Vertices overlap when there's only one component in igraph - r

So I noticed that the default layout in igraph sometimes behave weird when there's only one component of the graph, specifically vertices form a cluster that overlap. Consider the following matrix:
mat <- structure(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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 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, 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, 1, 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, 1, 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,
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0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 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, 1, 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,
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0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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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, 1, 0, 0,
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0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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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, 1,
0, 0, 0, 0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 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, 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, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0,
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0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
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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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 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, 1, 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,
1, 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, 1, 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), .Dim = c(65L, 65L), .Dimnames = list(
NULL, NULL))
g <- graph_from_adjacency_matrix(mat)
plot(g)
Compared to when I add vertex 29 to the main component
mat[29, 44] <- 1
g <- graph_from_adjacency_matrix(mat)
plot(g)
In which case I get the following figure which is much uglier.
Of course, I can work around this by adding an invisible vertex or by creating my own layout but I would like to know why this happens or what is an appropriate layout, similar to the default layout, in the igraph package that doesn't cause the vertices to overlap when I remove the singleton component.


You can try
plot(add_edges(g, c("29", "44")), layout = layout_nicely(g))

Related

R: How can I convert list of lists data into a dataframe

I have a list of lists of matrices as shown below, I have posted the code that got me closest to my desired format and some reproducible data.
Here is picture showing the format of my data:
I would like it to be in a dataframe format, similar to that shown below:
V1 V2
1 2
1 2
1 2
1 2
1 2
1 2
The closest I got was with this code:
dt_list <- purrr::map(a, data.table::as.data.table)
dt <- data.table::rbindlist(dt_list, fill = TRUE)
But instead of putting the second list into a second row, this code adds it on to the bottom of the column.
Reproducible data:
data <- list(list(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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, 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)), list(c(2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 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, 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, 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, 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, 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)))

You can try unlist with option recursive = FALSE, e.g.,
list2DF(unlist(data, recursive = FALSE))
or use data.frame (thank #akrun's comment)
setNames(data.frame(data), paste0("V", seq_along(data)))

I think purrr::map_dfc(data, as.data.frame) may solve your problem.

Why are the number of row results always different to the number of data frame rows and how can I make them match?

