library(ggplot2); library(scales) load('exp_clean_complete.RData') USColor <- '#30475E' CNColor <- '#F05454' defaultFill <- '#222831' defaultColor <- '#DDDDDD' # factual evaluation (T/F questions index) fact_plot_des <- ggplot(data = d, aes(x = d$fact)) + geom_bar(aes(y = (..count..)/sum(..count..)), fill = defaultFill,color =defaultColor, width=0.5) + geom_text(stat = "count", aes(y = ((..count..)/sum(..count..)), label = scales::percent((..count..)/sum(..count..))), vjust = -0.4, size = 5) + scale_y_continuous(labels = percent) + labs(title = "Distribution of Factual Evaluation", x = "", y = "Percent") + theme_fivethirtyeight() fact_plot_des #ggsave("fact_plot_des.png") # perceived type of society # type_china_plot_des <- ggplot(data = d, aes(x = d$type_china)) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = CNColor,color = defaultColor, width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 5) + # scale_y_continuous(labels = percent) + # labs(title = "Perceived Type of Society", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=20), axis.text.x = element_text(size=12, hjust = 1), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # type_china_plot_des # ggsave("type_china_plot_des.png") # type_us_plot_des <- ggplot(data = d, aes(x = d$type_us)) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = USColor,color = defaultColor, width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 5) + # scale_y_continuous(labels = percent) + # labs(title = "Perceived Type of Society", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=20), axis.text.x = element_text(size=12, hjust = 1), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # type_us_plot_des # ggsave("type_us_plot_des.png") d %>% gather(type_china,type_us,key='country',value='type') %>% group_by(country,type) %>% summarise(count = n()) %>% group_by(country) %>% mutate(percent = count/sum(count)) %>% ggplot( aes(x = type, y=percent, fill=country) )+ geom_bar(stat = 'identity',width=0.6,position = position_dodge(width=0.7), color = defaultColor)+ geom_text( aes(y = percent, label = scales::percent(percent,accuracy = 0.1)), vjust = -0.4, position = position_dodge(.7), size = 5) + scale_y_continuous(labels = percent)+ scale_x_discrete( labels = c('Type A','Type B','Type C','Type D','Type E',"Don't Know") )+ scale_fill_manual( values = c(CNColor,USColor), #limits = c('China','USA'), labels = c('China','U.S.') )+ labs( title = 'Perceived Type of Society', fill = 'Country', y='Percent' )+ theme_fivethirtyeight( )+ theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(size=15)) # Perceived severity # severe_china_plot_des <- ggplot(data = subset(d, !is.na(d$severe_china)), aes(x = factor(na.omit(d$severe_china)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("Not Serious At All", "Not too serious", "Somewhat Serious", "Relatively Serious", "Very Serious"))+ # labs(title = "Perceived Severity of Inequality in China", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # severe_china_plot_des # ggsave("severe_china_plot_des.png") # severe_us_plot_des <- ggplot(data = subset(d, !is.na(d$severe_us)), aes(x = factor(na.omit(d$severe_us)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("Not Serious At All", "Not too serious", "Somewhat Serious", "Relatively Serious", "Very Serious"))+ # labs(title = "Perceived Severity of Inequality in USA", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # severe_us_plot_des # ggsave("severe_us_plot_des.png") d %>% filter(!is.na(severe_china),!is.na(severe_us)) %>% gather(severe_china,severe_us,key='country',value='type') %>% group_by(country,type) %>% summarise(count = n()) %>% group_by(country) %>% mutate(percent = count/sum(count)) %>% ggplot( aes(x = type, y=percent, fill=country) )+ geom_bar(stat = 'identity',width=0.6,position = position_dodge(width=0.7), color = defaultColor)+ geom_text( aes(y = percent, label = scales::percent(percent,accuracy = 0.1)), vjust = -0.4, position = position_dodge(.7), size = 5) + scale_y_continuous(labels = percent)+ # scale_x_discrete( # labels = c('Type A','Type B','Type C','Type D','Type E',"Don't Know") # )+ scale_fill_manual( values = c(CNColor,USColor), #limits = c('China','USA'), labels = c('China','U.S.') )+ labs( title = 'Perceived Severity of Inequality in China and the U.S.', fill = 'Country', y='Percent' )+ theme_fivethirtyeight( )+ theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(size=15)) # Perceived instability due to inequality # instability_china_plot_des <- ggplot(data = subset(d, !is.na(d$instability_china)), aes(x = factor(na.omit(d$instability_china)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("No threat", "Some threat", "Big threat"))+ # labs(title = "To what extent does economic inequality in China/US threatens social stability", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # instability_china_plot_des # ggsave("instability_china_plot_des.png") # instability_us_plot_des <- ggplot(data = subset(d, !is.na(d$instability_us)), aes(x = factor(na.omit(d$instability_us)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("No threat", "Some threat", "Big