# Load data from star schema
etf_daily <- open_dataset(DAILY_PARQUET_DIR)
# Filter for stock_code 2800
etf_2800_all <- etf_daily %>%
filter(stock_code == STOCK_CODE) %>%
collect()
if (nrow(etf_2800_all) == 0) {
stop("No data found for stock_code ", STOCK_CODE)
}
# Ensure trade_date is Date type
etf_2800_all$trade_date <- as.Date(etf_2800_all$trade_date)
# Sort by date (most recent first)
etf_2800_all <- etf_2800_all %>%
arrange(desc(trade_date))
# Get last 5 days and last 30 days
etf_2800_recent <- etf_2800_all %>% slice_head(n = DAYS_TO_SHOW)
etf_2800_last30 <- etf_2800_all %>% slice_head(n = 30)
# Key variables
key_vars <- c("trade_date", "volume_cleaned", "turnover_cleaned", "aum_cleaned",
"closing_price", "nav", "day_high", "day_low",
"outstanding_units_cleaned", "premium_discount_percent")
# Calculate historical statistics (needed early for comparisons)
stats <- etf_2800_all %>%
summarise(
volume_median = median(volume_cleaned, na.rm = TRUE),
volume_mean = mean(volume_cleaned, na.rm = TRUE),
volume_min = min(volume_cleaned, na.rm = TRUE),
volume_max = max(volume_cleaned, na.rm = TRUE),
turnover_median = median(turnover_cleaned, na.rm = TRUE),
turnover_mean = mean(turnover_cleaned, na.rm = TRUE),
turnover_min = min(turnover_cleaned, na.rm = TRUE),
turnover_max = max(turnover_cleaned, na.rm = TRUE),
total_days = n()
)
# Prepare comparison data
comparison <- etf_2800_recent %>%
select(trade_date, volume_cleaned, turnover_cleaned) %>%
mutate(
volume_vs_median = ifelse(is.na(volume_cleaned), NA, volume_cleaned / stats$volume_median),
volume_vs_mean = ifelse(is.na(volume_cleaned), NA, volume_cleaned / stats$volume_mean),
turnover_vs_median = ifelse(is.na(turnover_cleaned), NA, turnover_cleaned / stats$turnover_median),
turnover_vs_mean = ifelse(is.na(turnover_cleaned), NA, turnover_cleaned / stats$turnover_mean),
volume_pct_rank = NA_real_,
turnover_pct_rank = NA_real_
)
# Calculate percentile ranks
for (i in 1:nrow(comparison)) {
if (!is.na(comparison$volume_cleaned[i])) {
comparison$volume_pct_rank[i] <- mean(etf_2800_all$volume_cleaned <= comparison$volume_cleaned[i], na.rm = TRUE) * 100
}
if (!is.na(comparison$turnover_cleaned[i])) {
comparison$turnover_pct_rank[i] <- mean(etf_2800_all$turnover_cleaned <= comparison$turnover_cleaned[i], na.rm = TRUE) * 100
}
}
Data Overview
cat("**Stock Code:**", STOCK_CODE, "\n\n")
## **Stock Code:** 3033
cat("**Total Records:**", nrow(etf_2800_all), "\n\n")
## **Total Records:** 65
cat("**Date Range:**", format(min(etf_2800_all$trade_date)), "to", format(max(etf_2800_all$trade_date)), "\n\n")
## **Date Range:** 2025-09-17 to 2025-12-19
cat("**Last Updated:**", format(Sys.Date()))
## **Last Updated:** 2025-12-20
Closing Price Trend
# Prepare data for chart - order by date ascending for line chart
chart_data <- etf_2800_all %>%
select(trade_date, closing_price) %>%
arrange(trade_date) %>%
mutate(
is_latest = trade_date == max(trade_date, na.rm = TRUE)
)
# Get latest date for labeling
latest_date <- max(chart_data$trade_date, na.rm = TRUE)
latest_price <- chart_data$closing_price[chart_data$trade_date == latest_date][1]
ggplot(chart_data, aes(x = trade_date, y = closing_price)) +
geom_line(color = "#007bff", size = 0.8) +
geom_point(data = chart_data %>% filter(is_latest),
color = "red", size = 3, shape = 19) +
geom_point(data = chart_data %>% filter(!is_latest),
color = "#007bff", size = 0.5, alpha = 0.6) +
labs(
title = paste("Closing Price Trend - ETF", STOCK_CODE),
subtitle = paste("Latest:", format(latest_date), "- Price:", ifelse(is.na(latest_price), "NA", formatC(latest_price, format = "f", digits = 4))),
x = "Date",
y = "Closing Price (HKD)",
caption = "Red dot indicates latest data point"
) +
