Vandermeer’s Paramecium Experiments

© 2018 Aaron A. King.

Load the data.

library(readr)
library(ggplot2)
library(magrittr)
library(reshape2)
library(plyr)
library(dplyr)

read_csv("https://kinglab.eeb.lsa.umich.edu/202/vandy/vandy_data.csv",
         comment="#") -> dat

unique(dat$species)

## [1] "Bl" "Pa" "Pb" "Pc"

unique(dat$experiment)

##  [1] "Bl"       "BlPa"     "BlPaPbPc" "BlPb"     "BlPc"     "Pa"      
##  [7] "PaPb"     "PaPc"     "Pb"       "PbPc"     "Pc"

unique(dat$rep)

## [1] 1 2 3 4 5 6 7 8

dat

Focus on just the Paramecium bursaria data, from the single-species experiments.

dat %>%
  subset(experiment=="Pb"&species=="Pb",select=-food) %>%
  ggplot(aes(x=day,y=count,group=rep))+
  geom_line()+
  facet_wrap(~rep,ncol=2,labeller=labeller(rep=label_both))+
  theme_bw() -> pl
pl

Plotting on the log scale gives a somewhat different perspective.

pl+scale_y_log10()

Let’s plot \(N_t\) vs \(N_{t-1}\).

dat %>%
  subset(experiment=="Pb"&species=="Pb",select=-food) %>%
  ddply(~rep,mutate,prev=lag(count,1)) %>%
  ggplot(aes(x=prev,y=count,group=rep))+
  geom_point()+geom_smooth()+
  facet_wrap(~rep,ncol=2,labeller=labeller(rep=label_both),scales="free")+
  labs(x=expression(N[t-1]),y=expression(N[t]))+
  theme_bw()

And \(N_t/N_{t-1}\) vs \(N_{t-1}\).

dat %>%
  subset(experiment=="Pb"&species=="Pb",select=-food) %>%
  ddply(~rep,mutate,prev=lag(count,1)) %>%
  ggplot(aes(x=prev,y=count/prev,group=rep))+
  geom_point()+geom_smooth()+
  facet_wrap(~rep,ncol=2,labeller=labeller(rep=label_both),scales="free")+
  labs(x=expression(N[t-1]),y=expression(N[t]/N[t-1]))+
  theme_bw()

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