# 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:** 2800
cat("**Total Records:**", nrow(etf_2800_all), "\n\n")
## **Total Records:** 62
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 2025-12-16
cat("**Last Updated:**", format(Sys.Date()))
## **Last Updated:** 2025-12-17
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 2800 - 2025-12-16 |
| Metric |
Value |
| Date |
2025-12-16 |
| Volume |
397,147,709 |
| Turnover |
10,089,945,020.00 |
| AUM |
NA |
| Closing Price |
25.4400 |
| NAV |
NA |
| Suspension Flag |
No |
| Volume vs Median |
0.80x (27.4% percentile) |
| Volume vs Mean |
0.73x |
| Turnover vs Median |
0.78x (19.4% percentile) |
| Turnover vs Mean |
0.70x |
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 2800 - Comparison of last 5 days |
| Date |
Volume
|
Turnover
|
| Volume |
vs Median |
vs Mean |
Percentile |
Turnover |
vs Median |
vs Mean |
Percentile |
| 12/16/2025 |
397,147,709 |
0.80x |
0.73x |
27.4% |
10,089,945,020.00 |
0.78x |
0.70x |
19.4% |
| 12/15/2025 |
494,817,249 |
1.00x |
0.91x |
50.0% |
12,830,176,890.00 |
0.99x |
0.89x |
48.4% |
| 12/12/2025 |
374,899,285 |
0.75x |
0.69x |
16.1% |
9,777,151,290.00 |
0.75x |
0.68x |
16.1% |
| 12/11/2025 |
422,527,872 |
0.85x |
0.78x |
30.6% |
10,904,808,490.00 |
0.84x |
0.76x |
30.6% |
| 12/10/2025 |
393,066,671 |
0.79x |
0.72x |
24.2% |
10,053,365,920.00 |
0.77x |
0.70x |
17.7% |
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 2800 |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/16/2025 |
397,147,709 |
10,089,945,020.00 |
NA |
25.4400 |
NA |
25.7600 |
25.3000 |
5,376,992,500 |
NA |
| 12/15/2025 |
494,817,249 |
12,830,176,890.00 |
138,960,000,000 |
25.8400 |
25.8400 |
26.0200 |
25.8000 |
5,376,992,500 |
−0.02 |
| 12/12/2025 |
374,899,285 |
9,777,151,290.00 |
140,840,000,000 |
26.1600 |
26.1900 |
26.2400 |
25.9200 |
5,376,992,500 |
−0.13 |
| 12/11/2025 |
422,527,872 |
10,904,808,490.00 |
138,430,000,000 |
25.7600 |
25.7500 |
26.0000 |
25.6800 |
5,376,992,500 |
0.06 |
| 12/10/2025 |
393,066,671 |
10,053,365,920.00 |
136,760,000,000 |
25.7000 |
25.7600 |
25.7000 |
25.4600 |
5,309,992,500 |
−0.22 |
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 2800 - Most recent dates first |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/16/2025 |
397,147,709 |
10,089,945,020.00 |
NA |
25.4400 |
NA |
25.7600 |
25.3000 |
5,376,992,500 |
NA |
| 12/15/2025 |
494,817,249 |
12,830,176,890.00 |
138,960,000,000 |
25.8400 |
25.8400 |
26.0200 |
25.8000 |
5,376,992,500 |
−0.02 |
| 12/12/2025 |
374,899,285 |
9,777,151,290.00 |
140,840,000,000 |
26.1600 |
26.1900 |
26.2400 |
25.9200 |
5,376,992,500 |
−0.13 |
| 12/11/2025 |
422,527,872 |
10,904,808,490.00 |
138,430,000,000 |
25.7600 |
25.7500 |
26.0000 |
25.6800 |
5,376,992,500 |
0.06 |
| 12/10/2025 |
393,066,671 |
10,053,365,920.00 |
136,760,000,000 |
25.7000 |
25.7600 |
25.7000 |
25.4600 |
5,309,992,500 |
−0.22 |
| 12/9/2025 |
395,383,580 |
10,183,826,380.00 |
136,180,000,000 |
25.6200 |
25.6500 |
26.0200 |
25.5800 |
5,309,992,500 |
−0.11 |
| 12/8/2025 |
326,054,948 |
8,507,777,976.00 |
137,770,000,000 |
25.9800 |
25.9800 |
26.3600 |
25.9600 |
5,302,992,500 |
NA |
| 12/5/2025 |
298,803,923 |
7,806,092,749.00 |
139,480,000,000 |
26.2800 |
26.3000 |
26.3600 |
25.9800 |
