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@hrbrmstr
Created April 18, 2018 16:07
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library(rvest)
library(stringi)
library(pluralize) # devtools::install_github("hrbrmstr/pluralize")
library(hrbrthemes)
library(tidyverse)
#' Retrieve the Federal Register main EO page so we can get the links for each POTUS
pg <- read_html("https://www.federalregister.gov/executive-orders")
#' Find the POTUS EO data nodes, excluding the one for "All"
html_nodes(pg, "ul.bulk-files") %>%
html_nodes(xpath = ".//li[span[a[contains(@href, 'json')]] and
not(span[contains(., 'All')])]") -> potus_nodes
#' Turn the POTUS info into a data frame with the POTUS name and EO JSON link,
#' then retrieve the JSON file and make a data frame of individual data elements
data_frame(
potus = html_nodes(potus_nodes, "span:nth-of-type(1)") %>% html_text(),
eo_link = html_nodes(potus_nodes, "a[href *= 'json']") %>%
html_attr("href") %>%
sprintf("https://www.federalregister.gov%s", .)
) %>%
mutate(eo = map(eo_link, jsonlite::fromJSON)) %>%
mutate(eo = map(eo, "results")) %>%
unnest() -> eo_df
glimpse(eo_df)
## Observations: 887
## Variables: 16
## $ potus <chr> "Donald Trump", "Donald Trump", "Donald Trump", "Donald Trump", "Donald Trump", "D...
## $ eo_link <chr> "https://www.federalregister.gov/documents/search.json?conditions%5Bcorrection%5D=...
## $ citation <chr> "82 FR 8351", "82 FR 8657", "82 FR 8793", "82 FR 8799", "82 FR 8977", "82 FR 9333"...
## $ document_number <chr> "2017-01799", "2017-02029", "2017-02095", "2017-02102", "2017-02281", "2017-02450"...
## $ end_page <int> 8352, 8658, 8797, 8803, 8982, 9338, 9341, 9966, 10693, 10696, 10698, 10700, 12287,...
## $ executive_order_notes <chr> NA, "See: EO 13807, August 15, 2017", NA, NA, "See: EO 13780, March 6, 2017", "Sup...
## $ executive_order_number <int> 13765, 13766, 13767, 13768, 13769, 13770, 13771, 13772, 13773, 13774, 13775, 13776...
## $ html_url <chr> "https://www.federalregister.gov/documents/2017/01/24/2017-01799/minimizing-the-ec...
## $ pdf_url <chr> "https://www.gpo.gov/fdsys/pkg/FR-2017-01-24/pdf/2017-01799.pdf", "https://www.gpo...
## $ publication_date <chr> "2017-01-24", "2017-01-30", "2017-01-30", "2017-01-30", "2017-02-01", "2017-02-03"...
## $ signing_date <chr> "2017-01-20", "2017-01-24", "2017-01-25", "2017-01-25", "2017-01-27", "2017-01-28"...
## $ start_page <int> 8351, 8657, 8793, 8799, 8977, 9333, 9339, 9965, 10691, 10695, 10697, 10699, 12285,...
## $ title <chr> "Minimizing the Economic Burden of the Patient Protection and Affordable Care Act ...
## $ full_text_xml_url <chr> "https://www.federalregister.gov/documents/full_text/xml/2017/01/24/2017-01799.xml...
## $ body_html_url <chr> "https://www.federalregister.gov/documents/full_text/html/2017/01/24/2017-01799.ht...
## $ json_url <chr> "https://www.federalregister.gov/api/v1/documents/2017-01799.json", "https://www.f...
#' Now, count by year and POTUS and see how many EOs were signed
mutate(eo_df, year = lubridate::year(signing_date)) %>%
mutate(year = as.Date(sprintf("%s-01-01", year))) %>%
count(year, potus) %>%
mutate(
potus = factor(
potus,
levels = c("Donald Trump", "Barack Obama", "George W. Bush", "William J. Clinton")
)
) %>%
ggplot(aes(year, n, group=potus)) +
geom_col(position = "stack", aes(fill = potus)) +
scale_x_date(
name = NULL,
expand = c(0,0),
breaks = as.Date(c("1993-01-01", "2001-01-01", "2009-01-01", "2017-01-01")),
date_labels = "%Y",
limits = as.Date(c("1992-01-01", "2020-12-31"))
) +
scale_y_comma(name = "# EOs") +
scale_fill_ipsum(name = NULL) +
labs(
title = "Number of Executive Orders Signed Per-Year, Per-POTUS",
subtitle = "1993-Present",
caption = "Source: Federal Register <https://www.federalregister.gov/executive-orders>"
) +
theme_ipsum_rc(grid = "Y") +
theme(legend.position = "bottom")
#' Famous first words?
mutate(eo_df, year = lubridate::year(signing_date)) %>%
mutate(year = as.Date(sprintf("%s-01-01", year))) %>%
mutate(first_word = stri_replace_first_regex(title, "^[[:digit:]]+\ *", "") %>%
stri_replace_first_regex("^(To|The|A|White House) ", "") %>%
stri_replace_first_fixed("Half-Day", "HalfDay") %>%
stri_extract_first_words()) -> titles_df
mutate(titles_df, first_word = singularize(first_word)) %>%
count(first_word, sort=TRUE) %>%
mutate(pct = n/sum(n)) %>%
filter(!stri_detect_regex(first_word, "President|Federal|National"))
#' Top 5 first words per POTUS
mutate(titles_df, first_word = singularize(first_word)) %>%
count(potus, first_word, sort=TRUE) %>%
filter(!stri_detect_regex(first_word, "President|Federal|National")) %>%
mutate(first_word = stri_replace_all_fixed(first_word, "Establishment", "Establishing")) %>%
mutate(first_word = stri_replace_all_fixed(first_word, "Amendment", "Amending")) -> first_words
group_by(first_words, potus) %>%
top_n(5) %>%
ungroup() %>%
distinct(first_word) %>%
pull(first_word) -> all_first_words
filter(first_words, first_word %in% all_first_words) %>%
mutate(
potus = factor(
potus,
levels = c("Donald Trump", "Barack Obama", "George W. Bush", "William J. Clinton")
)
) %>%
mutate(
first_word = factor(
first_word,
levels = rev(sort(unique(first_word)))
)
) -> first_df
ggplot(first_df, aes(n, first_word)) +
geom_segment(aes(xend=0, yend=first_word, color=potus), size=4) +
scale_x_comma(limits=c(0,40)) +
scale_y_discrete(limits = sort(unique(first_df$first_word))) +
facet_wrap(~potus, scales = "free", ncol = 2) +
labs(
x = "# EOs",
y = NULL,
title = "Top 5 Executive Order 'First Words' by POTUS",
subtitle = "1993-Present",
caption = "Source: Federal Register <https://www.federalregister.gov/executive-orders>"
) +
theme_ipsum_rc(grid="X", strip_text_face = "bold") +
theme(panel.spacing.x = unit(5, "lines")) +
theme(legend.position="none")
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