# 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:** 3067
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 3067 - 2025-12-19 |
| Metric |
Value |
| Date |
2025-12-19 |
| Volume |
16,795,752 |
| Turnover |
192,776,544.00 |
| AUM |
NA |
| Closing Price |
11.4900 |
| NAV |
NA |
| Suspension Flag |
No |
| Volume vs Median |
0.60x (16.9% percentile) |
| Volume vs Mean |
0.48x |
| Turnover vs Median |
0.53x (9.2% percentile) |
| Turnover vs Mean |
0.44x |
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 3067 - Comparison of last 5 days |
| Date |
Volume
|
Turnover
|
| Volume |
vs Median |
vs Mean |
Percentile |
Turnover |
vs Median |
vs Mean |
Percentile |
| 12/19/2025 |
16,795,752 |
0.60x |
0.48x |
16.9% |
192,776,544.00 |
0.53x |
0.44x |
9.2% |
| 12/18/2025 |
34,725,321 |
1.23x |
0.99x |
66.2% |
392,877,573.00 |
1.07x |
0.90x |
56.9% |
| 12/17/2025 |
19,441,556 |
0.69x |
0.56x |
24.6% |
221,077,666.00 |
0.60x |
0.51x |
23.1% |
| 12/16/2025 |
22,438,255 |
0.80x |
0.64x |
36.9% |
254,031,073.00 |
0.69x |
0.58x |
30.8% |
| 12/15/2025 |
12,797,694 |
0.46x |
0.37x |
4.6% |
148,260,517.00 |
0.40x |
0.34x |
4.6% |
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 3067 |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
16,795,752 |
192,776,544.00 |
NA |
11.4900 |
NA |
11.5500 |
11.3900 |
1,610,400,000 |
NA |
| 12/18/2025 |
34,725,321 |
392,877,573.00 |
NA |
11.3600 |
NA |
11.4000 |
11.2500 |
1,610,400,000 |
NA |
| 12/17/2025 |
19,441,556 |
221,077,666.00 |
18,430,000,000 |
11.4400 |
11.4500 |
11.4900 |
11.2900 |
1,610,400,000 |
−0.06 |
| 12/16/2025 |
22,438,255 |
254,031,073.00 |
18,240,000,000 |
11.3200 |
11.3300 |
11.5000 |
11.2200 |
1,610,400,000 |
−0.10 |
| 12/15/2025 |
12,797,694 |
148,260,517.00 |
18,570,000,000 |
11.5300 |
11.5300 |
11.7000 |
11.5200 |
1,610,850,000 |
−0.02 |
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 3067 - Most recent dates first |
| Date |
Volume |
Turnover |
AUM |
Close Price |
NAV |
Day High |
Day Low |
Outstanding Units |
Premium/Discount % |
| 12/19/2025 |
16,795,752 |
192,776,544.00 |
NA |
11.4900 |
NA |
11.5500 |
11.3900 |
1,610,400,000 |
NA |
| 12/18/2025 |
34,725,321 |
392,877,573.00 |
NA |
11.3600 |
NA |
11.4000 |
11.2500 |
1,610,400,000 |
NA |
| 12/17/2025 |
19,441,556 |
221,077,666.00 |
18,430,000,000 |
11.4400 |
11.4500 |
11.4900 |
11.2900 |
1,610,400,000 |
−0.06 |
| 12/16/2025 |
22,438,255 |
254,031,073.00 |
18,240,000,000 |
11.3200 |
11.3300 |
11.5000 |
11.2200 |
1,610,400,000 |
−0.10 |
| 12/15/2025 |
12,797,694 |
148,260,517.00 |
18,570,000,000 |
11.5300 |
11.5300 |
11.7000 |
11.5200 |
1,610,850,000 |
−0.02 |
| 12/12/2025 |
19,448,310 |
228,413,046.00 |
19,040,000,000 |
11.8200 |
11.8300 |
11.8400 |
11.6500 |
1,610,700,000 |
−0.04 |
| 12/11/2025 |
13,968,530 |
162,818,032.00 |
18,670,000,000 |
11.6100 |
11.6100 |
11.7900 |
11.5700 |
1,608,900,000 |
0.01 |
| 12/10/2025 |
21,881,585 |
254,388,013.00 |
18,830,000,000 |
11.6900 |
11.7100 |
11.7200 |
11.5400 |
1,608,900,000 |
−0.14 |
| 12/9/2025 |
17,533,658 |
205,261,094.00 |
