Stringr Explorer

What do you want to do with strings?

A couple of days ago, I passed by Sarah Drasner’s Array Explorer. It was through a retweet by Emily Robinson, who proposed the idea of a similar app for working with strings in R. I thought about giving it a try, and I deployed a preliminary Shiny App Stringr Explorer; which is still under development.

In the following sections, I will give a brief about the data extracted from the package documentation to use in the Stringr Explorer app, and I’ll be glad to get better suggestions and contributions.

First of all here’s the Shiny App

Creating a dataset with stringr functions info

I wanted to create a dataframe with stringr functions along with their usage and corresponding examples. I didn’t want to write everything from scratch, so I tried to extract whatever I can get from the documentation.

Package function names

To extract package functions I used:

str_fn_names <- ls("package:stringr") 

Function help text

Then I used utils:::.getHelpFile to get the help text associated with each function (This is what lives inside man/ directory)

str_fn_help <- map(str_fn_names,
                   ~ utils:::.getHelpFile(help(.x, package = "stringr")))

Function title

From the help text, I could get the title that corresponds to each function, as follows:

str_fn_title <- map_chr(str_fn_help,
                        ~ tools:::.Rd_get_metadata(.x, "title"))

so for instance, str_extract title will be Extract matching patterns from a string.

This might not be the best thing, but I wanted to start with the original titles, to save the time of writing everything. I said that I can modify them later, if they are not descriptive enough.

Function usage text

Then I got the usage text, removed any empty lines, and glued the extracted lines.

str_fn_usage <- map(str_fn_help, ~ tools:::.Rd_get_metadata(.x, "usage")) %>%  
  map(~.x[.x!=""]) %>% 
  map(~paste(.x, collapse = "\n")) %>% 

so here, str_extract usage will be:

str_extract(string, pattern)
str_extract_all(string, pattern, simplify = FALSE)

Using the extracted data, I created a dataframe to gather everything:

dat <- tibble(str_fn_names = str_fn_names,
              str_fn_help = str_fn_help,
              str_fn_title = str_fn_title,
              str_fn_usage = str_fn_usage)

But what about the functions examples?

I tried different ways to extract parts of the examples from the help text, but I wanted to have more control and get certain examples. So I had to write them in a dataframe and join with the created dat. The examples are listed here, and still being updated.

I wasn’t sure if the data structure I used was the best for the problem, but I started with it till I move on and revisit it, or maybe get recommendation from others!

So here’s the preliminary version of the app, I am still working on it. Meanwhile if anyone wants to share suggestions or contribute, you could send me or contribute on Github repo

See also