# Create a new formula based on a given DV and IVs (specified as a formulaa)
build_formula <- function(DV, IVs) {
  reformulate(attr(terms(IVs), "term.labels"), response = DV)
}

get_stargazer_labs <- function(model) {
  coefs <- tidy(model) %>%
    left_join(coef.names, by = c("term" = "coef.name")) %>%
    distinct(term, coef.clean) %>%
    mutate(coef.clean = as.character(coef.clean))
  
  return(coefs$coef.clean)
}

get_mode <- function(x) {
  names(which.max(table(x)))
}

predict_values <- function(model, model_name) {
  # Calculate the means of all five personality traits based on the data used in
  # the given model
  personality_means <- model$model %>%
    summarise_at(vars(OPENNESS, CONSCIENTIOUSNESS, EXTRAVERSION, 
                      AGREEABLENESS, NEUROTICISM), mean)
  
  # Row of 1s
  personality_1 <- data_frame(personality = colnames(personality_means),
                              value = 1) %>%
    spread(personality, value)
  
  # Row of 0s
  personality_0 <- data_frame(personality = colnames(personality_means),
                              value = 0) %>%
    spread(personality, value)
  
  # Data frame of possible personality values (mean, 0, and 1)
  personality_possibilities <- bind_rows(personality_means,
                                         personality_0, personality_1)
  
  # All combinations of mean, 0, and 1 personality values
  new_data_personalities <- expand.grid(personality_possibilities) %>%
    mutate(id = row_number()) %>%
    gather(key, value, -id) %>%
    group_by(id) %>%
    filter(sum(value == 0) == 1 & !any(value == 1) | 
             sum(value == 1) == 1 & !any(value == 0)) %>%
    spread(key, value) %>%
    mutate(index = 1) %>%
    ungroup() %>%
    select(-id)
  
  # Mean or modal values for all other model IVs
  control_means <- data_frame(index = 1, frame = 0:1,
                              sex_rev = get_mode(survey_clean$sex),
                              race_rev = get_mode(survey_clean$race),
                              age_cat = get_mode(survey_clean$age_cat),
                              democrat_rev = get_mode(survey_clean$democrat),
                              republican_rev = get_mode(survey_clean$republican),
                              POLITICAL_KNOWLEDGE = mean(model$model$POLITICAL_KNOWLEDGE), 
                              NC = mean(model$model$NC))
  
  # Add mean/modal values to each possible personality combination
  new_data_full <- new_data_personalities %>%
    left_join(control_means, by = "index") %>%
    select(-index)
  
  frame_labs <- case_when(
    model_name == model_kkk_name ~ frame_labs_kkk_guns_plot,
    model_name == model_guns_name ~ frame_labs_kkk_guns_plot,
    model_name == model_stemcells_name ~ frame_labs_stemcells_plot,
    model_name == model_af_ac_name ~ frame_labs_aa_plot
  )
  
  # Finally use new data in model to generate predicted values
  plot_predict <- augment(model, newdata = new_data_full) %>%
    gather(key, value, one_of(colnames(personality_means))) %>%
    left_join(coef.names, by = c("key" = "coef.name")) %>%
    filter(value %in% c(0, 1)) %>%
    mutate(value = factor(value, levels = c(0, 1),
                          labels = c("Low (0)    ", "High (1)"),
                          ordered = TRUE),
           frame = factor(frame, levels = c(0, 1),
                          labels = frame_labs, ordered = TRUE),
           coef.clean = fct_inorder(coef.clean),
           model_name = model_name)

  return(plot_predict)
}

Susceptibility to issue frames

models_tidy <- models %>%
  mutate(tidy_model = model %>% map(~ tidy(., conf.int = TRUE)))

coefs_to_plot <- c("OPENNESS", "CONSCIENTIOUSNESS", "EXTRAVERSION",
                   "AGREEABLENESS", "NEUROTICISM", "POLITICAL_KNOWLEDGE", "NC",
                   "frame:OPENNESS", "frame:CONSCIENTIOUSNESS", 
                   "frame:EXTRAVERSION", "frame:AGREEABLENESS", 
                   "frame:NEUROTICISM", "frame:POLITICAL_KNOWLEDGE", "frame:NC")

