Plotting means and error bars (ggplot2)

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/

http://www.inside-r.org/packages/cran/rmisc/docs/summarySE