In databrew/sfi:

# Packages
library(sfi)
library(webshot)
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",

              dev = c("png", "cairo_pdf"),
              fig.pos="!h",
              fig.path = 'figures/')
opts_knit$set(width=75)
options(xtable.comment = FALSE)

\newpage

This markdown is for Copus et al figures.

Copus et al 1 (version 1)

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

  # plot point, line, smooth data
  g1 <- ggplot(data, 
               aes(x, y)) +
    geom_point(size = 1,
               color = 'black', 
               alpha = 0.6) +
    geom_smooth(method = 'loess',
                linetype =0,
                fill = 'black',
                alpha = 0.4) +
    geom_smooth(method = 'lm',
                color = 'black',
                se = FALSE) +
    labs(x = '',
         y = '',
         title = 'Figure 1. In Sample Prediction') +
    theme_sfi(lp = 'none',
              x_axis_title_style = 'bold',
              y_axis_title_style = 'bold',
              title_style = 'bold') +
    theme(axis.text=element_text(size = 10, hjust = 1))

g1

# Copus 1 et al (version 2)

#  # plot point, line, smooth data
#   g2 <- ggplot(data, 
#                aes(x, y)) +
#     geom_point(size = 1.5,
#                color = 'black', 
#                alpha = 0.5) +
#     geom_smooth(method = 'loess',
#                 linetype = 0,
#                 se = TRUE,
#                 fill = 'black',
#                 alpha = 0.6) +
#     geom_smooth(method = 'lm',
#                 color = 'darkgrey',
#                 se = FALSE,
#                 alpha = 1) +
#     labs(x = '',
#          y = '',
#          title = 'Figure 1. In Sample Prediction') +
#     theme_sfi(lp = 'none',
#               x_axis_title_style = 'bold',
#               y_axis_title_style = 'bold',
#               title_style = 'bold') +
#     theme(axis.text=element_text(size = 10, hjust = 1))
# 
# g2

Copus et al 2a (Version 1)

# # no scientific notation
  # options(scipen = '999')
  # 
  # # get data 
  data <- all_data$copus$f2

  # plot point, line, smooth data
  g1 <- ggplot(data, 
               aes(Reinhardt, Leavy,
                   color = Percent.Defendant.Win)) +
    scale_color_gradient(name = 'Defendant Trial Winner %', 
                         low = 'grey', high = 'black') +
    xlim(c(0, 1)) + 
    ylim(c(0,1)) +
    xlab('Predicted Reversal Probability for Reinhardt') +
    ylab('Predicted Reversal Probability for Leavy') +
    geom_point(size = 1,
               alpha = 0.8) +
    geom_abline(intercept = 0,
                slope = 1,
                color = 'black') +
    labs(title = 'Figure 2. Predicting the Votes of Ninth Circuit Judges') +
    theme_sfi(lp = 'bottom',
              lkw = TRUE, 
              lkt = 'point', 
              legend_width = 30,
              y_axis_title_style = 'bold',
              x_axis_title_style = 'bold',
              title_style = 'bold')  +
    theme(axis.text=element_text(size = 10, hjust = 1))

  g1

Copus et al 2a (Version 2)

# Copus et al 2a (Version 2)

# # no scientific notation
  # options(scipen = '999')
  # 
  # # get data 
  # data <- all_data$copus$f2
  # 
  # # plot point, line, smooth data
  # g1 <- ggplot(data, 
  #              aes(Reinhardt, Leavy,
  #                  color = Percent.Defendant.Win)) +
  #   scale_color_gradient(name = 'Defendant Trial Winner %', 
  #                        low = 'black', high = 'darkgrey') +
  #   xlim(c(0, 1)) + 
  #   ylim(c(0,1)) +
  #   xlab('Predicted Reversal Probability for Reinhardt') +
  #   ylab('Predicted Reversal Probability for Leavy') +
  #   geom_point(size = 1.2,
  #              alpha = 0.6) +
  #   geom_abline(intercept = 0,
  #               slope = 1,
  #               color = 'black') +
  #   labs(title = 'Figure 2. Predicting the Votes of Ninth Circuit Judges') +
  #   theme_sfi(lp = 'bottom',
  #             lkw = TRUE, 
  #             lkt = 'point', 
  #             legend_width = 30,
  #             y_axis_title_style = 'bold',
  #             x_axis_title_style = 'bold',
  #             title_style = 'bold')  +
  #   theme(axis.text=element_text(size = 10, hjust = 1))
  # 
  # g1

Copus et al 2b (Version 1)

 # plot point, line, smooth data
  g2 <- ggplot(data, 
               aes(Pregerson, Kleinfeld,
                   color = Percent.Defendant.Win)) +
    scale_color_gradient(name = 'Defendant Trial Winner %', 
                         low = 'grey', high = 'black') +
    xlab('Predicted Reversal Probability for Pregerson') +
    ylab('Predicted Reversal Probability for Kleinfeld') +
    xlim(c(0, 1)) + 
    ylim(c(0,1)) +
    geom_point(size = 1,
               alpha = 0.8) +
    geom_abline(intercept = 0,
                slope = 1,
                color = 'black') +
    labs(title = 'Figure 2. Predicting the Votes of Ninth Circuit Judges') +
    theme_sfi(lp = 'bottom',
              lkw = TRUE, 
              lkt = 'point', 
              legend_width = 30,
              y_axis_title_style = 'bold',
              x_axis_title_style = 'bold',
              title_style = 'bold')  +
    theme(axis.text=element_text(size = 10, hjust = 1))
g2

# Copus et al 2a (Version 2)

 # plot point, line, smooth data
#   g2 <- ggplot(data, 
#                aes(Pregerson, Kleinfeld,
#                    fill = Percent.Defendant.Win)) +
#     scale_fill_gradient(name = 'Defendant Trial Winner %', low = 'black', high = 'white') +
#     xlim(c(0, 1)) + 
#     ylim(c(0,1)) +
#     xlab('Predicted Reversal Probability for Pregerson') +
#     ylab('Predicted Reversal Probability for Kleinfeld') +
#     geom_point(size = 2,
#                pch = 21,
#                color = '#575757',
#                alpha = 0.8) +
#     geom_abline(intercept = 0,
#                 slope = 1,
#                 color = 'black') +
#     labs(title = 'Figure 2. Predicting the Votes of Ninth Circuit Judges') +
#     theme_sfi(lp = 'bottom',
#               lkw = TRUE, 
#               lkt = 'point', 
#               legend_width = 30,
#               y_axis_title_style = 'bold',
#               x_axis_title_style = 'bold',
#               title_style = 'bold')  +
#     theme(axis.text=element_text(size = 10, hjust = 1))
#   
# g2