Generate dispersion (including plot) and summary metrics for a given vector

vecQ.R

# Plot histogram with density curve, and measures of dispersion from the mean
#
# dispersion x axis:
# V = Sample variance
# IQR = inter-quartile range (balanced)
# SD = standard deviation
# SE = Standard error
# CI = Confidence interval (see inputs)
#
# CV = coefficent of variation
#
# t = vector of numeric or interval data
# ci = z value for the desired confidence interval. Default is 1.96 (95%)
# label.y = Axis title for histogram x and dispersion y
# Example
# set.seed(42)
# a <- runif(100, min = 18, max = 27) 
# c <- rnorm(100, mean = 22.5, sd = 2.6)
# b <- c(a,c)
# vecQ(b, 1.96, "Measurement (mm)")[[2]]
  
vecQ <- function(t, ci = 1.96, label.y){
  
  require(grid)
  require(ggplot2)
  require(moments)
  require(ggpubr)
    
    df <- data.frame(cat = c("V", "IQR", "SD", "SE", "CI"), mean = NA, d.lower = NA, d.upper =NA)
    df$cat <- factor(df$cat, levels = unique(df$cat))
    
    m <- mean(t,na.rm = TRUE)
    v <- var(t, na.rm = TRUE)
    s <- sd(t, na.rm = TRUE)
    n <- length(t)
    se <- s/sqrt(n)
    z <- ci
    
    df$mean <- m
    df$d.lower[1] <- m - v
    df$d.upper[1] <- m + v
    df$d.lower[2] <- quantile(t)[2]
    df$d.upper[2] <- quantile(t)[4]
    df$d.lower[3] <- m - s
    df$d.upper[3] <- m + s
    df$d.lower[4] <- m - se
    df$d.upper[4] <- m + se
    df$d.lower[5] <- m - (z*se)
    df$d.upper[5] <- m + (z*se)
    
    cv <- sd(t, na.rm=TRUE)/
      mean(t, na.rm=TRUE)*100
    cv <- round(cv, 2)
    
    sk <- skewness(t)
    sk <- round(sk, 3)
    
    grob <- grobTree(textGrob(paste0("Skewness = ", sk), x=0.01,  y=0.95, hjust=0,
                              gp=gpar(fontsize=12)))
    
    kt <- kurtosis(t)
    kt <- round(kt, 3)
    
    grob2 <- grobTree(textGrob(paste0("Kurtosis = ", kt), x=0.01,  y=0.85, hjust=0,
                               gp=gpar(fontsize=12)))
    
    plot.a <- ggplot(df, aes(x = cat, y = mean)) +
      geom_point() + 
      geom_errorbar(aes(ymin = d.lower, ymax = d.upper), width=.3,)+
      annotate("text", x = 3, y = m+s+(se*2), label = paste0("CV = ", cv, size = 5)) +
      theme_bw() + 
      theme(axis.text = element_text(size = 16),
            axis.title = element_text(size=16,face="bold")) +
      ylab(label.y) +
      xlab("Measure of dispersion")
    
    plot.b <- ggplot(as.data.frame(t), aes(t)) + 
      geom_histogram(aes(y = ..density..),
                     binwidth = 0.5,
                     fill = I("white"), 
                     col = "black") +
      geom_density(col = "#009E73",
                   lwd = 2) +
      geom_vline(xintercept = median(t),
                 col = "#E69F00", 
                 lty = 1,
                 lwd = 2) +
      geom_vline(xintercept = mean(t),
                 col = "#0072B2", 
                 lty = 2,
                 lwd = 2) +
      annotation_custom(grob) + 
      annotation_custom(grob2) +
      theme_bw() + 
      theme(axis.text = element_text(size = 16),
            axis.title = element_text(size=16,face="bold")) +
      xlab(label.y) +
      ylab("Density")
    
    plot <- ggarrange(plot.b, plot.a,
                      ncol = 2, nrow = 1)
    
    getmode <- function(v) {
      uniqv <- unique(v)
      uniqv[which.max(tabulate(match(v, uniqv)))]
    }
    
    summary.list <- function(x)list(
      N.minus.NA = length(x[!is.na(x)]),
      NA.count = length(x[is.na(x)]),
      Skewness = sk,
      Kurtosis = kt, 
      Max.Min = range(x, na.rm=TRUE),
      Range = max(x, na.rm=TRUE) - min(x, na.rm=TRUE),
      Mode = getmode(x), 
      Median = median(x, na.rm=TRUE),
      Quantile = quantile(x, na.rm=TRUE), 
      Variance = v,
      Mean = m,
      Std.Dev = s,
      Coeff.Variation = cv,
      Std.Error = se
    )
    
    s <- summary.list(t)
    
    return(list(df, plot, s))
  }