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cat(rep('=', 80),
'\n\n',
'OUTPUT FROM: shorts/08_plot_shorts_figure.R',
'\n\n',
sep = ''
)
library(tidyverse)
library(janitor)
library(lubridate)
library(stargazer)
library(broom)
library(psych)
library(ggtext)
library(ggplot2)
# plotting w/ custom colors (optional)
red_mit = '#A31F34'
red_light = '#A9606C'
blue_mit = '#315485'
grey_light= '#C2C0BF'
grey_dark = '#8A8B8C'
black = '#353132'
vpurple = "#440154FF"
vyellow = "#FDE725FF"
vgreen = "#21908CFF"
## MODEL RESULTS
coefs_basecontrol <- read_csv("../results/padj_basecontrol_pretty_ytrecs_may2024.csv")
outcome_labels <- data.frame(outcome = c("Minimum wage<br>index"),
specificoutcome = c("mw_index"),
family = c(rep("Policy Attitudes<br>(unit scale, + is more conservative)",1)))
# HYP 1
#### THE effect of INCREASING vs. CONSTANT assignment among LIBERAL participants ####
coefs_hyp1 <- coefs_basecontrol %>%
filter(layer2_treatmentcontrast == "attitude.pro:recsys.pi - attitude.pro:recsys.pc" &
layer3_specificoutcome != "overall")
coefs_hyp1$outcome = outcome_labels$outcome[match(coefs_hyp1$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp1$family = outcome_labels$family[match(coefs_hyp1$layer3_specificoutcome,outcome_labels$specificoutcome)]
coefs_hyp1 <- mutate(coefs_hyp1,
family = factor(family,
levels = c("Policy Attitudes<br>(unit scale, + is more conservative)"
),ordered = T))
coefs_hyp1 <- coefs_hyp1 %>%
mutate(ci_lo_99 = est + qnorm(0.001)*se,
ci_hi_99 = est + qnorm(0.995)*se,
ci_lo_95 = est + qnorm(0.025)*se,
ci_hi_95 = est + qnorm(0.975)*se,
ci_lo_90 = est + qnorm(0.05)*se,
ci_hi_90 = est + qnorm(0.95)*se,
plotorder = nrow(coefs_hyp1):1
)
## HYP 2
#### THE effect of INCREASING vs. CONSTANT assignment among CONSERVATIVE participants ####
coefs_hyp2 <- coefs_basecontrol %>%
filter(layer2_treatmentcontrast == "attitude.anti:recsys.ai - attitude.anti:recsys.ac" &
layer3_specificoutcome != "overall")
coefs_hyp2$outcome = outcome_labels$outcome[match(coefs_hyp2$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp2$family = outcome_labels$family[match(coefs_hyp2$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp2 <- mutate(coefs_hyp2,
family = factor(family,levels = c("Policy Attitudes<br>(unit scale, + is more conservative)"
),ordered = T))
coefs_hyp2 <- coefs_hyp2 %>%
mutate(ci_lo_99 = est + qnorm(0.001)*se,
ci_hi_99 = est + qnorm(0.995)*se,
ci_lo_95 = est + qnorm(0.025)*se,
ci_hi_95 = est + qnorm(0.975)*se,
ci_lo_90 = est + qnorm(0.05)*se,
ci_hi_90 = est + qnorm(0.95)*se,
plotorder = nrow(coefs_hyp2):1
)
# HYP 3
#### THE effect of INCREASING vs. CONSTANT assignment among MODERATE participants assigned to a LIBERAL sequence ####
coefs_hyp3 <- coefs_basecontrol %>%
filter(layer2_treatmentcontrast == "attitude.neutral:recsys.pi - attitude.neutral:recsys.pc" &
layer3_specificoutcome != "overall")
coefs_hyp3$outcome = outcome_labels$outcome[match(coefs_hyp3$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp3$family = outcome_labels$family[match(coefs_hyp3$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp3 <- mutate(coefs_hyp3,
family = factor(family,levels = c("Policy Attitudes<br>(unit scale, + is more conservative)"
),ordered = T))
coefs_hyp3 <- coefs_hyp3 %>%
mutate(ci_lo_99 = est + qnorm(0.001)*se,
ci_hi_99 = est + qnorm(0.995)*se,
ci_lo_95 = est + qnorm(0.025)*se,
ci_hi_95 = est + qnorm(0.975)*se,
ci_lo_90 = est + qnorm(0.05)*se,
ci_hi_90 = est + qnorm(0.95)*se,
plotorder = nrow(coefs_hyp3):1
)
# HYP 4
#### THE effect of INCREASING vs. CONSTANT assignment among MODERATE participants assigned to a CONSERVATIVE sequence ####
coefs_hyp4 <- coefs_basecontrol %>%
filter(layer2_treatmentcontrast == "attitude.neutral:recsys.ai - attitude.neutral:recsys.ac" &
layer3_specificoutcome != "overall")
coefs_hyp4$outcome = outcome_labels$outcome[match(coefs_hyp4$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp4$family = outcome_labels$family[match(coefs_hyp4$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp4 <- mutate(coefs_hyp4,
