Ggplot2 Tutorial w Standard error function

##Import and examine dataset
library(ape)
data("carnivora")
head(carnivora)
?carnivora

##Open GGplot2
library(ggplot2)

##Lets look at the qplot function first
##Here's the usage
##qplot(x, y, data=, color=, shape=, size=, alpha=, geom=, method=, formula=, facets=, xlim=, ylim= xlab=, ylab=, main=, sub=)
##You can look up what all these mean very easily, we will only use a few of them
##Types of graphs you can make with qplot:  "point", "smooth", "boxplot", "line", "histogram", "density", "bar", and "jitter"
## Lets do boxplots first

qplot (SuperFamily, LS, data = carnivora, geom = "point", xlab = "Super Family", main= "Average Litter Size of Caniformia vs Feliformia" ,ylab = "Average Litter Size" )

##That's great, lets do something a little harder
#Let's leave qplot world

#I'm going to create a standard error function that I can use in my next plot
#you should totally steal this. It will work for any dataset in R, just run it as is.

seFunc <- function(x) {
  n <- sum(!is.na(x))
  se <- sd(x, na.rm = T)/sqrt(n)
  lims <- c(mean(x) - se, mean(x) + se)
  names(lims) <- c("ymin", "ymax")
  return(lims)}

#Lets make a graph that's really similar to that last one but with ggplot instead of qplot

ggplot(carnivora, aes(x = SuperFamily, y = LS)) +
  geom_boxplot()
  
#Lets do something harder and cooler



ggplot(carnivora,aes(LS, FB,color=Family, na.rm=TRUE)) +
  stat_summary(geom="errorbar",fun.data=seFunc,width=0,show.legend=F)+
  stat_summary(geom="point",fun.y="mean",size=3) + 
  stat_summary(geom="line",fun.y="mean")+
  ggtitle("Big Brain, Small Big Litter??")+
  xlab("Average Litter Size")+ylab("Average Brain Weight")+
  theme(panel.grid = element_blank(),
        axis.text=element_text(size=12),axis.title=element_text(size=14),
        legend.key=element_blank(), legend.title=element_text(size=12),
        legend.text=element_text(size=12),
        panel.background = element_blank() ,
        panel.border=element_rect(colour="black",fill=NA),
        axis.line.x = element_line(size=0.5,colour = "black"), 
        axis.line.y=element_line(size=0.5,colour="black"), 
        plot.title=element_text(lineheight=.8, face="bold",size=16))