theme_minimal() +
theme(
plot.title = element_text(size = 14, face = "bold"),
plot.subtitle = element_text(size = 11, color = "gray50"),
axis.title = element_text(size = 11),
axis.text = element_text(size = 9),
plot.caption = element_text(size = 9, color = "gray60", hjust = 0),
panel.grid.minor = element_blank()
) +
scale_x_date(date_labels = "%Y-%m-%d", date_breaks = "1 month") +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01))
Latest Date Verification
latest_date <- max(etf_2800_all$trade_date)
dec_16 <- etf_2800_recent %>%
filter(trade_date == latest_date)
if (nrow(dec_16) > 0) {
# Create a summary table
latest_summary <- data.frame(
Metric = c("Date", "Volume", "Turnover", "AUM", "Closing Price", "NAV", "Suspension Flag"),
Value = c(
format(latest_date),
ifelse(is.na(dec_16$volume_cleaned[1]), "NA (missing/suspended)",
formatC(dec_16$volume_cleaned[1], format = "d", big.mark = ",")),
ifelse(is.na(dec_16$turnover_cleaned[1]), "NA (missing/suspended)",
formatC(dec_16$turnover_cleaned[1], format = "f", digits = 2, big.mark = ",")),
ifelse(is.na(dec_16$aum_cleaned[1]), "NA",
formatC(dec_16$aum_cleaned[1], format = "f", digits = 0, big.mark = ",")),
ifelse(is.na(dec_16$closing_price[1]), "NA",
formatC(dec_16$closing_price[1], format = "f", digits = 4)),
ifelse(is.na(dec_16$nav[1]), "NA",
formatC(dec_16$nav[1], format = "f", digits = 4)),
ifelse(is.na(dec_16$suspension_flag[1]), "NA", dec_16$suspension_flag[1])
),
stringsAsFactors = FALSE
)
# Add comparison metrics if available
if (!is.na(dec_16$volume_cleaned[1]) && !is.na(dec_16$turnover_cleaned[1])) {
vol_idx <- which(comparison$trade_date == latest_date)
comparison_metrics <- data.frame(
Metric = c("Volume vs Median", "Volume vs Mean",
"Turnover vs Median", "Turnover vs Mean"),
Value = c(
sprintf("%.2fx (%s percentile)",
dec_16$volume_cleaned[1] / stats$volume_median,
sprintf("%.1f%%", comparison$volume_pct_rank[vol_idx])),
sprintf("%.2fx", dec_16$volume_cleaned[1] / stats$volume_mean),
sprintf("%.2fx (%s percentile)",
dec_16$turnover_cleaned[1] / stats$turnover_median,
sprintf("%.1f%%", comparison$turnover_pct_rank[vol_idx])),
sprintf("%.2fx", dec_16$turnover_cleaned[1] / stats$turnover_mean)
),
stringsAsFactors = FALSE
)
latest_summary <- rbind(latest_summary, comparison_metrics)
}
latest_summary %>%
gt() %>%
tab_header(
title = "Latest Date Data Summary",
subtitle = paste("ETF", STOCK_CODE, "-", format(latest_date))
) %>%
cols_label(
Metric = "Metric",
Value = "Value"
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_text(align = "left"),
locations = cells_body(columns = Metric)
) %>%
tab_style(
style = cell_text(align = "right"),
locations = cells_body(columns = Value)
) %>%
opt_table_font(font = "Arial") %>%
tab_options(
table.width = pct(60),
column_labels.background.color = "#f8f9fa",
table_body.hlines.color = "#e9ecef",
heading.border.bottom.color = "#dee2e6"
)
} else {
cat("⚠ Warning: No data found for latest date\n")
}
| Latest Date Data Summary |
| ETF 3033 - 2025-12-19 |
| Metric |
Value |
| Date |
2025-12-19 |
| Volume |
892,442,349 |
| Turnover |
4,787,090,098.00 |
| AUM |
NA |
| Closing Price |
5.3600 |
| NAV |
NA |
| Suspension Flag |
No |
| Volume vs Median |
0.68x (21.5% percentile) |
| Volume vs Mean |
0.63x |
| Turnover vs Median |
0.65x (13.8% percentile) |
| Turnover vs Mean |
0.58x |
Comparison: Recent vs Historical
Percentile Explanation: Percentile rank shows what
percentage of historical values are less than or equal to the current
value. For example, a 75th percentile means the current value is higher
than 75% of all historical values (higher is better for
volume/turnover). A 25th percentile means it’s higher than only 25% of
historical values (lower activity).