df_coef_plot <- models_tidy %>%
  unnest(tidy_model) %>%
  mutate(low = estimate - std.error,
         high = estimate + std.error) %>%
  left_join(coef.names, by = c("term" = "coef.name")) %>%
  filter(term != "(Intercept)") %>%
  mutate(coef.clean = fct_rev(fct_inorder(coef.clean, ordered = TRUE)),
         model_name = fct_rev(model_name)) %>%
  mutate(facet = case_when(
    term %in% coefs_to_plot & str_detect(term, ":") ~ "Frame ×\npersonality",
    term %in% coefs_to_plot & !str_detect(term, ":") ~ "Personality",
    TRUE ~ "Controls"
  )) %>%
  mutate(facet = factor(facet, 
                        levels = c("Personality", "Frame ×\npersonality", "Controls"),
                        ordered = TRUE))

coef_plot <- ggplot(df_coef_plot, aes(x = estimate, y = coef.clean, colour = model_name)) + 
  geom_vline(xintercept = 0, colour = "black") +
  geom_pointrangeh(aes(xmin = low, xmax = high), size = 0.25,
                   position = position_dodgev(0.5)) + 
  scale_colour_manual(values = framing.palette("palette.color")[5:1]) +
  guides(color = guide_legend(title = NULL, nrow = 1, byrow = TRUE, reverse = TRUE)) +
  labs(x = "Coefficient", y = NULL) +
  theme_framing() + theme(legend.position = "bottom") +
  facet_wrap(~ facet, scales = "free")

coef_plot

Susceptibility to issue frames
KKK rally Concealed handgun law Stem cell research Affirmative action
for women
(1) (2) (3) (4)
Intercept 0.11 2.75 3.73** 6.93***
(1.76) (2.04) (1.66) (1.76)
Sex (male) 0.47** -0.01 -0.18 -0.32*
(0.21) (0.24) (0.20) (0.19)
Race (white) 0.73*** -0.07 0.41* -0.57***
(0.22) (0.27) (0.21) (0.21)
Age (30–44) -0.16 -0.11 -0.27 0.12
(0.30) (0.34) (0.28) (0.28)
Age (45–59) 0.30 -0.05 -0.11 0.40
(0.29) (0.32) (0.28) (0.27)
Age (60+) 0.22 -0.27 0.34 0.35
(0.30) (0.35) (0.28) (0.29)
Democrat -0.26 -0.25 1.85*** 0.05
(0.61) (0.78) (0.58) (0.73)
Republican 0.18 1.49* -0.13 -0.73
(0.63) (0.79) (0.59) (0.74)
Frame 1.72 7.47*** 2.08 -5.68**
(2.40) (2.84) (2.27) (2.28)
Openness -0.62 2.58 1.33 0.26
(1.37) (1.58) (1.30) (1.25)
Conscientiousness 1.18 -0.31 -1.04 -1.09
(1.36) (1.57) (1.28) (1.10)
Extraversion 0.23 0.22 -0.99 1.26
(0.93) (1.07) (0.88) (0.84)
Agreeableness -1.04 -0.61 -1.46 -2.06
(1.33) (1.54) (1.26) (1.29)
Neuroticism 1.20 -0.77 -1.36 -0.83
(1.02) (1.18) (0.97) (0.93)
Political knowledge 1.88*** 0.02 2.34*** -2.43***
(0.58) (0.67) (0.55) (0.54)
Need for cognition -0.04 -0.71 0.43 1.52
(1.09) (1.25) (1.03) (1.12)
Openness × frame 3.93** -2.61 -1.51 -1.06
(1.94) (2.41) (1.84) (1.91)
Conscientiousness × frame -4.28** -0.49 1.92 1.22
(1.80) (2.19) (1.70) (1.65)
Extraversion × frame -1.84 0.38 2.48** -1.00
(1.31) (1.57) (1.24) (1.23)
Agreeableness × frame 0.81 -4.12* -2.02 6.15***
(1.93) (2.16) (1.83) (1.74)
Neuroticism × frame -1.13 -1.87 1.45 2.11
(1.42) (1.70) (1.34) (1.34)
Political knowledge × frame -0.97 -1.19 -1.83** 0.71
(0.78) (0.88) (0.74) (0.71)
Need for cognition × frame 1.76 0.82 -2.29 -0.66
(1.63) (1.91) (1.54) (1.57)
Observations 392 401 393 393
R2 0.24 0.19 0.32 0.25
Adjusted R2 0.20 0.15 0.27 0.20
Residual Std. Error 1.90 (df = 369) 2.19 (df = 378) 1.80 (df = 370) 1.73 (df = 370)
F Statistic 5.38*** (df = 22; 369) 4.12*** (df = 22; 378) 7.74*** (df = 22; 370) 5.53*** (df = 22; 370)
Note: p<0.1; p<0.05; p<0.01

