.Rhistory

test_that("get_ly_legislator_bills handles errors properly", {
# Test non-existent term/name combination
expect_error(
get_ly_legislator_bills(
term = 999,
name = "不存在的立委"
)
)
# Test invalid page number
expect_error(
get_ly_legislator_bills(
term = 9,
name = "王金平",
page = -1
)
)
# Test invalid limit
expect_error(
get_ly_legislator_bills(
term = 9,
name = "王金平",
limit = 0
)
)
})
# Test error handling
test_that("get_ly_legislator_bills handles errors properly", {
# Test non-existent term/name combination
expect_error(
get_ly_legislator_bills(
term = 999,
name = "不存在的立委"
)
)
# Test invalid page number
expect_error(
get_ly_legislator_bills(
term = 9,
name = "王金平",
page = -1
)
)
})
get_tly_stat()
library(legisTaiwan)
ly_info <- get_tly_stat()
library(legisTaiwan)
ly_info <- get_tly_stat()
ly_info
ly_info$meet$terms
print(ly_info$meet$terms, type = "html")
ly_info$bill$total
ly_info$bill
ly_info$bill$terms
ly_info$legislator$terms
ly_info$gazette$yearly_stats
ly_info$gazette$agenda_total
ly_info$meet$terms
ly_info$ivod$terms
ly_info$bill$terms
ly_info$meet$total
get_tly_stat()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
committees <- get_ly_committees_type(
type = "常設委員會"
)
committee <- get_ly_committees_type(
code = 15
)
committee
committee <- get_ly_committees_type(
)
roxygen2::roxygenise()
esult <- get_ly_committee_meets(committee_id = 22)
roxygen2::roxygenise()
esult <- get_ly_committee_meets(committee_id = 22)
page2_videos <- get_ly_ivod(
term = 9,
session_period = 1,
page = 2,
limit = 20
)
roxygen2::roxygenise()
esult <- get_ly_committee_meets(committee_id = 22)
# 取得第九屆第一會期的影片
videos <- get_ly_ivod(
term = 9,
session_period = 1,
limit = 5
)
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
file.edit("~/.Rprofile")
roxygen2::roxygenise()
stats <- get_tly_stat()
# 案例一：基本統計資訊分析
library(dplyr)
library(ggplot2)
# 取得立法院統計資料
stats <- get_tly_stat()
# 1. 分析各屆期法案統計
analyze_bills <- function(stats) {
# 計算各屆期法案數量和比例
bill_stats <- stats$bill$terms %>%
mutate(
percentage = round(count / sum(count) * 100, 2),
cumulative = cumsum(count)
)
# 繪製法案數量趨勢圖
ggplot(bill_stats, aes(x = term, y = count)) +
geom_line(color = "blue") +
geom_point(color = "red") +
labs(
title = "各屆期法案數量趨勢",
x = "屆期",
y = "法案數量"
) +
theme_minimal()
# 輸出統計摘要
cat("\n=== 法案統計摘要 ===\n")
cat(sprintf("總法案數：%d\n", stats$bill$total))
cat(sprintf("最後更新時間：%s\n", format(stats$bill$max_update_time)))
cat("\n前五屆法案數量：\n")
print(head(bill_stats, 5))
}
# 2. 分析會議資訊
analyze_meetings <- function(stats) {
# 整理會議資料
meeting_stats <- stats$meet$terms %>%
mutate(
議事錄比例 = round(議事錄_count / count * 100, 2),
最後會議日期 = format(max_meeting_date, "%Y-%m-%d")
)
# 繪製會議記錄完整性分析圖
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = 議事錄_count), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "各屆期會議記錄完整性分析",
x = "屆期",
y = "數量"
) +
theme_minimal()
# 輸出統計摘要
cat("\n=== 會議統計摘要 ===\n")
cat(sprintf("總會議數：%d\n", stats$meet$total))
cat("\n各屆期會議記錄統計：\n")
print(head(meeting_stats, 5))
}
# 3. 分析影片資料
analyze_ivod <- function(stats) {
# 整理影片資料
ivod_stats <- stats$ivod$terms %>%
mutate(
開始日期 = format(start_date, "%Y-%m-%d"),
結束日期 = format(end_date, "%Y-%m-%d"),
期間天數 = as.numeric(difftime(end_date, start_date, units = "days")),
平均每日影片數 = round(count / 期間天數, 2)
)
