Get a tidy data frame of classifications of all functions used in your analysis

Source: R/get_classifications.R

Get a tidy data frame of classifications of all functions used in your analysis

get_classifications(lexicon = NULL, include_duplicates = TRUE)

Arguments

lexicon

Character. The classification lexicon to retrieve. Either "crowdsource" or "leeklab". If NULL (default), will return all lexicons.
include_duplicates

Logical. Indicates whether to include all functions and classifications along with their score (default, TRUE) - this may result in multiple lines (with multiple classifications) for a single function. If FALSE, the most prevalent classification will be selected.

Value

A tbl_df with columns:

  • func: the function

  • classification: the classification

If include_duplicates = TRUE, will include a column:

  • score: the score

If lexicon is NULL, will include a column:

  • lexicon: the classification lexicon

Examples

# Get a data frame of all classifications
get_classifications()
#> # A tibble: 4,181 x 4
#>    lexicon     func  classification  score
#>    <chr>       <chr> <chr>           <dbl>
#>  1 crowdsource -     data cleaning  0.386 
#>  2 crowdsource -     visualization  0.174 
#>  3 crowdsource -     modeling       0.159 
#>  4 crowdsource -     evaluation     0.102 
#>  5 crowdsource -     exploratory    0.0833
#>  6 crowdsource -     setup          0.0458
#>  7 crowdsource -     import         0.0367
#>  8 crowdsource -     communication  0.0133
#>  9 crowdsource :     data cleaning  0.458 
#> 10 crowdsource :     exploratory    0.115 
#> # … with 4,171 more rows

# Get a data frame of the most prevalent classifications
get_classifications(include_duplicates = FALSE)
#> # A tibble: 1,957 x 3
#>    lexicon     func      classification
#>    <chr>       <chr>     <chr>         
#>  1 crowdsource -         data cleaning 
#>  2 crowdsource :         data cleaning 
#>  3 crowdsource ::        data cleaning 
#>  4 crowdsource :=        data cleaning 
#>  5 crowdsource !         data cleaning 
#>  6 crowdsource !=        data cleaning 
#>  7 crowdsource .         data cleaning 
#>  8 crowdsource .libPaths setup         
#>  9 crowdsource (         data cleaning 
#> 10 crowdsource [         data cleaning 
#> # … with 1,947 more rows

# Get a data frame of only `leeklab` classifications
get_classifications("leeklab")
#> # A tibble: 642 x 3
#>    func          classification  score
#>    <chr>         <chr>           <dbl>
#>  1 %%            data cleaning  1     
#>  2 %>%           data cleaning  0.864 
#>  3 %>%           exploratory    0.0909
#>  4 %>%           setup          0.0455
#>  5 %in%          data cleaning  1     
#>  6 %within%      data cleaning  1     
#>  7 abline        evaluation     1     
#>  8 abs           data cleaning  0.667 
#>  9 abs           modeling       0.333 
#> 10 ad.data.frame exploratory    1     
#> # … with 632 more rows