Predicted means

plot_labs <- tribble(
  ~model_name, ~add_legend, ~add_ylab, ~first,
  model_kkk_name, FALSE, FALSE, TRUE,
  model_guns_name, FALSE, FALSE, FALSE,
  model_stemcells_name, FALSE, TRUE, FALSE,
  model_af_ac_name, TRUE, FALSE, FALSE
) %>%
  mutate(model_name_char = model_name,
         model_name = fct_inorder(model_name, ordered = TRUE))

plot_predicted <- function(df, model_name, add_legend, add_ylab, first) {
  p <- ggplot(df, aes(x = frame, y = .fitted, color = value)) +
    geom_pointrange(aes(ymin = .fitted + (qnorm(0.025) * .se.fit),
                        ymax = .fitted + (qnorm(0.975) * .se.fit)),
                    position = position_dodge(width = 0.5),
                    size = 1, fatten = 1) +
    labs(x = NULL, y = NULL, title = model_name) +
    guides(color = guide_legend(title = "Trait score",
                                override.aes = list(size = 0.4))) +
    scale_colour_manual(values = framing.palette("palette.bw2"), name = NULL) +
    scale_y_continuous(breaks = c(1, 3, 5, 7)) +
    coord_cartesian(ylim = c(0, 9)) +
    theme_framing() + theme(panel.grid.minor = element_blank(),
                            panel.grid.major.x = element_blank(),
                            strip.text = element_text(size = rel(0.8)),
                            plot.title = element_text(size = rel(1.1)),
                            axis.text = element_text(size = rel(0.7))) +
    facet_wrap(~ coef.clean, nrow = 1)
  
  if (!add_legend) {
    p <- p + guides(color = FALSE)
  } 
  
  if (add_ylab) {
    p <- p + labs(y = "Average predicted support")
  }
  
  if (!first) {
    p <- p + theme(plot.title = element_text(margin = margin(t = 20, b = 3)))
  }
  
  p
}

models_predicted <- models %>%
  left_join(plot_labs, by = "model_name") %>%
  mutate(predicted = map2(.x = .$model, .y = .$model_name, 
                          .f = predict_values)) %>%
  mutate(plot = pmap(list(df = predicted, model_name_char, add_legend, add_ylab, first),
                     plot_predicted))

predicted_all <- wrap_plots(models_predicted$plot) + 
  plot_layout(ncol = 1)

# Save predicted data to CSV
models_predicted$predicted %>%
  bind_rows() %>% 
  write_csv(file.path(here(), "Output", "predicted_data.csv"))

The figure below demonstrates the marginal effect of having negative or positive personality traits on predicted support for a given issue, conditioned on the type of frame offered to respondents. All model variables are held at their means or the following modal values:

Variable Modal value
Sex Female
Race White/non-Hispanic
Age 45–59
Democrat Democrat
Republican Not Republican

Final output

In Output/ you can find:

