How to Plot Mean and Standard Error in Boxplot in R

How to plot mean and standard error in Boxplot in R

First write a function that compute the min, mean-1SEM, mean, mean+1SEM, and Max. Then map these 5 values onto a boxplot using stat_summary.

library(gridExtra)
library(ggplot2)

MinMeanSEMMax <- function(x) {
  v <- c(min(x), mean(x) - sd(x)/sqrt(length(x)), mean(x), mean(x) + sd(x)/sqrt(length(x)), max(x))
  names(v) <- c("ymin", "lower", "middle", "upper", "ymax")
  v
}

g1 <- ggplot(mtcars, aes(factor(am), mpg)) + geom_boxplot() +
  ggtitle("Regular Boxplot")

g2 <- ggplot(mtcars, aes(factor(am), mpg)) +
  stat_summary(fun.data=MinMeanSEMMax, geom="boxplot", colour="red") + 
  ggtitle("Boxplot: Min, Mean-1SEM, Mean, Mean+1SEM, Max")

grid.arrange(g1, g2, ncol=2)

How to add standard error bars to a box and whisker plot using ggplot2?

There is a mean_se function in ggplot2 which does exactly what you want.

library(ggplot2)
ggplot(PlantGrowth, aes(group, weight))+
  stat_boxplot( aes(group, weight), 
    geom='errorbar', linetype=1, width=0.5)+  #whiskers
  geom_boxplot( aes(group, weight),outlier.shape=1) +    
  stat_summary(fun.y=mean, geom="point", size=2) + 
  stat_summary(fun.data = mean_se, geom = "errorbar")

R manual boxplot with means and standard deviations (ggplot2)

I don't think you want a boxplot in this case. You could use something like geom_errorbar from the ggplot2 package. Please provide data or sample data to make your question reproducible.

df <- data.frame(means = rnorm(20, 5, 2),
                   sds = rnorm(20),
                 feats = c(paste0("Feature ", letters[1:10])),
                 group = rep(c("group 1", "group 2"), each = 2))
head(df)
#      means        sds     feats   group
# 1 7.298374 -1.1545645 Feature a group 1
# 2 6.124870 -0.0694843 Feature b group 1
# 3 3.855704  0.3802556 Feature c group 2
# 4 6.357659  2.2822757 Feature d group 2
# 5 3.572474 -0.9488784 Feature e group 1
# 6 3.526351  2.5956482 Feature f group 1

library(ggplot2)
ggplot(df, aes(x = feats, color = group)) +
  geom_errorbar(aes(ymax = means + sds, ymin = means - sds),
                position = "dodge")

How do I plot the mean instead of the median with geom_boxplot?

There are a few ways to do this:

1. Using middle

The easiest is to simply call:

plot <- ggplot(data = df, aes(y = dust, x = wind)) + 
        geom_boxplot(aes(middle = mean(dust))

2. Using fatten = NULL

You can also take advantage of the fatten parameter in geom_boxplot(). This controls the thickness of the median line. If we set it to NULL, then it will not plot a median line, and we can insert a line for the mean using stat_summary.

plot <- ggplot(data = df, aes(y = dust, x = wind)) + 
  geom_boxplot(fatten = NULL) +
  stat_summary(fun.y = mean, geom = "errorbar", aes(ymax = ..y.., ymin = ..y..),
               width = 0.75, size = 1, linetype = "solid")
print(plot)

Output using fatten = NULL

As you can see, the above method plots just fine, but when you evaluate the code it will output some warning messages because fatten is not really expected to take a NULL value.

The upside is that this method is possibly a bit more flexible, as we are essentially "erasing" the median line and adding in whatever we want. For example, we could also choose to keep the median, and add the mean as a dashed line.

ggplot: Boxplot with fill and standard errors

position = 'dodge' should solve your problem.

ggplot(mtcars, aes(factor(cyl), mpg, fill=factor(am))) + stat_summary(fun.data=boxes, geom="boxplot", position = 'dodge')

Plot mean, standard deviation, standard error of the mean, and confidence interval

names=data$method
x = 1:13*2-1
CI.up = as.numeric(data$mean)+as.numeric(data$ci)
CI.dn = as.numeric(data$mean)-as.numeric(data$ci)
plot(data$mean~x, cex=1.5,xaxt='n',ylim=c(0.3,0.40), xlab='',ylab='lalala!', main='blahblahblah',col='blue',pch=16)
axis(1, at=x, labels=names)
arrows(x,CI.dn,x,CI.up,code=3,length=0.2,angle=90,col='red')
legend("bottomleft",paste(names,": S.E=",data$se),ncol=6,text.width=1)

output

R - ggplot boxplot with standard deviation values printed in the plot?

You can try this a little bit hackish one:

d %>% 
  # transform from wide to long similar as you did already
  gather(k, v, -fruit, -ends_with("sd")) %>% 
  # add corresponding sd values 
  mutate(label = ifelse(k == "length", length_sd, weight_sd)) %>% 
  # prepare the label as expression
  mutate(label = paste0("sigma==", round(label, 3))) %>%       
  # add factor for alpha by adding the second group 
  group_by(k, add = T) %>% 
  mutate(Alpha=c(1, rep(0, n()-1))) %>% 
  ggplot(aes(fruit, v)) + 
  geom_boxplot() + 
  geom_text(aes(y=max(v) + 0.1,  
            label=label,
            alpha=factor(Alpha)), 
            size=3,
            show.legend = F, 
            parse = T) +
  facet_grid(~k) +
  scale_alpha_manual(values=c(0, 1))

You have to transform the data that the sd values correspond to the fruit and k column like in the column label. Then you have to add a binary factor to avoid overplotting using alpha parameter.

d %>% 
  gather(k, v, -fruit, -ends_with("sd")) %>% 
  mutate(label=ifelse(k == "length",length_sd,weight_sd )) %>% 
  group_by(k, add=T) %>% 
  mutate(Alpha=c(1,rep(0,n()-1))) %>% 
  head(3)
# A tibble: 3 x 7
# Groups:   fruit, k [1]
  fruit length_sd weight_sd k          v label Alpha
  <fct>     <dbl>     <dbl> <chr>  <dbl> <dbl> <dbl>
1 Apple     0.211    0.0292 length 0.531 0.211     1
2 Apple     0.211    0.0292 length 0.489 0.211     0
3 Apple     0.211    0.0292 length 0.503 0.211     0

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