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## ----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")
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