I use this loop to connect to Elasticsearch, retrieve some data and add it into my data frame based on a common identifier:
library(elastic); library(jsonlite)
for (i in first_chats_eu$associate) {
firstchats_eu_body <- paste0(
'{"size":1,"query":{"bool":{"must":[{"term":{"associate":"',
i
,'"}},{"term":{"type":"conversation-archived"}}]}},"sort":{"time":"asc"}}'
)
firstchats_eu_connection <- Search(
index = "my_index",
type = "my_type",
body = firstchats_eu_body
)
first_chats_eu$firstChat[first_chats_eu$associate == i] <- fromJSON(toJSON(firstchats_eu_connection$hits$hits), flatten = TRUE)[1]$`_source.time`
}
The problem is that it always produces the following message:
Error in `$<-.data.frame`(`*tmp*`, "firstChat", value = list("2017-06-08T00:17:01.118Z", :
replacement has 445 rows, data has 446
I have looked at other answers on SO but the solutions appear to be specific to each case, and thus not applicable to mine.
What could be causing this?
If it is of any use, this loop sometimes partially succeeds, in that it returns the first 34 results and then fails for all other rows after that point.
Data frame:
structure(list(associate = c("Pm0jYumSjx", "PbjmBqnIdY", "Vq3VblKpZn",
"JUCx0MOygt", "9IxVPXPGvQ", "FUUobAsS2Z", "uflgsKMNze", "VzeedmQuiv",
"BWYbDAIPkr", "PNCzrO3TRA", "zwemlUrxQE", "y19AiLUxLN", "4hhURCoC8H",
"Ak13YJF63y", "aoQI4Ncrt4", "syaulepdzW", "ISdTwUISIG", "wncQ5pXq88",
"MYbsg1E2EQ", "OhkAz5aren", "A2wqC9F1WR", "ZDVmRyHkUY", "n5kKRmTMzj",
"mfek2ukQXk", "9Tz6HWSBTd", "b7JdFFaL7M", "TetbzmItfe", "NfCPXPfA8G",
"fc3QfKEgoI", "lWOiSbqDkN", "PXU7CqGO1S", "kEgnBQeUVH", "Zk182RMS4W",
"vzF3lzDKlx", "92PThwjFeO", "hunSRzc1p9", "n2xvlXoTS3", "AzJIMGGn4I",
"8DAm5dFPtN", "Qk6Dl1wgAG", "b6Z1C2RgYk", "FsHvmjOWu8", "SHqTI9YFFx",
"xhKjx5JJin", "RAPQXUNJbF", "vuq1KKylZY", "sBcaWcjEsk", "B4Z2TYT02E",
"u6iEcDrjRL", "Tv8FVDmFlN", "lTS0ZT24vu", "i2I0kykYo0", "N9Nqwu7XiM",
"0wFw3bY5De", "KEcKE4DRdT", "JcZWhBasDE", "FfLrHsnrT5", "ibOCROEWcG",
"b9K3V27GH7", "1TMLQZS5eR", "Yfo5sO7Hyj", "Q09sg2jrsI", "byzMzoS8QV",
"NKLbLwDA4b", "iKkQUDBXln", "Rutbihe39R", "T00E44PAAf", "PSKIFW2Bi3",
"ewJjvrT8H6", "rudiPWdyHj", "gShZwgHn0m", "dQmluy3ilM", "kO2hP2SNzJ",
"hG8iLjN6BD", "GuDBMuoht6", "AOzBVHetmK", "inSAsVD12e", "tYSXKOEhQB",
"ie5m9sDcd5", "31XpEIYYEd", "qrh9kSiCTh", "6cGqFJB1vh", "VPqvH3dHnz",
"tmg2iLu1f6", "a7nBJlzYRt", "D8jHgvvZfY", "pMwjxAV1iP", "0hD2vZYxf7",
"JFuSGQZmYq", "dCu6ebaRzu", "8kFAGY52xO", "8dq2kAKbnP", "FL3RRY9dbb",
"kJhRRpLQDu", "bRkAyVrvBf", "mMHRUeQEjd", "3ATe60itju", "t1IdGh570n",
"LT7PLxWjKh", "zanyzD8KHy", "zo2fOcIflj", "pLhZhgBSd6", "iTzqXtTlVc",
"6ZGJSIjfUK", "bp0JOkgL2n", "4fHmIsuzoR", "wr8pS9BIGn", "XjBFbgCbA6",
"aBDfBMlyhl", "bjpWS5Zi1d", "1m8RnFavmk", "BCbEV3YcWs", "WO3z2kXsHv",
"W4ziF0GFYp", "Dvdpoq3gZJ", "TeO490YGfr", "sMuDgfHnOY", "IBH2U18pLr",
"iWfUpGFrvN", "JiqDLRGEFX", "kyNo0jk8Z1", "93HElGBOHz", "5ZJCyJrVoO",
"2XS3g6lGJN", "BcLA5ChZHs", "kEVIPyQENZ", "xEHCoCz4kk", "2VP9YQHg0z",
"NE1PnnqgGk", "Ay1r8aB1p3", "KX6pPK8IYt", "oaPI42kqwl", "G8DQ2GKget",
"rrUNVhvaL6", "e1V2GXIIQi", "S1ABORMzvC", "P5SHJFevse", "G5oyD7KyNk",
"96uS2gpKKW", "oVokuIkcBH", "uvZwOIZkhC", "9g3a3lkaqK", "sL3CM1Ydmp",
"CeRfpJxt5l", "gywxLgJfwk", "ykryX7GvTZ", "6G3LCq9dTM", "zcbQHDmYlm",
"vb9zQCBcsC", "CHQR1sudJt", "aSPIsQtBW9", "vUF6TTncxg", "mfZwbqTdqs",
"6KFAVH1JfU", "5VC1MoGAPL", "dBT7WbCPbg", "hsiLXdTOlX", "RYCLZhFY90",
"q9SsmoL8Ig", "eHawNS1jr0", "GVgIzKB9UB", "v1RSMGUZAb", "2arkjePApM",
"FVNiv2T5i2", "2YI036CYSi", "bEe9hMOuPw", "SYHAbSA4Bo", "kIxHklYUvm",
