I have a data frame with words and I want to extract the letter and bigram composition for each word.

Data:

df$text

[1] "table"
[2] "run"
[3] "mug"`

And in the end I want to receive the output:

 1      a b c d e..z aa ab bb...zz 
table   1 1 0 0 0..0  0 1  0    0

First, I was trying to extract all the letters using Quanteda:

text <- c("table", "run", "mug")

dict <- dictionary(list(a= "a",
                        b = "b",
                        c = "c",
                        d = "d", 
                        e = "e",
                        f = "f",
                        g = "g", 
                        h = "h",
                        i = "i",
                        j = "j",
                        k = "k",
                        l ="l",
                        m = "m",
                        n = "n",
                        o = "o",
                        p = "p",
                        q = "q",
                        r = "r", 
                        s = "s",
                        t = "t",
                        u = "u",
                        v = "v", 
                        w = "w",
                        x = "x",
                        y = "y",
                        z = "z"))


corp<- corpus(text)

tokens(corp) |>
  tokens_lookup(dictionary = dict) |>
  dfm()

But it did not work out:

Document-feature matrix of: 3 documents, 26 features (100.00% sparse) and 0 docvars.
       features
docs    a b c d e f g h i j
  text1 0 0 0 0 0 0 0 0 0 0
  text2 0 0 0 0 0 0 0 0 0 0
  text3 0 0 0 0 0 0 0 0 0 0
[ reached max_nfeat ... 16 more features ]

I am totally new in this, and if you have any hint how to do that, please, help. Thank you!