plotR.R

hist_bin_report <- function(x,
                            bin_width = 5,
                            bin_start = NULL,
                            bin_end = NULL,
                            main = "Histogram",
                            xlab = "x",
                            ylab = "Count",
                            col = "blue",
                            density = FALSE) {

  x <- as.numeric(x)
  x <- x[is.finite(x)]
  n <- length(x)
  if (n == 0) stop("x rỗng hoặc không có số hợp lệ.")

  # auto start/end nếu không nhập
  if (is.null(bin_start)) bin_start <- floor(min(x) / bin_width) * bin_width
  if (is.null(bin_end))   bin_end   <- ceiling(max(x) / bin_width) * bin_width
  breaks <- seq(bin_start, bin_end, by = bin_width)

  # --- plot ---
  h <- hist(x, breaks = breaks, right = FALSE, include.lowest = TRUE,
            col = col, border = "white",
            main = main, xlab = xlab, ylab = ylab)

  if (density) {
    d <- density(x)
    # scale density lên cùng trục y (để overlay hợp lý)
    scale_fac <- max(h$counts) / max(d$y)
    lines(d$x, d$y * scale_fac, lwd = 2)
  }

  # --- summary stats ---
  miss <- sum(!is.finite(as.numeric(x)))
  stats <- c(
    n = n,
    miss = miss,
    mean = mean(x),
    sd = sd(x),
    min = min(x),
    mdn = median(x),
    max = max(x)
  )

  # --- outliers theo boxplot ---
  out <- boxplot.stats(x)$out

  # --- bin table giống lessR ---
  lower <- breaks[-length(breaks)]
  upper <- breaks[-1]
  mid   <- (lower + upper) / 2

  bins <- cut(x, breaks = breaks, right = FALSE, include.lowest = TRUE)
  cnt  <- as.integer(table(bins))
  prop <- cnt / n
  cumc <- cumsum(cnt)
  cump <- cumsum(prop)

  tab <- data.frame(
    Bin = paste0(lower, " > ", upper),
    Midpnt = mid,
    Count = cnt,
    Prop = round(prop, 2),
    Cumul.c = cumc,
    Cumul.p = round(cump, 2),
    row.names = NULL
  )

  # In report
  cat("\n--- Summary ---\n")
  print(stats)

  cat("\n--- Outliers (boxplot) ---\n")
  cat("Number of outliers:", length(out), "\n")
  if (length(out)) print(sort(out))

  cat("\n--- Bin table ---\n")
  cat("Bin Width:", bin_width, "\n")
  cat("Number of Bins:", nrow(tab), "\n\n")
  print(tab)

  invisible(list(hist = h, stats = stats, outliers = out, table = tab))
}

two_group_tplot <- function(data, x, group,
                            group_levels = NULL,    # ví dụ c("M","F")
                            var.equal = TRUE,       # "Classic t-test" như hình (df = n1+n2-2) => TRUE
                            conf_level = 0.95,
                            main = "Two-Group Plot with Means",
                            sub = NULL,
                            xlab = NULL,
                            show_box = TRUE) {

  x_name <- deparse(substitute(x))
  g_name <- deparse(substitute(group))

  xv <- data[[x_name]]
  gv <- data[[g_name]]

  ok <- is.finite(as.numeric(xv)) & !is.na(gv)
  xv <- as.numeric(xv[ok])
  gv <- gv[ok]

  gv <- as.character(gv)
  if (!is.null(group_levels)) {
    gv <- factor(gv, levels = group_levels)
  } else {
    gv <- factor(gv)
  }
  if (nlevels(gv) != 2) stop("group phải có đúng 2 nhóm.")

  lev <- levels(gv)
  x1 <- xv[gv == lev[1]]
  x2 <- xv[gv == lev[2]]

  n1 <- length(x1); n2 <- length(x2)
  m1 <- mean(x1);   m2 <- mean(x2)
  s1 <- sd(x1);     s2 <- sd(x2)

  # pooled SD (s-within)
  s_within <- sqrt(((n1 - 1) * s1^2 + (n2 - 1) * s2^2) / (n1 + n2 - 2))
  diff <- m2 - m1
  smd  <- diff / s_within

