数据告诉你，垃圾回收动起来（只要开始，都不算晚）！

 1global_plastics_production%>%clean_names() %>%filter(year>2000) %>% 
 2ggplot(aes(x=year,y=global_plastics_production_million_tonnes_tonnes/1000000)) +geom_point()+geom_line()+
 3
 4  labs(x = "年",
 5       y = "全球制造的塑料垃圾（百万吨）",
 6       title = "人类制造越来越多的垃圾",
 7       subtitle = "每年制造近4亿吨塑料垃圾",
 8       caption ="统计时间:2000-2015年;数据源:ourworldindata.org
 9       @Tony Duan"
10       )+theme(plot.title = element_text(hjust = 0.5))

 1g1 <- plastic_waste %>%
 2  arrange(-total_population) %>%
 3  ggplot(aes(gdp_per_capita,plastic_waste_per_capita)) +
 4  geom_point(aes(size = total_population,fill = gdp_per_capita_group), shape = 21, show.legend = F, alpha = 0.7)+
 5  geom_text(aes(label = country), size=3,vjust = 1, hjust = 1, check_overlap = TRUE) +
 6  geom_smooth(method = "lm") +
 7  labs(x = "人均GDP(美元)",
 8       y = "人均每天生成的塑料垃圾(千克)",
 9       title = "塑料垃圾与人口收入的关系",
10       subtitle = "颜色为人均GPD组;点的大小代表国家总人口数量",
11       caption ="统计时间:2010年;数据源:ourworldindata.org
12       @Tony Duan"
13       )+theme(plot.title = element_text(hjust = 0.5))
14
15g1+ guides(fill=FALSE, color=FALSE)

 1g2 <- plastic_waste %>%
 2  arrange(-total_population) %>%
 3  mutate(pct_population_coastal = pmin(1, coastal_population / total_population),
 4         high_coastal_pop = ifelse(pct_population_coastal >= .8, ">=80%", "<80%")) %>%
 5
 6  ggplot(aes(gdp_per_capita, managed_per_capita_rate)) +
 7
 8  geom_point(aes(size = total_population,fill = gdp_per_capita_group), shape = 21, show.legend = F, alpha = 0.7)+
 9
10  geom_text(aes(label = country), size=3,vjust = 1, hjust = 1, check_overlap = TRUE) +
11  scale_x_log10(labels = dollar_format()) +
12  scale_size_continuous(guide = FALSE) +
13  labs(x = "人均GDP(美元)",
14       y = "塑料垃圾回收率",
15       title = "塑料垃圾回收率与人口收入的关系",
16       subtitle = "颜色为人均GPD组;点的大小代表国家总人口数量",
17       caption ="统计时间:2010年;数据源:ourworldindata.org
18       @Tony Duan"
19       )+theme(plot.title = element_text(hjust = 0.5))
20
21g2+ guides(fill=FALSE, color=FALSE)