"ea0HZio45e", "bJLyBCIDLq", "UOX1xE4I4m", "DUlIH2h7wi", "MYF1ueeXfe",
"1tOV5TEEK5", "oPt6FcBGj5", "jvyfvuEgaZ", "lNVTpT4hA9", "HqDMYSu1lC",
"al11gInzgN", "hIC4Q5JlBH", "ujvEUtatGU", "wU4B2gFWsH", "UbC2Fnef7t",
"4PMafQHS86", "pzgyPSDDR2", "HajTJzwD6s", "Vmb16EuXQd", "MLImxndCP8",
"JioVLn85XG", "ezeN1Jrfz7", "X3dVvzFpO7", "jXWNcEJF40", "1Dlxb9sFY1",
"I2rDM0GCQY", "ItEc62tlMD", "BQSEE0aw5j", "PcKSaFFCjc", "UZOQv40QpR",
"TpTUfAkH2j", "102Qub695d", "iYMYFVeO3a", "9q6smEyLHE", "Oft5GPL1Kz",
"RNgREArWYp", "6JpBEX0t5E", "FQGSnKhEHZ", "qhH4yU4GF4", "smal1c8nZI",
"XM22Vo5TMb", "wVNGHFxsrw", "bOhSejjY4F", "XstvxG2evH", "YSeaBFnCxC",
"aHvosVBvWF", "ECa32iNWUP", "ntNOwcH86q", "6sBSxrENuN", "TKrEDKGBP4",
"7goCPMibBX", "owne4NwFBU", "DzbUeksk3x", "GFmnRMNXLn", "z03bGKnR4K",
"yrickhqzXO", "orNL3qgRs5", "aZDhxK3MnT", "GfjLnYebLi", "ABzJScG4aF",
"MzDl5sIcEv", "nDPQ5ryX60", "wzYLWcEGt7", "fg4yEOCrMg", "cOxDNFSVJ1",
"N3xJGamqYg", "YCWeSx7FdA", "nS7j6dlHW1", "v8xHPHvF9I", "WTgftXNmQv",
"JhhRGHAiAv", "kA95elFmbl", "6SBhLJ10MA", "zB8PDYd3yi", "Id0K9bUgkh",
"wBkgvnElHb", "kQnwwfc7kJ", "6hNV4Y1C4A", "AmA4Y13eQd", "flON1gpb6N",
"f5DopCEFwK", "boFD2o0FU2", "yd2OuCsCSL", "nDzEFEpz2D", "fweWiQ6x6l",
"G1mT217Z6l", "rFt9fevsgu", "MGtbd36gdp", "K48dwo2BDj", "fzDa2HHq8k",
"q3MmPsZKB4", "O9yTAKQlCN", "tDpjeKpWIY", "wOgm4P7503", "4kXj6O1TWk",
"GTgOP6Ik3U", "AcCdmoQqtn", "F5fwdhj5l3", "AJnCAwFOdi", "ssvby6VgQp",
"qzh7xCHMLl", "T2VySRQ0M0", "0yfnFPWynB", "L7PqHs1f0H", "83GzTa9bxh",
"DOGXIreUyo", "PDsqEWLsu5", "sN3JstovsG", "knZx7l2hsk", "asCBJPBgTg",
"ytAmW9sTAP", "S9vuzwn13O", "nrXFr6Ww5O", "sFUB1beOAx", "CyCvAIDs5q",
"z2jmTzZVAo", "Zdd2fiDPVe", "MOQP5vAj8W", "rjx4tHVh6E", "2G3dquHI2q",
"BruK8mqzVI", "GAB1feRTKE", "TPwJ8jp6j2", "mprSpIdRuJ", "qBaU7KERwq",
"QNfCFKWXv5", "6IdSIlfhrz", "19SMR3CQID", "LxnNlx8DsD", "TEJeH1bZXz",
"bTmafqEayN", "9X18BUOA36", "NoYo4tT3oG", "yj5TyZZyNT", "kPVRWBJRXM",
"H3kVwfzGTe", "vq7qUUqGUr", "GLDCR4av3A", "xiYU2MDc6c", "qOGqanHpjw",
"5BaW31f0Nr", "4dJwcfCqkA", "UJzHhwFZRe", "dec0Qlz3eK", "0l7WDqbZb0",
"MMwd72FDCL", "EDMkdKIWMy", "2zLQmEuKpY", "1SUMAUhkZc", "BxXiWBTb8w",
"zFdVjcL7qz", "pfPOEmMTei", "HzjyCETszF", "b26xszWUlP", "AseQsOAqqY",
"lvDbmLqnyg", "L5WxvWWAyt", "X5WUvRm2BY", "t6XCwcOinz", "FG7pLFG8Uw",
"yLpMyvmHb2", "7Ac9pVAF0B", "sDN9UHNGm4", "9Z1L7RtlA3", "bVb8hjn1ju",
"F4nLAs89VO", "kB4aIQEpkf", "lov4Q42w2B", "jnFjq8covm", "tIuHe5uCaC",
"qsAssEo3Qr", "mW3HVtDHQj", "pFoLBb4EfI", "5UA1lbRTrF", "s1qrkLbTIU",
"RQrW6CtY7C", "xVIb7ymcmW", "Iw3AvXAlZK", "yW0P3oHRKp", "Ivf463pZ3k",
"07OAzJ3LMW", "5nB6O3rLm3", "QsI7HSwDZ6", "YN6V6flLDk", "B5RXoyGmeY",
"gt1VgDERw1", "FS2yKGf1vg", "BpUytkzSeS", "fKFSxBU4QG", "ueBhiB7lig",
"c901c019c5", "MKADBViZNz", "Vcb76RpM0z", "PnIACGOY1I", "EIYwDIB3od",
"B30kGNViyD", "YuAuj81ULa", "HBRNcgXjly", "CNFYrTCnkB", "0NG6oAIhrv",
"39OL0da3JB", "pEYsCPrejG", "imUsAjHivl", "D6QCEqCeEN", "pbARKO6WHG",
"mtx6Brb5rP", "oaLUE44A5W", "AgrZaWAFsg", "yvOMkkIZ1m", "Z6fXosz4Fa",
"2fthCJ5DpQ", "yzDHP6VC3C", "oACH3KkPl4", "OUpJGlVe7Q", "rTUsgI4HB1",
"BG0bfaNntS", "T6DgmUfrMS", "tjMwvgas9N", "gSYzWEbwH6", "6P9pRD9jft",
"h7jgprw5ZI", "9RYqSSNP4s", "pv51ewuo8J", "kPHNzPAsy7", "qDg9oiE5vO",
"8PxDnGjkT9", "czyeqBbnOJ", "uHSx9FbBZy", "Z2mdkJk7Tk", "A9lBq2pPB7",
"1MjXRGUpCL", "oS6oAKFqvi", "Ro2Cpuw7nB", "4LjJWDFiJU", "ASrmSlOd4j",
"f9BBXMzBju", "VHdzp3okgg", "zgCdOvoFoJ", "vm7dLCBAXu", "aohXymMv9M",
"rsATydK2xE", "l6erf0NNN8", "wtcNWItEK5", "XO9BfYHSuJ", "FIsDrDMEM6",
"wThiyvldqW", "n49e2LohLA", "ISPVKeaL8B", "QavdvrKCrJ", "Oc5GaqMvsD",