# 繪製影片數量分布圖
ggplot(ivod_stats, aes(x = term, y = count)) +
geom_bar(stat = "identity", fill = "darkgreen") +
labs(
title = "各屆期影片記錄數量",
x = "屆期",
y = "影片數量"
) +
theme_minimal()
# 輸出統計摘要
cat("\n=== 影片統計摘要 ===\n")
cat(sprintf("總影片數：%d\n", stats$ivod$total))
cat(sprintf("資料期間：%s 至 %s\n",
format(stats$ivod$date_range$start, "%Y-%m-%d"),
format(stats$ivod$date_range$end, "%Y-%m-%d")))
cat("\n各屆期影片統計：\n")
print(head(ivod_stats, 5))
}
# 4. 整合分析報告
generate_report <- function(stats) {
cat("\n==========================================")
cat("\n       立法院資料統計分析報告")
cat("\n==========================================\n")
# 1. 法案統計
cat("\nI. 法案統計")
cat("\n------------------------------------------")
cat(sprintf("\n總法案數：%d", stats$bill$total))
cat(sprintf("\n最新法案更新：%s", format(stats$bill$max_update_time, "%Y-%m-%d")))
# 2. 委員統計
cat("\n\nII. 委員統計")
cat("\n------------------------------------------")
cat(sprintf("\n歷史委員總數：%d", stats$legislator$total))
cat("\n各屆期委員人數：")
print(head(stats$legislator$terms, 5))
# 3. 公報統計
cat("\nIII. 公報統計")
cat("\n------------------------------------------")
cat(sprintf("\n公報總數：%d", stats$gazette$total))
cat(sprintf("\n議程總數：%d", stats$gazette$agenda_total))
cat(sprintf("\n最後會議日期：%s", format(stats$gazette$last_meeting, "%Y-%m-%d")))
# 4. 影片統計
cat("\n\nIV. 影片統計")
cat("\n------------------------------------------")
cat(sprintf("\n影片總數：%d", stats$ivod$total))
cat(sprintf("\n影片記錄期間：%s 至 %s",
format(stats$ivod$date_range$start, "%Y-%m-%d"),
format(stats$ivod$date_range$end, "%Y-%m-%d")))
cat("\n\n==========================================\n")
}
# 使用範例：
# 1. 執行基本分析
stats <- get_tly_stat()
analyze_bills(stats)
analyze_meetings(stats)
analyze_ivod(stats)
# 2. 產生完整報告
generate_report(stats)
# 3. 特定分析：計算每屆期的法案通過率趨勢
calculate_bill_trends <- function(stats) {
bill_trends <- stats$bill$terms %>%
left_join(stats$meet$terms, by = "term") %>%
mutate(
bills_per_meeting = round(count.x / count.y, 2),
bills_per_day = round(count.x / as.numeric(difftime(max_meeting_date,
lag(max_meeting_date), units = "days")), 2)
) %>%
select(term, count.x, count.y, bills_per_meeting, bills_per_day) %>%
rename(
bills = count.x,
meetings = count.y
)
print(bill_trends)
return(bill_trends)
}
# 4. 繪製互動式視覺化（需要安裝 plotly 套件）
library(plotly)
create_interactive_plot <- function(stats) {
bill_data <- stats$bill$terms
p <- plot_ly(bill_data, x = ~term, y = ~count, type = "scatter", mode = "lines+markers",
text = ~paste("屆期:", term, "<br>法案數:", count),
hoverinfo = "text") %>%
layout(title = "各屆期法案數量趨勢",
xaxis = list(title = "屆期"),
yaxis = list(title = "法案數量"))
return(p)
}
install.packages(c("dplyr", "ggplot2", "plotly", "lubridate"))
stats <- get_tly_stat()
analyze_bills(stats)
analyze_meetings(stats)
analyze_ivod(stats)
analyze_ivod(stats)
analyze_meetings(stats)
calculate_bill_trends <- function(stats) {
bill_trends <- stats$bill$terms %>%
left_join(stats$meet$terms, by = "term") %>%
mutate(
bills_per_meeting = round(count.x / count.y, 2),
bills_per_day = round(count.x / as.numeric(difftime(max_meeting_date,
lag(max_meeting_date), units = "days")), 2)
) %>%
select(term, count.x, count.y, bills_per_meeting, bills_per_day) %>%
rename(
bills = count.x,
meetings = count.y
)
print(bill_trends)
return(bill_trends)
}
calculate_bill_trends()
calculate_bill_trends(stats)
create_interactive_plot(stats )
