widyr | Widen , process , and re-tidy a dataset | Development Tools library

I have some data on published papers that looks like this:

I am new to shiny/flexdashboard and so far have been able to render plots and filter dataframe by using values from selectInput with help of req(input$user_input_value) .

ISSUE: To run kmeans I am taking user input for number of clusters which I am not able to code it in reactive format and getting error: object of type closure is not subsettable.

I want to cluster words that are similar using R and the tidytext package. I have created my tokens and would now like to convert it to a matrix in order to cluster it. I would like to try out a number of token techniques to see which provides the most compact clusters.

My code is as follows (taken from the docs of widyr package). I just cant make the next step. Can anyone help?

I am trying to calculate how many species are shared between region pairs. So if species A, B, and C all occur in, say, the US and Canada, but species D only occurs in Canada, then the number of shared species for the US-Canada pair == 3. I've used widyr() package in R to do this on a large dataset (>10,000 species in 50 countries), but I'm getting two errors:

1. If I analyze the whole dataset, some region pairs are missing, whereas if I just subset the two regions, then it works (gives the number of shared species for that pair). and 2) I tried with just a subset of the regions, and I get different answers for how many species are shared, compared to using the full dataset.

I've seen some similar posts about calculating the distance between pairs, and unique pairs, but it's not quite what I'm trying to do. I've never used widyr before and I can't figure out what the mistake is - I'm wondering if it's something with "first" and "last"?

I would like to understand the practical differences of following cases:

1. Use function fcm(objectname # generate feature co-occurrence matrix to calculate the absolute frequenies. Finally plot with function textplot_network().
2. I read tutorials like tidytextmining or a tutorial written by Andreas Niekler and Gregor Wiedemann who use igraph or widyr package. I want to plot correlated word pairs. Inspirated by tidytextmining tutorial which use the phi coefficient I will plot this correlation according the lambda coefficient.

I don't know how to plot the correlated word pairs with package quanteda. My idea is (maybe is not an efficient way) to compute textstat_collocations() and transform it to a tibble object and plot it with the functions of the widyr package. My open questions are: How can I split column collocation into two separate columns like item1 item2 and add select column lambda and save it and assign to a tibble object?

When I unnest_tokens for a list I enter manually; the output includes the row number each word came from.

I try to calculate the correlation between articles to get an indication of how often different article numbers appeared together in a document (invoice).

I have a Table from SQL query with two columns: Document Number, Article Number The Table is quite huge with 21k lines.

I have 5k document numbers and 700 different articles like the sample shown below. Its a data frame "db_belege".