  • Word versions of all tables (saved as .docx files)
  • Print-ready PDF versions of all figures (saved as .pdf files)
  • High quality PNG versions of all figures (for use in Word and PowerPoint; saved as .png files)
  • Captions for all figures (saved as .txt files)
---
title: "Modeling susceptibility"
author: "Meredith Conroy and Andrew Heiss"
date: "`r format(Sys.time(), '%B %e, %Y')`"
editor_options: 
  chunk_output_type: console
---

```{r setup, message=FALSE}
knitr::opts_chunk$set(cache = FALSE, fig.retina = 2,
                      tidy.opts = list(width.cutoff = 120),  # For code
                      width = 120)  # For output
```

```{r load-libraries-data, message=FALSE, warning=FALSE}
library(tidyverse)
library(broom)
library(ggstance)
library(patchwork)
library(pander)
library(stargazer)
library(here)

source(file.path(here(), "lib", "pander_options.R"))
source(file.path(here(), "lib", "graphics.R"))
source(file.path(here(), "lib", "labels.R"))

stargazer2word <- FALSE

# By default, R uses polynomial contrasts for ordered factors in linear models
# options("contrasts") 
# So make ordered factors use treatment contrasts instead
options(contrasts = rep("contr.treatment", 2))
# Or do it on a single variable:
# contrasts(df$x) <- "contr.treatment"

survey_clean <- readRDS(file.path(here(), "Data", "survey_clean.rds"))
```

```{r helpful-functions}
# Create a new formula based on a given DV and IVs (specified as a formulaa)
build_formula <- function(DV, IVs) {
  reformulate(attr(terms(IVs), "term.labels"), response = DV)
}

get_stargazer_labs <- function(model) {
  coefs <- tidy(model) %>%
    left_join(coef.names, by = c("term" = "coef.name")) %>%
    distinct(term, coef.clean) %>%
    mutate(coef.clean = as.character(coef.clean))
  
  return(coefs$coef.clean)
}

get_mode <- function(x) {
  names(which.max(table(x)))
}

predict_values <- function(model, model_name) {
  # Calculate the means of all five personality traits based on the data used in
  # the given model
  personality_means <- model$model %>%
    summarise_at(vars(OPENNESS, CONSCIENTIOUSNESS, EXTRAVERSION, 
                      AGREEABLENESS, NEUROTICISM), mean)
  
  # Row of 1s
  personality_1 <- data_frame(personality = colnames(personality_means),
                              value = 1) %>%
    spread(personality, value)
  
  # Row of 0s
  personality_0 <- data_frame(personality = colnames(personality_means),
                              value = 0) %>%
    spread(personality, value)
  
  # Data frame of possible personality values (mean, 0, and 1)
  personality_possibilities <- bind_rows(personality_means,
                                         personality_0, personality_1)
  
  # All combinations of mean, 0, and 1 personality values
  new_data_personalities <- expand.grid(personality_possibilities) %>%
    mutate(id = row_number()) %>%
    gather(key, value, -id) %>%
    group_by(id) %>%
    filter(sum(value == 0) == 1 & !any(value == 1) | 
             sum(value == 1) == 1 & !any(value == 0)) %>%
    spread(key, value) %>%
    mutate(index = 1) %>%
    ungroup() %>%
    select(-id)
  
  # Mean or modal values for all other model IVs
  control_means <- data_frame(index = 1, frame = 0:1,
                              sex_rev = get_mode(survey_clean$sex),
                              race_rev = get_mode(survey_clean$race),
                              age_cat = get_mode(survey_clean$age_cat),
                              democrat_rev = get_mode(survey_clean$democrat),
                              republican_rev = get_mode(survey_clean$republican),
                              POLITICAL_KNOWLEDGE = mean(model$model$POLITICAL_KNOWLEDGE), 
                              NC = mean(model$model$NC))
  
  # Add mean/modal values to each possible personality combination
  new_data_full <- new_data_personalities %>%
    left_join(control_means, by = "index") %>%
    select(-index)
  
  frame_labs <- case_when(
    model_name == model_kkk_name ~ frame_labs_kkk_guns_plot,
    model_name == model_guns_name ~ frame_labs_kkk_guns_plot,
    model_name == model_stemcells_name ~ frame_labs_stemcells_plot,
    model_name == model_af_ac_name ~ frame_labs_aa_plot
  )
  
  # Finally use new data in model to generate predicted values
  plot_predict <- augment(model, newdata = new_data_full) %>%
    gather(key, value, one_of(colnames(personality_means))) %>%
    left_join(coef.names, by = c("key" = "coef.name")) %>%
    filter(value %in% c(0, 1)) %>%
    mutate(value = factor(value, levels = c(0, 1),
                          labels = c("Low (0)    ", "High (1)"),
                          ordered = TRUE),
           frame = factor(frame, levels = c(0, 1),
                          labels = frame_labs, ordered = TRUE),
           coef.clean = fct_inorder(coef.clean),
           model_name = model_name)

  return(plot_predict)
}
```