calculate_bill_trends(stats)
stats
# 2. Meeting information analysis
analyze_meetings <- function(stats) {
# Process meeting data
meeting_stats <- stats$meet$terms %>%
mutate(
minutes_ratio = round(議事錄_count / count * 100, 2),
last_meeting_date = format(max_meeting_date, "%Y-%m-%d")
)
# Create meeting records completeness analysis plot
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = 議事錄_count), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "Meeting Records Completeness by Term",
x = "Term",
y = "Count"
) +
theme_minimal()
# Output statistical summary
cat("\n=== Meeting Statistics Summary ===\n")
cat(sprintf("Total Meetings: %d\n", stats$meet$total))
cat("\nMeeting Records Statistics by Term:\n")
print(head(meeting_stats, 5))
}
# 3. Video data analysis
analyze_ivod <- function(stats) {
# Process video data
ivod_stats <- stats$ivod$terms %>%
mutate(
start_date_fmt = format(start_date, "%Y-%m-%d"),
end_date_fmt = format(end_date, "%Y-%m-%d"),
period_days = as.numeric(difftime(end_date, start_date, units = "days")),
avg_daily_videos = round(count / period_days, 2)
)
# Create video count distribution plot
ggplot(ivod_stats, aes(x = term, y = count)) +
geom_bar(stat = "identity", fill = "darkgreen") +
labs(
title = "Video Records Count by Term",
x = "Term",
y = "Number of Videos"
) +
theme_minimal()
# Output statistical summary
cat("\n=== Video Statistics Summary ===\n")
cat(sprintf("Total Videos: %d\n", stats$ivod$total))
cat(sprintf("Data Period: %s to %s\n",
format(stats$ivod$date_range$start, "%Y-%m-%d"),
format(stats$ivod$date_range$end, "%Y-%m-%d")))
cat("\nVideo Statistics by Term:\n")
print(head(ivod_stats, 5))
}
# 4. Integrated analysis report
generate_report <- function(stats) {
cat("\n==========================================")
cat("\n    Legislative Yuan Data Analysis Report")
cat("\n==========================================\n")
# 1. Bill statistics
cat("\nI. Bill Statistics")
cat("\n------------------------------------------")
cat(sprintf("\nTotal Bills: %d", stats$bill$total))
cat(sprintf("\nLast Bill Update: %s", format(stats$bill$max_update_time, "%Y-%m-%d")))
# 2. Legislator statistics
cat("\n\nII. Legislator Statistics")
cat("\n------------------------------------------")
cat(sprintf("\nHistorical Total Legislators: %d", stats$legislator$total))
cat("\nLegislators by Term:")
print(head(stats$legislator$terms, 5))
# 3. Gazette statistics
cat("\nIII. Gazette Statistics")
cat("\n------------------------------------------")
cat(sprintf("\nTotal Gazettes: %d", stats$gazette$total))
cat(sprintf("\nTotal Agendas: %d", stats$gazette$agenda_total))
cat(sprintf("\nLast Meeting Date: %s", format(stats$gazette$last_meeting, "%Y-%m-%d")))
# 4. Video statistics
cat("\n\nIV. Video Statistics")
cat("\n------------------------------------------")
cat(sprintf("\nTotal Videos: %d", stats$ivod$total))
cat(sprintf("\nVideo Recording Period: %s to %s",
format(stats$ivod$date_range$start, "%Y-%m-%d"),
format(stats$ivod$date_range$end, "%Y-%m-%d")))
cat("\n\n==========================================\n")
}
# 2. Meeting information analysis
analyze_meetings <- function(stats) {
# Process meeting data
meeting_stats <- stats$meet$terms %>%
mutate(
minutes_ratio = round(議事錄_count / count * 100, 2),
