Convert a DataFrame into Adjacency/Weights Matrix in R












7















I have a DataFrame, df.



n is a column denoting the number of groups in the x column.
x is a column containing the comma-separated groups.



df <- data.frame(n = c(2, 3, 2, 2), 
x = c("a, b", "a, c, d", "c, d", "d, b"))

> df
n x
2 a, b
3 a, c, d
2 c, d
2 d, b


I would like to convert this DataFrame into a weights matrix where the row and column names are the unique values of the groups in df$c, and the elements represent the number of times each of the groups appear together in df$c.



The output should look like this:



m <- matrix(c(0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 2, 0), nrow = 4, ncol = 4)
rownames(m) <- letters[1:4]; colnames(m) <- letters[1:4]

> m
a b c d
a 0 1 1 1
b 1 0 0 1
c 1 0 0 2
d 1 1 2 0









share|improve this question


















  • 1





    your question is unclear. I can't see c in df. it only has n and x

    – YOLO
    4 hours ago













  • c is one of the x values. Its a frequency table of how often different letters appear in the same line in x

    – RAB
    4 hours ago











  • Do you mean df$x instead of df$c in the bolded part of the question?

    – mikoontz
    3 hours ago
















7















I have a DataFrame, df.



n is a column denoting the number of groups in the x column.
x is a column containing the comma-separated groups.



df <- data.frame(n = c(2, 3, 2, 2), 
x = c("a, b", "a, c, d", "c, d", "d, b"))

> df
n x
2 a, b
3 a, c, d
2 c, d
2 d, b


I would like to convert this DataFrame into a weights matrix where the row and column names are the unique values of the groups in df$c, and the elements represent the number of times each of the groups appear together in df$c.



The output should look like this:



m <- matrix(c(0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 2, 0), nrow = 4, ncol = 4)
rownames(m) <- letters[1:4]; colnames(m) <- letters[1:4]

> m
a b c d
a 0 1 1 1
b 1 0 0 1
c 1 0 0 2
d 1 1 2 0









share|improve this question


















  • 1





    your question is unclear. I can't see c in df. it only has n and x

    – YOLO
    4 hours ago













  • c is one of the x values. Its a frequency table of how often different letters appear in the same line in x

    – RAB
    4 hours ago











  • Do you mean df$x instead of df$c in the bolded part of the question?

    – mikoontz
    3 hours ago














7












7








7








I have a DataFrame, df.



n is a column denoting the number of groups in the x column.
x is a column containing the comma-separated groups.



df <- data.frame(n = c(2, 3, 2, 2), 
x = c("a, b", "a, c, d", "c, d", "d, b"))

> df
n x
2 a, b
3 a, c, d
2 c, d
2 d, b


I would like to convert this DataFrame into a weights matrix where the row and column names are the unique values of the groups in df$c, and the elements represent the number of times each of the groups appear together in df$c.



The output should look like this:



m <- matrix(c(0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 2, 0), nrow = 4, ncol = 4)
rownames(m) <- letters[1:4]; colnames(m) <- letters[1:4]

> m
a b c d
a 0 1 1 1
b 1 0 0 1
c 1 0 0 2
d 1 1 2 0









share|improve this question














I have a DataFrame, df.



n is a column denoting the number of groups in the x column.
x is a column containing the comma-separated groups.



df <- data.frame(n = c(2, 3, 2, 2), 
x = c("a, b", "a, c, d", "c, d", "d, b"))

> df
n x
2 a, b
3 a, c, d
2 c, d
2 d, b


I would like to convert this DataFrame into a weights matrix where the row and column names are the unique values of the groups in df$c, and the elements represent the number of times each of the groups appear together in df$c.



The output should look like this:



m <- matrix(c(0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 2, 1, 1, 2, 0), nrow = 4, ncol = 4)
rownames(m) <- letters[1:4]; colnames(m) <- letters[1:4]

> m
a b c d
a 0 1 1 1
b 1 0 0 1
c 1 0 0 2
d 1 1 2 0






r matrix adjacency-matrix






share|improve this question













share|improve this question











share|improve this question




share|improve this question










asked 4 hours ago









Rich PaulooRich Pauloo

2,188930




2,188930








  • 1





    your question is unclear. I can't see c in df. it only has n and x

    – YOLO
    4 hours ago













  • c is one of the x values. Its a frequency table of how often different letters appear in the same line in x

    – RAB
    4 hours ago











  • Do you mean df$x instead of df$c in the bolded part of the question?

    – mikoontz
    3 hours ago














  • 1





    your question is unclear. I can't see c in df. it only has n and x

    – YOLO
    4 hours ago













  • c is one of the x values. Its a frequency table of how often different letters appear in the same line in x

    – RAB
    4 hours ago











  • Do you mean df$x instead of df$c in the bolded part of the question?

