if (FALSE) {
library(agridat)
data(bailey.cotton.uniformity)
dat <- bailey.cotton.uniformity
dat <- transform(dat, env=paste(year,loc))
# Data check. Matches Bailey 1926 Table 1. 28.13, , 46.02, 31.74, 13.52
libs(dplyr)
dat
libs(desplot)
desplot(dat, yield ~ col*row|env)
# The yield scales are quite different at each loc, and the dimensions
# are different, so plot each location separately.
# Note: Bailey does not say if plots are 7x15 meters, or 15x7 meters.
# The choices here seem most likely in our opinion.
filter(dat, env=="1921 Sakha")
desplot(yield ~ col*row, main="1921 Sakha", aspect=(20*8.5)/(8*15))
filter(dat, env=="1921 Gemmeiza")
desplot(yield ~ col*row, main="1921 Gemmeiza", aspect=(20*8.5)/(8*15))
filter(dat, env=="1922 Gemmeiza")
desplot(yield ~ col*row, main="1922 Gemmeiza", aspect=(20*8.5)/(8*15))
filter(dat, env=="1921 Giza")
desplot(yield ~ col*row, main="1921 Giza", aspect=(11*6)/(14*8.5))
# 1923 Giza has alternately hi/lo yield rows. Not noticed by Bailey.
filter(dat, env=="1923 Giza")
desplot(yield ~ col*row, main="1923 Giza", aspect=(20*6)/(8*8.5))
}
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