Analysis

knitr::opts_chunk$set(cache=FALSE, fig.retina=2,
                      tidy.opts=list(width.cutoff=120),  # For code
                      options(width=120))  # For output

library(tidyverse)
library(haven)
library(stargazer)
library(xtable)
library(DT)
library(broom)
library(ggstance)
library(forcats)
library(stringr)
library(psych)
library(here)

source(file.path(here(), "Analysis", "graphics.R"))

stargazer2word <- FALSE

unlabelled <- function(x) {
  if (!haven:::is.labelled(x))
    return(x)
  attributes(x) <- NULL
  x
}

df.raw <- read_sav(file.path(here(), "Data", "personality 2016.sav")) %>%
  mutate_all(funs(unlabelled(.))) %>%
  mutate(KKKFrame_CivLibs.factor = factor(KKKFrame_CivLibs, levels=0:1,
                                          labels=c("Public safety frame",
                                                   "Civil liberties frame")),
         GunFrame_CivLibs.factor = factor(GunFrame_CivLibs, levels=0:1,
                                          labels=c("Public safety frame",
                                                   "Civil liberties frame"))) %>%
  mutate(sex = factor(PPGENDER, levels=c(1, 0), ordered=TRUE,
                      labels=c("Male", "Female")),
         age = factor(ppagect4, levels=1:4, ordered=TRUE,
                      labels=c("18–29", "30–44", "45–59", "60+")),
         race = factor(PPETHM, levels=c(1, 0), ordered=TRUE,
                       labels=c("White/non-Hispanic", "Other race specified")),
         education = factor(PPEDUCAT, levels=1:4, ordered=TRUE,
                            labels=c("Less than high school", "High school",
                                     "Some college", "Bachelor’s degree or above")))

saveRDS(df.raw, file.path(here(), "Data", "survey_clean.rds"))

df.small <- df.raw %>%
  select(caseid, weight, KKK_Support, Gun_Support,
         OPENNESS, CONSCIENTIOUSNESS, EXTRAVERSION, AGREEABLENESS, NEUROTICISM,
         POLITICAL_KNOWLEDGE, NC, KKKFrame_CivLibs, GunFrame_CivLibs)

Table 1

Text-based; not generated with code

Table 2: Participant demographics

dem.sex <- df.raw %>%
  group_by(sex) %>%
  summarise(Frequency = n()) %>%
  mutate(Percentage = Frequency / sum(Frequency),
         `Cumulative %` = cumsum(Percentage)) %>%
  mutate(Demographics = as.character(sex)) %>% 
  select(Demographics, everything(), -sex)

dem.age <- df.raw %>%
  group_by(age) %>%
  summarise(Frequency = n()) %>%
  mutate(Percentage = Frequency / sum(Frequency),
         `Cumulative %` = cumsum(Percentage)) %>%
  mutate(Demographics = as.character(age)) %>% 
  select(Demographics, everything(), -age)

dem.race <- df.raw %>%
  group_by(race) %>%
  summarise(Frequency = n()) %>%
  mutate(Percentage = Frequency / sum(Frequency),
         `Cumulative %` = cumsum(Percentage)) %>%
  mutate(Demographics = as.character(race)) %>% 
  select(Demographics, everything(), -race)

dem.education <- df.raw %>%
  group_by(education) %>%
  summarise(Frequency = n()) %>%
  mutate(Percentage = Frequency / sum(Frequency),
         `Cumulative %` = cumsum(Percentage)) %>%
  mutate(Demographics = as.character(education)) %>% 
  select(Demographics, everything(), -education)

title.row <- function(x) {
  data_frame(Demographics = as.character(x), Frequency = NA,
             Percentage = NA, `Cumulative %` = NA)
}

dem.full <- bind_rows(title.row("Sex"), dem.sex, 
                      title.row("Age"), dem.age, 
                      title.row("Race/Ethnicity"), dem.race,
                      title.row("Education"), dem.education) %>%
  mutate(Percentage = ifelse(!is.na(Frequency), 
                             scales::percent(Percentage), NA),
         `Cumulative %` = ifelse(!is.na(Frequency), 
                                 scales::percent(`Cumulative %`), NA),
         Demographics = ifelse(is.na(Frequency), 
                               pandoc.strong.return(Demographics),
                               Demographics))

caption <- "Participant demographics"
tbl.demographics <- pandoc.table.return(dem.full, caption=caption,
                                        justify="lccc")

cat(tbl.demographics)

