In databrew/sfi:

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

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

This markdown is for Laqueur and Venancio figures.

\newpage

Laqueur and Venancio 1 (version 1a)

 # get data 
  data <- all_data$laqueur$f1

  # version 1
  g1 <- 
    ggplot(data, 
           aes(x = year, 
               y = number_of_grants)) +
    geom_smooth(method = 'loess',
                alpha = 0.7,
                color = 'black',
                linetype = 0) + 
    geom_point(size = 3, 
               color = 'black',
               alpha = 0.8,
               pch = 16) +
    geom_text_repel(aes(label = number_of_grants),
              size = 2,
              color = 'black') +
    labs(x = '',
         y = 'Number of grants',
         title = 'Figure 1',
         subtitle = 'Number of Hearings Resulting in a Grant: 1978-2015',
         caption = paste0('Smoothed with a local regression', '\n', 
                          'with bands representing standard errors')) +
    theme_sfi(lp = 'none',
              y_axis_title_style = 'bold',
              x_axis_title_style = 'bold',
              title_style = 'bold') +
    theme(axis.text=element_text(size = 10, hjust = 1))


 g1

Laqueur and Venancio 1 (version 1b)

 # get data 
  data <- all_data$laqueur$f1

  # version 1
  g2 <- 
    ggplot(data, 
           aes(x = year, 
               y = number_of_grants)) +
    geom_smooth(method = 'loess',
                alpha = 0.7,
                color = 'black',
                linetype = 0) + 
    geom_point(size = 3, 
               color = 'black',
               alpha = 0.8,
               pch = 16) +
    geom_label_repel(aes(label = number_of_grants),
              size = 2,
              color = 'black',
              label.r = .40,
              label.padding = 0.3) +
    labs(x = '',
         y = 'Number of grants',
         title = 'Figure 1',
         subtitle = 'Number of Hearings Resulting in a Grant: 1978-2015',
         caption = paste0('Smoothed with a local regression', '\n', 
                          'with bands representing standard errors')) +
    theme_sfi(lp = 'none',
              y_axis_title_style = 'bold',
              x_axis_title_style = 'bold',
              title_style = 'bold') +
    theme(axis.text=element_text(size = 10, hjust = 1))


 g2

# Laqueur and Venancio 1 (version 2)

 # # get data 
 #  data <- all_data$laqueur$f1
 #  
 #  # version 1
 #  g1 <- 
 #    ggplot(data, 
 #           aes(x = year, 
 #               y = number_of_grants)) +
 #    geom_smooth(method = 'loess',
 #                alpha = 0.7,
 #                color = 'black',
 #                linetype = 0) + 
 #    geom_point(size = 3, 
 #               color = 'black',
 #               alpha = 0.8,
 #               pch = 16) +
 #    labs(x = '',
 #         y = 'Number of grants',
 #         title = 'Figure 1',
 #         subtitle = 'Number of Hearings Resulting in a Grant: 1978-2015',
 #         caption = paste0('Smoothed with a local regression', '\n', 
 #                          'with bands representing standard errors')) +
 #    theme_sfi(lp = 'none',
 #              y_axis_title_style = 'bold',
 #              x_axis_title_style = 'bold',
 #              title_style = 'bold') +
 #    theme(axis.text=element_text(size = 10, hjust = 1))
 #  
 # 
 # g1

Laqueur and Venancio 2 (version 1)

 # get data 
  data <- all_data$laqueur$f2

  # version 6
  g1 <- 
    ggplot(data, 
           aes(x = year, 
               y = percent_of_conducted_hearings_resulting_in_a_grant)) +
    ylim(c(0, 50)) +
    geom_smooth(method = 'lm',
                alpha = 0.4,
                fill = 'black',
                linetype = 0) + 
    geom_point(size = 4, 
               color = 'black',
               alpha = 0.8,
               pch = 16) +
    geom_text(aes(label = paste0(percent_of_conducted_hearings_resulting_in_a_grant, 
                                 '%')),
              size = 2,
              color = 'black',
              nudge_y = 0,
              vjust = -2) +
    labs(x = '',
         y = '% of hearings resulting in a grant',
         title = 'Figure 2',
         subtitle = 'Rate of Parole Grant: 2007-2014',
         caption = paste0('Smoothed with a local regression', '\n', 
                          'with bands representing standard errors')) +
    theme_sfi(lp = 'none',
              y_axis_title_style = 'bold',
              x_axis_title_style = 'bold',
              title_style = 'bold') +
    theme(axis.text=element_text(size = 10, hjust = 1))

g1

# Laqueur and Venancio 2 (version 2)

 # get data 
#   data <- all_data$laqueur$f2
#   
#   # version 6
#   g1 <- 
#     ggplot(data, 
#            aes(x = year, 
#                y = percent_of_conducted_hearings_resulting_in_a_grant)) +
#     ylim(c(0, 50)) +
#     geom_smooth(method = 'lm',
#                 alpha = 0.4,
#                 fill = 'black',
#                 linetype = 0) + 
#     geom_point(size = 4, 
#                color = 'black',
#                alpha = 0.8,
#                pch = 16) +
#     labs(x = '',
#          y = '% of hearings resulting in a grant',
#          title = 'Figure 2',
#          subtitle = 'Rate of Parole Grant: 2007-2014',
#          caption = paste0('Smoothed with a local regression', '\n', 
#                           'with bands representing standard errors')) +
#     theme_sfi(lp = 'none',
#               y_axis_title_style = 'bold',
#               x_axis_title_style = 'bold',
#               title_style = 'bold') +
#     theme(axis.text=element_text(size = 10, hjust = 1))
#  
# g1