The goal of fedregs is to allow for easy exploration and analysis of
the Code of Federal
Regulation.
You can install fedregs using:
install.packages("fedregs")
# Or: devtools::install_github("NOAA-EDAB/fedregs")The Code of Federal
Regulation
is organized according to a consistent hierarchy: title, chapter, part,
subpart, section, and subsection. Each title within the CFR is (somewhat
haphazardly) divided into volumes and over time each chapter isn’t
consistently in the same volume. The cfr_text() function is the main
function in the package and it will return the text for a specified
part, including the associated subparts and sections. Behind the scenes,
cfr_text() and associated helper functions gather the volumes for a
given title/year combination and parses XML to determine the chapters,
parts, and subparts associated with each volume. Next, the text is
extracted for each subpart. The return_tidytext = TRUE argument will
return a tibble with the text in a
tidytext format. If
ngrams are your game, set token = "ngrams" and specify n.
library(fedregs)
library(dplyr)
library(tidyr)
library(ggplot2)
library(quanteda)
regs <- cfr_text(year = 2023,
title_number = 50,
chapter = 6,
part = 648,
#token = "ngrams", # uncomment for ngrams of length 2
#n = 2, # uncomment for ngrams of length 2
return_tidytext = TRUE,
verbose = FALSE)
head(regs)
## # A tibble: 6 × 6
## # Groups: year, title_number, chapter, part, subpart [6]
## subpart year title_number chapter part data
## <chr> <dbl> <dbl> <chr> <dbl> <list>
## 1 Subpart A—General Provisions 2023 50 VI 648 <tibble>
## 2 Subpart B—Management Measures for t… 2023 50 VI 648 <tibble>
## 3 Subpart C—Management Measures for A… 2023 50 VI 648 <tibble>
## 4 Subpart D—Management Measures for t… 2023 50 VI 648 <tibble>
## 5 Subpart E—Management Measures for t… 2023 50 VI 648 <tibble>
## 6 Subpart F—Management Measures for t… 2023 50 VI 648 <tibble>Now, we can unnest the tibble and take a peek at the data to see what data we have to play with.
regs %>%
unnest(cols = c(data)) %>% head(20) %>% pull(word)
## [1] "c" "a" "this" "part" "implements"
## [6] "the" "fishery" "management" "plans" "fmps"
## [11] "for" "the" "atlantic" "mackerel" "atlantic"
## [16] "chub" "mackerel" "longfin" "squid" "n"Not entirely unexpected, but there are quite a few common words that don’t mean anything. These “stop words” typically don’t have important significance and and are filtered out from search queries.
head(stopwords("english"))
## [1] "i" "me" "my" "myself" "we" "our"There are some other messes like punctuation, numbers, iths, Roman Numerals, web sites, and random letters (probably from indexed lists) that can be removed with some simple regex-ing. We can also convert the raw words to word stems to further aggregate our data.
stop_words <- tibble(word = stopwords("english"))
clean_words <- regs %>%
unnest(cols = c(data)) %>%
mutate(word = gsub("[[:punct:]]", "", word), # remove any remaining punctuation
word = gsub("^[[:digit:]]*", "", word)) %>% # remove digits (e.g., 1st, 1881a, 15th, etc)
anti_join(stop_words, by = "word") %>% # remove "stop words"
filter(is.na(as.numeric(word)),
!grepl("^m{0,4}(cm|cd|d?c{0,3})(xc|xl|l?x{0,3})(ix|iv|v?i{0,3})$",
word), # adios Roman Numerals
!grepl("\\b[a-z]{1}\\b", word), # get rid of one letter words
!grepl("\\bwww*.", word)) # get rid of web addresses
head(clean_words)
## # A tibble: 6 × 9
## # Groups: year, title_number, chapter, part, subpart [1]
## subpart year title_number chapter part SECTION_NAME SECTION_NUMBER values
## <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr>
## 1 Subpart A… 2023 50 VI 648 Purpose and… § 648.1 648.1
## 2 Subpart A… 2023 50 VI 648 Purpose and… § 648.1 648.1
## 3 Subpart A… 2023 50 VI 648 Purpose and… § 648.1 648.1
## 4 Subpart A… 2023 50 VI 648 Purpose and… § 648.1 648.1
## 5 Subpart A… 2023 50 VI 648 Purpose and… § 648.1 648.1
## 6 Subpart A… 2023 50 VI 648 Purpose and… § 648.1 648.1
## # ℹ 1 more variable: word <chr>Now we can look at binning and plotting the words
count_words <- clean_words %>%
group_by(word) %>%
summarise(n = n()) %>%
ungroup() %>%
arrange(-n) %>%
top_n(n = 50, wt = n) %>%
mutate(word = reorder(word, n))ggplot(count_words, aes(word, n)) +
geom_col() +
labs(xlab = NULL,
title = "Code of Federal Regulations",
subtitle = "Title 50, Chapter VI, Part 648",
caption = sprintf("Data accessed on %s from:\n https://www.gpo.gov/fdsys/browse/collectionCfr.action?collectionCode=CFR",
format(Sys.Date(), "%d %B %Y"))) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1),
axis.text.y = element_text(size = 8),
legend.direction = "horizontal",
legend.position = "bottom") +
coord_flip() +
NULL
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