Participant demographics
Demographics	Frequency	Percentage	Cumulative %
Sex
Male	396	48.9%	48.9%
Female	413	51.1%	100.0%
Age
18–29	123	15.2%	15.2%
30–44	204	25.2%	40.4%
45–59	241	29.8%	70.2%
60+	241	29.8%	100.0%
Race/Ethnicity
White/non-Hispanic	579	71.6%	71.6%
Other race specified	230	28.4%	100.0%
Education
Less than high school	97	12.0%	12.0%
High school	256	31.6%	43.6%
Some college	223	27.6%	71.2%
Bachelor’s degree or above	233	28.8%	100.0%

cat(tbl.demographics, file=file.path(here(), "Output", "table_demographics.md"))

Information about survey distribution

dem.groups <- df.raw %>%
  group_by(XNANO) %>%
  summarise(total = n())

The 809 participants were distributed amongst three slightly different surveys (Group 1 N = 267, Group 2 N = 273, Group 3 N = 269).

Table 3: Civil liberties and public safety frames

Text-based; not generated with code

Table 4: Personality mean scores for entire sample

df.personality.raw <- df.raw %>%
  select(OPENNESS, CONSCIENTIOUSNESS, EXTRAVERSION, AGREEABLENESS, NEUROTICISM)

personality.alphas <- psych::alpha(as.data.frame(df.personality.raw),
                                   check.keys=TRUE, warnings=FALSE)$alpha.drop %>%
  select(raw_alpha) %>%
  mutate(Trait = gsub("-", "", rownames(.)))

df.personality <- df.personality.raw %>%
  summarise_all(funs(mean = mean(., na.rm=TRUE),
                     SD = sd(., na.rm=TRUE),
                     N = sum(!is.na(.)))) %>%
  mutate_all(as.numeric) %>%
  gather(key, value) %>%
  separate(key, c("Trait", "key"), sep="_") %>%
  spread(key, value) %>%
  left_join(personality.alphas, by="Trait") %>%
  mutate(Trait = str_to_title(Trait)) %>%
  mutate(Trait = factor(Trait, levels=c("Openness", "Conscientiousness", 
                                        "Extraversion", "Agreeableness", 
                                        "Neuroticism"), 
                        ordered=TRUE)) %>%
  arrange(Trait) %>%
  select(Trait, `Sample mean` = mean, SD, `Cronbach’s α` = raw_alpha, N)

caption <- "Personality mean scores for entire sample"
tbl.personality <- pandoc.table.return(df.personality, caption=caption,
                                       justify="lcccc", digits=2)

cat(tbl.personality)

Personality mean scores for entire sample
Trait	Sample mean	SD	Cronbach’s α	N
Openness	0.72	0.13	0.65	760
Conscientiousness	0.78	0.13	0.61	763
Extraversion	0.63	0.16	0.68	755
Agreeableness	0.79	0.12	0.62	753
Neuroticism	0.54	0.16	0.59	771

cat(tbl.personality, file=file.path(here(), "Output", "table_personality.md"))

Table 5: Framing effects for KKK rally and concealed handgun law

# KKK experiment
kkk.summary <- df.raw %>% 
  filter(!is.na(KKKFrame_CivLibs.factor)) %>%
  group_by(KKKFrame_CivLibs.factor) %>%
  summarise(N = n(),
            Mean = Hmisc::wtd.mean(KKK_Support, weight, na.rm=TRUE),
            SD = sqrt(Hmisc::wtd.var(KKK_Support, weight, na.rm=TRUE))) %>%
  rename(Frame = KKKFrame_CivLibs.factor) %>%
  arrange(rev(Frame))

# ANOVA
kkk.anova <- aov(KKK_Support ~ KKKFrame_CivLibs.factor,
                 weights=weight, data=df.raw)

# Round to nearest ten-thousandth
kkk.sig <- ceiling(tidy(kkk.anova)[1,]$p.value * 10000) / 10000

kkk.summary.sig <- kkk.summary %>%
  mutate(`Sig.` = c(NA, kkk.sig),
         Issue = c("Public KKK rally", NA)) %>%
  select(Issue, everything())