18,730,000,000 |
11.6400 |
11.6500 |
11.8800 |
11.6200 |
1,608,000,000 |
−0.08 |
| 12/8/2025 |
11,505,730 |
136,953,698.00 |
19,090,000,000 |
11.8700 |
11.8800 |
11.9600 |
11.8400 |
1,608,000,000 |
−0.04 |
| 12/5/2025 |
21,025,033 |
249,216,117.00 |
19,170,000,000 |
11.9100 |
11.9400 |
11.9900 |
11.7000 |
1,606,500,000 |
−0.22 |
| 12/4/2025 |
17,477,227 |
205,007,488.00 |
19,010,000,000 |
11.8300 |
11.8400 |
11.9000 |
11.6000 |
1,606,500,000 |
−0.06 |
| 12/3/2025 |
18,230,348 |
213,555,994.00 |
18,740,000,000 |
11.6700 |
11.6600 |
11.8500 |
11.6400 |
1,606,800,000 |
0.05 |
| 12/2/2025 |
15,377,631 |
182,903,385.00 |
19,070,000,000 |
11.8700 |
11.8500 |
12.0000 |
11.7900 |
1,609,800,000 |
0.15 |
| 12/1/2025 |
22,240,295 |
264,073,199.00 |
19,210,000,000 |
11.9000 |
11.9000 |
11.9400 |
11.7700 |
1,615,200,000 |
0.04 |
| 11/28/2025 |
11,363,530 |
134,233,212.00 |
19,050,000,000 |
11.8300 |
11.8000 |
11.8700 |
11.7300 |
1,615,200,000 |
0.26 |
| 11/27/2025 |
20,281,900 |
240,548,327.00 |
19,220,000,000 |
11.8500 |
11.8000 |
11.9700 |
11.7800 |
1,629,750,000 |
0.44 |
| 11/26/2025 |
23,528,953 |
279,629,611.00 |
19,250,000,000 |
11.8500 |
11.8400 |
11.9500 |
11.8200 |
1,626,000,000 |
0.08 |
| 11/25/2025 |
41,623,189 |
493,295,994.00 |
19,120,000,000 |
11.8400 |
11.8300 |
11.9600 |
11.7200 |
1,617,300,000 |
0.11 |
| 11/24/2025 |
53,757,936 |
623,181,942.00 |
19,350,000,000 |
11.6700 |
11.6900 |
11.7400 |
11.4100 |
1,656,450,000 |
−0.15 |
| 11/21/2025 |
206,309,214 |
2,355,798,711.00 |
19,050,000,000 |
11.3400 |
11.3700 |
11.5500 |
11.3100 |
1,675,950,000 |
−0.28 |
| 11/20/2025 |
45,246,804 |
531,078,698.00 |
19,410,000,000 |
11.7700 |
11.7500 |
11.9500 |
11.6100 |
1,652,250,000 |
0.18 |
| 11/19/2025 |
44,858,128 |
531,304,909.00 |
19,380,000,000 |
11.8300 |
11.8200 |
11.9800 |
11.7600 |
1,640,850,000 |
0.11 |
| 11/18/2025 |
33,209,825 |
396,270,570.00 |
19,480,000,000 |
11.9000 |
11.9000 |
12.0900 |
11.8300 |
1,637,250,000 |
0.01 |
| 11/17/2025 |
22,477,187 |
273,404,215.00 |
19,860,000,000 |
12.1400 |
12.1300 |
12.3000 |
12.0600 |
1,636,950,000 |
0.06 |
| 11/14/2025 |
21,906,498 |
270,168,771.00 |
20,070,000,000 |
12.2700 |
12.2500 |
12.4400 |
12.2200 |
1,638,600,000 |
0.15 |
| 11/13/2025 |
16,550,479 |
206,719,815.00 |
20,870,000,000 |
12.6300 |
12.6100 |
12.7000 |
12.3700 |
1,655,550,000 |
0.19 |
| 11/12/2025 |
16,454,299 |
205,188,378.00 |
20,700,000,000 |
12.5000 |
12.5100 |
12.5800 |
12.3900 |
1,655,550,000 |
−0.05 |
| 11/11/2025 |
16,646,558 |
207,324,084.00 |
20,430,000,000 |
12.4800 |
12.4900 |
12.5800 |
12.3400 |
1,636,500,000 |
−0.05 |
| 11/10/2025 |
19,364,358 |
239,494,240.00 |
20,580,000,000 |
12.4500 |
12.4700 |
12.4800 |
12.2400 |
1,651,350,000 |
−0.15 |
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 |
28,126,427 |
| Mean |
35,010,922 |
| Minimum |
11,363,530 |
| Maximum |
206,309,214 |
| 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 |
366,944,925.00 |
| Mean |
436,470,173.65 |
| Minimum |
134,233,212.00 |
| Maximum |
2,355,798,711.00 |
Report generated: 2025-12-20 08:23:32.701546