Implement data_seek()

[strengejacke]

@easystats/maintainers What do you think about this function? I find it useful when I see a questionnaire of a data set and am looking for particular variables where the pattern I know are present in value or variable labels.

One problem is the name - this functions tries to find variables, but we already have data_find() (and find_columns() as alias), which work differently and also return the extracted variables - seek_variables(), on contrast, returns a data frame with a summary of found matches. find_variables()` is already taken in insight.

data_seek() is currently an alias.

[strengejacke]

btw, this is a slightly modified version of sjmisc::find_in_data()

[strengejacke]

Examples:

library(datawizard)

# seek variables with "Length" in variable name or labels
seek_variables(iris, "Length")
#> index |       column |       labels
#> -----------------------------------
#>     1 | Sepal.Length | Sepal.Length
#>     3 | Petal.Length | Petal.Length

# seek variables with "dependency" in names or labels
# column "e42dep" has a label-attribute "elder's dependency"
data(efc)
seek_variables(efc, "dependency")
#> index | column |             labels
#> -----------------------------------
#>     3 | e42dep | elder's dependency

# "female" only appears as value label attribute - default search is in
# variable names and labels only, so no match
seek_variables(efc, "female")
#> Can't export table to 
#>   text
#>   , data frame is empty.

# when we seek in all sources, we find the variable "e16sex"
seek_variables(efc, "female", seek = "all")
#> index | column |         labels
#> -------------------------------
#>     2 | e16sex | elder's gender

# typo, no match
seek_variables(iris, "Lenght")
#> Can't export table to 
#>   text
#>   , data frame is empty.

# typo, fuzzy match
seek_variables(iris, "Lenght", fuzzy = TRUE)
#> index |       column |       labels
#> -----------------------------------
#>     1 | Sepal.Length | Sepal.Length
#>     3 | Petal.Length | Petal.Length

# multiple pattern
seek_variables(efc, c("female", "dependency"), seek = "all")
#> index | column |             labels
#> -----------------------------------
#>     2 | e16sex |     elder's gender
#>     3 | e42dep | elder's dependency

^{Created on 2023-09-12 with reprex v2.0.2}

[rempsyc]

Ich mag, especially the integration with labels :)

[DominiqueMakowski]

As someone that doesn't use labels, I don't expect to use this function over data_find(), but I can see the benefits

[etiennebacher]

I like this, it would have been useful for me before. How about data_search() for the name?

[strengejacke]

As someone that doesn't use labels

You could at least use it for factor levels :-)

datawizard::data_seek(iris, "setosa", seek = "all")
#> index |  column |  labels
#> -------------------------
#>     5 | Species | Species

^{Created on 2023-09-12 with reprex v2.0.2}

[etiennebacher]

Thank you @strengejacke !