"dBgH5abgm4", "jbxXIMi5T3", "wOEbpBhvJO", "EcSEO5x8Aa", "IPYmcletaC",
"RccyJrXYSQ", "F044DDAL9U", "xCUcsCDnQd", "wkF3tx7M5N", "RNMO5oH2ce",
"KEUArjG58B", "TfBcBOhVZQ", "BpjQjjlTDg", "wSsnje6q39", "ITAESdcmae",
"XTzpZzZFxy", "UutDWic510", "TMgewBSCgi", "mhEM43wLRC", "FwXswHZJzn",
"jqvBb6v6CY", "1YJDIwy9q2", "ea7Hx9RXNl", "cAYtXFKDvK", "R8DhTwSDmg",
"a54nasvwuX", "bnNkM8nimD", "FSSGwmo5Qh"), accountCreation = structure(c(1521647203.675,
1519339418.982, 1520978846.791, 1521462847.464, 1521219528.448,
1525602918.744, 1521039530.235, 1521026513.49, 1521802344.631,
1523790193.342, 1523699716.79, 1522159355.724, 1520935894.587,
1525529531.887, 1524575967.739, 1521127935.332, 1521047835.094,
1521101424.346, 1520997141.963, 1521025271.346, 1521106469.437,
1520952168.279, 1522939980.072, 1521644559.484, 1520962152.598,
1520946377.448, 1521114036.503, 1523303212.556, 1520948768.796,
1521281304.856, 1521484713.426, 1521314445.804, 1521632072.396,
1524399853.238, 1524063742.962, 1524048668.223, 1521231047.543,
1524157639.65, 1521209831.34, 1521475186.089, 1521724609.138,
1523533039.228, 1523702955.539, 1522488605.67, 1521890183.374,
1521642579.3, 1522228581.673, 1523525372.293, 1521195229.708,
1520940268.528, 1521044364.138, 1527678100.482, 1524600058.621,
1522777239.275, 1523870518.667, 1521369133.225, 1522232575.542,
1521198325.682, 1523360584.136, 1521813258.386, 1521627226.595,
1521033049.721, 1523452387.623, 1522236488.509, 1521198246.297,
1524671994.275, 1521720640.095, 1522242402.263, 1521637447.34,
1521718413.354, 1521290525.148, 1521530545.831, 1521022229.054,
1521054255.295, 1522147616.899, 1521115359.397, 1525349801.15,
1520943213.673, 1523610221.63, 1521478193.229, 1521889281.441,
1523701184.22, 1521810788.09, 1520946831.645, 1521471304.719,
1521561858.612, 1521893506.908, 1521048628.327, 1521116277.724,
1522765167.032, 1521286311.531, 1521125237.2, 1521887929.78,
1525159037.328, 1524135123.072, 1519230466.413, 1521030848.111,
1520956865.733, 1521592739.521, 1521457270.104, 1521736081.412,
1521736093.529, 1522159760.701, 1524053386.664, 1524496291.977,
1524216088.817, 1520936977.191, 1520953090.937, 1521212807.586,
1521803151.728, 1521801343.725, 1524055557.569, 1522767163.557,
1521729513.656, 1524316353.148, 1520935603.264, 1523960278.276,
1521499028.577, 1521643199.88, 1524483302.128, 1525335512.042,
1521373117.292, 1520879030.594, 1521023257.287, 1521022157.451,
1523800300.841, 1520935205.484, 1521738187.203, 1521280970.99,
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1524295064.907, 1521025774.074, 1522320956.043, 1521655757.989,
1521195220.823, 1521016193.598, 1521730985.238, 1521739086.354,
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1520932451.164, 1520932450.409, 1524233637.038, 1523542112.437,
1521891391.694, 1520941112.597, 1521981704.61, 1524126540.184,
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1524135121.181, 1521225075.835, 1521200485.608, 1521463364.621,
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1521111379.722, 1522405319.784, 1521021777.312, 1521062469.007,
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1521393592.616, 1521377252.798, 1521455570.819, 1521477410.136,
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1521476092.325, 1521150151.59, 1521565928.114, 1522758779.981,
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1521471989.26, 1524744993.667, 1520950428.393, 1523536553.54,
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446L), class = "data.frame")