  # t-test (classic if var.equal=TRUE)
  tt <- t.test(xv ~ gv, var.equal = var.equal, conf.level = conf_level)
  ci <- tt$conf.int
  tval <- unname(tt$statistic)
  df   <- unname(tt$parameter)
  pval <- unname(tt$p.value)

  # densities
  d1 <- density(x1)
  d2 <- density(x2)

  xlim <- range(c(d1$x, d2$x))
  ylim <- c(0, max(d1$y, d2$y) * 1.15)

  if (is.null(sub)) sub <- sprintf("Compare %s for %s %s and %s", x_name, g_name, lev[2], lev[1])
  if (is.null(xlab)) xlab <- x_name

  oldpar <- par(no.readonly = TRUE)
  on.exit(par(oldpar), add = TRUE)

  par(mar = c(5, 4, 7, 2))  # chừa chỗ tiêu đề phía trên

  plot(NA, xlim = xlim, ylim = ylim, xlab = xlab, ylab = "", main = "", axes = TRUE)

  # fill colors (nhẹ như hình)
  col1_fill <- adjustcolor("#4C72B0", alpha.f = 0.18)  # xanh
  col2_fill <- adjustcolor("#C49A3A", alpha.f = 0.18)  # vàng nâu
  col1_line <- "#4C72B0"
  col2_line <- "#C49A3A"

  polygon(d1$x, d1$y, col = col1_fill, border = NA)
  polygon(d2$x, d2$y, col = col2_fill, border = NA)
  lines(d1$x, d1$y, col = col1_line, lwd = 2)
  lines(d2$x, d2$y, col = col2_line, lwd = 2)

  # mean lines
  abline(v = m1, col = adjustcolor(col1_line, 0.45), lwd = 1.5)
  abline(v = m2, col = adjustcolor(col2_line, 0.45), lwd = 1.5)

  # bottom bracket for mean diff
  yb <- ylim[2] * 0.10
  segments(m1, yb, m2, yb, col = "grey40", lwd = 2)
  segments(m1, yb*0.92, m1, yb*1.08, col = "grey40", lwd = 2)
  segments(m2, yb*0.92, m2, yb*1.08, col = "grey40", lwd = 2)
  text((m1 + m2)/2, yb*1.12, sprintf("%.3f\ndiff", diff), col = "grey35")

  # top axis in standardized units (0,1,2) where 0 at mean of group1
  axis_at <- m1 + s_within * c(0, 1, 2)
  axis(3, at = axis_at, labels = c("0","1","2"), tck = -0.02, col = "grey40", col.axis = "grey25")

  # top bracket showing SMD
  yt <- ylim[2] * 0.92
  segments(m1, yt, m2, yt, col = "grey55", lwd = 2)
  text((m1 + m2)/2, yt*1.03, sprintf("%.3f\nsmd", smd), col = "grey35")
  text(m1 + s_within, ylim[2] * 0.98, sprintf("s-within\n%.2f", s_within), col = "grey40")

  # side text blocks like lessR
  if (show_box) {
    text(xlim[1] + diff(xlim)*0.02, ylim[2]*0.86,
         sprintf("%s %s\nn = %d\nm = %.3f\ns = %.3f", g_name, lev[1], n1, m1, s1),
         adj = c(0, 1))
    text(xlim[2] - diff(xlim)*0.02, ylim[2]*0.86,
         sprintf("%s %s\nn = %d\nm = %.3f\ns = %.3f", g_name, lev[2], n2, m2, s2),
         adj = c(1, 1))
  }

  # header text (giống hình)
  title(main = main, sub = sub, line = 4)
  mtext(sprintf("Classic t-test of 0 Mean Diff:   t = %.3f,  df = %.0f,  p-value = %.3f",
                tval, df, pval),
        side = 3, line = 2, cex = 1.05)
  mtext(sprintf("%d%% Confidence Interval for Mean Difference:  %.3f to %.3f",
                round(conf_level*100), ci[1], ci[2]),
        side = 3, line = 1, cex = 1.05)

  invisible(list(
    group_levels = lev,
    n = c(n1, n2),
    mean = c(m1, m2),
    sd = c(s1, s2),
    diff = diff,
    s_within = s_within,
    smd = smd,
    ttest = tt
  ))
}