    – mikoontz
    3 hours ago








1




1





your question is unclear. I can't see c in df. it only has n and x

– YOLO
4 hours ago







your question is unclear. I can't see c in df. it only has n and x

– YOLO
4 hours ago















c is one of the x values. Its a frequency table of how often different letters appear in the same line in x

– RAB
4 hours ago





c is one of the x values. Its a frequency table of how often different letters appear in the same line in x

– RAB
4 hours ago













Do you mean df$x instead of df$c in the bolded part of the question?

– mikoontz
3 hours ago





Do you mean df$x instead of df$c in the bolded part of the question?

– mikoontz
3 hours ago












2 Answers
2






active

oldest

votes


















4














Here's a very rough and probably pretty inefficient solution using tidyverse for wrangling and combinat to generate permutations.



library(tidyverse)
library(combinat)

df <- data.frame(n = c(2, 3, 2, 2),
x = c("a, b", "a, c, d", "c, d", "d, b"))

df %>%
## Parse entries in x into distinct elements
mutate(split = map(x, str_split, pattern = ', '),
flat = flatten(split)) %>%
## Construct 2-element subsets of each set of elements
mutate(combn = map(flat, combn, 2, simplify = FALSE)) %>%
unnest(combn) %>%
## Construct permutations of the 2-element subsets
mutate(perm = map(combn, permn)) %>%
unnest(perm) %>%
## Parse the permutations into row and column indices
mutate(row = map_chr(perm, 1),
col = map_chr(perm, 2)) %>%
count(row, col) %>%
## Long to wide representation
spread(key = col, value = nn, fill = 0) %>%
## Coerce to matrix
column_to_rownames(var = 'row') %>%
as.matrix()





share|improve this answer































    2














    Using Base R, you could do something like below



    a = strsplit(as.character(df$x),', ')
    b = unique(unlist(a))
    d = unlist(sapply(a,combn,2,toString))
    e = data.frame(table(factor(d,c(paste(b,b,sep=','),combn(b,2,toString)))))
    f = read.table(text = do.call(paste,c(sep =',', e)),sep=',',strip.white = T)
    g = xtabs(V3~V1+V2,f)
    g[lower.tri(g)] = t(g)[lower.tri(g)]
    g
    V2
    V1 a b c d
    a 0 1 1 1
    b 1 0 0 0
    c 1 0 0 2
    d 1 0 2 0





    share|improve this answer























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      2 Answers
      2






      active

      oldest

      votes








      2 Answers
      2






      active

      oldest

      votes









      active

      oldest

      votes






      active

      oldest

      votes









      4














      Here's a very rough and probably pretty inefficient solution using tidyverse for wrangling and combinat to generate permutations.



      library(tidyverse)
      library(combinat)

      df <- data.frame(n = c(2, 3, 2, 2),
      x = c("a, b", "a, c, d", "c, d", "d, b"))

      df %>%
      ## Parse entries in x into distinct elements
      mutate(split = map(x, str_split, pattern = ', '),
      flat = flatten(split)) %>%
      ## Construct 2-element subsets of each set of elements
      mutate(combn = map(flat, combn, 2, simplify = FALSE)) %>%
      unnest(combn) %>%
      ## Construct permutations of the 2-element subsets
      mutate(perm = map(combn, permn)) %>%
      unnest(perm) %>%
      ## Parse the permutations into row and column indices
      mutate(row = map_chr(perm, 1),
      col = map_chr(perm, 2)) %>%
      count(row, col) %>%
      ## Long to wide representation
      spread(key = col, value = nn, fill = 0) %>%
      ## Coerce to matrix
      column_to_rownames(var = 'row') %>%
      as.matrix()





      share|improve this answer




























        4














        Here's a very rough and probably pretty inefficient solution using tidyverse for wrangling and combinat to generate permutations.



        library(tidyverse)
        library(combinat)

        df <- data.frame(n = c(2, 3, 2, 2),
        x = c("a, b", "a, c, d", "c, d", "d, b"))

        df %>%
        ## Parse entries in x into distinct elements
        mutate(split = map(x, str_split, pattern = ', '),
        flat = flatten(split)) %>%
        ## Construct 2-element subsets of each set of elements
        mutate(combn = map(flat, combn, 2, simplify = FALSE)) %>%
        unnest(combn) %>%
        ## Construct permutations of the 2-element subsets
        mutate(perm = map(combn, permn)) %>%
        unnest(perm) %>%
        ## Parse the permutations into row and column indices
        mutate(row = map_chr(perm, 1),
        col = map_chr(perm, 2)) %>%
        count(row, col) %>%
        ## Long to wide representation
        spread(key = col, value = nn, fill = 0) %>%
        ## Coerce to matrix
        column_to_rownames(var = 'row') %>%
        as.matrix()





        share|improve this answer


























          4












          4








          4







          Here's a very rough and probably pretty inefficient solution using tidyverse for wrangling and combinat to generate permutations.



