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| require(lfe)
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| require(ggridges)
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| require(tidyverse)
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| require(gridExtra)
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| rm(list =ls())
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| gc()
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| source('./helper_functions.R')
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| load('../Data/france_data.RData')
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| p1 <- toanal %>%
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| mutate(mainstream = ifelse(mainstream == 0,'Non-Mainstream','Mainstream'),
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| period = factor(ifelse(date == as.Date('2020-03-15'),'March Elections','June Elections'),
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| levels = c('March Elections','June Elections'))) %>%
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| group_by(mainstream,period) %>%
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| summarise(votes = mean(votes)) %>%
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| ungroup() %>%
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| filter(complete.cases(.)) %>%
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| ggplot(aes(x = period,y = votes,fill = factor(mainstream))) +
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| stat_summary(fun.y = mean,geom = "bar",position = position_dodge(width = .9),
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| size = 3,alpha = .7) +
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| scale_fill_manual(name = '',values = c('Mainstream' = 'grey80','Non-Mainstream' = 'grey30')) +
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| theme_ridges() + xlab('Period') + ylab('Aggregate Vote Share')
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| p2 <- toanal %>%
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| mutate(mainstream = ifelse(mainstream == 0,'Non-Mainstream','Mainstream'),
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| period = factor(ifelse(date == as.Date('2020-03-15'),'March Elections','June Elections'),
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| levels = c('March Elections','June Elections')),
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| logcases = log(deaths+1)) %>%
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| filter(!is.na(period)) %>%
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| ggplot(aes(x = logcases,y = votes,color = mainstream)) +
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| geom_point(alpha = .7,size = 2) +
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| geom_smooth(method = 'lm') +
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| scale_color_manual(name = "",values = c('Mainstream' = 'grey80',"Non-Mainstream" = "grey30")) +
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| theme_ridges() + xlab("Deaths (logged)") + ylab('Aggregate Vote Share')
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| pdf('../figures/figure8.pdf',width = 7,height = 4)
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| grid.arrange(p1 + theme(legend.position = 'bottom'),
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| p2 + theme(legend.position = 'bottom'),ncol = 2)
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| dev.off()
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| toanal$post <- ifelse(toanal$date == '2020-03-15',0,1)
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| toanal$antiEst <- (toanal$mainstream - 1)^2
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| summary(mod1 <- felm(votes ~ antiEst*post | dpt_code | 0 | dpt_code,toanal))
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| summary(mod2 <- felm(votes ~ antiEst*log(deaths+1) | post + dpt_code | 0 | dpt_code,toanal))
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| toplot <- data.frame(interaction_plot_continuous(mod1,plot = T,pointsplot = T,colr = 'black',alph = 200,num_points = 2,
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| xlabel = "Election Wave",ylabel = 'Marginal Coefficient of Mainstream')) %>%
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| mutate(type = 'Temporal')
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| toplot <- bind_rows(toplot,data.frame(interaction_plot_continuous(mod2,plot = T,pointsplot = T,
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| colr = 'black',alph = 200,num_points = 20,
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| xlabel = "Election Wave",ylabel = 'Marginal Coefficient of Mainstream')) %>%
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| mutate(type = 'Geographic'))
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| p1 <- toplot %>%
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| filter(type == 'Temporal') %>%
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| mutate(x = factor(ifelse(x_2 == 0,'March Elections','June Elections'),levels = c('March Elections','June Elections'))) %>%
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| ggplot(aes(x = x,y = delta_1)) +
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| geom_point() +
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| geom_errorbar(aes(ymin = lb,ymax = ub),width = .1) +
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| geom_hline(yintercept = 0,linetype = 'dashed') +
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| ylab('Margnial Coefficient on Non-Mainstream') + xlab('') +
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| theme_ridges() +
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| ggtitle('Temporal Variation')
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| p2 <- toplot %>%
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| filter(type == 'Geographic') %>%
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| ggplot(aes(x = x_2,y = delta_1)) +
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| geom_point() +
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| geom_errorbar(aes(ymin = lb,ymax = ub),width = .1) +
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| geom_hline(yintercept = 0,linetype = 'dashed') +
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| ylab('Margnial Coefficient on Non-Mainstream') + xlab('Logged Deaths') +
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| theme_ridges() +
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| ggtitle('Geographic Variation')
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| pdf('../Figures/SI_figure19.pdf',width = 8,height = 4)
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| grid.arrange(p1,p2,ncol = 2)
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| dev.off()
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