This R package, ProPubBills, is an API wrapper around the ProPublica API for U.S. Congressional Bills. Users can include their API key, U.S. Congress, branch, and offset ranges, to return a dataframe of all results within those parameters.
getBills(key, congress, branch, type, numFrom, numTo)
returns a dataframe with all bills and info from the API within the arguments’ parameters
These examples (code on Github here) compare the number of enacted legislations by political party, from the 116th Congress (current) to the 113th Congress. For Congresses 110 to 112, the examples show word clouds for the top 50 words all introduced bills.
Here are the graphs created, with the code below
House Enacted Legislation by Party, 113th to 115th Congress
Senate Enacted Legislation by Party, 113th to 115th Congress
House and Senate Enacted Legislation by Party, 113th to 115th Congress
library(proPubBills)
library(reshape2)
library(dplyr)
library(ggplot2)
library(stringr)
library(tm)
library(wordcloud)
library(RColorBrewer)
library(SnowballC)
api_key <- Sys.getenv('api_key')
h_enact_115 <- getBills(api_key, "115", "house","enacted",1,8000 )
s_enact_115 <- getBills(api_key, "115", "senate","enacted",1,8000 )
h_enact_114 <- getBills(api_key,"114", "house","enacted",1,8000 )
s_enact_114 <- getBills(api_key,"114", "senate","enacted",1,8000 )
h_enact_113 <- getBills(api_key,"113", "house","enacted",1,500 )
s_enact_113 <- getBills(api_key,"113", "senate","enacted",1,500 )
h_enact_115$dup <- duplicated(h_enact_115$bill_id)
s_enact_115$dup <- duplicated(s_enact_115$bill_id)
hs_enact_115 <- rbind(h_enact_115, s_enact_115) %>%
filter(dup == FALSE) %>%
filter(!(str_detect(latest_major_action, "^Presented")))
h_enact_114$dup <- duplicated(h_enact_114$bill_id)
s_enact_114$dup <- duplicated(s_enact_114$bill_id)
hs_enact_114 <- rbind(h_enact_114, s_enact_114) %>%
filter(dup == FALSE)
hs_enact_114_1 <- hs_enact_114 %>%
filter((str_detect(latest_major_action, "^Became")) | (str_detect(latest_major_action, "^By")) | (str_detect(latest_major_action, "^Referred")))
h_enact_113$dup <- duplicated(h_enact_113$bill_id)
s_enact_113$dup <- duplicated(s_enact_113$bill_id)
hs_enact_113 <- rbind(h_enact_113, s_enact_113) %>%
filter(dup == FALSE)
hs_115_party <- as.data.frame(table(hs_enact_115$sponsor_party, hs_enact_115$bill_type)) %>%
mutate(total_p = sum(Freq)) %>%
mutate(congress = '115')
hs_114_party <- as.data.frame(table(hs_enact_114_1$sponsor_party, hs_enact_114_1$bill_type)) %>%
mutate(total_p = sum(Freq)) %>%
mutate(congress = '114')
hs_113_party <- as.data.frame(table(hs_enact_113$sponsor_party, hs_enact_113$bill_type)) %>%
mutate(total_p = sum(Freq)) %>%
mutate(congress = '113')
hs_113_to_115 <- rbind(hs_115_party, hs_114_party, hs_113_party) %>%
mutate(chamber = ifelse(str_detect(Var2, "^h"),"H","S")) %>%
select(-Var2) %>%
group_by(Var1, congress, chamber,total_p) %>%
summarise_all(funs(sum)) %>%
arrange(congress)
hs_113_to_115_1 <- hs_113_to_115 %>%
group_by(congress, chamber) %>%
summarise(chamber_total_p = sum(Freq))
hs_113_to_115_2 <- hs_113_to_115 %>%
left_join(hs_113_to_115_1, by=c("congress", "chamber")) %>%
mutate(party_chamber_percent = round((Freq/chamber_total_p),2)) %>%
mutate(party_congress_percent = round((Freq/total_p),2)) %>%
data.frame()
hs_113_to_115_3 <- hs_113_to_115_2 %>%
select(Var1, congress, chamber, party_chamber_percent) %>%
melt(id.var=c("Var1", "congress","chamber"))
hs_113_to_115_3_h <- hs_113_to_115_3 %>%
filter(chamber == 'H')
hs_113_to_115_3_s <- hs_113_to_115_3 %>%
filter(chamber == 'S')
house_113_to_115_graph <- ggplot(hs_113_to_115_3_h, aes(x=congress, y=value, group=Var1, fill=Var1)) +
geom_bar(stat="identity", position="dodge", size=0.25, width=0.8) +
scale_fill_manual(values=c("blue","red", "purple"))
senate_113_to_115_graph <- ggplot(hs_113_to_115_3_s, aes(x=congress, y=value, group=Var1, fill=Var1)) +
