Regular expressions are your friends:

R> ## also adds missing ')' and sets column name
R> group<-data.frame(group=c("12357e", "12575e", "197e18", "e18947"))  )
R> group
   group
1 12357e
2 12575e
3 197e18
4 e18947

Now use gsub() with the simplest possible replacement pattern: empty string:

R> group$groupNoE <- gsub("e", "", group$group)
R> group
   group groupNoE
1 12357e    12357
2 12575e    12575
3 197e18    19718
4 e18947    18947
R> 

It's not clear to me how you want to deal with entries where you have more than one number. That aside and generally, a more convenient option is to use readr::parse_number, rather than using stringr::str_detect/stringr::str_remove. parse_number takes care of additional text, units and thousands separators.

If you want to keep only the first number (in the cases where there are more than one number per entry), you can do

library(tidyverse)
Example %>% mutate(Column1 = parse_number(Column1))
#  Column1
#1    1000
#2   50000
#3    1000
#4     100

Or if you want to keep both/multiple numbers, I suggest using separate_rows to separate entries based on a comma followed by a whitespace, before using readr::parse_number.

Example %>%
    separate_rows(Column1, sep = ",\\s") %>%
    mutate(Column1 = parse_number(Column1))
## A tibble: 5 × 1
#  Column1
#    <dbl>
#1    1000
#2   50000
#3    1000
#4     100
#5    1000

Update

To separate keys and values, here is an option; please see inline comments for explanations:

library(tidyverse)
Example %>%
    # Separate multiple comma-separated entries into rows
    separate_rows(Column1, sep = ",\\s") %>%
    # Swap "(value) key" > "key (value)" %>%
    mutate(Column1 = str_replace(
        Column1, "^(\\(.+\\))\\s(\\w+)$", "\\2 \\1")) %>%
    # Separate "key (value)" into columns
    separate(Column1, c("key", "value"), sep = "\\s", fill = "left") %>%
    # Parse number
    mutate(value = parse_number(value))
## A tibble: 5 × 2
#  key       value
#  <chr>     <dbl>
#1 Pineapple  1000
#2 Roger     50000
#3 NA         1000
#4 First       100
#5 Second     1000

Sample data

Example <- data.frame(Column1 = c(
    "Pineapple (50,000) Roger", 
    "($1,000)", 
    "First ($100), Second ($1,000)"))