In databrew/sfi:

# Packages
library(sfi)
library(kmodR)
library(directlabels)
library(webshot)
library(ggplot2)
library(dplyr)
library(ggrepel)
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)

This markdown is for Chen voice figures: correlation plot and audio plot

\newpage

Chen voice figure 1 (normalized data)

data <- all_data$chenvoice$f1
#  make correlation plot 
g1 <-ggplot(data,
       aes(zresponse1, zresponse2)) +
  geom_point(size = 2) +
  geom_smooth(method = 'lm', 
              se = TRUE, 
              linetype = 0) +
  labs(title = 'Correlation plot',
       x = '1 Mean response',
       y = '0 Mean response',
       caption = 'Std error estimated with a linear model') +
   theme_sfi() +
   theme(axis.text=element_text(size = 10, hjust = 1),
          plot.title = element_text(size =12)) +
  facet_wrap(~qtype, nrow = 2)

g1

Chen voice figure 2 (jitter by importance value)

data <- all_data$chenvoice$f2
data <- as.data.frame(rbind(names(data), data))
names(data) <- c('var1', 'var2', 'value')

# recode
data$var1 <- gsub('X1', 'Both', data$var1)
data$var2 <- gsub('1', 'Intercept', data$var2)

# restructure 
data$var1 <- as.factor(data$var1)
data$var2 <- as.factor(data$var2)
data$value <- as.numeric(data$value)

data$jitter_value <- ave(as.numeric(data$value), data$value,
                         FUN = function(x) x + rnorm(length(x), sd = .3))


# plot
g1 <- ggplot(data, 
       aes(reorder(var2, -jitter_value), 
           jitter_value, 
           group = var1, 
           col = var1)) +
           geom_segment(aes(x=reorder(var2, jitter_value), 
                     xend=var2, 
                     y=0, 
                     yend=jitter_value),
                 size = 1,
                 alpha = 0.5,
                 position = 'stack') + 
    geom_point(size= 2, 
               alpha = 1,
               aes(color = var1)) + scale_color_manual(name='',
                      values = c('#AEAEAE', '#181818','#6C6C6C')) +
  labs(title = 'Audio features', 
       subtitle = 'feature importance (jittered by importance)',
       x = 'Names of features', 
       y = 'Importance score for features') +
  coord_flip()+
  theme_sfi(gM = FALSE) + 
  theme(axis.text=element_text(size = 10, hjust = 1),
        axis.text.x = element_text(),
          plot.title = element_text(size =12)) 
g1

Figure 2 (jittr by petitioner and responded. Equivalent to position = dodge)

g1 <- ggplot(data, 
       aes(reorder(var2, -value), 
           value, 
           group = var1)) +
  geom_point(size= 2, 
               alpha = 1,
               position = position_dodge(0.5),
               aes(color = var1)) + scale_color_manual(name='',
                      values = c('#AEAEAE', '#181818','#6C6C6C')) +
           geom_linerange(aes(ymin =0 , 
                              ymax =value, 
                              color = var1), 
                          size = 0.5,
                          position = position_dodge(0.5)) +
  labs(title = 'Audio features', 
       subtitle = 'feature importance (jittered by petitioner and respondent)',
       x = 'Names of features', 
       y = 'Importance score for features') +
  coord_flip()+
  theme_sfi(gM = FALSE) + 
  theme(axis.text=element_text(size = 10, hjust = 1),
        axis.text.x = element_text(),
          plot.title = element_text(size =12)) 
g1