5,302,992,500 |
−0.09 |
| 12/4/2025 |
498,794,543 |
12,991,441,220.00 |
139,230,000,000 |
26.1400 |
26.1500 |
26.2000 |
25.8600 |
5,323,992,500 |
−0.05 |
| 12/3/2025 |
398,237,220 |
10,365,822,890.00 |
140,160,000,000 |
25.9400 |
25.9600 |
26.1800 |
25.9000 |
5,399,992,500 |
−0.07 |
| 12/2/2025 |
339,903,581 |
8,934,172,670.00 |
140,200,000,000 |
26.2400 |
26.2600 |
26.4400 |
26.1400 |
5,338,992,500 |
−0.08 |
| 12/1/2025 |
321,924,139 |
8,422,362,995.00 |
139,770,000,000 |
26.2000 |
26.1800 |
26.3400 |
26.0400 |
5,338,992,500 |
0.08 |
| 11/28/2025 |
230,461,371 |
6,000,255,473.00 |
139,120,000,000 |
26.0400 |
26.0000 |
26.1600 |
25.9600 |
5,349,992,500 |
0.14 |
| 11/27/2025 |
546,820,348 |
14,281,995,180.00 |
141,930,000,000 |
26.0600 |
26.0900 |
26.2600 |
25.9600 |
5,439,992,500 |
−0.12 |
| 11/26/2025 |
537,131,355 |
14,044,518,780.00 |
146,480,000,000 |
26.0800 |
26.0700 |
26.2800 |
26.0400 |
5,617,992,500 |
0.03 |
| 11/25/2025 |
533,695,075 |
13,899,951,510.00 |
142,480,000,000 |
26.0400 |
26.0400 |
26.2000 |
25.8800 |
5,471,992,500 |
0.00 |
| 11/24/2025 |
490,723,815 |
12,631,415,320.00 |
146,960,000,000 |
25.8200 |
25.8600 |
25.9200 |
25.5200 |
5,682,992,500 |
−0.16 |
| 11/21/2025 |
727,619,188 |
18,543,114,350.00 |
144,430,000,000 |
25.3600 |
25.3600 |
25.6600 |
25.3200 |
5,694,992,500 |
−0.01 |
| 11/20/2025 |
1,187,476,759 |
30,855,474,080.00 |
142,500,000,000 |
26.0000 |
25.9800 |
26.2000 |
25.8200 |
5,484,992,500 |
0.07 |
| 11/19/2025 |
661,002,576 |
17,190,756,400.00 |
143,100,000,000 |
25.9800 |
25.9800 |
26.1800 |
25.8800 |
5,508,992,500 |
0.02 |
| 11/18/2025 |
504,318,531 |
13,183,014,150.00 |
144,640,000,000 |
26.0600 |
26.0800 |
26.3200 |
25.9600 |
5,546,992,500 |
−0.06 |
| 11/17/2025 |
689,586,244 |
18,283,162,870.00 |
145,550,000,000 |
26.5200 |
26.5300 |
26.6800 |
26.4000 |
5,485,992,500 |
−0.05 |
| 11/14/2025 |
595,660,489 |
15,996,781,990.00 |
151,850,000,000 |
26.7200 |
26.7200 |
27.0400 |
26.6800 |
5,682,992,500 |
0.00 |
| 11/13/2025 |
735,260,946 |
19,901,956,980.00 |
154,710,000,000 |
27.1800 |
27.2200 |
27.3200 |
26.8600 |
5,682,992,500 |
−0.16 |
| 11/12/2025 |
462,244,627 |
12,495,378,720.00 |
153,010,000,000 |
27.0400 |
27.0700 |
27.1600 |
26.9000 |
5,651,992,500 |
−0.12 |
| 11/11/2025 |
575,306,746 |
15,399,258,560.00 |
150,800,000,000 |
26.8400 |
26.8400 |
26.9400 |
26.6600 |
5,617,992,500 |
−0.01 |
| 11/10/2025 |
331,961,140 |
8,835,370,880.00 |
150,510,000,000 |
26.8000 |
26.8000 |
26.8400 |
26.4400 |
5,616,992,500 |
0.01 |
| 11/7/2025 |
386,895,252 |
10,208,929,210.00 |
147,670,000,000 |
26.3400 |
26.3800 |
26.5000 |
26.3000 |
5,597,992,500 |
−0.15 |
| 11/6/2025 |
554,722,356 |
14,657,484,840.00 |
149,100,000,000 |
26.6000 |
26.6300 |
26.6400 |
26.1800 |
5,599,992,500 |
−0.10 |
| 11/5/2025 |
537,528,465 |
13,901,467,440.00 |
146,200,000,000 |
26.0400 |
26.0600 |
26.1000 |
25.6000 |
5,610,992,500 |
−0.07 |
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 |
496,805,896 |
| Mean |
542,322,622 |
| Minimum |
164,529,440 |
| Maximum |
1,317,057,344 |
| Total Days |
62 |
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 |
12,999,645,215.00 |
| Mean |
14,382,061,931.32 |
| Minimum |
4,562,791,823.00 |
| Maximum |
34,709,271,360.00 |
Report generated: 2025-12-17 08:41:49.177438