family = factor(family,levels = c("Policy Attitudes<br>(unit scale, + is more conservative)"
),ordered = T))
coefs_hyp4 <- coefs_hyp4 %>%
mutate(ci_lo_99 = est + qnorm(0.001)*se,
ci_hi_99 = est + qnorm(0.995)*se,
ci_lo_95 = est + qnorm(0.025)*se,
ci_hi_95 = est + qnorm(0.975)*se,
ci_lo_90 = est + qnorm(0.05)*se,
ci_hi_90 = est + qnorm(0.95)*se,
plotorder = nrow(coefs_hyp4):1
)
# HYP 5
#### THE effect of CONSERVATIVE vs. LIBERAL assignment among MODERATE participants assigned to an INCREASING sequence ####
coefs_hyp5 <- coefs_basecontrol %>%
filter(layer2_treatmentcontrast == "attitude.neutral:recsys.ai - attitude.neutral:recsys.pi" &
layer3_specificoutcome != "overall")
coefs_hyp5$outcome = outcome_labels$outcome[match(coefs_hyp5$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp5$family = outcome_labels$family[match(coefs_hyp5$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp5 <- mutate(coefs_hyp5,
family = factor(family,levels = c("Policy Attitudes<br>(unit scale, + is more conservative)"
),ordered = T))
coefs_hyp5 <- coefs_hyp5 %>%
mutate(ci_lo_99 = est + qnorm(0.001)*se,
ci_hi_99 = est + qnorm(0.995)*se,
ci_lo_95 = est + qnorm(0.025)*se,
ci_hi_95 = est + qnorm(0.975)*se,
ci_lo_90 = est + qnorm(0.05)*se,
ci_hi_90 = est + qnorm(0.95)*se,
plotorder = nrow(coefs_hyp5):1
)
# HYP 6
#### THE effect of CONSERVATIVE vs. LIBERAL assignment among MODERATE participants assigned to an CONSTANT sequence ####
coefs_hyp6 <- coefs_basecontrol %>%
filter(layer2_treatmentcontrast == "attitude.neutral:recsys.ac - attitude.neutral:recsys.pc" &
layer3_specificoutcome != "overall")
coefs_hyp6$outcome = outcome_labels$outcome[match(coefs_hyp6$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp6$family = outcome_labels$family[match(coefs_hyp6$layer3_specificoutcome,
outcome_labels$specificoutcome)]
coefs_hyp6 <- mutate(coefs_hyp6,
family = factor(family,levels = c("Policy Attitudes<br>(unit scale, + is more conservative)"
),ordered = T))
#### THE effect of CONSERVATIVE vs. LIBERAL assignment among MODERATE participants assigned to an CONSTANT sequence ####
coefs_hyp6 <- coefs_hyp6 %>%
mutate(ci_lo_99 = est + qnorm(0.001)*se,
ci_hi_99 = est + qnorm(0.995)*se,
ci_lo_95 = est + qnorm(0.025)*se,
ci_hi_95 = est + qnorm(0.975)*se,
ci_lo_90 = est + qnorm(0.05)*se,
ci_hi_90 = est + qnorm(0.95)*se,
plotorder = nrow(coefs_hyp6):1,
)
# Combine all data frames into one
all_coefs <- bind_rows(
mutate(coefs_hyp1, hypothesis = "**Increasing vs. Constant**<br>Liberal Seed<br>Liberal Ideologues", Sample="**Increasing vs. Constant**<br>Liberal Seed"),
mutate(coefs_hyp2, hypothesis = "**Increasing vs. Constant**<br>Conservative Seed<br>Conservative Ideologues", Sample="**Increasing vs. Constant**<br>Conservative Seed"),
mutate(coefs_hyp3, hypothesis = "**Increasing vs. Constant**<br>Liberal Seed<br>Moderates", Sample="**Increasing vs. Constant**<br>Liberal Seed"),
mutate(coefs_hyp4, hypothesis = "**Increasing vs. Constant**<br>Conservative Seed<br>Moderates", Sample="**Increasing vs. Constant**<br>Conservative Seed"),
mutate(coefs_hyp5, hypothesis = "**Conservative vs. Liberal**<br>Increasing Extremity<br>Moderates", Sample="**Conservative vs. Liberal**<br>Increasing Extremity"),
mutate(coefs_hyp6, hypothesis = "**Conservative vs. Liberal**<br>Constant Extremity<br>Moderates", Sample="**Conservative vs. Liberal**<br>Constant Extremity")
)
# Define the order of hypotheses
hypothesis_order <- c("**Increasing vs. Constant**<br>Liberal Seed<br>Liberal Ideologues",
"**Increasing vs. Constant**<br>Conservative Seed<br>Conservative Ideologues",
"**Increasing vs. Constant**<br>Liberal Seed<br>Moderates",
"**Increasing vs. Constant**<br>Conservative Seed<br>Moderates",
"**Conservative vs. Liberal**<br>Increasing Extremity<br>Moderates",
"**Conservative vs. Liberal**<br>Constant Extremity<br>Moderates")