Error in solving set of linear equation in R even when the determinant of coefficient matrix is not zero

I have coefficient matrix f as 55 by 55 matrix which det(f) is not zero :
f = structure(c(0.2, 1, 4, 12, 24, 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, 0.25,
1, 3, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.333333333333333, 1, 2,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.2, 1, 4, 12, 24, 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, -6, 0.25, 1, 3, 6,
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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, -2, 0, 0.333333333333333, 1, 2, 2, 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, -1, 0, 0, 0.5, 1, 1, 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, -1, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2,
1, 4, 12, 24, 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, -6, 0.25, 1, 3, 6, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -2, 0, 0.333333333333333, 1, 2, 2, 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, -1, 0, 0, 0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1,
1, 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, 0.2, 1, 4,
12, 24, 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, -6, 0.25, 1, 3, 6, 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,
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0, 0, 0, -2, 0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4, 12, 24,
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, -6, 0.25, 1, 3, 6, 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,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-2, 0, 0.333333333333333, 1, 2, 2, 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, -1, 0,
0, 0.5, 1, 1, 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, -1, 0, 0, 0, 1, 1, 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, 0.2, 1, 4, 12, 24, 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, -6, 0.25, 1, 3, 6, 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, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 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, -1, 0, 0,
0.5, 1, 1, 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, -1, 0, 0, 0, 1, 1, 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, 0.2, 1, 4, 12, 24, 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, -6, 0.25, 1, 3, 6, 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, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 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, -1, 0, 0,
0.5, 1, 1, 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, -1, 0, 0, 0, 1, 1, 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, 0.2, 1, 4, 12, 24, 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, -6, 0.25, 1, 3, 6, 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, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 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, -1, 0, 0,
0.5, 1, 1, 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, -1, 0, 0, 0, 1, 1, 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, 0.2, 1, 4, 12, 24, 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, -6, 0.25, 1, 3, 6, 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, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 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, -1, 0, 0,
0.5, 1, 1, 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, -1, 0, 0, 0, 1, 1, 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, 0.2, 1, 4, 12, 24, 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, -6, 0.25, 1, 3, 6, 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, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 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, -1, 0, 0,
0.5, 1, 1, 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, -1, 0, 0, 0, 1, 1, 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, 0.2, 0, 0, 0, 24, 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, -6, 0.25, 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, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0,
0.333333333333333, 0, 2, 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, -1, 0, 0, 0.5,
1, 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, -1, 0, 0, 0, 1, 0, 0, 0, 0), .Dim = c(55L,
55L))
and a vector p like :
p=c(0.02, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0.02,
0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.03, 0, 0, 0,
0, 0.03, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0.04,
0, 0, 0, 0)
To solve f by using solve(f,p) I'm getting the error :
Error in solve.default(f, p) :
system is computationally singular: reciprocal condition number = 1.31432e-16
Since det(f) is not zero and is inverse-able, why I'm getting this error and how could I resolve that ?

