Use language objects for commands

[wlandau]

drake currently uses character strings to represent code.

drake_plan(x = simulate_data())
#> # A tibble: 1 x 2
#>   target command        
#>   <chr>  <chr>          
#> 1 x      simulate_data()

Now that we have a language-based DSL (#233) we can start to move away from text. Specific considerations:

1. Printing. Better display for list columns if all elements have the same type r-lib/pillar#34 has been quiet, so we may need a special S3 class and a print() method for drake plans. The trouble is that data frame operations often drop attributes.
2. Wildcard functions. In functions like evaluate_plan(), we should deparse the command column, do the text replacement, safe_parse() to get language objects, and then restore the S3 class.
3. Command standardization. I think this part will clean up and speed up nicely. We just need to remember to use all.vars(functions = TRUE) instead of grepl() to set look_for_ignore in standardize_command().

[wlandau]

Using a new_tibble(subclass = "drake_plan") with custom printing actually seems to work. In the 700 branch, drake deparses list columns of language objects and then overloads pillar::type_sum() to show <lang> instead of <chr> when printing. Deparsing should not be a bottleneck because it happens after the rows are subsetted.

Now, I need to update the wildcard functions and the tests. Could take some time.

[wlandau]

Fixed via #708. cc @krlmlr

[wlandau]

Printing seems to work out okay now. Note the <expr> type summary below. Hopefully the deparsing is not too slow. I have not yet decided whether to skip most of the deparsing or how best to do that.

library(drake)
drake_plan(
  raw_data = readxl::read_excel(file_in("raw_data.xlsx")),
  data = raw_data %>%
    mutate(Species = forcats::fct_inorder(Species)),
  hist = create_plot(data),
  fit = lm(Sepal.Width ~ Petal.Width + Species, data),
  report = rmarkdown::render(
    knitr_in("report.Rmd"),
    output_file = file_out("report.html"),
    quiet = TRUE
  )
)
#> # A tibble: 5 x 2
#>   target   command                                                         
#>   <chr>    <expr>                                                          
#> 1 raw_data readxl::read_excel(file_in("raw_data.xlsx"))                   …
#> 2 data     raw_data %>% mutate(Species = forcats::fct_inorder(Species))   …
#> 3 hist     create_plot(data)                                              …
#> 4 fit      lm(Sepal.Width ~ Petal.Width + Species, data)                  …
#> 5 report   rmarkdown::render(knitr_in("report.Rmd"), output_file = file_ou…