# Guns experiment
guns.summary <- df.raw %>% 
  filter(!is.na(GunFrame_CivLibs.factor)) %>%
  group_by(GunFrame_CivLibs.factor) %>%
  summarise(N = n(),
            Mean = Hmisc::wtd.mean(Gun_Support, weight, na.rm=TRUE),
            SD = sqrt(Hmisc::wtd.var(Gun_Support, weight, na.rm=TRUE))) %>%
  rename(Frame = GunFrame_CivLibs.factor) %>%
  arrange(rev(Frame))

# ANOVA
guns.anova <- aov(Gun_Support ~ GunFrame_CivLibs.factor,
                  weights=weight, data=df.raw)

# Round to nearest thousandth
guns.sig <- ceiling(tidy(guns.anova)[1,]$p.value * 1000) / 1000

guns.summary.sig <- guns.summary %>%
  mutate(`Sig.` = c(NA, guns.sig),
         Issue = c("Concealed handgun law", NA)) %>%
  select(Issue, everything())

# Everything in one table
framing.summary <- bind_rows(kkk.summary.sig, guns.summary.sig)

caption <- "Framing effects for KKK rally and concealed handgun law"
tbl.framing <- pandoc.table.return(framing.summary, caption=caption,
                                   justify="llcccc", digits=3)

notes <- c("*Notes*: “Mean” table entries are average responses, where minimum response is 1, and maximum response is 7, where 7 is maximum support.", 
           "", 
           "Question measuring support for the KKK rally: In general, do you oppose or support allowing members of the Ku Klux Klan to hold public rallies? Question measuring support for the concealed handgun law: How strongly do you support or oppose a proposed state law allowing citizens to carry concealed handguns?")

tbl.framing.notes <- pandoc.p.return(notes)

cat(tbl.framing)

Framing effects for KKK rally and concealed handgun law
Issue	Frame	N	Mean	SD	Sig.
Public KKK rally	Civil liberties frame	267	3.25	2.17
	Public safety frame	273	2.67	1.88	0.001
Concealed handgun law	Civil liberties frame	269	4.22	2.27
	Public safety frame	273	3.39	2.28	0.001

cat(tbl.framing.notes)

Notes: “Mean” table entries are average responses, where minimum response is 1, and maximum response is 7, where 7 is maximum support.

Question measuring support for the KKK rally: In general, do you oppose or support allowing members of the Ku Klux Klan to hold public rallies? Question measuring support for the concealed handgun law: How strongly do you support or oppose a proposed state law allowing citizens to carry concealed handguns?

cat(paste0(tbl.framing, tbl.framing.notes), 
    file=file.path(here(), "Output", "table_framing_anova.md"))

Table 6: Susceptibility to KKK rally frames

model.small.kkk <- lm(KKK_Support ~ KKKFrame_CivLibs +
                        OPENNESS * KKKFrame_CivLibs + 
                        CONSCIENTIOUSNESS * KKKFrame_CivLibs +
                        EXTRAVERSION * KKKFrame_CivLibs +
                        AGREEABLENESS * KKKFrame_CivLibs +
                        NEUROTICISM * KKKFrame_CivLibs,
                      data=df.small, weights=weight)

model.expanded.kkk <- lm(KKK_Support ~ KKKFrame_CivLibs +
                           OPENNESS * KKKFrame_CivLibs + 
                           CONSCIENTIOUSNESS * KKKFrame_CivLibs +
                           EXTRAVERSION * KKKFrame_CivLibs +
                           AGREEABLENESS * KKKFrame_CivLibs +
                           NEUROTICISM * KKKFrame_CivLibs +
                           POLITICAL_KNOWLEDGE * KKKFrame_CivLibs +
                           NC * KKKFrame_CivLibs,
                         data=df.small, weights=weight)

coef.names.kkk <- names(coef(model.small.kkk))
coef.plot.kkk <- bind_rows(mutate(tidy(model.small.kkk, conf.int=TRUE), 
                                  model="Simple"),
                           mutate(tidy(model.expanded.kkk, conf.int=TRUE), 
                                  model="Full")) %>%
  mutate(low = estimate - std.error,
         high = estimate + std.error) %>%
  left_join(coef.names, by=c("term" = "coef.name")) %>%
  mutate(coef.clean = fct_rev(fct_inorder(coef.clean)),
         model = factor(model, levels=c("Simple", "Full"),
                        labels=c("Simple   ", "Full")))