If your matrix is near singular you can perform regularization with a truncated SVD or truncated QR.
tsvd(x, nu, tol = 0.1, maxit = 20, tx, v, pca = FALSE)
the solution then is simply the inverse of this multiplied by your vector p.
The tsvd method uses Baglama and Reichel's efficient agumented
implicitly restarted Lanczos method for computing a truncated SVD of
large dense or sparse matrices. The method works best for the hard
case of close to square matrices. For matrices where the row and
column dimensions are very different (tall, skinny or short, fat
matrices), consider the computationally cheaper eigenvalue
decomposition of the small matrix cross product instead.
https://www.rdocumentation.org/packages/scidb/versions/1.2-0/topics/tsvd

R cut based on prespecified levels

I am trying to cut my numeric values such that I also get a count for the number of zeros. Not sure how to accomplish that. These are my goals.
1) I specifically get a count of number of zeros.
2) Option to cut the remaining non-zero values into many different
bins.
Right now I tried this below and I cannot get any count of number of zeros.
c1 <- cut(df$Col1, breaks = seq(0, 1442, by = 53.25))
Expected Output
(0] (0,53.2] (53.2,106] (106,160] (160,213] (213,266] (266,320] (320,373] (373,426] (426,479]
1652 1 6 1 34 6 1 1 8 2
(479,532] (532,586] (586,639] (639,692] (692,746] (746,799] (799,852] (852,905] (905,958]
0 0 4 1 0 0 1 0 0
(958,1.01e+03] (1.01e+03,1.06e+03] (1.06e+03,1.12e+03] (1.12e+03,1.17e+03] (1.17e+03,1.22e+03] (1.22e+03,1.28e+03] (1.28e+03,1.33e+03] (1.33e+03,1.38e+03] (1.38e+03,1.44e+03]
0 0 0 0 0 0 0 0 0
dput(df$Col1)
structure(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, 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, 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, 198, 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, 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, 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, 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, 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, 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, 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, 182.71, 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, 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, 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,
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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0,
199, 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, 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, 445, 0, 0, 176.02, 0, 192,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 198, 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, 204, 0, 0, 0, 207, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 209, 0, 0, 161.19, 0, 0, 106, 0, 0, 0, 0, 0, 0, 0,
0, 100, 0, 0, 0, 0, 0, 0, 0, 200, 0, 0, 0, 195, 0, 0, 0, 0, 398,
0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 204, 0, 89.37, 0, 0, 0,
0, 0, 0, 194, 0, 0, 0, 0, 212, 0, 0, 0, 0, 212, 211, 0, 402,
219, 0, 0, 244, 194, 0, 183.75, 0, 0, 0, 0, 0, 0, 0, 104, 197,
0, 0, 53.25, 0, 0, 0, 0, 0, 0, 0, 103, 0, 0, 0, 0, 0, 0, 383,
314, 202, 0, 0, 0, 0, 204, 227, 0, 205, 211, 670, 230.39, 0,
0, 110, 801, 595, 0, 0, 0, 438, 0, 397, 203, 209, 0, 209, 0,
258, 0, 0, 213, 0, 201, 174.84, 213, 0, 407, 208, 218, 365.7,
205, 595, 0, 608, 601, 183, 381.56, 421, 1442, 408), label = "Col1", class = c("labelled",
"numeric"))

The ( of (0,53.2] on the left of each bin means an "open-end", meaning values above that boundary. (x is your df$Col1.)
And it looks like you want the table of the cut, so this is the starting point:
head(table(cut(x, breaks = seq(0, 1442, by = 53.25))))
# (0,53.2] (53.2,106] (106,160] (160,213] (213,266] (266,320]
# 1 6 1 34 6 1
Two options. Either use right-closed:
head(table(cut(x, breaks = seq(0, 1442, by = 53.25), right = FALSE)))
# [0,53.2) [53.2,106) [106,160) [160,213) [213,266) [266,320)
# 1652 7 1 32 8 1
(Realize that this will change some of your bin counts, as you can see above.) Or explicitly provide something "to the left" of your first bin:
head(table(cut(x, breaks = c(-Inf, seq(0, 1442, by = 53.25)))))
# (-Inf,0] (0,53.2] (53.2,106] (106,160] (160,213] (213,266]
# 1652 1 6 1 34 6
This retains the original bin counts and ensures you have all of your zeroes (and, if present, any negative values).