comparison_display <- comparison %>%
select(trade_date, volume_cleaned, volume_vs_median, volume_vs_mean, volume_pct_rank,
turnover_cleaned, turnover_vs_median, turnover_vs_mean, turnover_pct_rank)
comparison_display %>%
gt() %>%
tab_header(
title = "Recent Performance vs Historical Statistics",
subtitle = paste("ETF", STOCK_CODE, "- Comparison of last 5 days")
) %>%
fmt_date(columns = trade_date, date_style = "yMd") %>%
fmt_number(columns = volume_cleaned, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = volume_vs_median, decimals = 2, pattern = "{x}x") %>%
fmt_number(columns = volume_vs_mean, decimals = 2, pattern = "{x}x") %>%
fmt_number(columns = volume_pct_rank, decimals = 1, pattern = "{x}%") %>%
fmt_number(columns = turnover_cleaned, decimals = 2, use_seps = TRUE) %>%
fmt_number(columns = turnover_vs_median, decimals = 2, pattern = "{x}x") %>%
fmt_number(columns = turnover_vs_mean, decimals = 2, pattern = "{x}x") %>%
fmt_number(columns = turnover_pct_rank, decimals = 1, pattern = "{x}%") %>%
sub_missing(columns = everything(), missing_text = "NA") %>%
cols_label(
trade_date = "Date",
volume_cleaned = "Volume",
volume_vs_median = "vs Median",
volume_vs_mean = "vs Mean",
volume_pct_rank = "Percentile",
turnover_cleaned = "Turnover",
turnover_vs_median = "vs Median",
turnover_vs_mean = "vs Mean",
turnover_pct_rank = "Percentile"
) %>%
tab_spanner(
label = "Volume",
columns = c(volume_cleaned, volume_vs_median, volume_vs_mean, volume_pct_rank)
) %>%
tab_spanner(
label = "Turnover",
columns = c(turnover_cleaned, turnover_vs_median, turnover_vs_mean, turnover_pct_rank)
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_text(align = "right"),
locations = cells_body()
) %>%
tab_style(
style = cell_text(weight = "bold", align = "center"),
locations = cells_column_spanners()
) %>%
opt_table_font(font = "Arial") %>%
tab_options(
table.width = pct(100),
column_labels.background.color = "#f8f9fa",
table_body.hlines.color = "#e9ecef",
heading.border.bottom.color = "#dee2e6"
)
| Recent Performance vs Historical Statistics |
| ETF 3033 - Comparison of last 5 days |
| Date |
Volume
|
Turnover
|
| Volume |
vs Median |
vs Mean |
Percentile |
Turnover |
vs Median |
vs Mean |
Percentile |
| 12/19/2025 |
892,442,349 |
0.68x |
0.63x |
21.5% |
4,787,090,098.00 |
0.65x |
0.58x |
13.8% |
| 12/18/2025 |
1,139,429,792 |
0.87x |
0.80x |
40.0% |
6,020,161,755.00 |
0.81x |
0.72x |
36.9% |
| 12/17/2025 |
1,691,790,573 |
1.29x |
1.19x |
75.4% |
8,988,359,290.00 |
1.21x |
1.08x |
64.6% |
| 12/16/2025 |
858,662,184 |
0.65x |
0.60x |
15.4% |
4,537,412,993.00 |
0.61x |
0.55x |
6.2% |
| 12/15/2025 |
1,239,277,553 |
0.94x |
0.87x |
49.2% |
6,724,758,896.00 |
0.91x |
0.81x |
43.1% |
Recent 5 Days
display_data <- etf_2800_recent %>%
select(any_of(key_vars))
display_data %>%
gt() %>%
tab_header(
title = "Recent 5 Days - Key Metrics",
subtitle = paste("ETF", STOCK_CODE)
) %>%
fmt_date(columns = trade_date, date_style = "yMd") %>%
