内容简介：PHOTOGRAPHS BY RANDY OLSON
PHOTOGRAPHS BY RANDY OLSON
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上海市垃圾分类立法 在
2019年7月1日起施行。个人混投垃圾最高可罚200元，单位混装混运最高可罚5万元。丢垃圾要分为4类:
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- 可回收物 :废纸张、废塑料、废玻璃制品、废金属、废织物等
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- 有害垃圾 :废电池、废灯管、废药品、废油漆及其容器等
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- 湿垃圾 :食材废料、剩菜剩饭、过期食品、瓜皮果核、花卉绿植等
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- 干垃圾 :除可回收物、有害垃圾、湿垃圾以外的其它生活废弃物
《上海市生活垃圾管理条例》同时规定:旅馆不得主动提供客房一次性日用品。餐饮服务不得主动提供一次性餐具。
package
library(tidyverse) library(dplyr) library(scales) library(ggplot2) library(readr) library(janitor) library(ggthemes) theme_set(theme_light())
导入数据
coast_vs_waste <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/coastal-population-vs-mismanaged-plastic.csv")
mismanaged_vs_gdp <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-mismanaged-plastic-waste-vs-gdp-per-capita.csv")
waste_vs_gdp <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-21/per-capita-plastic-waste-vs-gdp-per-capita.csv")
# data source shttps://ourworldindata.org/plastic-pollution
global_plastics_production=read_csv("https://raw.githubusercontent.com/TonyFly3000/kaggle/master/global-plastics-production.csv")
global_plastics_production
## # A tibble: 66 x 4 ## Entity Code Year `Global plastics production (million tonnes) (ton~ ## <chr> <chr> <int> <int> ## 1 World OWID_WRL 1950 2000000 ## 2 World OWID_WRL 1951 2000000 ## 3 World OWID_WRL 1952 2000000 ## 4 World OWID_WRL 1953 3000000 ## 5 World OWID_WRL 1954 3000000 ## 6 World OWID_WRL 1955 4000000 ## 7 World OWID_WRL 1956 5000000 ## 8 World OWID_WRL 1957 5000000 ## 9 World OWID_WRL 1958 6000000 ## 10 World OWID_WRL 1959 7000000 ## # ... with 56 more rows
数据清洗
coast_vs_waste%>%glimpse()
## Observations: 20,093 ## Variables: 6 ## $ Entity <chr> "Afghanistan", "Afghanista... ## $ Code <chr> "AFG", "AFG", "AFG", "AFG"... ## $ Year <int> 1800, 1820, 1870, 1913, 19... ## $ `Mismanaged plastic waste (tonnes)` <int> NA, NA, NA, NA, NA, NA, NA... ## $ `Coastal population` <int> NA, NA, NA, NA, NA, NA, NA... ## $ `Total population (Gapminder)` <int> 3280000, 3280000, 4207000,...
用janitor package 清理变量名, 如 Mismanaged plastic waste (tonnes) 清理成了mismanaged_plastic_waste_tonnes
coast_vs_waste %>% clean_names() %>%glimpse()
## Observations: 20,093 ## Variables: 6 ## $ entity <chr> "Afghanistan", "Afghanistan", ... ## $ code <chr> "AFG", "AFG", "AFG", "AFG", "A... ## $ year <int> 1800, 1820, 1870, 1913, 1950, ... ## $ mismanaged_plastic_waste_tonnes <int> NA, NA, NA, NA, NA, NA, NA, NA... ## $ coastal_population <int> NA, NA, NA, NA, NA, NA, NA, NA... ## $ total_population_gapminder <int> 3280000, 3280000, 4207000, 573...
mismanaged_vs_gdp %>% clean_names() %>%glimpse()
## Observations: 22,204 ## Variables: 6 ## $ entity <chr> ... ## $ code <chr> ... ## $ year <int> ... ## $ per_capita_mismanaged_plastic_waste_kilograms_per_person_per_day <dbl> ... ## $ gdp_per_capita_ppp_constant_2011_international_rate <dbl> ... ## $ total_population_gapminder <int> ...