Which of elements come together in different data

I'm trying to write a script to analyze my data. Would be great if you can help me with that.
Let's start with my data:
> dput(tbl_alles[1:100,])
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, 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), `20` = 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, 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), `52.5` = 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.7306675, 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), `81` = c(0, 0, 1, 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, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0.91538769,
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, 1, 0, 0, 0), `110` = 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, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0.85441768, 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, 0.61947266, 0, 0, 0), `140.5` = 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, 0, 0.5664111,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.6800275, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 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, 0.6300494, 0, 0, 0), `189` = 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, 0, 0.6234154, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.987181, 0, 1, 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, 0.6194727,
0, 0, 0), `222.5` = 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, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0.8632862, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 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, 0.3456471, 1, 0, 0), `278` = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0.61502309,
0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0.44036184, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.42691496, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.32234716, 0, 0, 0, 0.08890253, 0.67918373,
0, 0), `340` = c(0, 0, 0, 0, 0, 0, 0.583163048, 0, 0, 0, 0, 0,
1, 1, 0, 0, 0, 0, 1, 0.218194067, 0.325932107, 1, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.663889907, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0.789927058, 0, 0.44201215,
0, 0, 0, 0, 0.134339392, 0, 0.668372028, 0, 0, 0, 0, 0, 0, 0,
0.387740087, 0, 0, 0, 0, 0, 0, 0), `397` = c(0, 1, 0, 0, 0, 0,
1, 0, 1, 0, 0, 0, 0, 0.63953839, 0, 1, 0, 0, 0, 1, 1, 0.81888525,
0.89884151, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.51459601, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 0.75141988, 0, 0, 0, 0, 0, 0, 0, 0, 0.65763553, 1, 0, 0,
0, 0, 1, 0, 1, 0.67607045, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0,
0, 0.80260185, 0, 0, 0, 0, 0, 0, 1), `453.5` = c(0, 0.66069369,
0, 0, 0, 1, 0.57541627, 1, 1, 0, 0, 0, 1, 0.64615661, 0, 0.45209671,
0, 0, 0, 0.17022498, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0.02056466, 0.08765142, 1, 0, 0, 0, 0, 0, 1, 0.53107365,
0, 0, 1, 0, 0.47454662, 1, 0.58701126, 1, 0.83194495, 0, 0, 1,
0, 0, 0, 0, 0.04550448, 0, 1, 0, 0.65763553, 1, 1, 0.05581525,
0, 0, 0.78992706, 1, 0.80859968, 0.80441503, 1, 1, 0, 0.5866736,
0, 0, 0.75623688, 0.36602167, 0, 0, 1, 0, 0.31053744, 1, 0.52549512,
1, 0, 0, 0, 0, 0.51742419), `529` = c(0, 0.521435654, 0, 0, 1,
0, 0.175996209, 0, 0, 0, 1, 0, 0, 0, 0, 0.886059888, 0, 0, 0,
0.17022498, 0, 0, 0.643526362, 0, 0, 0, 0, 0, 0.438237074, 0.01594858,
0, 0.383182117, 0, 0, 0, 0, 0, 0, 1, 0.101027654, 1, 0, 0, 0,
0, 0, 0, 0.733614607, 0.89648659, 0, 0, 0.703255538, 0, 1, 0.383200069,
0.868653232, 1, 0.065540432, 0, 0, 0.221275397, 0.229618981,
0, 0, 0, 0.14131076, 0, 0.665883882, 0, 0.399096177, 0.570186116,
0.917797708, 0.349222091, 0, 0, 0.872987981, 0, 0, 1, 1, 0.58275186,
0, 0.211497647, 0, 0, 0.929066091, 0.826799766, 0, 0, 0, 0, 0.148043509,
0.802601847, 1, 0.780383116, 0, 0, 0, 0, 0.340224249), `580` = c(0,
0.437291195, 0, 0, 1, 0, 0.20731698, 0, 0, 0, 1, 0, 0, 0, 0,
0.719755907, 0, 0, 0, 0.033248127, 0, 0, 0, 0, 0, 0, 0, 0, 0.443305568,
0, 0, 0.558877749, 0, 0, 0, 0, 0, 0, 1, 0.171621995, 1, 0, 0,
0, 0, 0, 0, 0.28952456, 1, 1, 0, 0.470920245, 0, 0.690299657,
0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0.111377617, 0.740623952, 0,
0, 0.53029633, 0, 0.917797708, 0.395559317, 0, 0, 0.484708125,
0, 0, 0.174273053, 0, 0.693355663, 0, 0.050471201, 1, 0, 0, 1,
0, 0, 0, 0, 0.698359908, 0.298609118, 0.702753583, 0.926794372,
0, 0, 0, 0, 0.320673115), `630.5` = c(0, 0.52204783, 0, 0, 0,
0, 0.48815538, 0, 0, 0, 0, 1, 0, 0, 0, 0.82709638, 0, 0, 0, 0.09539534,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0.45656691,
0.74836669, 0, 0, 0, 0, 0, 0, 0, 0.95701562, 1, 0, 0.67884433,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0.65763553,
0, 0, 1, 0, 0, 0.77875219, 0, 0, 0.25002477, 0, 1, 0, 0, 0, 0,
0, 0.82679977, 0, 0, 0, 0, 0.84843874, 0.38138487, 0.79820877,
0, 0, 0, 0, 0, 0.51876177), `683.5` = c(0, 0.52429838, 0, 0,
0, 0, 0.59605685, 0, 0, 0, 0, 0, 0, 0, 0, 0.27845748, 0.28224351,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0.94942976, 0, 0, 0, 0, 0, 0.74467188, 0, 0, 0.31501511,
0, 0, 0, 0, 0, 0, 0, 0.73190143, 0, 0, 0, 0, 0, 0.90254266, 0.42921624,
0, 0, 0.56841245, 0, 0, 0.48306937, 0.54177946, 0, 0.70689046,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0.72875619, 0, 0, 0, 0, 1, 0.26676304,
0.77778861, 0, 0, 0, 0, 0, 0.17064527), `735.5` = c(1, 0.3768651,
0, 1, 0, 0, 0.51381348, 0, 0, 0, 0, 0, 0, 0, 0, 0.39914361, 0.22206677,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0.42663351, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.61727598,
0, 0, 0, 0, 0, 0, 0.70045244, 0, 0, 0, 0, 1, 0.62631217, 0, 0,
0, 0, 0, 0, 0.14653411, 1, 1, 0.27513455, 0, 0, 0, 0, 0.75025613,
