ggplot2 plotting with error bars

Liang / 2018-05-03

Problem

As a biologist, we need to plot a lot of data every day. To make our result more convincing, usually we need to add error bars on the bar plot results. In this Poster, I will show how to use ggplot2 to make a bar plot with error bar.

Solution

Prepare Data

The data we are using in this practice is ToothGrowth, we can get the data in datasets library. first we can look at the dataset.

library(datasets)
library(ggplot2)

data(ToothGrowth)
summary(ToothGrowth)

##       len        supp         dose      
##  Min.   : 4.20   OJ:30   Min.   :0.500  
##  1st Qu.:13.07   VC:30   1st Qu.:0.500  
##  Median :19.25           Median :1.000  
##  Mean   :18.81           Mean   :1.167  
##  3rd Qu.:25.27           3rd Qu.:2.000  
##  Max.   :33.90           Max.   :2.000

head(ToothGrowth)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

Next, We will use summarySE function to summary the dataset. Please consider about the difference between summary and summarySE. summarySE is in Rmisc library, so first require Rmisc

library(Rmisc)
tg <- ToothGrowth
# summarySE provides the standard deviation, standard error of the mean, and a (default 95%) confidence interval
tgc <- summarySE(tg, measurevar="len", groupvars=c("supp","dose"))
tgc

##   supp dose  N   len       sd        se       ci
## 1   OJ  0.5 10 13.23 4.459709 1.4102837 3.190283
## 2   OJ  1.0 10 22.70 3.910953 1.2367520 2.797727
## 3   OJ  2.0 10 26.06 2.655058 0.8396031 1.899314
## 4   VC  0.5 10  7.98 2.746634 0.8685620 1.964824
## 5   VC  1.0 10 16.77 2.515309 0.7954104 1.799343
## 6   VC  2.0 10 26.14 4.797731 1.5171757 3.432090

#>   supp dose  N   len       sd        se       ci
#> 1   OJ  0.5 10 13.23 4.459709 1.4102837 3.190283
#> 2   OJ  1.0 10 22.70 3.910953 1.2367520 2.797727
#> 3   OJ  2.0 10 26.06 2.655058 0.8396031 1.899314
#> 4   VC  0.5 10  7.98 2.746634 0.8685620 1.964824
#> 5   VC  1.0 10 16.77 2.515309 0.7954104 1.799343
#> 6   VC  2.0 10 26.14 4.797731 1.5171757 3.432090

After the data is summarized, we can make the graph. I will show the basic bar graph in ggplot2 with error bars representing either the standard error of the mean, or 95% confidence interval.

Bar plot with ggplot

# Use dose as a factor rather than numeric
tgc$dose <- factor(tgc$dose)

ggplot(tgc, aes(x=dose, y=len, fill=supp)) + 
    geom_bar(position=position_dodge(), stat="identity",
             colour="black", # Use black outlines,
             size=.3) +      # Thinner lines
    geom_errorbar(aes(ymin=len-se, ymax=len+se),
                  size=.3,    # Thinner lines
                  width=.2,
                  position=position_dodge(.9)) + # adjust errorbar`s position
    xlab("Dose (mg)") +
    ylab("Tooth length") +
    scale_fill_hue(name="Supplement type", # Legend label, use darker colors
                   breaks=c("OJ", "VC"),
                   labels=c("Orange juice", "Ascorbic acid")) +
    ggtitle("The Effect of Vitamin C on\nTooth Growth in Guinea Pigs") +
    scale_y_continuous(breaks=0:20*4) +
    theme_classic()

scale_fill_hue is one way to modify the legend. And also there some other way in ggplot2.

Reference: Plotting means and error bars (ggplot2)

Last modified on 2018-05-03