stargazer.labs <- coef.plot.kkk %>%
  distinct(term, coef.clean) %>%
  mutate(coef.clean = as.character(coef.clean)) %>%
  mutate(term = recode(term, `(Intercept)` = "Constant"),
         term = paste0("^", term, "$"))

title <- "Susceptibility to KKK rally frames"
out.file <- file.path(here(), "Output", "table_kkk_results.html")

stargazer(model.small.kkk, model.expanded.kkk,
          type="html", intercept.bottom=FALSE, digits=2,
          title=title, out=out.file,
          column.labels=c("Simple", "Full"),
          dep.var.caption="", dep.var.labels.include=FALSE,
          report="vc*s", order=stargazer.labs$term,
          covariate.labels=stargazer.labs$coef.clean)

**Susceptibility to KKK rally frames**

	Simple	Full
	(1)	(2)

Intercept	2.27	-0.09
	(1.61)	(1.69)

KKK frame	1.52	2.28
	(2.20)	(2.45)

Openness	0.30	-1.58
	(1.15)	(1.41)

Conscientiousness	1.78	1.89
	(1.34)	(1.36)

Extraversion	-0.95	0.12
	(0.91)	(0.92)

Agreeableness	-0.96	-0.95
	(1.32)	(1.33)

Neuroticism	0.33	1.70
	(0.98)	(1.03)

Openness × frame	3.90^**	5.23^***
	(1.61)	(1.97)

Conscientiousness × frame	-5.10^***	-4.92^***
	(1.78)	(1.83)

Extraversion × frame	-1.22	-2.29^*
	(1.27)	(1.31)

Agreeableness × frame	1.58	0.79
	(1.86)	(1.94)

Neuroticism × frame	-0.47	-1.78
	(1.37)	(1.45)

Political knowledge		2.66^***
		(0.55)

Need for cognition		0.33
		(1.11)

Political knowledge × frame		-0.95
		(0.80)

Need for cognition × frame		1.15
		(1.66)


Observations	447	398
R²	0.08	0.17
Adjusted R²	0.06	0.14
Residual Std. Error	2.02 (df = 435)	1.98 (df = 382)
F Statistic	3.42^*** (df = 11; 435)	5.30^*** (df = 15; 382)

Note:	p<0.1; p<0.05; p<0.01

coef.kkk <- ggplot(coef.plot.kkk, aes(x=estimate, y=coef.clean, colour=model)) + 
  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.bw1"), name=NULL) +
  labs(x="Coefficient", y=NULL) +
  theme_framing() + theme(legend.position="bottom")
coef.kkk

caption <- "Coefficients for KKK frame models"
save.fig.caption(coef.kkk, filename="coefs_kkk", 
                 width=5, height=4, caption=caption)

Table 7: Susceptibility to concealed handgun frames

model.small.guns <- lm(Gun_Support ~ GunFrame_CivLibs + 
                         GunFrame_CivLibs * OPENNESS + 
                         CONSCIENTIOUSNESS * GunFrame_CivLibs +
                         EXTRAVERSION * GunFrame_CivLibs +
                         AGREEABLENESS * GunFrame_CivLibs +
                         NEUROTICISM * GunFrame_CivLibs,
                       data=df.small, weights=weight)

model.expanded.guns <- lm(Gun_Support ~ GunFrame_CivLibs +
                            OPENNESS * GunFrame_CivLibs + 
                            CONSCIENTIOUSNESS * GunFrame_CivLibs +
                            EXTRAVERSION * GunFrame_CivLibs +
                            AGREEABLENESS * GunFrame_CivLibs +
                            NEUROTICISM * GunFrame_CivLibs +
                            POLITICAL_KNOWLEDGE * GunFrame_CivLibs +
                            NC * GunFrame_CivLibs,
                          data=df.small, weights=weight)

coef.names.guns <- names(coef(model.small.guns))
coef.plot.guns <- bind_rows(mutate(tidy(model.small.guns, conf.int=TRUE), 
                                   model="Simple"),
                            mutate(tidy(model.expanded.guns, conf.int=TRUE), 
                                   model="Full")) %>%
  mutate(low = estimate - std.error,
         high = estimate + std.error) %>%
  left_join(coef.names, by=c("term" = "coef.name")) %>%
  mutate(coef.clean = fct_rev(fct_inorder(coef.clean)),
         model = factor(model, levels=c("Simple", "Full"),
                        labels=c("Simple   ", "Full")))

