R separate lines into columns specified by start and end - r

Question

I'd like to split a dataset made of character strings into columns specified by start and end.
My dataset looks something like this:
>head(templines,3)
[1] "201801 1 78"
[2] "201801 2 67"
[3] "201801 1 13"
and i'd like to split it by specifying my columns using the data dictionary:
>dictionary
col_name col_start col_end
year 1 4
week 5 6
gender 8 8
age 11 12
so it becomes:
year week gender age
2018 01 1 78
2018 01 2 67
2018 01 1 13
In reality the data comes from a long running survey and the white spaces between some columns represent variables that are no longer collected. It has many variables so i need a solution that would scale.
In tidyr::separate it looks like you can only split by specifying the position to split at, rather than the start and end positions. Is there a way to use start / end?
I thought of doing this with read_fwf but I can't seem to be able to use it on my already loaded dataset. I only managed to get it to work by first exporting as a txt and then reading from this .txt:
write_lines(templines,"t1.txt")
read_fwf("t1.txt",
fwf_positions(start = dictionary$col_start,
end = dictionary$col_end,
col_names = dictionary$col_name)
is it possible to use read_fwf on an already loaded dataset?

Answer 1

Answering your question directly: yes, it is possible to use read_fwf with already loaded data. The relevant part of the docs is the part about the argument file:
Either a path to a file, a connection, or literal data (either a single string or a raw vector).
...
Literal data is most useful for examples and tests.
It must contain at least one new line to be recognised as data (instead of a path).
Thus, you can simply collapse your data and then use read_fwf:
templines %>%
paste(collapse = "\n") %>%
read_fwf(., fwf_positions(start = dictionary$col_start,
end = dictionary$col_end,
col_names = dictionary$col_name))
This should scale to multiple columns, and is fast for many rows (on my machine for 1 million rows and four columns about half a second).
There are a few warnings regarding parsing failures, but they stem from your dictionary. If you change the last line to age, 11, 12 it works as expected.

Answer 2

A solution with substring:
library(data.table)
x <- transpose(lapply(templines, substring, dictionary$col_start, dictionary$col_end))
setDT(x)
setnames(x, dictionary$col_name)
# > x
# year week gender age
# 1: 2018 01 1 78
# 2: 2018 01 2 67
# 3: 2018 01 1 13

Answer 3

How about this?
data.frame(year=substr(templines,1,4),
week=substr(templines,5,6),
gender=substr(templines,7,8),
age=substr(templines,11,13))

Answer 4

Using base R:
m = list(`attr<-`(dat$col_start,"match.length",dat$col_end-dat$col_start+1))
d = do.call(rbind,regmatches(x,rep(m,length(x))))
setNames(data.frame(d),dat$col_name)
year week gender age
1 2018 01 1 78
2 2018 01 2 67
3 2018 01 1 13
DATA USED:
x = c("201801 1 78", "201801 2 67", "201801 1 13")
dat=read.table(text="col_name col_start col_end
year 1 4
week 5 6
gender 8 8
age 11 13 ",h=T)

Answer 5

We could use separate from tidyverse
library(tidyverse)
data.frame(Col = templines) %>%
separate(Col, into = dictionary$col_name, sep= head(dictionary$col_end, -1))
# year week gender age
#1 2018 01 1 78
#2 2018 01 2 67
#3 2018 01 1 13
The convert = TRUE argument can also be used with separate to have numeric columns as output
tibble(Col = templines) %>%
separate(Col, into = dictionary$col_name,
sep= head(dictionary$col_end, -1), convert = TRUE)
# A tibble: 3 x 4
# year week gender age
# <int> <int> <int> <int>
#1 2018 1 1 78
#2 2018 1 2 67
#3 2018 1 1 13
data
dictionary <- structure(list(col_name = c("year", "week", "gender", "age"),
col_start = c(1L, 5L, 8L, 11L), col_end = c(4L, 6L, 8L, 13L
)), .Names = c("col_name", "col_start", "col_end"),
class = "data.frame", row.names = c(NA, -4L))
templines <- c("201801 1 78", "201801 2 67", "201801 1 13")

Answer 6

This is an explicit function which seems to be working the way you wanted.
split_func<-function(char,ref,name,start,end){
res<-data.table("ID" = 1:length(char))
for(i in 1:nrow(ref)){
res[,ref[[name]][i] := substr(x = char,start = ref[[start]][i],stop = ref[[end]][i])]
}
return(res)
}
I have created the same input files as you:
templines<-c("201801 1 78","201801 2 67","201801 1 13")
dictionary<-data.table("col_name" = c("year","week","gender","age"),"col_start" = c(1,5,8,11),
"col_end" = c(4,6,8,13))
# col_name col_start col_end
#1: year 1 4
#2: week 5 6
#3: gender 8 8
#4: age 11 13
As for the arguments,
char - The character vector with the values you want to split
ref - The reference table or dictionary
name - The column number in the reference table containing the column names you want
start - The column number in the reference table containing the start points
end - The column number in the reference table containing the stop points
If I use this function with these inputs, I get the following result:
out<-split_func(char = templines,ref = dictionary,name = 1,start = 2,end = 3)
#>out
# ID year week gender age
#1: 1 2018 01 1 78
#2: 2 2018 01 2 67
#3: 3 2018 01 1 13
I had to include an "ID" column to initiate the data table and make this easier. In case you want to drop it later you can just use:
out[,ID := NULL]
Hope this is closer to the solution you were looking for.