geom_bar(stat="identity", position="dodge", size=0.25, width=0.8) +
scale_fill_manual(values=c("blue","red", "purple"))
hs_113_to_115_4 <- hs_113_to_115_2 %>%
select(Var1, congress, party_congress_percent) %>%
melt(id.var=c("Var1", "congress"))
house_senate_113_to_115_graph <- ggplot(hs_113_to_115_4, aes(x=congress, y=value, group=Var1, fill=Var1)) +
geom_bar(stat="identity", position="dodge", size=0.25, width=0.8) +
scale_fill_manual(values=c("blue","red", "purple"))
110th Congress, Introduced Bills' Titles Wordcloud: Top 50 Words
111th Congress, Introduced Bills' Titles Wordcloud: Top 50 Words
112th Congress, Introduced Bills' Titles Wordcloud: Top 50 Words
h_intro_112 <- getBills(api_key,"112","house", "introduced", 1,12000)
s_intro_112 <- getBills(api_key,"112","senate", "introduced", 1,6000)
t112 <- as.data.frame(as.character(h_intro_112$title))
t112$`as.character(h_intro_112$title)` <- gsub('Act', '', t112$`as.character(h_intro_112$title)`)
t112$`as.character(h_intro_112$title)` <- gsub('2011', '', t112$`as.character(h_intro_112$title)`)
t112$`as.character(h_intro_112$title)` <- gsub('2012', '', t112$`as.character(h_intro_112$title)`)
t112$`as.character(h_intro_112$title)` <- gsub('United States', 'U.S.', t112$`as.character(h_intro_112$title)`)
t112$`as.character(h_intro_112$title)` <- gsub('amend', '', t112$`as.character(h_intro_112$title)`)
t112_2 <- paste(unlist(t112), collapse =" ")
t112_3 <- Corpus(VectorSource(t112_2))
t112_4 <- as.data.frame(as.character(t112_3))
t112_5 <- tm_map(t112_3, PlainTextDocument)
t112_6 <- tm_map(t112_5, removePunctuation)
t112_7 <- tm_map(t112_6, removeWords, stopwords('english'))
pal2 <- brewer.pal(11,"Spectral")
wordcloud_112 <- wordcloud(t112_7, max.words = 50, random.order = FALSE, color=pal2)
wordcloud_112
h_intro_111 <- getBills(api_key,"111","house", "introduced", 1,12000)
s_intro_111 <- getBills(api_key,"111","senate", "introduced", 1,6000)
t111 <- as.data.frame(as.character(h_intro_111$title))
t111$`as.character(h_intro_111$title)` <- gsub('Act', '', t111$`as.character(h_intro_111$title)`)
t111$`as.character(h_intro_111$title)` <- gsub('2010', '', t111$`as.character(h_intro_111$title)`)
t111$`as.character(h_intro_111$title)` <- gsub('2009', '', t111$`as.character(h_intro_111$title)`)
t111$`as.character(h_intro_111$title)` <- gsub('United States', 'U.S.', t111$`as.character(h_intro_111$title)`)
t111$`as.character(h_intro_111$title)` <- gsub('amend', '', t111$`as.character(h_intro_111$title)`)
t111_2 <- paste(unlist(t111), collapse =" ")
t111_3 <- Corpus(VectorSource(t111_2))
t111_5 <- tm_map(t111_3, PlainTextDocument)
t111_6 <- tm_map(t111_5, removePunctuation)
t111_7 <- tm_map(t111_6, removeWords, stopwords('english'))
wordcloud_111 <- wordcloud(t111_7, max.words = 50, random.order = FALSE, colors=pal2)
h_intro_110 <- getBills(api_key,"110","house", "introduced", 1,12000)
s_intro_110 <- getBills(api_key,"110","senate", "introduced", 1,6000)
t110 <- as.data.frame(as.character(h_intro_110$title))
t110$`as.character(h_intro_111$title)` <- gsub('Act', '', t110$`as.character(h_intro_110$title)`)
t110$`as.character(h_intro_111$title)` <- gsub('2010', '', t110$`as.character(h_intro_110$title)`)
t110$`as.character(h_intro_111$title)` <- gsub('2009', '', t110$`as.character(h_intro_110$title)`)
t110$`as.character(h_intro_111$title)` <- gsub('United States', 'U.S.', t110$`as.character(h_intro_110$title)`)
t110$`as.character(h_intro_111$title)` <- gsub('amend', '', t110$`as.character(h_intro_110$title)`)
t110_2 <- paste(unlist(t110), collapse =" ")
t110_3 <- Corpus(VectorSource(t110_2))
t110_5 <- tm_map(t110_3, PlainTextDocument)
t110_6 <- tm_map(t110_5, removePunctuation)
t110_7 <- tm_map(t110_6, removeWords, stopwords('english'))
wordcloud_110 <- wordcloud(t110_7, max.words = 50, random.order = FALSE, colors=pal2)
Bug reports and pull requests are welcome on GitHub here. This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.