This is highly expermantal
parsesum provides tools to analyze source code. To this end, it leverages the R base parser.
For example, did you want to know how many functions you declared in your R package? Here is how you can get the names of all your named delared functions, enriched with some useful information, for example how many line each function declaration spans.
library(parsesum)
decs <- ls_fun_decs(".")
decs
#> # A tibble: 15 x 4
#> text n_lines source line1
#> <chr> <int> <chr> <int>
#> 1 ls_named_fun_decs_of_pd 14 ./R/analyse.R 3
#> 2 n_lines_of_expr 4 ./R/analyse.R 18
#> 3 ls_fun_decs 5 ./R/analyse.R 41
#> 4 fetch_child 3 ./R/analyse.R 47
#> 5 ls_fun_calls 5 ./R/analyse.R 56
#> 6 ls_fun_calls_of_pd 9 ./R/analyse.R 64
#> 7 token_is_fun_dec 3 ./R/expr-is.R 1
#> 8 token_is_left_assign 3 ./R/expr-is.R 5
#> 9 set_na_to 4 ./R/expr-is.R 9
#> 10 token_is_fun_call 3 ./R/expr-is.R 14
#> 11 token_is_namespace 3 ./R/expr-is.R 18
#> 12 parse_file 5 ./R/parse.R 4
#> 13 parse_r 4 ./R/parse.R 11
#> 14 parse_tests 4 ./R/parse.R 16
#> 15 parse_dir 8 ./R/parse.R 21Wanna know which file contains most delcarations?
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following object is masked from 'package:testthat':
#>
#> matches
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
decs %>%
group_by(.data$source) %>%
count() %>%
arrange(desc(n))
#> # A tibble: 3 x 2
#> # Groups: source [3]
#> source n
#> <chr> <int>
#> 1 ./R/analyse.R 6
#> 2 ./R/expr-is.R 5
#> 3 ./R/parse.R 4Or list according to longest name?
decs %>%
mutate(nchar = nchar(.data$text)) %>%
arrange(desc(.data$nchar))
#> # A tibble: 15 x 5
#> text n_lines source line1 nchar
#> <chr> <int> <chr> <int> <int>
#> 1 ls_named_fun_decs_of_pd 14 ./R/analyse.R 3 23
#> 2 token_is_left_assign 3 ./R/expr-is.R 5 20
#> 3 ls_fun_calls_of_pd 9 ./R/analyse.R 64 18
#> 4 token_is_namespace 3 ./R/expr-is.R 18 18
#> 5 token_is_fun_call 3 ./R/expr-is.R 14 17
#> 6 token_is_fun_dec 3 ./R/expr-is.R 1 16
#> 7 n_lines_of_expr 4 ./R/analyse.R 18 15
#> 8 ls_fun_calls 5 ./R/analyse.R 56 12
#> 9 ls_fun_decs 5 ./R/analyse.R 41 11
#> 10 fetch_child 3 ./R/analyse.R 47 11
#> 11 parse_tests 4 ./R/parse.R 16 11
#> 12 parse_file 5 ./R/parse.R 4 10
#> 13 set_na_to 4 ./R/expr-is.R 9 9
#> 14 parse_dir 8 ./R/parse.R 21 9
#> 15 parse_r 4 ./R/parse.R 11 7Or wanna see the function that is the shortest in terms of lines used?
decs %>%
arrange(.data$n_lines)
#> # A tibble: 15 x 4
#> text n_lines source line1
#> <chr> <int> <chr> <int>
#> 1 fetch_child 3 ./R/analyse.R 47
#> 2 token_is_fun_dec 3 ./R/expr-is.R 1
#> 3 token_is_left_assign 3 ./R/expr-is.R 5
#> 4 token_is_fun_call 3 ./R/expr-is.R 14
#> 5 token_is_namespace 3 ./R/expr-is.R 18
#> 6 n_lines_of_expr 4 ./R/analyse.R 18
#> 7 set_na_to 4 ./R/expr-is.R 9
#> 8 parse_r 4 ./R/parse.R 11
#> 9 parse_tests 4 ./R/parse.R 16
#> 10 ls_fun_decs 5 ./R/analyse.R 41
#> 11 ls_fun_calls 5 ./R/analyse.R 56
#> 12 parse_file 5 ./R/parse.R 4
#> 13 parse_dir 8 ./R/parse.R 21
#> 14 ls_fun_calls_of_pd 9 ./R/analyse.R 64
#> 15 ls_named_fun_decs_of_pd 14 ./R/analyse.R 3How many function calls does your package contain?
calls <- ls_fun_calls()
calls
#> # A tibble: 33 x 4
#> text source line1 namespace
#> <chr> <chr> <int> <chr>
#> 1 token_is_fun_dec ./R/analyse.R 4 <NA>
#> 2 getParseData ./R/parse.R 5 utils
#> 3 parse ./R/parse.R 5 <NA>
#> 4 token_is_left_assign ./R/analyse.R 5 <NA>
#> 5 c ./R/expr-is.R 6 <NA>
#> 6 which ./R/analyse.R 6 <NA>
#> 7 as_tibble ./R/parse.R 6 <NA>
#> 8 lead ./R/analyse.R 7 <NA>
#> 9 add_column ./R/parse.R 7 <NA>
#> 10 lead ./R/analyse.R 8 <NA>
#> # … with 23 more rows
calls %>%
nrow()
#> [1] 33