fmt_number(columns = volume_cleaned, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = turnover_cleaned, decimals = 2, use_seps = TRUE) %>%
fmt_number(columns = aum_cleaned, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = closing_price, decimals = 4) %>%
fmt_number(columns = nav, decimals = 4) %>%
fmt_number(columns = day_high, decimals = 4) %>%
fmt_number(columns = day_low, decimals = 4) %>%
fmt_number(columns = outstanding_units_cleaned, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = premium_discount_percent, decimals = 2) %>%
cols_label(
trade_date = "Date",
volume_cleaned = "Volume",
turnover_cleaned = "Turnover",
aum_cleaned = "AUM",
closing_price = "Close Price",
nav = "NAV",
day_high = "Day High",
day_low = "Day Low",
outstanding_units_cleaned = "Outstanding Units",
premium_discount_percent = "Premium/Discount %"
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_text(align = "right"),
locations = cells_body()
) %>%
opt_table_font(font = "Arial") %>%
tab_options(
table.width = pct(100),
column_labels.background.color = "#f8f9fa",
table_body.hlines.color = "#e9ecef",
heading.border.bottom.color = "#dee2e6"
)
| Recent 5 Days - Key Metrics |
| ETF 3033 |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
892,442,349 |
4,787,090,098.00 |
NA |
5.3600 |
NA |
5.3900 |
5.3200 |
13,642,400,200 |
NA |
| 12/18/2025 |
1,139,429,792 |
6,020,161,755.00 |
72,420,000,000 |
5.3100 |
5.3100 |
5.3200 |
5.2600 |
13,642,400,200 |
0.02 |
| 12/17/2025 |
1,691,790,573 |
8,988,359,290.00 |
72,950,000,000 |
5.3500 |
5.3500 |
5.3700 |
5.2800 |
13,642,400,200 |
0.04 |
| 12/16/2025 |
858,662,184 |
4,537,412,993.00 |
72,750,000,000 |
5.2900 |
5.2900 |
5.3700 |
5.2400 |
13,743,400,200 |
−0.07 |
| 12/15/2025 |
1,239,277,553 |
6,724,758,896.00 |
75,140,000,000 |
5.3800 |
5.3900 |
5.4700 |
5.3800 |
13,947,400,200 |
−0.05 |
Recent 30 Days
table_30days <- etf_2800_last30 %>%
select(any_of(key_vars)) %>%
arrange(desc(trade_date))
table_30days %>%
gt() %>%
tab_header(
title = "Recent 30 Days - Complete Data",
subtitle = paste("ETF", STOCK_CODE, "- Most recent dates first")
) %>%
fmt_date(columns = trade_date, date_style = "yMd") %>%
fmt_number(columns = volume_cleaned, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = turnover_cleaned, decimals = 2, use_seps = TRUE) %>%
fmt_number(columns = aum_cleaned, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = closing_price, decimals = 4) %>%
fmt_number(columns = nav, decimals = 4) %>%
fmt_number(columns = day_high, decimals = 4) %>%
fmt_number(columns = day_low, decimals = 4) %>%
fmt_number(columns = outstanding_units_cleaned, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = premium_discount_percent, decimals = 2) %>%
sub_missing(columns = everything(), missing_text = "NA") %>%
cols_label(
trade_date = "Date",
volume_cleaned = "Volume",
turnover_cleaned = "Turnover",
aum_cleaned = "AUM",
closing_price = "Close Price",
nav = "NAV",
day_high = "Day High",
day_low = "Day Low",
outstanding_units_cleaned = "Outstanding Units",
