load('gss.Rdata')
# realy hard work here to supress annoying messages
suppressWarnings(library(ggplot2))
suppressPackageStartupMessages(suppressWarnings(library(dplyr)))
suppressPackageStartupMessages(suppressWarnings(library(gridExtra)))
library(statsr)

gss_job = gss %>%
    select(jobinc, jobsec, jobpromo, jobmeans, class)

summary(gss_job)

gss_job = na.omit(gss_job) 

gr_inc = ggplot(gss_job, aes(jobinc, fill=jobinc)) + geom_bar() + labs(title = "Income") + guides(fill=FALSE)
gr_sec = ggplot(gss_job, aes(jobsec, fill=jobsec)) + geom_bar() + labs(title = "Security") + guides(fill=FALSE)
gr_promo = ggplot(gss_job, aes(jobpromo, fill=jobpromo)) + geom_bar() + labs(title = "Promotion") + guides(fill=FALSE)
gr_means = ggplot(gss_job, aes(jobmeans, fill=jobmeans)) + geom_bar() + labs(title = "Meaning") + guides(fill=FALSE)

grid.arrange(gr_inc, gr_sec, gr_promo, gr_means, ncol=2, nrow =2)

gss_job %>%
    group_by(class) %>%
    summarise(income = median(as.numeric(jobinc)), 
              security = median(as.numeric(jobsec)),
              promotion = median(as.numeric(jobpromo)),
              meaning = median(as.numeric(jobmeans)),
              count = n())

gss_job %>%
    group_by(class) %>%
    summarise(income = mean(as.numeric(jobinc)), 
              security = mean(as.numeric(jobsec)),
              promotion = mean(as.numeric(jobpromo)),
              meaning = mean(as.numeric(jobmeans)),
              count = n())

# setup printing options
# options(repr.plot.width = 4)
options(repr.plot.height = 4)

inference(jobsec, class, data=gss_job, type = "ht", method = "theoretical", alternative="greater", null=0, statistic = "proportion")

inference(jobmeans, class, data=gss_job, type = "ht", method = "theoretical", alternative="greater", null=0, statistic = "proportion")

chisq.test(gss_job$class, gss_job$jobsec)