library(swepophist) library(ggplot2) library(dplyr)
d <- filter(mfrt, age == "total") %>% mutate( col = ifelse(county_code %in% c(9,2), "low", "norm"), col = ifelse(county_code %in% c(25,24), "high", col), col = ifelse(is.na(county_code), "total", col) ) ggplot(d, aes(to, fert, group = context, color = col)) + xlim(1880,1961) + geom_line() + theme_minimal() + scale_color_manual(values = c("red", "blue","grey","black")) + theme(legend.position = "bottom")
df1 <- pop_fert %>% filter(context == "Sweden") %>% group_by(end) %>% summarise(tfr = (sum(fert)*5)/1000) %>% mutate(rate = "Marital") df2 <- d %>% filter(context == "Sweden") %>% select(end = to, tfr = fert) %>% mutate(rate = "TFR") %>% rbind(df1) ggplot(df2, aes(end, tfr, group = rate)) + geom_line(aes(color = rate), size = 2) + annotate("text", x = c(1895,1935), y = c(3.2,6.9), label = c("TFR", "Martial"), color = c("red", "blue")) + theme_classic() + theme(legend.position = "none")
d1 <- pop_fert %>% filter(context != "Sweden", end >= 1850) d2 <- pop_fert %>% filter(context == "Sweden", end >= 1850) ggplot() + geom_line(data = d1, aes(end, fert, group = context), color = "grey") + geom_line(data = d2, aes(end, fert, group = context), color = "black", size = 1) + facet_wrap(~age_g, nrow = 1) + theme_classic()
df <- pop_age %>% group_by(year) %>% summarise(p = sum(pop, na.rm = T)) ggplot(df, aes(year, p/1000000, 1)) + geom_line() + geom_point() + theme_classic() # cut by span cuts <- pop_age %>% filter(year == 1750) %>% select(age) %>% distinct() %>% .$age cuts <- c(cuts, 80) df2 <- pop_age %>% mutate(age_g = cut(age, cuts, right = FALSE)) %>% group_by(year, age_g) %>% summarise(p = sum(pop, na.rm = T)) df3 <- df2 %>% filter(age_g %in% levels(age_g)[c(1:4)]) %>% ungroup() ggplot(df3, aes(year, p, group = age_g, color = age_g)) + geom_line() + scale_color_grey() + theme_classic()
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