Published
Edited
May 21, 2021
1 fork
md`## 1 Maternal Mortality Rate (1/100,000) in China from 2010 to 2018
### 1.1 An Overview: Urban-Rural Gap`
{
const urban_rural1 = vl.markArea({opacity: 0.5})
.data(mortality_data_china)
.encode(
vl.x().fieldT("Year").timeUnit("year").title("2010-2018"),
vl.y().mean("Maternal").stack(null).title("Maternal Mortality Rate"),
vl.color().fieldN("Urban-Rural").scale({scheme: "goldorange"}),
)
const urban_rural2 = vl.markLine({strokeWidth: 3, interpolate: 'monotone'})
.data(mortality_data_china)
.encode(
vl.x().fieldT("Year").timeUnit("year"),
vl.y().fieldQ('Maternal'),
vl.color().fieldN('Urban-Rural').legend(null),
vl.tooltip().fieldN('Urban-Rural')
)
.width(700)
.height(500)
return vl.layer(urban_rural1, urban_rural2).render();
}
vl.markCircle()
.data(mortality_data_provincial)
.transform(vl.filter("datum.Year == '" + selectYear + "'"))
.encode(vl.x().fieldQ("City").title("Urban Maternal Mortality Rate"),
vl.y().fieldQ("County").title("Rural Maternal Mortality Rate"),
vl.color().fieldN("Total").scale({scheme: "lightgreyred"}).title("Total Maternal Mortality Rate"),
vl.size().fieldN("Total"),
vl.tooltip().fieldN("Provincial"))
.width(700)
.height(500)
.render()
md`### 1.3 Urban-Rural Gap at Provincial Level`
vl.markBar()
.data(mortality_data_province)
.transform(vl.filter("datum.Location == '" + selectLocation + "'"))
.encode(vl.y().fieldN("Urban_Rural"),
vl.x().fieldO("Year"),
vl.color().mean(vl.repeat("column")).scale({scheme: "lightgreyred"}).title("Maternal Mortality Rate"),
vl.tooltip().mean(vl.repeat("column")))
.width(400)
.height(120)
.repeat({column: ["Mortality"]})
.resolve({scale: {color: 'independent'}})
.render()
{
const totalrate = vl.markBar()
.data(maternal_data_china)
.encode(vl.y().fieldQ("Value").title("Mortality Rate"),
vl.x().fieldN("Year"),
vl.color().fieldN("Causes").scale({scheme: "inferno"}).title("Major Causes"),
vl.tooltip().fieldQ("Value"))
.width(360)
.height(360)
return vl.layer(totalrate)
.facet({column: vl.field("Location")})
.data(maternal_data_china)
.render()
}
vl.markBar()
.data(mortality_data_2019)
.width(580)
.height(400)
.encode(
vl.y().fieldN("Causes").title("Major Causes in urban/rural area"),
vl.x().fieldQ("Value").title("% of Total Maternal Mortality"),
vl.color().fieldQ("Value").scale({scheme: "lightorange"}).title("% of Total Maternal Mortality"),
vl.tooltip().fieldQ("Value"))
.render()
countries_url = "https://gitee.com/riverscn/china-geojson/raw/master/china.json"
d3 = require("d3-dsv@1", "d3@5")
md`## mortality_file_china`
import {vl} from '@vega/vega-lite-api'
mortality_file_china = FileAttachment("mortality_china2@2.csv")
mortality_text_china = mortality_file_china.text()
mortality_data_china = d3.csvParse(mortality_text_china, d3.autoType)
import {printTable} from '@uwdata/data-utilities'
printTable(mortality_data_china.slice(0, 10))
md`## mortality_file_provincial`
mortality_file_provincial = FileAttachment("mortality_pro.csv")
mortality_text_provincial = mortality_file_provincial.text()
mortality_data_provincial = d3.csvParse(mortality_text_provincial, d3.autoType)
printTable(mortality_data_provincial.slice(0, 10))
md`## mortality_file_province`
mortality_file_province = FileAttachment("mortality_province2.csv")
mortality_text_province = mortality_file_province.text()
mortality_data_province = d3.csvParse(mortality_text_province, d3.autoType)
printTable(mortality_data_province.slice(0, 10))
md`## maternal_file_china`
maternal_file_china = FileAttachment("maternal_total2@2.csv")
maternal_text_china = maternal_file_china.text()
maternal_data_china = d3.csvParse(maternal_text_china, d3.autoType)
printTable(maternal_data_china.slice(0, 10))
mortality_file_2019 = FileAttachment("mortality_2019a@1.csv")
mortality_text_2019 = mortality_file_2019.text()
mortality_data_2019 = d3.csvParse(mortality_text_2019, d3.autoType)
printTable(mortality_data_2019.slice(0, 10))
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