Separate string column by row efficiently - r

Question

I'm trying to separate a string column into two pieces based on chopping up the string. It's best illustrated with example below. rowwise does work, but given the size of the data.frame, I'd like to use a more efficient method. How can I avoid using rowwise?
library(dplyr)
library(stringr)
library(tidyr)
#make data
a <- "(1, 10)"
b <- "(10, 20)"
c <- "(20, 30)"
df <- data.frame(size = c(a,b,c))
# Goal is to separate the 'size' column into 'lower' and 'upper' by
# extracting the value contained in the parens and split by a comma.
# Once the column is split into 'upper' and 'lower' I will perform
# additional operations.
# DESIRED RESULT
size lower upper
<fct> <chr> <chr>
1 (1, 10) 1 10
2 (10, 20) 10 20
3 (20, 30) 20 30
# WHAT I HAVE TRIED
> #This works... but too inefficient
> df %>%
+ rowwise() %>%
+ mutate(lower = str_split(size, ",") %>% .[[1]] %>% .[1] %>%
+ str_split("\\(") %>% .[[1]] %>% .[2])
size lower
<fct> <chr>
1 (1, 10) 1
2 (10, 20) 10
3 (20, 30) 20
> # I'm not sure why this doesn't work
> df %>%
+ mutate(lower = str_split(size, ",") %>% .[[1]] %>% .[1] %>%
+ str_split("\\(") %>% .[[1]] %>% .[2])
size lower
1 (1, 10) 1
2 (10, 20) 1
3 (20, 30) 1
> #Not obivous how to use separate (tidyr)
> df %>%
+ separate(size, sep=",", c("lower", "upper"))
lower upper
1 (1 10)
2 (10 20)
3 (20 30)

Answer 1

You don't state your goal explicitly, but it seems like you want to extract the first number from a string. This is easy with stringi::str_extract_first_regex
library(stringi)
stri_extract_first_regex(df$size, "[0-9]+")
# [1] "1" "10" "20"
So in your case,
df %>% mutate(lower = as.numeric(stri_extract_first_regex, size, "[0-9]+"))
You can extract all numbers with stri_extract_all_regex.
Based on your edits:
df$nums = str_extract_all(df$size, "[0-9]+")
df$lower = as.numeric(sapply(df$nums, `[[`, 1))
df$upper = as.numeric(sapply(df$nums, `[[`, 2))
df
# size nums lower upper
# 1 (1, 10) 1, 10 1 10
# 2 (10, 20) 10, 20 10 20
# 3 (20, 30) 20, 30 20 30
Another way to go is to get rid of the parens and whitespace and then use separate:
df %>%
mutate(just_nums = str_replace_all(size, "[^0-9,]", "")) %>%
separate(just_nums, into = c("lower", "upper"))
# size lower upper
# 1 (1, 10) 1 10
# 2 (10, 20) 10 20
# 3 (20, 30) 20 30
The regex pattern "[^0-9,]" matches everything except numbers and commas.

Answer 2

For rowwise operations, I prefer data.table.
Try this
library(data.table)
library(stringi)
#make data
a <- "(1, 10)"
b <- "(10, 20)"
c <- "(20, 30)"
dt <- data.table(c(a,b,c))
dt[, lower := tstrsplit(V1, ",")[1]]
dt[, lower:= stri_replace_all_regex(lower, '\\(', '')]
dt

Answer 3

An option is to use tidyr::separate after removing both ( and ) from the data.
library(tidyverse)
df %>% mutate(size = gsub("\\(|)","",size)) %>% # Both ( and ) has been removed.
separate(size, c("Min", "Max"), sep = ",")
# Min Max
# 1 1 10
# 2 10 20
# 3 20 30

Answer 4

You are almost there. Here is my explanation for two approach, one is similar to yours:
In the first code, I have used unnest_tokens from tidytext package, which can split words on a different rows, since you want to extract the first item before the comma(I have assumed it basis your example, although you should mention it). I have choosen the first row basis this by using filter command.
In the second code, I have used the regex (note you can also use here str_replace also). Here I am using map(since the items returned by str_split is a list) to iterate the returned items and pass each by gsub, which can replace the regex matched with the back referencing items. Also to select only the first item, I have used [[1]] in the end of gsub.
library(tidyverse)
library(stringr)
library(tidytext)
df %>%
unnest_tokens(lower,size, token="words",drop=F) %>%
filter(row_number()%%2==T)
df %>%
mutate(lower = map(str_split(df$size, ","), function(x)gsub("\\((\\w+)","\\1",x)[[1]]))
Output:
# size lower
# 1 (1, 10) 1
# 2 (10, 20) 10
# 3 (20, 30) 20
In case you want to extract both the terms before and after the commas, you can use extract function as well.
tidyr::extract(df, size, c("lower", "upper"), regex= "\\((\\w+),\\s+(\\w+)\\)")
Output:
# lower upper
# 1 1 10
# 2 10 20
# 3 20 30