各国犯罪率和枪支数量
GongCC
2022-08-19
各国枪支数量1
gun <- fread("./各国枪支数量csvData.csv")
gun## country firearms per100 military lawEnf pop2022
## 1: United States 393347000 120.5 4535380 1016000 338289.857
## 2: Falkland Islands 2000 62.1 NA 30 3.780
## 3: Yemen 14859000 52.8 28500 NA 33696.614
## 4: New Caledonia 115000 42.5 NA 700 289.950
## 5: Serbia 2719000 39.1 384422 53100 7221.365
## ---
## 212: Timor-Leste 3000 0.3 2527 4000 1341.296
## 213: South Korea 79000 0.2 2688020 115000 51815.810
## 214: Solomon Islands 1000 0.2 NA 70 724.273
## 215: Indonesia 82000 0.0 1711450 429000 275501.339
## 216: Taiwan 10000 0.0 2022150 76000 23893.394crim <- fread("./各国犯罪率.csv", header = T)
crim## Country Name 2020 2019 2018 2017 2016
## 1: Jamaica 44.949 45.450 43.920 56.388 46.589
## 2: Honduras 36.327 42.007 38.926 40.980 55.551
## 3: South Africa 33.462 36.417 36.399 35.671 33.832
## 4: Mexico 28.371 28.737 29.071 25.709 19.913
## 5: St. Lucia 28.318 25.165 20.342 27.079 16.664
## ---
## 141: Guam 0.000 4.184 0.000 0.000 0.000
## 142: Iran 0.000 0.000 2.194 0.000 0.000
## 143: UAE 0.000 0.655 0.000 0.000 0.000
## 144: Cameroon 0.000 0.000 0.000 1.388 1.154
## 145: Ghana 0.000 0.000 0.000 2.091 1.928ggthemr::ggthemr()
melt(crim, id = 1)[,
`:=`("variable" = as.integer(as.character(variable)),
"Country" = forcats::fct_reorder(`Country Name`, value)
)
][] |>
ggplot(mapping = aes(value, Country)) +
geom_point() +
labs(x = "犯罪率（%）", y = "") +
gganimate::transition_time(variable) +
ggtitle("各国犯罪率变化（{frame_time}年）")+
theme(axis.title.x = element_text(family = "yahei")) ->p
gganimate::animate(p, width = 800, height = 1500)
# gganimate::anim_save("./res.gif", p, width = 800, height = 1500,res = 100)