Create Chicklet (Rounded Segmented Column) Charts
https://cinc.rud.is/web/packages/ggchicklet/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
264 lines
8.5 KiB
264 lines
8.5 KiB
## code to prepare `debates2019` dataset goes here
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# read_csv(
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# file = "https://rud.is/data/2019-dem-debates.csv.gz",
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# col_types = cols(
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# elapsed = col_double(),
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# timestamp = col_time(format = ""),
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# speaker = col_character(),
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# topic = col_character()
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# )
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# ) -> debates2019
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#
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#
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# usethis::use_data(debates2019, overwrite = TRUE)
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library(rvest)
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library(stringi)
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library(tidyverse)
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if (!file.exists(here::here("data-raw/2019-06-26-us-elections-debate-speaking-time.html"))) download.file("https://www.nytimes.com/interactive/2019/06/26/us/elections/debate-speaking-time.html", here::here("data-raw/2019-06-26-us-elections-debate-speaking-time.html"))
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if (!file.exists(here::here("data-raw/2019-06-27-us-elections-debate-speaking-time.html"))) download.file("https://www.nytimes.com/interactive/2019/06/27/us/elections/debate-speaking-time.html", here::here("data-raw/2019-06-27-us-elections-debate-speaking-time.html"))
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if (!file.exists(here::here("data-raw/2019-07-30-us-elections-debate-speaking-time.html"))) download.file("https://www.nytimes.com/interactive/2019/07/30/us/elections/debate-speaking-time.html", here::here("data-raw/2019-07-30-us-elections-debate-speaking-time.html"))
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if (!file.exists(here::here("data-raw/2019-07-31-us-elections-debate-speaking-time.html"))) download.file("https://www.nytimes.com/interactive/2019/07/31/us/elections/debate-speaking-time.html", here::here("data-raw/2019-07-31-us-elections-debate-speaking-time.html"))
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if (!file.exists(here::here("data-raw/2019-09-12-us-elections-debate-speaking-time.html"))) download.file("https://www.nytimes.com/interactive/2019/09/12/us/elections/debate-speaking-time.html", here::here("data-raw/2019-09-12-us-elections-debate-speaking-time.html"))
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read_html(here::here("data-raw/2019-06-26-us-elections-debate-speaking-time.html")) %>%
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html_nodes(xpath = ".//script[contains(., 'NYTG_DEMDEBATES')]") %>%
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html_text() %>%
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stri_split_lines() %>%
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unlist() %>%
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.[3] %>%
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stri_replace_first_regex("^.*NYTG_DEMDEBATES = ", "") %>%
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jsonlite::fromJSON() %>%
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mutate(
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elapsed = as.numeric(elapsed)/60,
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debate_date = as.Date("2019-09-13"),
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speaker = stri_trans_totitle(speaker),
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timestamp = parse_time(timestamp),
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topic = stri_trans_totitle(topic),
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debate_group = 1,
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night = 1
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) %>%
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mutate(
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speaker = case_when(
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speaker == "Orourke" ~ "O'Rourke",
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speaker == "Deblasio" ~ "de Blasio",
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TRUE ~ speaker
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)
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) %>%
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mutate(
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topic = case_when(
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topic == "" ~ "Other",
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grepl("Campaign", topic) ~ "Campaign Finance Reform",
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grepl("Civil", topic) ~ "Civil Rights",
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grepl("Climate", topic) ~ "Climate",
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grepl("Foreign", topic) ~ "Foreign Policy",
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grepl("Gun", topic) ~ "Gun Control",
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grepl("Election", topic) ~ "Elections Reform",
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grepl("Health", topic) ~ "Healthcare",
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grepl("Party", topic) ~ "Party Strategy",
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grepl("Women", topic) ~ "Women's Rights",
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TRUE ~ topic
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)
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) %>%
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filter(
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!is.na(timestamp),
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speaker != "",
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speaker != "Moderator"
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) %>%
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as_tibble() -> jun_day_1
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read_html(here::here("data-raw/2019-06-27-us-elections-debate-speaking-time.html")) %>%
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html_nodes(xpath = ".//script[contains(., 'NYTG_DEMDEBATES')]") %>%
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html_text() %>%
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stri_split_lines() %>%
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unlist() %>%
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.[3] %>%
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stri_replace_first_regex("^.*NYTG_DEMDEBATES = ", "") %>%
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jsonlite::fromJSON() %>%
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mutate(
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elapsed = as.numeric(elapsed)/60,
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debate_date = as.Date("2019-09-13"),
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speaker = stri_trans_totitle(speaker),
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timestamp = parse_time(timestamp),
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topic = stri_trans_totitle(topic),
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debate_group = 1,
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night = 2
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) %>%
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mutate(
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speaker = case_when(
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speaker == "Orourke" ~ "O'Rourke",
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speaker == "Deblasio" ~ "de Blasio",
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TRUE ~ speaker
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)
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) %>%
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mutate(
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topic = case_when(
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topic == "" ~ "Other",
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grepl("Campaign", topic) ~ "Campaign Finance Reform",
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grepl("Civil", topic) ~ "Civil Rights",
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grepl("Climate", topic) ~ "Climate",
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grepl("Foreign", topic) ~ "Foreign Policy",
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grepl("Gun", topic) ~ "Gun Control",
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grepl("Election", topic) ~ "Elections Reform",
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grepl("Health", topic) ~ "Healthcare",
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grepl("Party", topic) ~ "Party Strategy",
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grepl("Women", topic) ~ "Women's Rights",
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TRUE ~ topic
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)
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) %>%
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filter(
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!is.na(timestamp),
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speaker != "",
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speaker != "Moderator"
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) %>%
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as_tibble() -> jun_day_2
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read_html(here::here("data-raw/2019-07-30-us-elections-debate-speaking-time.html")) %>%
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html_nodes(xpath = ".//script[contains(., 'NYTG_DEMDEBATES')]") %>%
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html_text() %>%
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stri_split_lines() %>%
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unlist() %>%
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.[2] %>%
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stri_replace_first_regex("^.*NYTG_DEMDEBATES = ", "") %>%
