Splitting collumn with numbers "by nothing" in a dataframe-CodePudding

I am currently trying to import a txt dataset which I only have numbers in it. When I am importing it I use;

k2= read.delim("dummy.txt", stringsAsFactors = F, header = F, sep="",colClasses = "character")

but unfortunately it seems that this gives the columns combined with only one column like this. And it didn't separate "by nothing". Read table gives the same result.

I am trying to separate the column into multiple columns where every single number will be in a separate column (there are 297 numbers and all will be separate). I tried these with tidyr;

1- k2 %>% separate(as.character(k2$V1), 1:297, "")

2- apply(k2,2,k2 %>% separate(as.character(k2$V1), 1:297, sep= ""))

But get this message in both: Must extract column with a single valid subscript. x Subscript var has size 555514 but must be size 1.

I would like to either import my dataset with everything splitted or split them with a code.

I am not well with the apply family and good tutorial suggestions are also heartfully welcome.

CodePudding user response：

You may scan the file as character and use strsplit on "nothing", rbind it and type.convert.

scan('foo.txt', what='A', quiet=TRUE) |> strsplit('') |> do.call(what=rbind) |> 
  type.convert(as.is=TRUE) |> as.data.frame()
#   V1 V2 V3 V4 V5 V6 V7 V8 V9 V10
# 1  1  1  1  1  2  2  2  1  9   9
# 2  1  1  2  2  2  9  9  1  1   9
# 3  1  9  1  1  2  9  2  1  2   2
# 4  9  9  2  2  2  2  1  1  2   2
# 5  9  9  1  1  2  2  2  2  2   9

Note: R >= 4.1 used.

Data:

set.seed(42)
replicate(5, paste(sample(c(1, 2, 9), 10, replace=TRUE), collapse='')) |> 
  as.matrix() |> writeLines('foo.txt')

CodePudding user response：

library(dplyr)

# nums <- read.table('nums.txt', colClasses = "character")

Say you have file structure like this:

nums <- structure(list(V1 = c("003111222", "212251256")), 
                  class = "data.frame", row.names = c(NA, -2L))

Then you can iterate over all values and put substring values into separate columns which will form you a dataframe.

outs <- lapply(nums, function(x){
  x <- unname(x)
  rows <- lapply(1:nchar(x), function(i){
    val <- substr(x, i, i)
    data.frame(val)
  }) %>% 
    bind_cols
  colnames(rows) <- paste0('col',1:nchar(x))
  rows
}) %>% bind_rows

print(outs)