In ying14/yingtools2: Ying's R Tools

source(here::here("common.R"))

In this example we will generate a plot of diversity. This is the same plot that was published as Figure 1 in the Clin Infect Dis (2012) paper.

library(tidyverse)
library(yingtools2)
library(phyloseq)

Use get.samp() to extract the sample data from the phyloseq object. By specifying stats=TRUE, you can calculate alpha diversity for each sample. Note that several types of diversity are calculated.

s <- get.samp(cid.phy,stats=TRUE)
s

With this table you can now plot:

ggplot(s,aes(x=day,y=Shannon)) + geom_point()

We can add a smoothed conditional mean to the plot:

ggplot(s,aes(x=day,y=Shannon)) + geom_point() + geom_smooth()

This is fairly equivalent to Figure 1 in the CID 2012 paper. To make it match that plot even more closely, you could perform the following aesthetic operations: (1) change the X and Y axis titles, using xlab and ylab, (2) add a verticle line at X=0 to indicate the time of stem cell infusion, (3) change the color of the smoothed mean to black, (4) change the shape and color of the points to match, and specify alpha to make them slightly translucent, (5) draw the perimeter of the points in a separate layer (because those are not translucent), (6) change the order of the layers, such that the points are drawn over the smoothed mean.

ggplot(s,aes(x=day,y=Shannon)) + 
  geom_smooth(color="black") + 
  geom_point(shape=16,color="dark gray",size=3,alpha=0.4) +
  geom_point(shape=1,size=3) + 
  geom_vline(xintercept=0,linetype="longdash") +
  xlab("Day of transplant") +
  ylab("Biodiversity index (Shannon)")