0 0 0 0 0 ... X0.50 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.51 : int 0 0 0 0 0 0 0 0 0 0 ... X0.52 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.53 : int 0 0 0 0 0 0 0 0 0 0 ... X0.54 : int 0 0 0 0 0 0 0 1 0 0 ...
X0.55 : int 0 0 0 0 0 0 0 0 0 0 ... X0.56 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.57 : int 0 0 0 0 0 0 0 0 0 0 ... X0.58 : int 0 1 0 0 0 0 0 0 0 0 ...
X0.59 : int 0 0 0 0 0 0 0 0 0 0 ... X0.60 : int 0 0 0 0 0 0 0 0 0 0 ...
X1.2 : int 0 0 0 0 0 0 0 1 1 1 ... X0.61 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.62 : int 0 0 0 0 0 0 0 0 0 0 ... X0.63 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.64 : int 0 0 0 0 0 0 0 0 0 0 ... X1.3 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.65 : int 0 0 0 0 0 0 0 0 0 0 ... X0.66 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.67 : int 0 0 0 0 0 0 0 0 0 0 ... X0.68 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.69 : int 0 0 0 0 0 0 0 0 0 0 ... X0.70 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.71 : int 0 0 0 0 0 0 0 0 0 0 ... X0.72 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.73 : int 0 0 0 0 0 0 0 0 0 0 ... X0.74 : int 0 0 0 0 0 0 0 0 0 0 ...
X1.4 : int 0 1 1 1 1 1 1 1 0 0 ... X0.75 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.76 : int 0 0 0 0 0 0 0 0 0 0 ... X0.77 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.78 : int 0 0 0 0 0 0 0 0 0 0 ... X0.79 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.80 : int 0 0 1 0 0 0 1 0 0 0 ... X0.81 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.82 : int 0 0 0 0 0 0 0 0 0 0 ... X0.83 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.84 : int 0 0 0 0 0 0 0 0 0 0 ... X0.85 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.86 : int 0 0 0 0 0 0 0 0 0 0 ... X0.87 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.88 : int 0 0 0 1 0 0 1 0 0 0 ... X0.89 : int 0 0 0 0 0 0 0 0 0 0 ...
X0.90 : int 0 0 0 0 0 0 0 0 0 0 ... X0.91 : int 0 0 0 0 0 0 0 0 0 0 ...
$ X0.92 : int 0 0 0 0 0 0 0 0 0 0 ...
[list output truncated]
head(mydata)
ï..3000300 X0 X0.1 X0.2 X0.3 X0.4 X0.5 X0.6 X0.7 X0.8 X0.9 X0.10 X0.11 X0.12 X0.13 X1 X0.14 X0.15 X0.16 X0.17
X0.18 X0.19 X0.20 X0.21 X0.22 X0.23 X0.24 X0.25 X0.26 X0.27 X0.28 X0.29 X0.30 X1.1 X0.31 X0.32 X0.33 X0.34
X0.35 X0.36 X0.37 X0.38 X0.39 X0.40 X0.41 X0.42 X0.43 X0.44 X0.45 X0.46 X0.47 X0.48 X0.49 X0.50 X0.51 X0.52
X0.53 X0.54 X0.55 X0.56 X0.57 X0.58 X0.59 X0.60 X1.2 X0.61 X0.62 X0.63 X0.64 X1.3 X0.65 X0.66 X0.67 X0.68 X0.69
