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