## ----include = FALSE---------------------------------------------------------- LOCAL <- identical(Sys.getenv("LOCAL"), "TRUE") knitr::opts_chunk$set(comment = "#>", collapse = TRUE, message = FALSE) ## ----eval = LOCAL------------------------------------------------------------- # library(legislatoR) # library(tibble) # # # get "Core" table for the United States House ------------------------------------------ # usa_house_core <- get_core(legislature = "usa_house") # glimpse(usa_house_core) # # # get "Political" table for the German Bundestag ---------------------------------------- # deu_political <- get_political(legislature = "deu") # glimpse(deu_political) # # # get "IDs" table for the Spanish Congreso ---------------------------------------------- # esp_ids <- get_ids(legislature = "esp") # glimpse(esp_ids) ## ----eval = LOCAL------------------------------------------------------------- # library(dplyr) # # # combine "Core" and "Political" tables for the Irish Dail ------------------------------ # irl_join <- left_join(x = get_core(legislature = "irl"), # y = get_political(legislature = "irl"), # by = "pageid") # glimpse(irl_join) # # # then add the "Social" table ----------------------------------------------------------- # irl_join <- left_join(x = irl_join, # y = get_social(legislature = "irl"), # by = "wikidataid") # glimpse(irl_join) # # # get "Core" table for Scottish Liberal Democrats # sco_subset <- semi_join(x = get_core(legislature = "sco"), # y = filter(get_political(legislature = "sco"), # party == "Scottish Liberal Democrats"), # by = "pageid") # glimpse(sco_subset) # # # combine "Core" and "Political" tables for German Bundestag CDU/CSU and AfD members ---- # deu_subset <- inner_join(x = get_core(legislature = "deu"), # y = filter(get_political(legislature = "deu"), # party %in% c("CDU", "CSU", "AfD")), # by = "pageid") # glimpse(deu_subset) # # # combine "Core" and "Political" tables for female legislators from the 37th Canadian # # House of Commons ---------------------------------------------------------------------- # can_subset <- inner_join(x = filter(get_core(legislature = "can"), sex == "female"), # y = filter(get_political(legislature = "can"), session == 37), # by = "pageid") # glimpse(can_subset) # # # combine "Core", "Traffic", and "Social" tables for UK House Commons members with # # Twitter handles ----------------------------------------------------------------------- # uk_subset <- left_join(x = inner_join(x = get_core(legislature = "gbr"), # y = filter(get_social(legislature = "gbr"), !is.na(twitter)), # by = "wikidataid"), # y = get_traffic(legislature = "gbr"), # by = "pageid") # glimpse(uk_subset) ## ----eval = LOCAL------------------------------------------------------------- # library(magrittr) # # # combine "Core", "IDs", and "Portraits" tables for the Austrian Nationalrat ------------ # aut_join <- get_core(legislature = "aut") %>% # left_join(get_ids(legislature = "aut"), # by = "wikidataid") %>% # left_join(get_portrait(legislature = "aut"), # by = "pageid") # glimpse(aut_join) # # # get "Core" table for high-profile politicians (top 1% of Wikipedia page views) of # # French Assemblée ---------------------------------------------------------------------- # fra_subset <- get_traffic(legislature = "fra") %>% # group_by(pageid) %>% # summarise(total_traffic = sum(traffic)) %>% # filter(total_traffic >= quantile(total_traffic, probs = 0.99)) %>% # semi_join(x = get_core(legislature = "fra"), # y = ., # by = "pageid") # glimpse(fra_subset) ## ----eval = LOCAL------------------------------------------------------------- # library(stringr) # # # import ParlSpeech example and rename ID to match CLD ---------------------------------- # parlspeech_example <- readRDS("parlspeech_example") %>% # rename(parlspeech = speaker) # # # remove whitespace from start and end of the ID in ParlSpeech -------------------------- # parlspeech_example$parlspeech <- str_trim(parlspeech_example$parlspeech) # # # integrate CLD with ParlSpeech example ------------------------------------------------- # esp_speeches <- get_core(legislature = "esp") %>% # left_join(get_ids(legislature = "esp"), # by = "wikidataid") %>% # filter(!is.na(parlspeech)) %>% # inner_join(parlspeech_example, # by = "parlspeech") # # # import Database of Parliamentary Speeches in Ireland example and rename ID ------------ # dpsi_example <- readRDS("dpsi_example") %>% # rename(dpsi = memberID) # # # integrate CLD with ParlSpeech example ------------------------------------------------- # irl_speeches <- get_core(legislature = "irl") %>% # inner_join(filter(get_political(legislature = "irl"), session == 28), # by = "pageid") %>% # left_join(get_ids(legislature = "irl"), # by = "wikidataid") %>% # inner_join(dpsi_example, # by = "dpsi") ## ----eval = LOCAL------------------------------------------------------------- # library(purrr) # # # get "Core" table for all legislatures ------------------------------------------------- # all_core <- cld_content() %>% # names() %>% # map(get_core) # glimpse(all_core) # # # get "Core" and "Political" tables for last three sessions of all legislatures ---------- # recent_sessions <- cld_content() %>% # names() %>% # map(~ { # get_core(legislature = .x) %>% # inner_join(filter(get_political(legislature = .x), # session %in% tail(cld_content(.x)[[1]], 3)), # by = "pageid") # }) # glimpse(recent_sessions) ## ----eval = FALSE------------------------------------------------------------- # library(haven) # # # save data as .csv for use with Excel -------------------------------------------------- # write.csv(fra_subset, "fra_subset.csv") # # # save data as .sas for use with SAS ---------------------------------------------------- # write_sas(sco_subset, "sco_subset.sas") # # # save data as .dta for use with STATA -------------------------------------------------- # write_dta(irl_join, "irl_join.dta") # # # save data as .sav for use with SPSS --------------------------------------------------- # write_sav(esp_speeches, "esp_speeches.sav")