library(dplyr)
fdtdata <- read.csv("/home/-redacted-/Documents/femthread.csv")
names(fdtdata)
fdtdata

# t-test: fem/mra pctg misinfo
fdtdata <- data.frame(fdtdata)
feminists <- filter(fdtdata, cat=="fem")
mras <- filter(fdtdata, cat == "mra")
t_misinfo <- t.test(feminists$pctg_misinfo, mras$pctg_misinfo)
# No significant difference
boxplot(fdtdata$pctg_misinfo ~ fdtdata$cat,
        main="Misinformation content percentage as a function of ideology",
        xlab="",
        ylab="% of misinformation posts (PM/PF)",
        names=c("Feminists", "Neutral", "MRAs"),)

# t-test: fem/mra pctg counter misinfo
fem_ctrmisinfo <- c(feminists$pctg_ctrmisinfo_arg + feminists$pctg_ctrmisinfo_def)
mra_ctrmisinfo <- c(mras$pctg_ctrmisinfo_arg + mras$pctg_ctrmisinfo_def)
t_cmisinfo <- t.test(mra_ctrmisinfo, fem_ctrmisinfo)
# no significant difference
boxplot(fdtdata$pctg_ctrmisinfo_arg + fdtdata$pctg_ctrmisinfo_def ~ fdtdata$cat,
        main="Counter-misinformation content % vs ideology",
        xlab="",
        ylab="% of counter-misinformation posts",
        names=c("Feminists", "Neutral", "MRAs"),)

# t-test: difference in PCI
t_pci <- t.test(feminists$pci, mras$pci)
# no significant difference
boxplot(fdtdata$pci ~ fdtdata$cat,
        main="PCI as a function of ideology",
        xlab="",
        ylab="PCI (non-frequency-corrected)",
        names=c("Feminists", "Neutral", "MRAs"),)

# t-test: difference in FPCI
t_fpci <- t.test(feminists$fpci, mras$fpci)
# no significant difference
boxplot(fdtdata$fpci ~ fdtdata$cat,
        main="Frequency-adjusted PCI as a function of ideology",
        xlab="",
        ylab="FPCI",
        names=c("Feminists", "Neutral", "MRAs"),)

# anova: gender pctg misinfo
aov_misinfo <- aov(fdtdata$pctg_misinfo ~ fdtdata$sex)
summary(aov_misinfo)
# No significant difference
boxplot(fdtdata$pctg_misinfo ~ fdtdata$sex,
        main="Misinformation content percentage as a function of gender",
        xlab="",
        ylab="% of misinformation posts (PM/PF)",
        names=c("Females", "Males", "Unknown/other"),)

# anova: gender pctg counter misinfo
aov_cmisinfo <- aov(fdtdata$pctg_ctrmisinfo_arg + fdtdata$pctg_ctrmisinfo_def ~ fdtdata$sex)
summary(aov_cmisinfo)
# not significant
boxplot(fdtdata$pctg_ctrmisinfo_arg + fdtdata$pctg_ctrmisinfo_def ~ fdtdata$sex,
        main="Counter-misinfo. content % vs gender",
        xlab="",
        ylab="% of counter-misinformation posts",
        names=c("Females", "Males", "Unknown/other"),)

# anova: gender pci
aov_pci <- aov(fdtdata$pci ~ fdtdata$sex)
summary(aov_pci)
# aggressively insignificant
boxplot(fdtdata$pci ~ fdtdata$sex,
        main="PCI as a function of gender",
        xlab="",
        ylab="PCI",
        names=c("Females", "Males", "Unknown/other"),)

# anova: gender fpci
aov_fpci <- aov(fdtdata$fpci ~ fdtdata$sex)
summary(aov_fpci)
# even more aggressively insignificant
boxplot(fdtdata$fpci ~ fdtdata$sex,
        main="Frequency-adjusted PCI as a function of gender",
        xlab="",
        ylab="FPCI",
        names=c("Females", "Males", "Unknown/other"),)

# make pie charts
# misinformation by ideology (duh)
misinfo_by_ideology <- c(sum(feminists$p), sum(mras$p))
pie(misinfo_by_ideology, labels=c("Pro-feminist", "Pro-MRA"), main="Alignment of misinformation posts", radius=1)
# gender ratios
gender_ratio <- c(
        count(filter(fdtdata, sex == "f", preserve=TRUE)),
        count(filter(fdtdata, sex == "m", preserve=TRUE)),
        count(filter(fdtdata, sex == "u", preserve=TRUE))
)
# for some unknown reason, pie won't work using the above, so i just converted the values directly into integers for this
pie(c(3,10,3), labels=c("Female", "Male", "Unknown/other"), main="Gender ratio", radius=1)
# ideology ratios
ideology_ratio <- c(
        count(filter(fdtdata, cat == "fem", preserve=TRUE)),
        count(filter(fdtdata, cat == "meh", preserve=TRUE)),
        count(filter(fdtdata, cat == "mra", preserve=TRUE))
)
# same problem as with gender - directly converted again
pie(c(6,3,7), labels=c("Feminist", "Neutral", "MRA"), main="Ideology ratio", radius=1)