X0.70 X0.71 X0.72 X0.73 X0.74 X1.4 X0.75 X0.76 X0.77 X0.78 X0.79 X0.80 X0.81 X0.82 X0.83 X0.84 X0.85 X0.86
X0.87 X0.88 X0.89 X0.90 X0.91 X0.92 X0.93 X0.94 X0.95 X0.96 X0.97 X0.98 X0.99 X0.100 X0.101 X0.102 X0.103
X0.104 X0.105 X0.106 X0.107 X0.108 X0.109 X0.110 X0.111 X0.112 X0.113 X0.114 X0.115 X0.116 X0.117 X0.118 X0.119
X0.120 X0.121 X0.122 X0.123 X0.124 X0.125 X0.126 X0.127 X0.128 X0.129 X0.130 X0.131 X0.132 X0.133 X0.134 X0.135
X0.136 X0.137 X0.138 X0.139 X0.140 X0.141 X0.142 X0.143 X0.144 X0.145 X0.146 X0.147 X0.148 X0.149 X0.150 X0.151
X0.152 X0.153 X0.154 X0.155 X0.156 X0.157 X0.158 X0.159 X0.160 X0.161 X0.162 X0.163 X0.164 X0.165 X0.166 X0.167
X0.168 X0.169 X0.170 X0.171 X0.172 X0.173 X0.174 X0.175 X0.176 X0.177 X1.5 X0.178 X0.179 X0.180 X0.181 X0.182
X0.183 X0.184 X0.185 X0.186 X0.187 X0.188 X0.189 X0.190 X0.191 X0.192 X0.193 X0.194 X0.195 X0.196 X0.197 X0.198
X0.199 X0.200 X0.201 X0.202 X0.203 X0.204 X0.205 X0.206 X1.6 X0.207 X0.208 X0.209 X0.210 X0.211 X0.212 X0.213
X0.214 X0.215 X0.216 X0.217 X0.218 X0.219 X0.220 X0.221 X0.222 X0.223 X0.224 X0.225 X0.226 X0.227 X0.228 X1.7
X0.229 X0.230 X0.231 X0.232 X0.233 X0.234 X0.235 X0.236 X0.237 X0.238 X0.239 X0.240 X0.241 X0.242 X0.243 X0.244
X0.245 X0.246 X0.247 X0.248 X0.249 X0.250 X0.251 X0.252 X0.253 X0.254 X0.255 X0.256 X0.257 X0.258 X0.259 X0.260
X0.261 X0.262 X0.263 X0.264 X0.265 X0.266 X0.267 X0.268 X0.269 X0.270 X0.271 X0.272 X0.273 X0.274 X0.275 X0.276
X0.277 X0.278 X0.279 X0.280 X0.281 X0.282 X0.283 X0.284 X1.8 X0.285 X0.286 X0.287 X0.288 X0.289 X0.290 X0.291
X0.292 X0.293 X0.294 X0.295 X0.296 X0.297 X0.298 X0.299 X0.300 X0.301 X0.302 X0.303 X0.304 X0.305 X0.306 X0.307
X0.308 X0.309 X0.310 X0.311 X1.9 X0.312 X0.313 X0.314 X0.315 X0.316 X0.317 X0.318 X0.319 X0.320 X0.321 X0.322
X0.323 X0.324 X0.325 X0.326 X0.327 X0.328 X0.329 X0.330 X0.331 X0.332 X0.333 X0.334 X0.335 X0.336 X0.337 X0.338
X0.339 X0.340 X0.341 X0.342 X0.343 X0.344 X1.10 X0.345 X0.346 X0.347 X1.11 X1.12 X0.348 X0.349 X0.350 X0.351
X0.352 X0.353 X0.354 X0.355 X0.356 X0.357 X0.358 X0.359 X0.360 X0.361 X0.362 X0.363 X0.364 X0.365 X0.366 X0.367
X0.368 X0.369 X0.370 X0.371 X0.372 X0.373 X0.374 X0.375 X0.376 X0.377 X0.378 X0.379 X0.380 X0.381 X0.382 X0.383
X0.384 X0.385 X0.386 X0.387 X0.388 X0.389 X0.390 X0.391 X0.392 X0.393 X0.394 X0.395 X0.396 X0.397 X0.398 X0.399
X0.400 X0.401 X0.402 X0.403 X0.404 X0.405 X0.406 X0.407 X0.408 X0.409 X0.410 X0.411 X0.412 X0.413 X1.13 X0.414
X0.415 X0.416 X0.417 X0.418 X0.419 X0.420 X0.421 X0.422 X0.423 X0.424 X0.425 X0.426 X0.427 X0.428 X0.429 X0.430
