Analyze Lines of R Code the Tidy Way

Analyze lines of R code using tidy principles. This allows you to input lines of R code and output a data frame with one row per function included. Additionally, it facilitates code classification via included lexicons.

tidycode

The goal of tidycode is to allow users to analyze R expressions in a tidy way.

Installation

You can install tidycode from CRAN with:

install.packages("tidycode")

You can install the development version of tidycode from github with:

# install.packages("remotes")
remotes::install_github("LucyMcGowan/tidycode")

Example

Read in existing code

Using the matahari package, we can read in existing code, either as a string or a file, and turn it into a matahari tibble using matahari::dance_recital().

code <- "
library(broom)
library(glue)
m <- lm(mpg ~ am, data = mtcars)
t <- tidy(m)
glue_data(t, 'The point estimate for term {term} is {estimate}.')
"

m <- matahari::dance_recital(code)

Alternatively, you may already have a matahari tibble that was recorded during an R session.

Load the tidycode library.

library(tidycode)

We can use the expressions from this matahari tibble to extract the names of the packages included.

(pkg_names <- ls_packages(m$expr))
#> [1] "broom" "glue"

Create a data frame of your expressions, splitting each into individual functions.

u <- unnest_calls(m, expr)

Add in the function classifications!

u %>%
  dplyr::inner_join(
    get_classifications("crowdsource", include_duplicates = FALSE)
    )
#> Joining, by = "func"
#> # A tibble: 8 x 8
#>   value      error  output   warnings messages func   args   classification
#>   <list>     <list> <list>   <list>   <list>   <chr>  <list> <chr>         
#> 1 <chr [8]>  <NULL> <chr [1… <chr [1… <chr [0… libra… <list… setup         
#> 2 <chr [9]>  <NULL> <chr [1… <chr [1… <chr [0… libra… <list… setup         
#> 3 <lm>       <NULL> <chr [1… <chr [0… <chr [0… <-     <list… data cleaning 
#> 4 <lm>       <NULL> <chr [1… <chr [0… <chr [0… lm     <list… modeling      
#> 5 <lm>       <NULL> <chr [1… <chr [0… <chr [0… ~      <list… modeling      
#> 6 <tibble [… <NULL> <chr [1… <chr [0… <chr [0… <-     <list… data cleaning 
#> 7 <tibble [… <NULL> <chr [1… <chr [0… <chr [0… tidy   <list… modeling      
#> 8 <glue>     <NULL> <chr [1… <chr [0… <chr [0… glue_… <list… communication

We can also remove a list of “stopwords”. We have a function, get_stopfuncs() that lists common “stopwords”, frequently used operators, like %>% and +.

u %>%
  dplyr::inner_join(
    get_classifications("crowdsource", include_duplicates = FALSE)
    ) %>%
  dplyr::anti_join(get_stopfuncs()) %>%
  dplyr::select(func, classification)
#> Joining, by = "func"
#> Joining, by = "func"
#> # A tibble: 5 x 2
#>   func      classification
#>   <chr>     <chr>         
#> 1 library   setup         
#> 2 library   setup         
#> 3 lm        modeling      
#> 4 tidy      modeling      
#> 5 glue_data communication