Date: 2024-10-01

# Important Miscellany

#### 2024-10-01

## The Importance of this miscellany

The features of `strex` that were deemed the most interesting have been given their own vignettes. However, the package was intended as a miscellany of useful functions, so the functions demonstrated here encapsulate the spirit of this package, i.e. functions that save R string manipulators time.

    library(strex)

## Could this be numeric?

Sometimes you don’t want to know whether something is numeric, just whether or not it could be. Now you can find out with `str_can_be_numeric()`.

    str_can_be_numeric(c("1a", "abc", "5", "2e7", "seven"))
    #> [1] FALSE FALSE  TRUE  TRUE FALSE

## Currency

To get currencies and amounts mentioned in strings, there are `str_extract_currencies()` and `str_nth_currency()`, `str_first_currency()` and `str_last_currency()`. `str_first_currency()` just returns the first currency amount. `str_last_currency()` returns the last. `str_nth_currency()` allows you to get the second, third and so on. `str_extract_currencies()` returns all currency amounts mentioned in a string.

    string <- c("Alan paid £5", "Joe paid $7")
    str_first_currency(string)
    #>   string_num       string curr_sym amount
    #> 1          1 Alan paid £5        £      5
    #> 2          2  Joe paid $7        $      7
    string <- c("€1 is $1.17", "£1 is $1.29")
    str_nth_currency(string, n = c(1, 2))
    #>   string_num      string curr_sym amount
    #> 1          1 €1 is $1.17        €   1.00
    #> 2          2 £1 is $1.29        $   1.29
    str_last_currency(string) # only gets the first mentioned
    #>   string_num      string curr_sym amount
    #> 1          1 €1 is $1.17        $   1.17
    #> 2          2 £1 is $1.29        $   1.29
    str_extract_currencies(string)
    #>   string_num      string curr_sym amount
    #> 1          1 €1 is $1.17        €   1.00
    #> 2          1 €1 is $1.17        $   1.17
    #> 3          2 £1 is $1.29        £   1.00
    #> 4          2 £1 is $1.29        $   1.29

## Extract a single element of a string

This is a simple wrapper around `stringr::str_sub()`.

    string <- "abcdefg"
    str_sub(string, 3, 3)
    #> [1] "c"
    str_elem(string, 3) # simpler and more exressive
    #> [1] "c"

## Extract numbers and non-numeric elements

    string <- c("aa1bbb2ccc3", "xyz7ayc8jzk99elephant")
    str_extract_numbers(string)
    #> [[1]]
    #> [1] 1 2 3
    #>
    #> [[2]]
    #> [1]  7  8 99
    str_extract_non_numerics(string)
    #> [[1]]
    #> [1] "aa"  "bbb" "ccc"
    #>
    #> [[2]]
    #> [1] "xyz"      "ayc"      "jzk"      "elephant"

## Split a string by its numbers

    string <- c("aa1bbb2ccc3", "xyz7ayc8jzk99elephant")
    str_split_by_numbers(string)
    #> [[1]]
    #> [1] "aa"  "1"   "bbb" "2"   "ccc" "3"
    #>
    #> [[2]]
    #> [1] "xyz"      "7"        "ayc"      "8"        "jzk"      "99"       "elephant"

## Force a file name to have an extension

We can give files a given extension, leaving them alone if they already have it.

    string <- c("spreadsheet1.csv", "spreadsheet2")
    str_give_ext(string, "csv")
    #> [1] "spreadsheet1.csv" "spreadsheet2.csv"

If the file already has an extension, we can append one or replace it.

    str_give_ext(string, "xls") # append
    #> [1] "spreadsheet1.csv.xls" "spreadsheet2.xls"
    str_give_ext(string, "csv", replace = TRUE) # replace
    #> [1] "spreadsheet1.csv" "spreadsheet2.csv"

## Strip away a file extension

    string <- c("spreadsheet1.csv", "spreadsheet2")
    str_before_last_dot(string)
    #> [1] "spreadsheet1" "spreadsheet2"

## Remove quoted bits from a string

    string <- "I hate having these \"quotes\" in the middle of my strings."
    cat(string)
    #> I hate having these "quotes" in the middle of my strings.
    str_remove_quoted(string)
    #> [1] "I hate having these  in the middle of my strings."

## Split camel case

I’m not mad on CamelCase, I often want to deconstruct it.

    string <- c("CamelVar1", c("CamelVar2"))
    str_split_camel_case(string)
    #> [[1]]
    #> [1] "Camel" "Var1"
    #>
    #> [[2]]
    #> [1] "Camel" "Var2"

## Convert a string to a vector

This is something I did a lot to avoid using regular expression. Don’t do it for that purpose. Learn regex. https://regexone.com/ is a very good start.

    string <- "R is good."
    str_to_vec(string)
    #>  [1] "R" " " "i" "s" " " "g" "o" "o" "d" "."

## Trim anything, not just whitespace

What if something is needlessly surrounded by parentheses and we want to get rid of them?

    string <- "(((Why all the parentheses?)))"
    string %>%
      str_trim_anything(coll("("), side = "left") %>%
      str_trim_anything(coll(")"), side = "r")
    #> [1] "Why all the parentheses?"

## Remove duplicated bits of strings

    string <- c("I often write the word *my* twice in a row in my my sentences.")
    str_singleize(string, " my")
    #> [1] "I often write the word *my* twice in a row in my sentences."