X0.431 X0.432 X0.433 X0.434 X0.435 X0.436 X0.437 X0.438 X0.439 X0.440 X0.441 X0.442 X0.443 X0.444 X0.445 X0.446
X0.447 X0.448 X0.449 X0.450 X0.451 X0.452 X0.453 X0.454 X0.455 X0.456 X0.457 X0.458 X0.459 X0.460 X0.461 X1.14
X0.462 X0.463 X0.464 X0.465 X0.466 X0.467 X0.468 X0.469 X0.470 X0.471 X0.472 X0.473 X0.474 X0.475 X0.476 X0.477
X0.478 X0.479 X0.480 X0.481 X0.482 X0.483 X0.484 X0.485 X0.486 X0.487 X0.488 X0.489 X0.490 X0.491 X0.492 X0.493
X0.494 X0.495 X0.496 X0.497 X0.498 X0.499 X0.500 X0.501 X0.502 X0.503 X0.504 X0.505 X0.506 X0.507 X0.508 X0.509
X0.510 X0.511 X0.512 X0.513 X0.514 X0.515 X0.516 X0.517 X0.518 X0.519 X0.520 X0.521 X0.522 X0.523 X0.524 X0.525
X0.526 X0.527 X0.528 X0.529 X0.530 X0.531 X0.532 X0.533 X0.534 X0.535 X0.536 X0.537 X0.538 X0.539 X0.540 X0.541
X0.542 X0.543 X0.544 X0.545 X1.15 X0.546 X0.547 X0.548 X0.549 X0.550 X0.551 X0.552 X0.553 X0.554 X0.555 X0.556
X0.557 X0.558 X0.559 X0.560 X0.561 X0.562 X0.563 X0.564 X0.565 X0.566 X0.567 X0.568 X0.569 X0.570 X0.571 X0.572
X0.573 X0.574 X0.575 X0.576 X0.577 X0.578 X0.579 X0.580 X0.581 X0.582 X0.583 X0.584 X0.585 X0.586 X0.587 X0.588
X0.589 X0.590 X0.591 X0.592 X0.593 X0.594 X0.595 X0.596 X0.597 X0.598 X0.599 X1.16 X0.600 X0.601 X0.602 X0.603
X0.604 X0.605 X0.606 X0.607 X0.608 X0.609 X0.610 X0.611 X0.612 X0.613 X0.614 X0.615 X0.616 X0.617 X0.618 X0.619
X0.620 X0.621 X0.622 X0.623 X0.624 X0.625 X0.626 X0.627 X0.628 X0.629 X0.630 X0.631 X1.17 X0.632 X0.633 X0.634
X0.635 X0.636 X0.637 X0.638 X0.639 X0.640 X0.641 X0.642 X0.643 X0.644 X0.645 X0.646 X0.647 X0.648 X0.649 X0.650
X0.651 X0.652 X0.653 X0.654 X0.655 X0.656 X0.657 X0.658 X0.659 X0.660 X0.661 X0.662 X0.663 X0.664 X0.665 X0.666
X0.667 X0.668 X0.669 X0.670 X0.671 X0.672 X0.673 X0.674 X0.675 X1.18 X0.676 X0.677 X0.678 X0.679 X0.680 X0.681
X0.682 X0.683 X0.684 X0.685 X0.686 X0.687 X0.688 X0.689 X0.690 X0.691 X0.692 X0.693 X0.694 X0.695 X0.696 X0.697
X0.698 X0.699 X0.700 X0.701 X0.702 X0.703 X0.704 X0.705 X1.19 X0.706 X0.707 X0.708 X0.709 X0.710 X0.711 X0.712
X0.713 X0.714 X0.715 X0.716 X0.717 X0.718 X0.719 X0.720 X0.721 X0.722 X0.723 X0.724 X0.725 X0.726 X0.727 X0.728
X0.729 X0.730 X0.731 X0.732 X0.733 X0.734 X0.735 X0.736 X0.737 X0.738 X0.739 X0.740 X0.741 X0.742 X0.743 X0.744
X0.745 X0.746 X0.747 X0.748 X0.749 X0.750 X0.751 X0.752 X0.753 X0.754 X0.755 X0.756 X0.757 X0.758 X0.759 X0.760
X0.761 X0.762 X0.763 X0.764 X0.765 X0.766 X0.767 X0.768 X0.769 X0.770 X0.771 X0.772 X0.773 X0.774 X1.20 X0.775
X0.776 X0.777 X0.778 X0.779 X0.780 X0.781 X0.782 X0.783 X0.784 X1.21 X0.785 X0.786 X0.787 X0.788 X0.789 X0.790