## Susceptibility to issue frames

```{r build-models}
# Base model (sans DV, since that gets added later)
indep_vars <- ~
  # Main controls
  sex_rev + race_rev + age_cat + democrat_rev + republican_rev +
  # Frame
  frame +
  # Personality
  OPENNESS * frame + 
  CONSCIENTIOUSNESS * frame +
  EXTRAVERSION * frame +
  AGREEABLENESS * frame +
  NEUROTICISM * frame +
  # Other traits
  POLITICAL_KNOWLEDGE * frame +
  NC * frame


# KKK
df_kkk <- survey_clean %>%
  rename(frame = KKKFrame_CivLibs)

model_kkk <- lm(build_formula("KKK_Support", indep_vars),
                data = df_kkk, weights = weight)

# Guns
df_guns <- survey_clean %>%
  rename(frame = GunFrame_CivLibs)

model_guns <- lm(build_formula("Gun_Support", indep_vars),
                 data = df_guns, weights = weight)

# Stem cell research
df_stemcells <- survey_clean %>%
  rename(frame = StemCellsFrame_Positive)

model_stemcells <- lm(build_formula("StemCellsSupport", indep_vars),
                      data = df_stemcells, weights = weight)

# Affirmative action
df_af_ac <- survey_clean %>%
  rename(frame = AAFrame_positive)

model_af_ac <- lm(build_formula("AASupport", indep_vars),
                  data = df_af_ac, weights = weight)

models <- tribble(
  ~model, ~model_name,
  model_kkk, model_kkk_name,
  model_guns, model_guns_name,
  model_stemcells, model_stemcells_name,
  model_af_ac, model_af_ac_name
) %>%
  mutate(model_name = fct_inorder(model_name, ordered = TRUE))
```

```{r models-plot, fig.width=8, fig.height=5}
models_tidy <- models %>%
  mutate(tidy_model = model %>% map(~ tidy(., conf.int = TRUE)))

coefs_to_plot <- c("OPENNESS", "CONSCIENTIOUSNESS", "EXTRAVERSION",
                   "AGREEABLENESS", "NEUROTICISM", "POLITICAL_KNOWLEDGE", "NC",
                   "frame:OPENNESS", "frame:CONSCIENTIOUSNESS", 
                   "frame:EXTRAVERSION", "frame:AGREEABLENESS", 
                   "frame:NEUROTICISM", "frame:POLITICAL_KNOWLEDGE", "frame:NC")

df_coef_plot <- models_tidy %>%
  unnest(tidy_model) %>%
  mutate(low = estimate - std.error,
         high = estimate + std.error) %>%
  left_join(coef.names, by = c("term" = "coef.name")) %>%
  filter(term != "(Intercept)") %>%
  mutate(coef.clean = fct_rev(fct_inorder(coef.clean, ordered = TRUE)),
         model_name = fct_rev(model_name)) %>%
  mutate(facet = case_when(
    term %in% coefs_to_plot & str_detect(term, ":") ~ "Frame ×\npersonality",
    term %in% coefs_to_plot & !str_detect(term, ":") ~ "Personality",
    TRUE ~ "Controls"
  )) %>%
  mutate(facet = factor(facet, 
                        levels = c("Personality", "Frame ×\npersonality", "Controls"),
                        ordered = TRUE))

coef_plot <- ggplot(df_coef_plot, aes(x = estimate, y = coef.clean, colour = model_name)) + 
  geom_vline(xintercept = 0, colour = "black") +
  geom_pointrangeh(aes(xmin = low, xmax = high), size = 0.25,
                   position = position_dodgev(0.5)) + 
  scale_colour_manual(values = framing.palette("palette.color")[5:1]) +
  guides(color = guide_legend(title = NULL, nrow = 1, byrow = TRUE, reverse = TRUE)) +
  labs(x = "Coefficient", y = NULL) +
  theme_framing() + theme(legend.position = "bottom") +
  facet_wrap(~ facet, scales = "free")

coef_plot

caption <- c("Coefficients for all four issue models")
save.fig.caption(coef_plot, filename = "coef_plot", 
                 width = 8, height = 5, caption = caption)
```


```{r models-show, results="asis"}
title <- "Susceptibility to issue frames"
out.file <- file.path(here(), "Output", "table_model_results.html")

stargazer(model_kkk, model_guns, model_stemcells, model_af_ac,
          type = "html", digits = 2, intercept.bottom = FALSE,
          title = title, out = out.file,
          report = "vc*s", dep.var.caption = "",
          dep.var.labels = c(model_kkk_name, model_guns_name, model_stemcells_name, 
                             str_replace(model_af_ac_name_plot, "\\n", "<br>")),
          covariate.labels = get_stargazer_labs(model_kkk))
```