waste_vs_gdp %>% clean_names() %>%glimpse()
## Observations: 22,204 ## Variables: 6 ## $ entity <chr> ... ## $ code <chr> ... ## $ year <int> ... ## $ per_capita_plastic_waste_kilograms_per_person_per_day <dbl> ... ## $ gdp_per_capita_ppp_constant_2011_international_constant_2011_international <dbl> ... ## $ total_population_gapminder <int> ...
coast_vs_waste2 = clean_names(coast_vs_waste) %>%filter(year>2000) %>% group_by(year)%>%summarise(mismanaged_plastic_waste_tonnes=sum(mismanaged_plastic_waste_tonnes,na.rm = TRUE))
生成清理 function ‘clean_dataset’
clean_dataset <- function(tbl) {
tbl %>%
clean_names() %>% # 清理变量名
rename(country = entity,country_code = code) %>% # 改变量名
filter(year == 2010) %>% # 选2010年
select(-year) # 删除year变量
}
plastic_waste <- clean_dataset(coast_vs_waste) %>% # 清洗数据 coast_vs_waste
select(-total_population_gapminder) %>% # 删除total_population_gapminder变量
inner_join(clean_dataset(mismanaged_vs_gdp) %>% # 清洗数据 mismanaged_vs_gdp
select(-total_population_gapminder), # 删除total_population_gapminder变量
by = c("country", "country_code")) %>% # inner join by "country", "country_code"
inner_join(clean_dataset(waste_vs_gdp), # 清洗数据 waste_vs_gdp
by = c("country", "country_code")) %>% # inner join by "country", "country_code"
# 选变量
select(country,
country_code,
mismanaged_waste = mismanaged_plastic_waste_tonnes,
coastal_population,
total_population = total_population_gapminder,
mismanaged_per_capita = per_capita_mismanaged_plastic_waste_kilograms_per_person_per_day,
plastic_waste_per_capita=per_capita_plastic_waste_kilograms_per_person_per_day,
gdp_per_capita = gdp_per_capita_ppp_constant_2011_international_rate) %>%
mutate(gdp_per_capita=round(gdp_per_capita))%>%
filter(!is.na(mismanaged_waste),country!='Trinidad and Tobago')%>% # 选mismanaged_waste 非空的记录
mutate(gdp_per_capita_group=cut_number(gdp_per_capita/1000, n = 4) # 按gdp_per_capita 排序 将国家分为 4组
,mismanaged_per_capita_rate=mismanaged_per_capita/plastic_waste_per_capita,
managed_per_capita_rate=1-mismanaged_per_capita_rate
)
glimpse(plastic_waste)
## Observations: 186 ## Variables: 11 ## $ country <chr> "Albania", "Algeria", "Angola", "An... ## $ country_code <chr> "ALB", "DZA", "AGO", "AIA", "ATG", ... ## $ mismanaged_waste <int> 29705, 520555, 62528, 52, 1253, 157... ## $ coastal_population <int> 2530533, 16556580, 3790041, 14561, ... ## $ total_population <int> 3204284, 35468208, 19081912, 15358,... ## $ mismanaged_per_capita <dbl> 0.032, 0.086, 0.045, 0.010, 0.051, ... ## $ plastic_waste_per_capita <dbl> 0.069, 0.144, 0.062, 0.252, 0.660, ... ## $ gdp_per_capita <dbl> 9927, 12871, 5898, NA, 19213, 18712... ## $ gdp_per_capita_group <fct> "(4.41,11.9]", "(11.9,28.7]", "(4.4... ## $ mismanaged_per_capita_rate <dbl> 0.46376812, 0.59722222, 0.72580645,... ## $ managed_per_capita_rate <dbl> 0.5362319, 0.4027778, 0.2741935, 0....
作图
人类制造越来越多的垃圾 !