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jsonlite::fromJSON() %>%
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mutate(
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elapsed = as.numeric(elapsed)/60,
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debate_date = as.Date("2019-09-13"),
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speaker = stri_trans_totitle(speaker),
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timestamp = parse_time(timestamp),
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topic = stri_trans_totitle(topic),
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debate_group = 2,
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night = 1
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) %>%
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mutate(
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speaker = case_when(
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speaker == "Orourke" ~ "O'Rourke",
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speaker == "Deblasio" ~ "de Blasio",
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TRUE ~ speaker
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)
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) %>%
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mutate(
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topic = case_when(
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topic == "" ~ "Other",
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grepl("Campaign", topic) ~ "Campaign Finance Reform",
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grepl("Civil", topic) ~ "Civil Rights",
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grepl("Climate", topic) ~ "Climate",
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grepl("Foreign", topic) ~ "Foreign Policy",
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grepl("Gun", topic) ~ "Gun Control",
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grepl("Election", topic) ~ "Elections Reform",
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grepl("Health", topic) ~ "Healthcare",
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grepl("Party", topic) ~ "Party Strategy",
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grepl("Women", topic) ~ "Women's Rights",
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TRUE ~ topic
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)
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) %>%
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filter(
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!is.na(timestamp),
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speaker != "",
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speaker != "Moderator"
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) %>%
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as_tibble() -> jul_day_1
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read_html(here::here("data-raw/2019-07-31-us-elections-debate-speaking-time.html")) %>%
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html_nodes(xpath = ".//script[contains(., 'NYTG_DEMDEBATES')]") %>%
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html_text() %>%
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stri_split_lines() %>%
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unlist() %>%
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.[2] %>%
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stri_replace_first_regex("^.*NYTG_DEMDEBATES = ", "") %>%
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jsonlite::fromJSON() %>%
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mutate(
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elapsed = as.numeric(elapsed)/60,
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debate_date = as.Date("2019-09-13"),
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speaker = stri_trans_totitle(speaker),
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timestamp = parse_time(timestamp),
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topic = stri_trans_totitle(topic),
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debate_group = 2,
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night = 2
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) %>%
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mutate(
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speaker = case_when(
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speaker == "Orourke" ~ "O'Rourke",
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speaker == "Deblasio" ~ "de Blasio",
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TRUE ~ speaker
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)
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) %>%
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mutate(
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topic = case_when(
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topic == "" ~ "Other",
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grepl("Campaign", topic) ~ "Campaign Finance Reform",
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grepl("Civil", topic) ~ "Civil Rights",
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grepl("Climate", topic) ~ "Climate",
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grepl("Foreign", topic) ~ "Foreign Policy",
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grepl("Gun", topic) ~ "Gun Control",
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grepl("Election", topic) ~ "Elections Reform",
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grepl("Health", topic) ~ "Healthcare",
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grepl("Party", topic) ~ "Party Strategy",
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grepl("Women", topic) ~ "Women's Rights",
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TRUE ~ topic
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)
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) %>%
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filter(
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!is.na(timestamp),
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speaker != "",
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speaker != "Moderator"
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) %>%
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as_tibble() -> jul_day_2
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read_html(here::here("data-raw/2019-09-12-us-elections-debate-speaking-time.html")) %>%
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html_nodes(xpath = ".//script[contains(., 'NYTG_DEMDEBATES')]") %>%
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html_text() %>%
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stri_split_lines() %>%
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unlist() %>%
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.[3] %>%
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stri_replace_first_regex("^.*NYTG_DEMDEBATES = ", "") %>%
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jsonlite::fromJSON() %>%
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mutate(
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elapsed = as.numeric(elapsed)/60,
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debate_date = as.Date("2019-09-13"),
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speaker = stri_trans_totitle(speaker),
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timestamp = parse_time(timestamp),
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topic = stri_trans_totitle(topic),
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debate_group = 3,
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night = 1
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) %>%
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mutate(
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speaker = case_when(
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speaker == "Orourke" ~ "O'Rourke",
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speaker == "Deblasio" ~ "de Blasio",
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TRUE ~ speaker
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)
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) %>%
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mutate(
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topic = case_when(
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topic == "" ~ "Other",
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grepl("Campaign", topic) ~ "Campaign Finance Reform",
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grepl("Civil", topic) ~ "Civil Rights",
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grepl("Climate", topic) ~ "Climate",
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grepl("Foreign", topic) ~ "Foreign Policy",
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grepl("Gun", topic) ~ "Gun Control",
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grepl("Election", topic) ~ "Elections Reform",
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grepl("Health", topic) ~ "Healthcare",
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grepl("Party", topic) ~ "Party Strategy",
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grepl("Women", topic) ~ "Women's Rights",
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TRUE ~ topic
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)
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) %>%
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filter(
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!is.na(timestamp),
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speaker != "",
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speaker != "Moderator"
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) %>%
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as_tibble() -> sep_day_1
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bind_rows(
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jun_day_1,
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jun_day_2,
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jul_day_1,
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jul_day_2,
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sep_day_1
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) -> debates2019
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usethis::use_data(debates2019, overwrite = TRUE)
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