premium_discount_percent = "Premium/Discount %"
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_text(align = "right"),
locations = cells_body()
) %>%
opt_table_font(font = "Arial") %>%
tab_options(
table.width = pct(100),
column_labels.background.color = "#f8f9fa",
table_body.hlines.color = "#e9ecef",
heading.border.bottom.color = "#dee2e6",
table.font.size = px(12)
)
| Recent 30 Days - Complete Data |
| ETF 3033 - Most recent dates first |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
892,442,349 |
4,787,090,098.00 |
NA |
5.3600 |
NA |
5.3900 |
5.3200 |
13,642,400,200 |
NA |
| 12/18/2025 |
1,139,429,792 |
6,020,161,755.00 |
72,420,000,000 |
5.3100 |
5.3100 |
5.3200 |
5.2600 |
13,642,400,200 |
0.02 |
| 12/17/2025 |
1,691,790,573 |
8,988,359,290.00 |
72,950,000,000 |
5.3500 |
5.3500 |
5.3700 |
5.2800 |
13,642,400,200 |
0.04 |
| 12/16/2025 |
858,662,184 |
4,537,412,993.00 |
72,750,000,000 |
5.2900 |
5.2900 |
5.3700 |
5.2400 |
13,743,400,200 |
−0.07 |
| 12/15/2025 |
1,239,277,553 |
6,724,758,896.00 |
75,140,000,000 |
5.3800 |
5.3900 |
5.4700 |
5.3800 |
13,947,400,200 |
−0.05 |
| 12/12/2025 |
972,331,101 |
5,349,672,951.00 |
78,160,000,000 |
5.5200 |
5.5200 |
5.5400 |
5.4300 |
14,147,900,200 |
−0.18 |
| 12/11/2025 |
864,808,203 |
4,703,813,108.00 |
76,730,000,000 |
5.4200 |
5.4200 |
5.5200 |
5.4000 |
14,147,900,200 |
0.03 |
| 12/10/2025 |
979,369,763 |
5,317,185,434.00 |
76,550,000,000 |
5.4600 |
5.4700 |
5.4700 |
5.3900 |
13,996,400,200 |
−0.26 |
| 12/9/2025 |
1,313,889,987 |
7,190,317,325.00 |
76,170,000,000 |
5.4400 |
5.4400 |
5.5500 |
5.4200 |
13,996,400,200 |
0.04 |
| 12/8/2025 |
664,780,210 |
3,696,484,973.00 |
77,490,000,000 |
5.5400 |
5.5500 |
5.6000 |
5.5400 |
13,967,400,200 |
−0.06 |
| 12/5/2025 |
1,043,813,555 |
5,752,290,779.00 |
77,510,000,000 |
5.5400 |
5.5500 |
5.5800 |
5.4400 |
13,967,400,200 |
−0.17 |
| 12/4/2025 |
902,847,524 |
4,932,497,746.00 |
77,060,000,000 |
5.5000 |
5.5000 |
5.5400 |
5.3900 |
14,001,400,200 |
−0.07 |
| 12/3/2025 |
699,980,187 |
3,809,071,589.00 |
76,200,000,000 |
5.4200 |
5.4200 |
5.5200 |
5.4100 |
14,050,900,200 |
−0.06 |
| 12/2/2025 |
619,644,746 |
3,425,344,789.00 |
76,630,000,000 |
5.5200 |
5.5100 |
5.5800 |
5.4800 |
13,905,900,200 |
0.17 |
| 12/1/2025 |
1,612,028,436 |
8,900,197,670.00 |
76,910,000,000 |
5.5200 |
5.5300 |
5.5600 |
5.4700 |
13,905,900,200 |
−0.20 |
| 11/28/2025 |
840,176,737 |
4,613,317,586.00 |
76,550,000,000 |
5.4900 |
5.4900 |
5.5300 |
5.4600 |
13,952,400,200 |
0.05 |
| 11/27/2025 |
1,222,467,487 |
6,739,034,971.00 |
77,380,000,000 |
5.4800 |
5.4900 |
5.5700 |
5.4700 |
14,106,400,200 |
−0.02 |
| 11/26/2025 |
951,646,982 |
5,257,031,164.00 |
79,970,000,000 |
5.5000 |
5.5100 |
5.5600 |
5.5000 |
14,523,900,200 |
−0.02 |
| 11/25/2025 |
1,569,684,609 |
8,653,403,170.00 |
77,860,000,000 |
5.5000 |
5.5000 |
5.5600 |
5.4500 |
14,157,900,200 |
0.00 |
| 11/24/2025 |
1,791,567,261 |
9,663,224,540.00 |
75,810,000,000 |
5.4200 |
5.4400 |
5.4600 |
5.3100 |
13,948,900,200 |
−0.19 |
| 11/21/2025 |
