In databrew/sfi:

# Packages
library(sfi)
library(webshot)
library(reshape2)
library(ggplot2)
library(dplyr)
library(plotly)
library(ggiraph)
library(scales)
library(tidyverse)
library(knitr)
library(Hmisc)
library(RColorBrewer)
library(extrafont)
library(kableExtra)
library(grid)

# webshot::install_phantomjs()

loadfonts()
## Global options
options(max.print="75")
opts_chunk$set(echo=FALSE,
            cache=FALSE,
              prompt=FALSE,


              tidy=TRUE,
              comment=NA,
              message=FALSE,
              warning=FALSE,
              dpi = 300,
              # dev = "cairo_pdf",
            fig.width = 7.25,
            fig.height = 4,
              dev = c("png", "cairo_pdf"),
              fig.pos="!h",
              fig.path = 'figures/')
opts_knit$set(width=75)
options(xtable.comment = FALSE)

This markdown is for Livermore, Grom, Eidelman figures.

\newpage

Livermore, Grom, Eidelman 1 (version 1)

# # no scientific notation
  # options(scipen = '999')
  # 
  # # get data 
  data <- all_data$livermoregrom$f1

  # get those columns 
  data <- data[, c('agency_ideol', 'sent_agency', 
                   'ml_mean_compress2', 'agency_ideol_E')]

  data$agency_ideol <- as.numeric(data$agency_ideol)

  # make long form
  data <- melt(data)

  g1 <- ggplot() + geom_point(aes(data$value[data$variable =='agency_ideol_E'], 
                                  data$value[data$variable =='ml_mean_compress2'], 
                                  size = 'Rule Sentiment'),
                              alpha = 0.9) +
    geom_point(aes(data$value[data$variable =='agency_ideol_E'], 
                   data$value[data$variable =='sent_agency'],
                   size = 'Agency Average Summit'), 
               color = '#6D6D6D', alpha = 0.1) +
    xlab('Agency Polarity') + ylab('Sentiment') +
    theme_sfi(lp = 'bottom', x_axis_title_style = 'bold', y_axis_title_style = 'bold') +
    scale_size_manual(name = '',
                      values = c(4,1)) +
    theme(axis.text=element_text(size = 10, hjust = 1))

  g1

Livermore, Grom, Eidelman 1 (version 1)

# # no scientific notation
  # options(scipen = '999')
  # 
  # # get data 
  data <- all_data$livermoregrom$f1

  # get those columns 
  data <- data[, c('agency_ideol', 'sent_agency', 
                   'ml_mean_compress2', 'agency_ideol_E')]

  data$agency_ideol <- as.numeric(data$agency_ideol)

  # make long form
  data <- melt(data)

  g2 <- ggplot() + geom_point(aes(data$value[data$variable =='agency_ideol_E'], 
                                  data$value[data$variable =='ml_mean_compress2'], 
                                  size = 'Rule Sentiment'),
                              alpha = 0.9) +
    geom_point(aes(data$value[data$variable =='agency_ideol_E'], 
                   data$value[data$variable =='sent_agency'],
                   size = 'Agency Average Summit'), 
               color = '#6D6D6D', alpha = 1) +
    xlab('Agency Polarity') + ylab('Sentiment') +
    theme_sfi(lp = 'bottom', x_axis_title_style = 'bold', y_axis_title_style = 'bold') +
    scale_size_manual(name = '',
                      values = c(4,2)) +
    theme(axis.text=element_text(size = 10, hjust = 1))

  g2