R: table frequencies of letters in string based on Alphabet - r

Question

I need to compute letter frequencies of a large list of words. For each of the locations in the word (first, second,..), I need to find how many times each letter (a-z) appeared in the list and then table the data according to the word positon.
For example, if my word list is: words <- c("swims", "seems", "gills", "draws", "which", "water")
then the result table should like that:
letter
first position
second position
third position
fourth position
fifth position
a
0
1
1
0
0
b
0
0
0
0
0
c
0
0
0
1
0
d
1
0
0
0
0
e
0
1
1
1
0
f
0
0
0
0
0
...continued until z
...
...
...
...
...
All words are of same length (5).
What I have so far is:
alphabet <- letters[1:26]
words.df <- data.frame("Words" = words)
words.df <- words.df %>% mutate("First_place" = substr(words.df$words,1,1))
words.df <- words.df %>% mutate("Second_place" = substr(words.df$words,2,2))
words.df <- words.df %>% mutate("Third_place" = substr(words.df$words,3,3))
words.df <- words.df %>% mutate("Fourth_place" = substr(words.df$words,4,4))
words.df <- words.df %>% mutate("Fifth_place" = substr(words.df$words,5,5))
x1 <- words.df$First_place
x1 <- table(factor(x1,alphabet))
x2 <- words.df$Second_place
x2 <- table(factor(x2,alphabet))
x3 <- words.df$Third_place
x3 <- table(factor(x3,alphabet))
x4 <- words.df$Fourth_place
x4 <- table(factor(x4,alphabet))
x5 <- words.df$Fifth_place
x5 <- table(factor(x5,alphabet))
My code is not effective and gives tables to each letter position sepretely. All help will be appreicated.

Answer 1

in base R use table:
table(let = unlist(strsplit(words,'')),pos = sequence(nchar(words)))
pos
let 1 2 3 4 5
a 0 1 1 0 0
c 0 0 0 1 0
d 1 0 0 0 0
e 0 1 1 1 0
g 1 0 0 0 0
h 0 1 0 0 1
i 0 1 2 0 0
l 0 0 1 1 0
m 0 0 0 2 0
r 0 1 0 0 1
s 2 0 0 0 4
t 0 0 1 0 0
w 2 1 0 1 0
Note that if you need all the values from a-z then use
table(factor(unlist(strsplit(words,'')), letters), sequence(nchar(words)))
Also to get a dataframe you could do:
d <- table(factor(unlist(strsplit(words,'')), letters), sequence(nchar(words)))
cbind(letters = rownames(d), as.data.frame.matrix(d))

Answer 2

Here is a tidyverse solution using dplyr, purrr, and tidyr:
strsplit(words.df$Words, "") %>%
map_dfr(~setNames(.x, seq_along(.x))) %>%
pivot_longer(everything(),
values_drop_na = T,
names_to = "pos",
values_to = "letter") %>%
count(pos, letter) %>%
pivot_wider(names_from = pos,
names_glue = "pos{pos}",
id_cols = letter,
values_from = n,
values_fill = 0L)
Output
letter pos1 pos2 pos3 pos4 pos5 pos6 pos7 pos8 pos9 pos10 pos11
1 a 65 127 88 38 28 17 14 5 3 0 0
2 b 58 4 7 9 2 4 2 0 1 0 0
3 c 83 14 45 37 20 19 8 3 2 0 0
4 C 2 0 0 0 0 0 0 0 0 0 0
5 d 43 8 33 47 21 22 9 3 1 1 0
6 e 45 156 81 132 114 69 48 23 14 2 2
7 f 54 11 18 10 5 2 1 0 0 0 0
8 g 23 7 27 21 15 8 7 1 0 0 0
9 h 38 56 6 28 21 10 3 3 1 1 0
10 i 25 106 51 58 38 28 8 4 1 0 0
11 j 6 0 2 2 0 0 0 0 0 0 0
12 k 9 1 6 22 12 0 0 0 0 0 0
13 l 45 41 54 54 36 9 7 6 0 2 0
14 m 45 8 31 19 8 8 4 2 0 0 0
15 n 23 42 75 53 34 41 16 16 4 2 0
16 o 28 167 76 41 38 9 11 2 1 0 0
17 p 72 20 34 30 8 3 1 1 1 0 0
18 q 7 2 1 0 0 0 0 0 0 0 0
19 r 46 74 92 59 56 45 12 9 1 1 0
20 s 119 8 67 35 31 22 18 4 1 0 0
21 t 65 30 73 83 57 42 31 9 6 3 1
22 u 12 66 39 36 20 7 7 2 0 0 0
23 v 8 7 20 12 5 5 1 0 0 0 0
24 w 53 8 13 10 2 3 0 1 0 0 0
25 y 6 4 16 15 17 15 10 5 6 1 1
26 x 0 12 5 0 0 0 0 0 0 0 0
27 z 0 0 1 0 0 0 1 1 0 0 0