0, 0, 0, 0, 0, 0.92484335, 0, 1, 0, 1, 0.84843874, 0.12198269,
0.58556836, 0, 0, 0, 0, 0, 0.09714178), `784` = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.45656691, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0.61727598, 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, 1, 1, 0, 0, 0.84843874, 0, 0, 0, 0, 0, 0, 0, 0
), `832` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.16189002, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0.2511846, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.23427262, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0.45750616,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0.86974453,
1, 0, 0, 0.48180864, 0, 0, 0, 0.93083267, 0, 0, 0, 0), `882.5` = 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, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3111616,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.63931007, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.73948906, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0), `926.5` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0.63485498, 0, 0, 0, 0, 0, 0, 1, 0,
0.68547559, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.35567368,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0.84973396, 0, 0, 1, 0), `973` = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.86100786, 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,
0.68128251, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.30811206, 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, 0.79599822, 0,
0, 0, 0), `1108` = 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, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0.75365872, 0, 0, 1, 0, 0, 0, 0, 0, 0.52862914,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.20061435, 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, 0.93083267, 0, 0, 0, 0), `1200` = 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, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0.67241551, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0.55638877, 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)), .Names = c("10",
"20", "52.5", "81", "110", "140.5", "189", "222.5", "278", "340",
"397", "453.5", "529", "580", "630.5", "683.5", "735.5", "784",
"832", "882.5", "926.5", "973", "1108", "1200"), row.names = c("at1g01050.1",
"at1g01080.1", "at1g01090.1", "at1g01220.1", "at1g01420.1", "at1g01470.1",
"at1g01800.1", "at1g01910.5", "at1g01920.2", "at1g01980.1", "at1g02020.2",
"at1g02100.2", "at1g02130.1", "at1g02140.1", "at1g02150.1", "at1g02500.2",
"at1g02560.1", "at1g02780.1", "at1g02880.3", "at1g02920.1", "at1g02930.2",
"at1g03030.1", "at1g03110.1", "at1g03130.1", "at1g03210.1", "at1g03220.1",
"at1g03230.1", "at1g03310.2", "at1g03330.1", "at1g03475.1", "at1g03630.2",
"at1g03680.1", "at1g03870.1", "at1g03900.1", "at1g04130.1", "at1g04170.1",
"at1g04190.1", "at1g04270.2", "at1g04350.1", "at1g04410.1", "at1g04420.1",
"at1g04530.1", "at1g04640.2", "at1g04690.1", "at1g04750.2", "at1g04810.1",
"at1g04850.1", "at1g04870.2", "at1g05010.1", "at1g05180.1", "at1g05320.3",
"at1g05350.1", "at1g05520.1", "at1g05560.1", "at1g05620.2", "at1g06000.1",
"at1g06110.1", "at1g06130.2", "at1g06290.1", "at1g06410.1", "at1g06550.1",
"at1g06560.1", "at1g06570.1", "at1g06620.1", "at1g06650.2", "at1g06680.2",
"at1g06900.1", "at1g07080.1", "at1g07110.1", "at1g07140.1", "at1g07240.1",
"at1g07250.1", "at1g07440.1", "at1g07750.1", "at1g07780.4", "at1g07890.8",
"at1g07990.1", "at1g08110.3", "at1g08200.1", "at1g08360.1", "at1g08490.1",
"at1g08520.1", "at1g08550.2", "at1g08820.2", "at1g08830.2", "at1g08980.1",
"at1g09010.1", "at1g09020.1", "at1g09130.2", "at1g09210.1", "at1g09300.2",
"at1g09310.1", "at1g09340.1", "at1g09430.1", "at1g09490.2", "at1g09620.1",
"at1g09640.1", "at1g09750.1", "at1g09760.1", "at1g09780.1"), class = "data.frame")
As you can see most of cells contain a number "0".
I would like to create a subsets of my data. Let's call them "clusters". I would like to put in the same cluster "elements" (row.names) which has any value (different than 0) in the same column.
So for example:
row.name Column1 Column2 Column3 Column4
at1g02560.1 0 0.12 0 0
at1g02020.2 1 0 0.55 0.31
at1g14560.2 0.15 0.47 0 0
As you see at1g02560.1 has a value only in column 2, there is one more row which contains any value in the column 2, it's at1g14560.2. That means that they belong to the same cluster (let's say cluster 1).
The problem is that at1g02020.2 and at1g14560.2 has a value different than 0 in the column 1. They should be in the same cluster aswell (cluster 2).
In summary at1g14560.2 belong to two different clusters (cluster 1 and cluster 2).
As a result I would like to add additional column to my data named clusters and fill it with the numbers.
Example:
row.name data data data data Clusters
at1g02560.1 1
at1g14560.2 1,2
at1g02020.2 2
Is it even possible to do with R ?

Yes, it is possible to do with R. Here's one way using apply:
res <- cbind(tbl_alles,
Clusters=apply(tbl_alles, 1, function(x) paste(which(x!=0), collapse=",")))
head(res[, c(1:5, ncol(res))])
# 10 20 52.5 81 110 Clusters
# at1g01050.1 0 0 0 0 0 17
# at1g01080.1 0 0 0 0 0 11,12,13,14,15,16,17
# at1g01090.1 0 0 0 1 0 4
# at1g01220.1 0 0 0 0 0 17
# at1g01420.1 0 0 0 0 0 13,14
# at1g01470.1 0 0 0 0 0 12

You may also try:
indx <- which(!!tbl_alles,arr.ind=T)
tbl_alles$Clusters <- tapply(indx[,2],indx[,1], FUN=paste, collapse=",")
tbl_alles[1:5,c(1:5, ncol(tbl_alles))]
# 10 20 52.5 81 110 Clusters
#at1g01050.1 0 0 0 0 0 17
#at1g01080.1 0 0 0 0 0 11,12,13,14,15,16,17
#at1g01090.1 0 0 0 1 0 4
#at1g01220.1 0 0 0 0 0 17
#at1g01420.1 0 0 0 0 0 13,14

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