          library(tidyverse)
          library(combinat)

          df <- data.frame(n = c(2, 3, 2, 2),
          x = c("a, b", "a, c, d", "c, d", "d, b"))

          df %>%
          ## Parse entries in x into distinct elements
          mutate(split = map(x, str_split, pattern = ', '),
          flat = flatten(split)) %>%
          ## Construct 2-element subsets of each set of elements
          mutate(combn = map(flat, combn, 2, simplify = FALSE)) %>%
          unnest(combn) %>%
          ## Construct permutations of the 2-element subsets
          mutate(perm = map(combn, permn)) %>%
          unnest(perm) %>%
          ## Parse the permutations into row and column indices
          mutate(row = map_chr(perm, 1),
          col = map_chr(perm, 2)) %>%
          count(row, col) %>%
          ## Long to wide representation
          spread(key = col, value = nn, fill = 0) %>%
          ## Coerce to matrix
          column_to_rownames(var = 'row') %>%
          as.matrix()





          share|improve this answer













          Here's a very rough and probably pretty inefficient solution using tidyverse for wrangling and combinat to generate permutations.



          library(tidyverse)
          library(combinat)

          df <- data.frame(n = c(2, 3, 2, 2),
          x = c("a, b", "a, c, d", "c, d", "d, b"))

          df %>%
          ## Parse entries in x into distinct elements
          mutate(split = map(x, str_split, pattern = ', '),
          flat = flatten(split)) %>%
          ## Construct 2-element subsets of each set of elements
          mutate(combn = map(flat, combn, 2, simplify = FALSE)) %>%
          unnest(combn) %>%
          ## Construct permutations of the 2-element subsets
          mutate(perm = map(combn, permn)) %>%
          unnest(perm) %>%
          ## Parse the permutations into row and column indices
          mutate(row = map_chr(perm, 1),
          col = map_chr(perm, 2)) %>%
          count(row, col) %>%
          ## Long to wide representation
          spread(key = col, value = nn, fill = 0) %>%
          ## Coerce to matrix
          column_to_rownames(var = 'row') %>%
          as.matrix()






          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 3 hours ago









          Dan HicksDan Hicks

          1876




          1876

























              2














              Using Base R, you could do something like below



              a = strsplit(as.character(df$x),', ')
              b = unique(unlist(a))
              d = unlist(sapply(a,combn,2,toString))
              e = data.frame(table(factor(d,c(paste(b,b,sep=','),combn(b,2,toString)))))
              f = read.table(text = do.call(paste,c(sep =',', e)),sep=',',strip.white = T)
              g = xtabs(V3~V1+V2,f)
              g[lower.tri(g)] = t(g)[lower.tri(g)]
              g
              V2
              V1 a b c d
              a 0 1 1 1
              b 1 0 0 0
              c 1 0 0 2
              d 1 0 2 0





              share|improve this answer




























                2














                Using Base R, you could do something like below



                a = strsplit(as.character(df$x),', ')
                b = unique(unlist(a))
                d = unlist(sapply(a,combn,2,toString))
                e = data.frame(table(factor(d,c(paste(b,b,sep=','),combn(b,2,toString)))))
                f = read.table(text = do.call(paste,c(sep =',', e)),sep=',',strip.white = T)
                g = xtabs(V3~V1+V2,f)
                g[lower.tri(g)] = t(g)[lower.tri(g)]
                g
                V2
                V1 a b c d
                a 0 1 1 1
                b 1 0 0 0
                c 1 0 0 2
                d 1 0 2 0





                share|improve this answer


























                  2












                  2








                  2







                  Using Base R, you could do something like below



                  a = strsplit(as.character(df$x),', ')
                  b = unique(unlist(a))
                  d = unlist(sapply(a,combn,2,toString))
                  e = data.frame(table(factor(d,c(paste(b,b,sep=','),combn(b,2,toString)))))
                  f = read.table(text = do.call(paste,c(sep =',', e)),sep=',',strip.white = T)
                  g = xtabs(V3~V1+V2,f)
                  g[lower.tri(g)] = t(g)[lower.tri(g)]
                  g
                  V2
                  V1 a b c d
                  a 0 1 1 1
                  b 1 0 0 0
                  c 1 0 0 2
                  d 1 0 2 0





                  share|improve this answer













                  Using Base R, you could do something like below



                  a = strsplit(as.character(df$x),', ')
                  b = unique(unlist(a))
                  d = unlist(sapply(a,combn,2,toString))
                  e = data.frame(table(factor(d,c(paste(b,b,sep=','),combn(b,2,toString)))))
                  f = read.table(text = do.call(paste,c(sep =',', e)),sep=',',strip.white = T)
                  g = xtabs(V3~V1+V2,f)
                  g[lower.tri(g)] = t(g)[lower.tri(g)]
                  g
                  V2
                  V1 a b c d
                  a 0 1 1 1
                  b 1 0 0 0
                  c 1 0 0 2
                  d 1 0 2 0






                  share|improve this answer












                  share|improve this answer



                  share|improve this answer










                  answered 2 hours ago









                  OnyambuOnyambu

                  15.5k1520




                  15.5k1520






























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