2010年每年制造近3亿万吨塑料垃圾.到2015年数字已上升到每年4亿万吨。这些垃圾不被回收的话将会污染我们的环境。
global_plastics_production%>%clean_names() %>%filter(year>2000) %>%
ggplot(aes(x=year,y=global_plastics_production_million_tonnes_tonnes/1000000)) +geom_point()+geom_line()+
labs(x = "年",
y = "全球制造的塑料垃圾（百万吨）",
#color = "Coastal population",
title = "人类制造越来越多的垃圾",
subtitle = "每年制造近4亿万吨塑料垃圾",
caption ="统计时间:2000-2015年;数据源:ourworldindata.org
@Tony Duan"
)+theme(plot.title = element_text(hjust = 0.5))
谁制造垃圾 ？
人均收入越高的国家，制造的塑料垃圾越多。如美国人均每天制造0.3千克塑料垃圾。中国人均0.15千克。
g1 <- plastic_waste %>%
arrange(-total_population) %>%
mutate(pct_population_coastal = pmin(1, coastal_population / total_population),
high_coastal_pop = ifelse(pct_population_coastal >= .8, ">=80%", "<80%")) %>%
ggplot(aes(gdp_per_capita,plastic_waste_per_capita)) +
geom_point(aes(size = total_population,fill = gdp_per_capita_group), shape = 21, show.legend = F, alpha = 0.7)+
geom_text(aes(label = country), size=3,vjust = 1, hjust = 1, check_overlap = TRUE) +
geom_smooth(method = "lm") +
#scale_x_log10(labels = dollar_format()) +
#scale_y_log10() +
#scale_size_continuous(guide = FALSE) +
labs(x = "人均GDP(美元)",
y = "人均每天生成的塑料垃圾(千克)",
#color = "Coastal population",
title = "塑料垃圾与人口收入的关系",
subtitle = "颜色为人均GPD组;点的大小代表国家总人口数量",
caption ="统计时间:2010年;数据源:ourworldindata.org
@Tony Duan"
)+theme(plot.title = element_text(hjust = 0.5))
g1+ guides(fill=FALSE, color=FALSE)
谁回收垃圾 ？
人均收入越高的国家，垃圾回收率越高。如美国塑料垃圾回收率为90%。中国仅为25%。 可见人均收入最高的25%国家已建立有效的垃圾回收系统。所以垃圾回收能明显高于其他国家。
g2 <- plastic_waste %>%
arrange(-total_population) %>%
mutate(pct_population_coastal = pmin(1, coastal_population / total_population),
high_coastal_pop = ifelse(pct_population_coastal >= .8, ">=80%", "<80%")) %>%
ggplot(aes(gdp_per_capita, managed_per_capita_rate)) +
geom_point(aes(size = total_population,fill = gdp_per_capita_group), shape = 21, show.legend = F, alpha = 0.7)+
geom_text(aes(label = country), size=3,vjust = 1, hjust = 1, check_overlap = TRUE) +
#geom_smooth(method = "lm") +
scale_x_log10(labels = dollar_format()) +
#scale_y_log10() +
scale_size_continuous(guide = FALSE) +
labs(x = "人均GDP(美元)",
y = "塑料垃圾回收率",
#color = "Coastal population",
title = "塑料垃圾回收率与人口收入的关系",
subtitle = "颜色为人均GPD组;点的大小代表国家总人口数量",
caption ="统计时间:2010年;数据源:ourworldindata.org
@Tony Duan"
)+theme(plot.title = element_text(hjust = 0.5))
g2+ guides(fill=FALSE, color=FALSE)
结论
随着经济的发展。中国人均每天将生成越来越多垃圾。目前中国还处在较低的垃圾回收率。所以建立有效的垃圾回收系统迫在眉睫。上海市的垃圾分类立法将成为其他城市的学习榜样。
Reference
https://www.nationalgeographic.com/magazine/2018/06/plastic-planet-waste-pollution-trash-crisis/
http://env.people.com.cn/n1/2019/0215/c1010-30715370.html
https://github.com/rfordatascience/tidytuesday/tree/master/data/2019/2019-05-21
https://www.youtube.com/watch?v=BRdLOYtJk9o&t=115s
https://github.com/dgrtwo/data-screencasts/blob/master/plastic-waste.Rmd
以上所述就是小编给大家介绍的《Data weekend:Global Plastic Waste》，希望对大家有所帮助，如果大家有任何疑问请给我留言，小编会及时回复大家的。在此也非常感谢大家对 码农网 的支持！
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