get_degree_df_wo_unk: Get degree df without unk
In ConesaLab/MDM: Complete MDM Network Analysis for 16S rRNA Data

Description Usage Arguments Value Examples

Calculate degree, betweenness, and closeness scores for each node in network (igraph), excluding MDM taxa at each rank, and store results in a dataframe. 7 dataframes are returned as output, 1 for each rank. Nodes are represented by row. Each column represents a separate network centrality measure.

1	get_degree_df_wo_unk(graph_wo_unk)

graph_wo_unk

List of 7 graphs, each only including known taxa for that taxonomic rank (from Kingdom to Species), produced by get_graph_wo_unk function.

Returns List of 7 dataframes of degree, betweenness, and closeness scores for all nodes in network.

`df1`	Dataframe of nodes with known Kingdom network measure scores
`df2`	Dataframe of nodes with known Phylum network measure scores
`df3`	Dataframe of nodes with known Class network measure scores
`df4`	Dataframe of nodes with known Order network measure scores
`df5`	Dataframe of nodes with known Family network measure scores
`df6`	Dataframe of nodes with known Genus network measure scores
`df7`	Dataframe of nodes with known Species network measure scores

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function(graph_wo_unk){
  met_degree_df_wo_unk_l = list()
  for(i in 1:length(graph_wo_unk)){
    graph_tax_level = names(graph_wo_unk)[[i]]
    print(graph_tax_level)
    graph = graph_wo_unk[[i]]
    graph_degree_df <- degree_calc_f(graph)
    met_degree_df_wo_unk_l[[graph_tax_level]] = graph_degree_df
  }
  return(met_degree_df_wo_unk_l)
}