Remove Last Character in R inplace - r

Question

I came from a Python background and I am working in R with this data df.
name age
1 Anon1 52a
2 Anon2 62
3 Anon3 44a
4 Anon4 30
5 Anon5 110a
Using R language, how can I remove the a in the last part of the age column and do data modification in place??
(just like Python using inplace=True)
Can I attain it using
df$Age[which(df$Age == `a pattern`)] <- ""

Answer 1

This is a perfect use case for parse_number from readr package (it is in tidyverse:
library(dplyr)
library(readr)
df %>%
mutate(age = parse_number(age))
name age
1 Anon1 52
2 Anon2 62
3 Anon3 44
4 Anon4 30
5 Anon5 110
data:
df <- structure(list(name = c("Anon1", "Anon2", "Anon3", "Anon4", "Anon5"
), age = c("52a", "62", "44a", "30", "110a")), class = "data.frame", row.names = c("1",
"2", "3", "4", "5"))

Answer 2

You could use sub here:
df$age <- sub("a$", "", df$age, fixed=TRUE)

Answer 3

#A tidy solution
library(dplyr)
library(stringr)
df <- data.frame(name=c("anon1","anon2"),age=c("52","37a"))
df <- df %>%
mutate(age = str_extract(age,"^\\d+"))
df
name age
1 anon1 52
2 anon2 37

Answer 4

Here are two approaches. No packages are used.
1) We remove all non-digit characters where in a regular expression \D means non-digit. If we knew that only a could appear as a non-digit we could , instead, use "a" as the first argument to gsub and if we knew it only appears once we could use sub instead of gsub.
Also it is easier to debug code if you don't overwrite variables since then you always know that a particular variable is in its original state. Instead assign the result to a new variable.
transform(DF, age = as.numeric(gsub("\\D", "", age)))
This could also be written using pipes:
transform(DF, age = age |> gsub(pattern = "\\D", replacement = "") |> as.numeric())
2) We can use scan specifying that a is a comment character.
transform(DF, age = scan(text = age, comment.char = "a", quiet = TRUE))
Note
Lines <- "
name age
1 Anon1 52a
2 Anon2 62
3 Anon3 44a
4 Anon4 30
5 Anon5 110a"
DF <- read.table(text = Lines)

Answer 5

The inplace modifier in python refers to making a change without creating a copy. The data.table package in R allows for this (called replace by reference).
df <- read.table(text="
name age
1 Anon1 52a
2 Anon2 62
3 Anon3 44a
4 Anon4 30
5 Anon5 110a")
library(data.table)
library(stringi)
setDT(df)[, age:=stri_extract(age, regex='^\\d+')]
df
The first clause (setDT(df)) converts df to a data.table by reference (e.g., without making a copy), and the second clause ([, age:=...]) replaces the values in column age with ... also by reference.