X0.791 X0.792 X0.793 X0.794 X0.795 X0.796 X0.797 X0.798 X0.799 X0.800 X0.801 X0.802 X0.803 X0.804 X0.805 X0.806
X0.807 X0.808 X0.809 X0.810 X0.811 X0.812 X0.813 X0.814 X0.815 X0.816 X0.817 X0.818 X0.819 X0.820 X0.821 X0.822
X0.823 X0.824 X0.825 X1.22 X0.826 X0.827 X0.828 X0.829 X0.830 X0.831 X0.832 X0.833 X0.834 X0.835 X0.836 X0.837
X0.838 X0.839 X0.840 X0.841 X0.842 X0.843 X0.844 X0.845 X0.846 X0.847 X0.848 X0.849 X0.850 X1.23 X0.851 X0.852
X0.853 X0.854 X0.855 X0.856 X0.857 X0.858 X0.859 X0.860 X0.861 X0.862 X0.863 X0.864 X0.865 X0.866 X0.867 X1.24
X0.868 X0.869 X0.870 X0.871 X0.872 X0.873 X0.874 X0.875 X0.876 X0.877 X0.878 X0.879 X0.880 X0.881 X0.882 X0.883
X0.884 X0.885 X0.886 X0.887 X0.888 X0.889 X0.890 X0.891 X0.892 X0.893 X0.894 X0.895 X0.896 X0.897 X0.898 X0.899
X0.900 X0.901 X0.902 X0.903 X0.904 X0.905 X0.906 X0.907 X0.908 X0.909 X0.910 X0.911 X0.912 X0.913 X0.914 X0.915
X0.916 X0.917 X0.918 X0.919 X0.920 X0.921 X0.922 X0.923 X0.924 X0.925 X0.926 X0.927 X0.928 X0.929 X0.930 X0.931
X0.932 X0.933 X0.934 X1.25 X0.935 X0.936 X0.937 X0.938 X0.939 X0.940 X0.941 X0.942 X0.943 X0.944 X0.945 X0.946
X0.947 X0.948 X0.949 X0.950 X0.951 X0.952 X0.953 X0.954 X0.955 X0.956 X0.957 X0.958 X0.959 X0.960 X0.961 X0.962
X0.963 X0.964 X0.965 X0.966 X0.967 X0.968 X0.969 X0.970 X0.971 X0.972 X0.973 X0.974 X0.975 X0.976 X0.977 X0.978
X0.979 X0.980 X0.981 X0.982 X0.983 X0.984 X0.985 X0.986 X0.987 X0.988 X0.989 X1.26 X0.990 X0.991 X0.992 X0.993
X0.994 X0.995
[ reached 'max' / getOption("max.print") -- omitted 6 rows ]
pairs(mydata)
Error in plot.new() : figure margins too large
z <- mydata[,-c(1,1)]
m <- apply(z,2,mean)
s <- apply(z, 2,sd)
z <- scale(z,m,s)
#calculate the Euclidean distance
distance <- dist(z)
Errore: cannot allocate vector of size 9.3 Gb #obviously it won't work anymore then
distance
Errore: oggetto "distance" non trovato
print(distance, digits = 3)
Error in print(distance, digits = 3) : oggetto "distance" non trovato
#clustering dindrogram
hc.1 <- hclust(distance)
Error in hclust(distance) : oggetto "distance" non trovato
plot(hc.1)
Error in plot(hc.1) : oggetto "hc.1" non trovato

It seems you do not have enough memory in the computer to calculate the distance matrix, which will have dimensions of about 50000 x 50000. I have no experience with such calculations. I suggest you start a new thread with the words "hierarchical clustering" in the title. That is more likely to attract the attention of someone with the right expertise.

1 Like

Thanks a lot. I'll try it!