threat"))+ # labs(title = "To what extent does economic inequality in China/US threatens social stability", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # instability_us_plot_des # ggsave("instability_us_plot_des.png") d %>% filter(!is.na(instability_china),!is.na(instability_us)) %>% gather(instability_china,instability_us,key='country',value='type') %>% group_by(country,type) %>% summarise(count = n()) %>% group_by(country) %>% mutate(percent = count/sum(count)) %>% ggplot( aes(x = type, y=percent, fill=country) )+ geom_bar(stat = 'identity',width=0.6,position = position_dodge(width=0.7), color = defaultColor)+ geom_text( aes(y = percent, label = scales::percent(percent,accuracy = 0.1)), vjust = -0.4, position = position_dodge(.7), size = 5) + scale_y_continuous(labels = percent)+ # scale_x_discrete( # labels = c('Type A','Type B','Type C','Type D','Type E',"Don't Know") # )+ scale_fill_manual( values = c(CNColor,USColor), labels = c('China','U.S.') )+ labs( title = 'To what extent does economic inequality in China/US threatens social stability', fill = 'Country', y='Percent' )+ theme_fivethirtyeight( )+ theme(panel.grid.major.x = element_blank(), axis.text.x = element_text(size=15)) # Perceived trend past 5 years # trend_past5y_china_plot_des <- ggplot(data = subset(d, !is.na(d$trend_past5y_china)), aes(x = factor(na.omit(d$trend_past5y_china)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("Reduced", "Stayed the same", "Increased"))+ # labs(title = "In the past 5 years, income inequality in China/US has __?", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # trend_past5y_china_plot_des # ggsave("trend_past5y_china_plot_des.png") # trend_past5y_us_plot_des <- ggplot(data = subset(d, !is.na(d$trend_past5y_us)), aes(x = factor(na.omit(d$trend_past5y_us)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("Reduced", "Stayed the same", "Increased"))+ # labs(title = "In the past 5 years, income inequality in China/US has __?", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # trend_past5y_us_plot_des # ggsave("trend_past5y_us_plot_des.png") # Perceived trend 5 years later # trend_5ylater_china_plot_des <- ggplot(data = subset(d, !is.na(d$trend_5ylater_china)), aes(x = factor(na.omit(d$trend_5ylater_china)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("Reduce", "Stay the same", "Increase"))+ # labs(title = "In the next 5 years, income inequality in China/US will __?", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # trend_5ylater_china_plot_des # ggsave("trend_5ylater_china_plot_des.png") # trend_5ylater_us_plot_des <- ggplot(data = subset(d, !is.na(d$trend_5ylater_us)), aes(x = factor(na.omit(d$trend_5ylater_us)))) + # geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + # geom_text(stat = "count", # aes(y = ((..count..)/sum(..count..)), # label = scales::percent((..count..)/sum(..count..))), # vjust = -0.4, # size = 4) + # scale_y_continuous(labels = percent) + # scale_x_discrete(labels= c("Reduce", "Stay the same", "Increase"))+ # labs(title = "In the next 5 years, income inequality in China/US will __?", x = "", y = "Percent") + # theme_classic() + # theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), # axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) # trend_5ylater_us_plot_des # ggsave("trend_5ylater_us_plot_des.png") ### Fairness fair_society_plot_des <- ggplot(data = subset(d, !is.na(d$fair_society)), aes(x = factor(na.omit(d$fair_society)))) + geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + geom_text(stat = "count", aes(y = ((..count..)/sum(..count..)), label = scales::percent((..count..)/sum(..count..))), vjust = -0.4, size = 4) + scale_y_continuous(labels = percent) + labs(title = "Fair Society", x = "", y = "Percent") + theme_classic() + theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) fair_society_plot_des ggsave("fair_society_plot_des.png") fair_income_plot_des <- ggplot(data = subset(d, !is.na(d$fair_income)), aes(x = factor(na.omit(d$fair_income)))) + geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + geom_text(stat = "count", aes(y = ((..count..)/sum(..count..)), label = scales::percent((..count..)/sum(..count..))), vjust = -0.4, size = 4) + scale_y_continuous(labels = percent) + labs(title = "Fair Income", x = "", y = "Percent") + theme_classic() + theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) fair_income_plot_des ggsave("fair_income_plot_des.png") ### Blame attribution blame_plot_des <- ggplot(data = subset(d, !is.na(d$Q35)), aes(x = factor(na.omit(d$Q35)))) + geom_bar(aes(y = (..count..)/sum(..count..)), fill = "paleturquoise4", width=0.5) + geom_text(stat = "count", aes(y = ((..count..)/sum(..count..)), label = scales::percent((..count..)/sum(..count..))), vjust = -0.4, size = 4) + scale_y_continuous(labels = percent) + labs(title = "Blame Attribution", x = "", y = "Percent") + theme_classic() + theme(legend.position = "none", plot.title = element_text(size=15), axis.text.x = element_text(size=12, hjust = 1, angle = 30), axis.text.y = element_text(size = 10), axis.title.y = element_text(size = 12)) blame_plot_des ggsave("blame_plot_des.png")