#' Load required libraries
library(magrittr)
library(tidyverse)
library(plotly)

#' Read in codes data
time_output <- read.csv('time_output.csv', stringsAsFactors = FALSE)

#' Summarise codes 
code_sum <- time_output %>% count(code, sort = TRUE)
#' Plot code frequency (Figure 3)
ggplot(code_sum, aes(x=as.factor(code), y=n, fill=as.factor(code), text = paste0('Code: ', code, '\nn = ', n))) + 
  geom_bar(stat="identity") +
  coord_flip() +
  theme(legend.position="none") + 
  scale_fill_viridis_d('Code') +
  xlab('Code') + 
  ylab('Frequency')

#' Summarise codes by speaker
spkr_code_sum <- time_output %>% count(speaker, code, sort = TRUE)
#' Plot code frequency by speakers (Figure 4)
ggplot(spkr_code_sum, aes(x=as.factor(speaker), y=n, fill=as.factor(code), text = paste0('Code: ', code, '\nn = ', n))) + 
  geom_bar(stat="identity") +
  theme(legend.position="right") + 
  scale_fill_viridis_d('Code') +
  xlab('Speaker') + 
  ylab('Frequency')