2,611,547,036 |
13,880,882,910.00 |
72,840,000,000 |
5.2700 |
5.2900 |
5.3700 |
5.2600 |
13,773,900,200 |
−0.35 |
| 11/20/2025 |
1,905,105,904 |
10,389,622,630.00 |
74,380,000,000 |
5.4700 |
5.4600 |
5.5600 |
5.4000 |
13,613,400,200 |
0.11 |
| 11/19/2025 |
1,855,234,737 |
10,226,882,860.00 |
73,390,000,000 |
5.5000 |
5.5000 |
5.5800 |
5.4700 |
13,353,900,200 |
−0.02 |
| 11/18/2025 |
1,383,768,691 |
7,681,487,586.00 |
73,720,000,000 |
5.5300 |
5.5300 |
5.6200 |
5.5000 |
13,320,900,200 |
−0.08 |
| 11/17/2025 |
901,677,839 |
5,101,257,206.00 |
74,060,000,000 |
5.6500 |
5.6400 |
5.7200 |
5.6200 |
13,124,900,200 |
0.12 |
| 11/14/2025 |
919,062,831 |
5,277,253,028.00 |
76,070,000,000 |
5.7000 |
5.7000 |
5.7900 |
5.6800 |
13,349,400,200 |
0.11 |
| 11/13/2025 |
1,166,473,139 |
6,772,321,736.00 |
78,280,000,000 |
5.8600 |
5.8600 |
5.9100 |
5.7600 |
13,349,400,200 |
−0.15 |
| 11/12/2025 |
1,604,332,817 |
9,308,318,670.00 |
77,420,000,000 |
5.8100 |
5.8200 |
5.8600 |
5.7600 |
13,308,400,200 |
−0.13 |
| 11/11/2025 |
834,860,520 |
4,834,475,518.00 |
77,800,000,000 |
5.8000 |
5.8100 |
5.8500 |
5.7400 |
13,394,900,200 |
−0.15 |
| 11/10/2025 |
868,099,374 |
4,997,647,223.00 |
77,510,000,000 |
5.7900 |
5.8000 |
5.8100 |
5.7000 |
13,365,400,200 |
−0.17 |
Historical Statistics
Volume Statistics
volume_stats_df <- data.frame(
Metric = c("Median", "Mean", "Minimum", "Maximum", "Total Days"),
Value = c(
stats$volume_median,
stats$volume_mean,
stats$volume_min,
stats$volume_max,
stats$total_days
)
)
volume_stats_df %>%
gt() %>%
fmt_number(columns = Value, rows = 1:4, decimals = 0, use_seps = TRUE) %>%
fmt_number(columns = Value, rows = 5, decimals = 0, use_seps = FALSE) %>%
cols_label(
Metric = "Metric",
Value = "Value"
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_text(align = "right"),
locations = cells_body(columns = Value)
) %>%
opt_table_font(font = "Arial") %>%
tab_options(
table.width = pct(50),
column_labels.background.color = "#f8f9fa",
table_body.hlines.color = "#e9ecef"
)
| Metric |
Value |
| Median |
1,313,889,987 |
| Mean |
1,425,819,881 |
| Minimum |
619,644,746 |
| Maximum |
3,737,847,066 |
| Total Days |
65 |
Turnover Statistics
turnover_stats_df <- data.frame(
Metric = c("Median", "Mean", "Minimum", "Maximum"),
Value = c(
stats$turnover_median,
stats$turnover_mean,
stats$turnover_min,
stats$turnover_max
)
)
turnover_stats_df %>%
gt() %>%
fmt_number(columns = Value, decimals = 2, use_seps = TRUE) %>%
cols_label(
Metric = "Metric",
Value = "Value"
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels()
) %>%
tab_style(
style = cell_text(align = "right"),
locations = cells_body(columns = Value)
) %>%
opt_table_font(font = "Arial") %>%
tab_options(
table.width = pct(50),
column_labels.background.color = "#f8f9fa",
table_body.hlines.color = "#e9ecef"
)
| Metric |
Value |
| Median |
7,416,426,523.00 |
| Mean |
8,304,287,494.49 |
| Minimum |
3,425,344,789.00 |
| Maximum |
22,304,783,010.00 |
Report generated: 2025-12-20 08:23:28.668204