updated the visualization info and added a dataset to play with

Author: Ryan Williams

R_visualization.md

@@ -77,3 +77,116 @@ ggplot(dataset)+geom_boxplot(aes(x=categorical_variable, y= variable))
 With this plot we can see the distributions of data (quantiles and median) categorized by our categorical variable.  While this visualization can show you how your data is distributed (is it skewed?), you can also begin comparing between categorical variables (is my variable greater under one category than another)?
 
 
+Applying Visualization to a Dataset
+-----------------------------------
+
+First we will install the following packages
+```
+install.packages("ggplot2")
+install.packages("reshape2")
+install.packages("plyr")
+install.pacakges("vegan")
+
+library(reshape2)
+library(plyr)
+library(ggplot2)
+library(vegan)
+```
+These packages include many visualization, statistical, and data management tools that can be used to summarize your data and produce publication-ready plots.
+
+First we will read in the data (this will be at the github repo too), we will remove some nonsenical labels for ease of visualization here.  We will also add a variable that allows us to count up SNPs in the database
+```
+data<-read.csv("~/Desktop/journal.pone.0081760.s004.csv")
+data<-subset(data, RefGenomeID != "" & locus_tag != "")
+data$count<-1
+
+```
+
+This dataset displays SNPs among multiple E.coli strains.  We are interested in looking at how many total SNPs there are per E.coli genome.  We will do this using ddply.
+
+```
+av_snps<-ddply(dataset_melt, .(RefGenomeID),summarise,
+average=mean(value),
+SD=sd(value)
+)
+```
+Here we could calculate multiple descriptive statistics that can be useful when visualizing relationships.
+Now we will use ggplot to produce visualizations of these data.  These functions work in a step-wise manner, where we add visual layers to our data.
+
+```
+ggplot(av_snps)
+
+```
+The errror shows that we have no layers in our plot, so lets add bars to show the average number of SNPs per genome.  
+
+```
+data<-arrange(data, -average)
+ggplot(av_snps)+geom_bar(aes(x=variable, y=average),stat="identity")
+
+```
+
+We can order them within the plot as well
+
+```
+ggplot(av_snps)+geom_bar(aes(x=reorder(variable,average), y=average),stat="identity")
+
+```
+
+These are hard to see.  We will arrange them, look  at the top 10, and flip the axes so we can read labels
+
+```
+av_snps<-arrange(av_snps,-average)
+ggplot(av_snps[1:10,])+geom_bar(aes(x=reorder(variable,average), y=average),stat="identity")+coord_flip()
+```
+
+Lets add a layer that will show the variability around these averages (standard deviation)
+
+```
+p<-ggplot(av_snps[1:10,])+geom_bar(aes(x=reorder(variable,average), y=average),stat="identity")+coord_flip()
+p+geom_errorbar(aes(x=reorder(variable,average),y=average,ymax=average+SD,ymin=average-SD))
+
+```
+
+We can also add informative labels easily
+
+```
+p<-ggplot(av_snps[1:10,])+geom_bar(aes(x=reorder(variable,average), y=average),stat="identity")+coord_flip()
+p.error.bars<-p+geom_errorbar(aes(x=reorder(variable,average),y=average,ymax=average+SD,ymin=average-SD))
+p.error.bars+labs(x="Average Number of SNPs",y="E. Coli Genomes")
+```
+
+Colors can be used as well
+
+```
+p<-ggplot(av_snps[1:10,])+geom_bar(aes(x=reorder(variable,average), y=average,fill=variable),stat="identity")+coord_flip()
+p.error.bars<-p+geom_errorbar(aes(x=reorder(variable,average),y=average,ymax=average+SD,ymin=average-SD,colour=variable))
+p.error.bars+labs(x="Average Number of SNPs",y="E. Coli Genomes")
+
+```
+
+We may also be interested in a multivariate analysis of these data.  We can ask the question, "Which genomes are most similar based on SNPs?"  This is analogous to saying, "Which of my samples are the most similar?"  We will do this using nonmetric multidimensional scaling or NMDS.
+
+First we will transform data to presence-absence 
+
+```
+library(vegan)
+transformed_data<-decostand(dataset[,-1],"pa")
+```
+
+Then we will produce the NMDS and look at one of the outputs
+
+```
+mds<-metaMDS(transformed_data, distance="jaccard",k=2,autotransform=F)
+scores<-data.frame(dataset[,1],scores(mds))
+head(scores)
+names(scores)[1]<-"RefGenomeID"
+```
+We changed the neame above of the first column for ease of understanding.
+Now let's look at the first two axes from our NMDS analysis and colour points by genome identity
+
+```
+ggplot(scores)+geom_point(aes(x=NMDS1,y=NMDS2,colour=RefGenomeID))
+```
+
+This visualization shows where points are in multidimensional space in relation to one another compressed down into a 2D form.  Points that are very close together are very similar while points that are very far apart are dissimilar.
+