library(ggplot2)
#############################################
# summarySE
#############################################
## Summarizes data.
## Gives count, mean, standard deviation, standard error of the mean, and confidence interval (default 95%).
## data: a data frame.
## measurevar: the name of a column that contains the variable to be summariezed
## groupvars: a vector containing names of columns that contain grouping variables
## na.rm: a boolean that indicates whether to ignore NA's
## conf.interval: the percent range of the confidence interval (default is 95%)
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
conf.interval=.95, .drop=TRUE) {
library(plyr)
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
#############################################
# Sample data
#############################################
library(ggplot2)
tg <- ToothGrowth
head(tg)
tgc <- summarySE(tg, measurevar="len", groupvars=c("supp","dose"))
tgc
#############################################
# Line graphs
#############################################
# Standard error of the mean
ggplot(tgc, aes(x=dose, y=len, colour=supp)) +
geom_errorbar(aes(ymin=len-se, ymax=len+se), width=.1) +
geom_line() +
geom_point()
# The errorbars overlapped, so use position_dodge to move them horizontally
pd <- position_dodge(0.1) # move them .05 to the left and right
ggplot(tgc, aes(x=dose, y=len, colour=supp)) +
geom_errorbar(aes(ymin=len-se, ymax=len+se), width=.1, position=pd) +
geom_line(position=pd) +
geom_point(position=pd)
# Use 95% confidence interval instead of SEM
ggplot(tgc, aes(x=dose, y=len, colour=supp)) +
geom_errorbar(aes(ymin=len-ci, ymax=len+ci), width=.1, position=pd) +
geom_line(position=pd) +
geom_point(position=pd)
# Black error bars - notice the mapping of 'group=supp' -- without it, the error
# bars won't be dodged!
ggplot(tgc, aes(x=dose, y=len, colour=supp, group=supp)) +
geom_errorbar(aes(ymin=len-ci, ymax=len+ci), colour="black", width=.1, position=pd) +
geom_line(position=pd) +
geom_point(position=pd, size=3)
# A finished graph with error bars representing the standard error of the mean might
# look like this. The points are drawn last so that the white fill goes on top of
# the lines and error bars.
ggplot(tgc, aes(x=dose, y=len, colour=supp, group=supp)) +
geom_errorbar(aes(ymin=len-se, ymax=len+se), colour="black", width=.1, position=pd) +
geom_line(position=pd) +
geom_point(position=pd, size=3, shape=21, fill="white") + # 21 is filled circle
xlab("Dose (mg)") +
ylab("Tooth length") +
scale_colour_hue(name="Supplement type", # Legend label, use darker colors
breaks=c("OJ", "VC"),
labels=c("Orange juice", "Ascorbic acid"),
l=40) + # Use darker colors, lightness=40
ggtitle("The Effect of Vitamin C on
Tooth Growth in Guinea Pigs") +
expand_limits(y=0) + # Expand y range
scale_y_continuous(breaks=0:20*4) + # Set tick every 4
theme_bw() +
theme(legend.justification=c(1,0),
legend.position=c(1,0)) # Position legend in bottom right
#############################################
# Bar graphs
#############################################
# Use dose as a factor rather than numeric
tgc2 <- tgc
tgc2$dose <- factor(tgc2$dose)
# Error bars represent standard error of the mean
ggplot(tgc2, aes(x=dose, y=len, fill=supp)) +
geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin=len-se, ymax=len+se),
width=.2, # Width of the error bars
position=position_dodge(.9))
# Use 95% confidence intervals instead of SEM
ggplot(tgc2, aes(x=dose, y=len, fill=supp)) +
geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin=len-ci, ymax=len+ci),
width=.2, # Width of the error bars
position=position_dodge(.9))
## A finished graph might look like this.
ggplot(tgc2, 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)) +
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
Tooth Growth in Guinea Pigs") +
scale_y_continuous(breaks=0:20*4) +
theme_bw()
REF:
http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_%28ggplot2%29/
http://www.rdocumentation.org/packages/bear/functions/summarySE
http://www.cookbook-r.com/Manipulating_data/Summarizing_data/