last_meeting_date = format(max_meeting_date, "%Y-%m-%d")
)
# Create meeting records completeness analysis plot
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = 議事錄_count), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "Meeting Records Completeness by Term",
x = "Term",
y = "Count"
) +
theme_minimal()
# Output statistical summary
cat("\n=== Meeting Statistics Summary ===\n")
cat(sprintf("Total Meetings: %d\n", stats$meet$total))
cat("\nMeeting Records Statistics by Term:\n")
print(head(meeting_stats, 5))
}
analyze_meetings(stats)
# Process meeting data
meeting_stats <- stats$meet$terms %>%
mutate(
minutes_ratio = round(議事錄_count / count * 100, 2),
last_meeting_date = format(max_meeting_date, "%Y-%m-%d")
)
meeting_stats
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = 議事錄_count), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "Meeting Records Completeness by Term",
x = "Term",
y = "Count"
) +
theme_minimal()
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5)
# Create meeting records completeness analysis plot
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = 議事錄_count), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "Meeting Records Completeness by Term",
x = "Term",
y = "Count"
) +
theme_minimal()
# Create meeting records completeness analysis plot
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = `議事錄_count`), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "Meeting Records Completeness by Term",
x = "Term",
y = "Count"
) +
theme_minimal()
# Create meeting records completeness analysis plot
x <- ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = `議事錄_count`), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "Meeting Records Completeness by Term",
x = "Term",
y = "Count"
) +
theme_minimal()
x
# Create meeting records completeness analysis plot
ggplot(meeting_stats, aes(x = term)) +
geom_bar(aes(y = count), stat = "identity", fill = "blue", alpha = 0.5) +
geom_bar(aes(y = `議事錄_count`), stat = "identity", fill = "red", alpha = 0.5) +
labs(
title = "Meeting Records Completeness by Term",
x = "Term",
y = "Count"
) +
theme_minimal()
stats <- get_tly_stat()
stats
stats$bill$terms
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
stats <- get_tly_stat()
analyze_bills(stats)
analyze_bills
analyze_bills(stats)
analyze_meetings(stats)
analyze_ivod(stats)
create_interactive_plot(stats)
library(pkgdown)
build_site()
build_site()
build_site()
build_site()
build_site()
deploy_to_branch()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
library(legisTaiwan)
devtools::check(args = c('--no-codoc', '--no-manual', '--no-check-ASCII'))
library(stringi)
# 读取文件内容
file_content <- readLines("R/bill.R", encoding = "UTF-8")
file_content
writeLines(file_content, "R/bill.R", useBytes = TRUE)
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
build_site()
roxygen2::roxygenise()
build_site()
deploy_to_branch()
library(pkgdown)
build_site()
build_site()
build_site()
roxygen2::roxygenize()
roxygen2::roxygenize()
build_site()
roxygen2::roxygenise()
pkgload::dev_help('.onAttach')
pkgload::dev_help('calculate_bill_trends')
pkgload::dev_help('calculate_bill_trends')
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
roxygen2::roxygenise()
build_site()
build_site()
build_site()
deploy_to_branch()