stargazer.labs <- coef.plot.guns %>%
  distinct(term, coef.clean) %>%
  mutate(coef.clean = as.character(coef.clean)) %>%
  mutate(term = recode(term, `(Intercept)` = "Constant"),
         term = paste0("^", term, "$"))

title <- "Susceptibility to concealed handgun frames"
out.file <- file.path(here(), "Output", "table_guns_results.html")

stargazer(model.small.guns, model.expanded.guns,
          type="html", intercept.bottom=FALSE, digits=2,
          title=title, out=out.file,
          column.labels=c("Simple", "Full"),
          dep.var.caption="", dep.var.labels.include=FALSE,
          report="vc*s", order=stargazer.labs$term,
          covariate.labels=stargazer.labs$coef.clean)

**Susceptibility to concealed handgun frames**

	Simple	Full
	(1)	(2)

Intercept	2.27	1.72
	(1.85)	(1.99)

Guns frame	5.86^**	7.63^**
	(2.75)	(2.97)

Openness	2.00	2.03
	(1.36)	(1.65)

Conscientiousness	0.53	0.44
	(1.54)	(1.60)

Extraversion	-0.47	-0.03
	(1.04)	(1.09)

Agreeableness	-0.07	-0.21
	(1.51)	(1.57)

Neuroticism	-0.74	-0.12
	(1.12)	(1.22)

Openness × frame	-1.56	-3.94
	(1.95)	(2.51)

Conscientiousness × frame	-1.46	-1.04
	(2.18)	(2.28)

Extraversion × frame	0.97	0.81
	(1.53)	(1.61)

Agreeableness × frame	-3.71^*	-4.33^*
	(2.11)	(2.22)

Neuroticism × frame	-0.86	-2.12
	(1.67)	(1.78)

Political knowledge		0.78
		(0.66)

Need for cognition		-0.64
		(1.31)

Political knowledge × frame		-1.46
		(0.92)

Need for cognition × frame		2.85
		(1.96)


Observations	453	408
R²	0.06	0.08
Adjusted R²	0.04	0.04
Residual Std. Error	2.31 (df = 441)	2.32 (df = 392)
F Statistic	2.57^*** (df = 11; 441)	2.17^*** (df = 15; 392)

Note:	p<0.1; p<0.05; p<0.01

coef.guns <- ggplot(coef.plot.guns, aes(x=estimate, y=coef.clean, colour=model)) + 
  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.bw1"), name=NULL) +
  labs(x="Coefficient", y=NULL) +
  theme_framing() + theme(legend.position="bottom")
coef.guns

caption <- "Coefficients for concealed handgun frame models"
save.fig.caption(coef.guns, filename="coefs_guns", 
                 width=5, height=4, caption=caption)

Figure 1: Predicted means of KKK support across frames and personality types

# Get just the means of personality types
personality.means <- model.expanded.kkk$model %>% 
  summarise_at(vars(OPENNESS, CONSCIENTIOUSNESS, EXTRAVERSION, AGREEABLENESS, 
                    NEUROTICISM, POLITICAL_KNOWLEDGE, NC),
               mean)

# Create rows of personality types with all 0s and all 1s
personality.1 <- data_frame(personality = colnames(personality.means),
                            value = 1) %>%
  spread(personality, value)
personality.0 <- data_frame(personality = colnames(personality.means),
                            value = 0) %>%
  spread(personality, value)

# Create base matrix of possible personality type values (mean, 0, and 1)
personality.possibilities <- bind_rows(personality.means,
                                       personality.0, personality.1)

# Expand the personality possibilities matrix to include all combinations of
# all variables, then filter out all rows where there are multiple 0 and 1
# values
new.data.personalities <- expand.grid(personality.possibilities) %>%
  mutate(id = row_number()) %>%
  gather(key, value, -id) %>%
  group_by(id) %>%
  # Select only rows where there's one 0 value and no 1 value, or one 1 value
  # and no 0 value
  filter(sum(value == 0) == 1 & !any(value == 1) | 
           sum(value == 1) == 1 & !any(value == 0)) %>%
  spread(key, value) %>%
  mutate(index = 1)