# Reorder the factor levels
all_coefs$hypothesis <- factor(all_coefs$hypothesis, levels = hypothesis_order)
all_coefs <- all_coefs %>%
mutate(
attitude = case_when(
row_number() == 1 ~ "Liberal Ideologues",
row_number() == 2 ~ "Conservative Ideologues",
TRUE ~ "Moderates"
),
alpha = ifelse(p.adj<0.05, T, F),
alpha = as.logical(alpha),
alpha = replace_na(alpha,F),
Sample_color = as.character(Sample),
Sample_color = replace(Sample_color,alpha==F,"insig")
)
all_coefs <- all_coefs %>%
mutate(
sign_color = case_when(
ci_lo_95 < 0 & ci_hi_95 > 0 ~ grey_dark, # black color code
TRUE ~ "darkgreen" # blue color code (or replace with your desired color code)
)
)
all_coefs <- all_coefs %>%
mutate(
attitude_color = case_when(
attitude == "Liberal Ideologues" ~ blue_mit,
attitude == "Conservative Ideologues" ~ red_mit,
attitude == "Moderates" ~ "darkgreen"
)
)
all_coefs <- all_coefs %>%
mutate(Sample = factor(Sample,levels=c("**Increasing vs. Constant**<br>Liberal Seed",
"**Increasing vs. Constant**<br>Conservative Seed",
"**Conservative vs. Liberal**<br>Increasing Extremity",
"**Conservative vs. Liberal**<br>Constant Extremity"),
ordered=T)) #%>%
#mutate(layer1_hypothesisfamily = recode(layer1_hypothesisfamily,
# "mwpolicy"="policy"),
# layer3_specificoutcome = recode(layer3_specificoutcome,
# "mw_index"="policyindex"))
# Create a data frame for attitude shapes
attitude_shapes <- data.frame(attitude = c("Liberal Ideologues", "Conservative Ideologues", "Moderates"))
# Plot the attitude shapes
attitude_bar <- ggplot(attitude_shapes, aes(x = attitude)) +
geom_point(aes(shape = attitude), size = 3) +
scale_shape_manual(values = c("Liberal Ideologues" = 16, "Conservative Ideologues" = 17, "Moderates" = 15)) +
theme_void() +
theme(legend.position = "none")
# Create a data frame for attitude shapes
attitude_shapes <- data.frame(attitude = c("Liberal Ideologues", "Conservative Ideologues", "Moderates"))
# Plot the attitude shapes
attitude_bar <- ggplot(attitude_shapes, aes(x = attitude)) +
geom_point(aes(shape = attitude), size = 5) +
scale_shape_manual(values = c("Liberal Ideologues" = 16, "Conservative Ideologues" = 17, "Moderates" = 15)) +
theme_void() +
theme(legend.position = "none")
# Plot
combined_plot <- ggplot(all_coefs, aes(x = est, y = Sample, group = attitude, shape = attitude)) +
# 95% CI: Adjust alpha based on significance
geom_errorbarh(aes(xmin = ci_lo_95, xmax = ci_hi_95, color = sign_color, alpha = 0.8),
height = 0, lwd = 1, position = position_dodge(width = 0.8)) +
# 90% CI: Adjust alpha based on significance
geom_errorbarh(aes(xmin = ci_lo_90, xmax = ci_hi_90, color = sign_color, alpha = 0.8),
height = 0, lwd = 1.5, position = position_dodge(width = 0.8)) +
# Points: Adjust alpha directly for better visibility of insignificant shapes
geom_point(aes(color = sign_color),
size = 4, position = position_dodge(width = 0.8),
alpha = ifelse(all_coefs$alpha, 1, 0.7)) + # Make insignificant points more visible with 0.7 alpha
# Labels: Adjust alpha based on significance
geom_text(data = all_coefs,
aes(x = est, label = attitude, color = attitude_color),
alpha = 1, size = 6,
position = position_dodge(width = 0.8), vjust = -0.6) +
geom_vline(xintercept = 0, lty = 2) +
facet_wrap(~ family, ncol = 1, scales = "free") +
coord_cartesian(xlim = c(-0.06, 0.18), clip="off") +
scale_x_continuous(" Minimum Wage Policy Effect Size\n(95% and 90% CIs)") +
scale_color_identity() + # Ensure that the color column is used directly
labs(y = NULL) + # Remove y-axis title
theme_bw(base_family = "sans") +
theme(strip.background = element_rect(fill = "white"),
legend.position = "none",
axis.text.y = element_markdown(color = "black", size=16),
axis.title.x = element_markdown(color = "black", size=16),
strip.text = element_markdown(size = 18)
)
combined_plot
ggsave(combined_plot, filename = "../results/shorts_combined_intervals.pdf", width = 8.5, height = 5)
rm(list = ls())
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