The goal of tidycode is to allow users to analyze R expressions in a tidy way.
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")
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.
We can use the expressions from this matahari tibble to extract the names of the packages included. We can also create a data frame that will include all functions of the packages included.
(pkg_names <- ls_packages(m$expr)) #> [1] "broom" "glue" pkg_functions <- get_package_functions(m$expr)
Create a data frame of your expressions, splitting each into individual functions.
u <- unnest_calls(m, expr)
Merge in the package names
u <- u %>% dplyr::left_join(pkg_functions) %>% dplyr::select(func, args, line, package) #> Joining, by = "func" u #> # A tibble: 8 x 4 #> func args line package #> <chr> <list> <int> <chr> #> 1 library <list [1]> 1 base #> 2 library <list [1]> 2 base #> 3 <- <list [2]> 3 base #> 4 lm <named list [2]> 3 stats #> 5 ~ <list [2]> 3 base #> 6 <- <list [2]> 4 base #> 7 tidy <list [1]> 4 broom #> 8 glue_data <list [2]> 5 glue
Add in the function classifications!
u %>%
dplyr::inner_join(
get_classifications("crowdsource", include_duplicates = FALSE)
)
#> Joining, by = "func"
#> # A tibble: 8 x 5
#> func args line package classification
#> <chr> <list> <int> <chr> <chr>
#> 1 library <list [1]> 1 base setup
#> 2 library <list [1]> 2 base setup
#> 3 <- <list [2]> 3 base data cleaning
#> 4 lm <named list [2]> 3 stats modeling
#> 5 ~ <list [2]> 3 base modeling
#> 6 <- <list [2]> 4 base data cleaning
#> 7 tidy <list [1]> 4 broom modeling
#> 8 glue_data <list [2]> 5 glue 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
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