library(readr)
library(ggplot2)
## update this to read the proper .Rdata file from /data
## D <- read_csv("./learning-strain-etal-1995.csv")
## Strain's categories
xtabs(~freq+reg, data=D)
## Our categories based on Tao's rubric
xtabs(~freqval+reg, data=D)
## Relating our CoCA derived frequencies to Strain's
## frequency categories. I see very little overlap between the two.
ggplot(aes(y=log_freq, x=freq, color=freq), data=D) +
geom_jitter(width=1/3, alpha=.5, size=3) +
ylab("log frequency (CoCA)") +
xlab("Frequency category (Strain)") +
labs(color = "Freq. (Strain)")
ggplot(aes(y=log_freq, x=freq, color=reg), data=D) +
geom_jitter(width=1/3, alpha=.5, size=3) +
ylab("log frequency (CoCA)") +
xlab("Frequency category (Strain)") +
labs(color = "Reg. (Strain)")
max(D$log_freq[D$freq=="L"], na.rm=TRUE) # 0.359
D[D$log_freq<.4 & D$freq=="H",]
ggplot(aes(y=log_freq, x=freqval, color=freq), data=D) +
geom_jitter(width=1/3, alpha=.5, size=3) +
ylab("log frequency (CoCA)") +
xlab("Frequency category (Tao)") +
labs(color = "Freq. (Strain)")
ggplot(aes(y=log_freq, x=freqval, color=reg), data=D) +
geom_jitter(width=1/3, alpha=.5, size=3) +
ylab("log frequency (CoCA)") +
xlab("Frequency category (Tao)") +
labs(color = "Reg. (Strain)")
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