# Create matrix of possible framing conditions
new.data.kkk <- data_frame(KKKFrame_CivLibs = 0:1, index = 1,
                           `(weights)` = 1) %>%
  left_join(new.data.personalities, by="index") %>%
  select(-index, -id)

plot.predict.kkk <- augment(model.expanded.kkk, newdata=new.data.kkk) %>%
  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),
         KKKFrame_CivLibs = factor(KKKFrame_CivLibs, levels=c(0, 1),
                                   labels=c("Public safety", "Civil liberties"),
                                   ordered=TRUE),
         coef.clean = fct_inorder(coef.clean))

pandoc.table(select(plot.predict.kkk, Personality = coef.clean, Value = value, 
                    Frame = KKKFrame_CivLibs, `Fitted value` = .fitted, `SE` = .se.fit))

Personality	Value	Frame	Fitted value	SE
Openness	Low (0)	Public safety	3.95	1.04
Openness	High (1)	Public safety	2.38	0.401
Openness	Low (0)	Civil liberties	0.636	1.01
Openness	High (1)	Civil liberties	4.29	0.412
Conscientiousness	Low (0)	Public safety	1.32	1.07
Conscientiousness	High (1)	Public safety	3.21	0.322
Conscientiousness	Low (0)	Civil liberties	5.67	0.963
Conscientiousness	High (1)	Civil liberties	2.63	0.306
Extraversion	Low (0)	Public safety	2.73	0.602
Extraversion	High (1)	Public safety	2.85	0.365
Extraversion	Low (0)	Civil liberties	4.67	0.601
Extraversion	High (1)	Civil liberties	2.5	0.374
Agreeableness	Low (0)	Public safety	3.57	1.08
Agreeableness	High (1)	Public safety	2.62	0.294
Agreeableness	Low (0)	Civil liberties	3.41	1.12
Agreeableness	High (1)	Civil liberties	3.25	0.336
Neuroticism	Low (0)	Public safety	1.89	0.578
Neuroticism	High (1)	Public safety	3.58	0.49
Neuroticism	Low (0)	Civil liberties	3.33	0.567
Neuroticism	High (1)	Civil liberties	3.25	0.497
Political knowledge	Low (0)	Public safety	0.734	0.437
Political knowledge	High (1)	Public safety	3.39	0.193
Political knowledge	Low (0)	Civil liberties	1.95	0.463
Political knowledge	High (1)	Civil liberties	3.66	0.196
Need for cognition	Low (0)	Public safety	2.58	0.794
Need for cognition	High (1)	Public safety	2.9	0.355
Need for cognition	Low (0)	Civil liberties	2.23	0.9
Need for cognition	High (1)	Civil liberties	3.71	0.373

plot.pred.kkk <- ggplot(plot.predict.kkk,
                        aes(x=KKKFrame_CivLibs, y=.fitted, 
                            colour=value, group=value)) + 
  geom_line(size=1) + 
  geom_ribbon(aes(ymax=.fitted + (qnorm(0.975) * .se.fit),
                  ymin=.fitted + (qnorm(0.025) * .se.fit),
                  fill=value),
              alpha=0.3, colour=NA) +
  labs(x=NULL, y="Average KKK support") + 
  scale_colour_manual(values=framing.palette("palette.bw2"), name=NULL) +
  scale_fill_manual(values=framing.palette("palette.bw2"), name=NULL) +
  coord_cartesian(ylim=c(0, 8)) +
  scale_y_continuous(breaks=c(1, 3, 5, 7)) +
  facet_wrap(~ coef.clean, ncol=2) + 
  theme_framing() + theme(panel.grid.minor=element_blank(),
                          panel.grid.major.x=element_blank(),
                          strip.text=element_text(size=rel(0.8)))
plot.pred.kkk

caption <- "Predicted means of KKK support"
save.fig.caption(plot.pred.kkk, filename="pred_means_kkk", 
                 width=5, height=4.5, caption=caption)