## Predicted means

```{r plot-predicted-means, warning=FALSE, message=FALSE}
plot_labs <- tribble(
  ~model_name, ~add_legend, ~add_ylab, ~first,
  model_kkk_name, FALSE, FALSE, TRUE,
  model_guns_name, FALSE, FALSE, FALSE,
  model_stemcells_name, FALSE, TRUE, FALSE,
  model_af_ac_name, TRUE, FALSE, FALSE
) %>%
  mutate(model_name_char = model_name,
         model_name = fct_inorder(model_name, ordered = TRUE))

plot_predicted <- function(df, model_name, add_legend, add_ylab, first) {
  p <- ggplot(df, aes(x = frame, y = .fitted, color = value)) +
    geom_pointrange(aes(ymin = .fitted + (qnorm(0.025) * .se.fit),
                        ymax = .fitted + (qnorm(0.975) * .se.fit)),
                    position = position_dodge(width = 0.5),
                    size = 1, fatten = 1) +
    labs(x = NULL, y = NULL, title = model_name) +
    guides(color = guide_legend(title = "Trait score",
                                override.aes = list(size = 0.4))) +
    scale_colour_manual(values = framing.palette("palette.bw2"), name = NULL) +
    scale_y_continuous(breaks = c(1, 3, 5, 7)) +
    coord_cartesian(ylim = c(0, 9)) +
    theme_framing() + theme(panel.grid.minor = element_blank(),
                            panel.grid.major.x = element_blank(),
                            strip.text = element_text(size = rel(0.8)),
                            plot.title = element_text(size = rel(1.1)),
                            axis.text = element_text(size = rel(0.7))) +
    facet_wrap(~ coef.clean, nrow = 1)
  
  if (!add_legend) {
    p <- p + guides(color = FALSE)
  } 
  
  if (add_ylab) {
    p <- p + labs(y = "Average predicted support")
  }
  
  if (!first) {
    p <- p + theme(plot.title = element_text(margin = margin(t = 20, b = 3)))
  }
  
  p
}

models_predicted <- models %>%
  left_join(plot_labs, by = "model_name") %>%
  mutate(predicted = map2(.x = .$model, .y = .$model_name, 
                          .f = predict_values)) %>%
  mutate(plot = pmap(list(df = predicted, model_name_char, add_legend, add_ylab, first),
                     plot_predicted))

predicted_all <- wrap_plots(models_predicted$plot) + 
  plot_layout(ncol = 1)

# Save predicted data to CSV
models_predicted$predicted %>%
  bind_rows() %>% 
  write_csv(file.path(here(), "Output", "predicted_data.csv"))
```

The figure below demonstrates the marginal effect of having negative or positive personality traits on predicted support for a given issue, conditioned on the type of frame offered to respondents. All model variables are held at their means or the following modal values:

```{r control-modes, results="asis"}
control_modes <- data_frame("Sex" = get_mode(survey_clean$sex),
                            "Race" = get_mode(survey_clean$race),
                            "Age" = get_mode(survey_clean$age_cat),
                            "Democrat" = get_mode(survey_clean$democrat),
                            "Republican" = get_mode(survey_clean$republican)) %>%
  gather(Variable, `Modal value`)

control_modes %>% pandoc.table()
```

```{r show-predicted, fig.width=6, fig.height=8.5}
predicted_all

caption <- c("Mean predicted values of issue support based on hypothetical", 
             "personality trait scores and frame exposure")
save.fig.caption(predicted_all, filename = "predicted_all", 
                 width = 6, height = 8.5, caption = caption)
```


## Final output

In `Output/` you can find:

- Word versions of all tables (saved as `.docx` files)
- Print-ready PDF versions of all figures (saved as `.pdf` files)
- High quality PNG versions of all figures (for use in Word and PowerPoint; saved as `.png` files)
- Captions for all figures (saved as `.txt` files)

```{r convert-tables}
# Convert Markdown tables to docx
capture.output({
  Sys.glob(file.path(here(), "Output", "*.md")) %>%
    map(~ Pandoc.convert(., format = "docx", footer = FALSE,
                         proc.time = FALSE, open = FALSE))
}, file = "/dev/null")

# Convert stargazer HTML tables to docx (macOS only)
if (Sys.info()['sysname'] == "Darwin" & stargazer2word) {
  change.dir <- paste('cd "', file.path(here(), "bin"), '"', sep = "")
  command <- paste("python3 stargazer2docx.py")
  full.command <- paste(change.dir, command, sep = "; ")
  system(full.command)
}
```