Figure 2: Predicted means of concealed handgun support across frames and personality types

personality.means <- model.expanded.guns$model %>% 
  summarise_at(vars(OPENNESS, CONSCIENTIOUSNESS, EXTRAVERSION, AGREEABLENESS, 
                    NEUROTICISM, POLITICAL_KNOWLEDGE, NC),
               mean)

personality.1 <- data_frame(personality = colnames(personality.means),
                            value = 1) %>%
  spread(personality, value)
personality.0 <- data_frame(personality = colnames(personality.means),
                            value = 0) %>%
  spread(personality, value)

personality.possibilities <- bind_rows(personality.means,
                                       personality.0, personality.1)

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)

new.data.guns <- data_frame(GunFrame_CivLibs = 0:1, index = 1,
                            `(weights)` = 1) %>%
  left_join(new.data.personalities, by="index") %>%
  select(-index, -id)

plot.predict.guns <- augment(model.expanded.guns, newdata=new.data.guns) %>%
  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),
         GunFrame_CivLibs = factor(GunFrame_CivLibs, levels=c(0, 1),
                                   labels=c("Public safety", "Civil liberties"),
                                   ordered=TRUE),
         coef.clean = fct_inorder(coef.clean))

pandoc.table(select(plot.predict.guns, Personality = coef.clean, Value = value, 
                    Frame = GunFrame_CivLibs, `Fitted value` = .fitted, `SE` = .se.fit))

Personality	Value	Frame	Fitted value	SE
Openness	Low (0)	Public safety	1.96	1.22
Openness	High (1)	Public safety	3.99	0.472
Openness	Low (0)	Civil liberties	5.6	1.38
Openness	High (1)	Civil liberties	3.68	0.546
Conscientiousness	Low (0)	Public safety	3.07	1.27
Conscientiousness	High (1)	Public safety	3.52	0.376
Conscientiousness	Low (0)	Civil liberties	4.69	1.31
Conscientiousness	High (1)	Civil liberties	4.09	0.369
Extraversion	Low (0)	Public safety	3.44	0.711
Extraversion	High (1)	Public safety	3.41	0.427
Extraversion	Low (0)	Civil liberties	3.72	0.754
Extraversion	High (1)	Civil liberties	4.5	0.486
Agreeableness	Low (0)	Public safety	3.59	1.27
Agreeableness	High (1)	Public safety	3.38	0.343
Agreeableness	Low (0)	Civil liberties	7.82	1.25
Agreeableness	High (1)	Civil liberties	3.29	0.377
Neuroticism	Low (0)	Public safety	3.49	0.683
Neuroticism	High (1)	Public safety	3.37	0.575
Neuroticism	Low (0)	Civil liberties	5.42	0.72
Neuroticism	High (1)	Civil liberties	3.18	0.625
Political knowledge	Low (0)	Public safety	2.82	0.522
Political knowledge	High (1)	Public safety	3.6	0.226
Political knowledge	Low (0)	Civil liberties	4.74	0.513
Political knowledge	High (1)	Civil liberties	4.05	0.232
Need for cognition	Low (0)	Public safety	3.88	0.932
Need for cognition	High (1)	Public safety	3.24	0.419
Need for cognition	Low (0)	Civil liberties	2.63	1.07
Need for cognition	High (1)	Civil liberties	4.84	0.445

plot.pred.guns <- ggplot(plot.predict.guns,
                         aes(x=GunFrame_CivLibs, y=.fitted, 
                             colour=value, group=value)) + 
  geom_line(size=1) + 
  geom_ribbon(aes(ymax=.fitted + (qnorm(0.975) * .se.fit),
                  ymin=.fitted + (qnorm(0.025) * .se.fit),
                  fill=value),
              alpha=0.3, colour=NA) +
  labs(x=NULL, y="Average concealed carry support") + 
  scale_colour_manual(values=framing.palette("palette.bw2"), name=NULL) +
  scale_fill_manual(values=framing.palette("palette.bw2"), name=NULL) +
  coord_cartesian(ylim=c(0, 8)) +
  scale_y_continuous(breaks=c(1, 3, 5, 7)) +
  facet_wrap(~ coef.clean, ncol=2) + 
  theme_framing() + theme(panel.grid.minor=element_blank(),
                          panel.grid.major.x=element_blank(),
                          strip.text=element_text(size=rel(0.8)))
plot.pred.guns

caption <- c("Predicted means of concealed handgun support")
save.fig.caption(plot.pred.guns, filename="pred_means_guns", 
                 width=5, height=4.5, 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)

# 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)
}