In New Zealand being kid of human.
We might make mistakes but we will make other things too. -
Michael Joseph Savage
margin = ({top: 50, bottom: 50, left: 75, right: 50})
height = 640
data = FileAttachment("nitrate_leaching_fixs.csv").csv()//{typed: true})
group = d3.group(data, d => d.geography_name, d => d.year, d=> d.animal)
/*
geography_type
geography_name
animal
year
no3_kg_per_yr
*/
group.get("Auckland").get("1990")// .get('Sheep') //[0].no3_kg_per_yr
hierarchy = d3.hierarchy(group)
hierarchy.height
hierarchy.data
hierarchy.children[0].data // region
hierarchy.children[0].children[0].data // year
hierarchy.children[0].children[0].children[0].data // Animal
hierarchy.children[0].children[0].children[0].children[0].data // the complete data at the bottom level
hierarchy.children[0].children[0].children[0].children[0].data.no3_kg_per_yr
{
const ff = []
hierarchy.descendants().map(
d => ff.push(`${"> ".repeat(d.depth)} ${d.data[0] || d.data["name"] || d.data.no3_kg_per_yr}`)
)
return html`<pre style="max-height: 15em; overflow-y: scroll;">${ff.join(
"\n"
)}`;
}
hierarchy.count().value // number of entries in the dataset
sums = hierarchy.copy().sum(d => {
if (!(d.geography_type=='Island'||d.geography_type=='New Zealand')) return d.no3_kg_per_yr}) // sum only the regions
sums.value
sums.find(d => d.data[0] === "Auckland").value // 1990 auckland total
sums.find(d => d.data[0] === "Sheep").value // 1990 auckland sheep
sums.find(d => d.data[0] === "Deer").value
sums.find(d => d.data[0] === '1990').value // auckland total for 1990
sums.find(d => d.data[0] === '2017').value // auckland total
hierarchy.leaves().map(d => d.data)//[111]
hierarchy.copy().sort((a, b) => d3.ascending(a.data[0], b.data[0])).children.map(d => d.data[0])
rollup = d3.rollup( // makes totals for each region each year.
data,
v => d3.sum(v, d => d.no3_kg_per_yr),
d => d.year,
d => d.geography_name
// d=>d.animal // this would return the same as the orgional data
)
R = d3.hierarchy(rollup)
R.leaves().map(d => [d.parent.data[0], ">", ...d.data].join(" "))
{
const names = [];
R.copy().sum(d => d[1]).eachBefore((d, index) =>
names.push(" ".repeat(d.depth) + `${d.depth}: ${d.data[0] || "All"}, ${d.value} (${index})`)
);
return html`<pre style="max-height: 15em; overflow-y: scroll;">${names.join(
"\n"
)}`;
}
{
const names = [];
R.copy().sum(d => d[1]).eachAfter((d, i) =>
names.push(" ".repeat(d.depth) + `${d.depth}: ${d.data[0] || "All"}, ${d.value} (${i})`)
);
return html`<pre style="max-height: 15em; overflow-y: scroll;">${names.join(
"\n"
)}`;
}
d3.extent(hierarchy, d => d.data.no3_kg_per_yr)
no3_rollup = d3.rollup(no3, v => v.length,d => d.year, d => d.geography_name, d => d.animal, d=>d.no3_kg_per_yr)
data_table = Table(no3_hierarchy.map(d => ({"Region": d.geography_name, "Nitrates (kg per year)": d.no3_kg_per_yr,"Stock type": d.animal,'year':d.year })),
{columns: ["Region", "Stock type",'year','Nitrates (kg per year)',' '],
layout:'auto',
align: { 'animal':'center','year':'center','no3_kg_per_yr':'right'},
width: { 'geography_name':'150px','animal':'120px','year':'90px','no3_kg_per_yr':'120px' },
})
no3_hierarchy = {
// if(nitrateData[nitrateData.length-1].children!='root') { // this added a root node
// nitrateData.push({parent: "",
// geography_type: "New Zealand",
// geography_name: "Aotearoa" ,
// children: "root",
// })
// }
let no3h = nitrateData.filter( d => {
// d.year=new Date(Date.UTC(d.year, 0, 1)) // can't use "Date" as I am using the year sting in the name hierarchy
d.year=''+d.year // fix the auto format make the year a string
if (d.no3_kg_per_yr==-1) d.no3_kg_per_yr =0 // fix up the NA -1 data to read zero as some regions used 0 and others NA
switch(d.geography_name){
case 'New Zealand':
d.children=d.geography_name+d.animal+d.year
d['parent']='root'
break;
case 'North Island':
case 'South Island':
d.children=d.geography_name+d.animal+d.year
d['parent']='New Zealand'+d.animal+d.year
break;
case "Auckland":
case "Bay of Plenty":
case "Gisborne":
case "Hawke's Bay":
case "Manawatu-Whanganui":
case "Waikato":
case "Wellington":
case "Northland":
case "Taranaki":
d.children = d.geography_name+d.animal+d.year
d.parent = 'North Island'+d.animal+d.year
break;
case "Otago":
case "Southland":
case "Marlborough":
case "Tasman":
case "West Coast":
case "Canterbury":
d.children = d.geography_name+d.animal+d.year
d.parent = 'South Island'+d.animal+d.year
break;
}
if (!(d.geography_type == 'New Zealand'||d.geography_type == 'Island')) return d // remover New Zealand, North and South Island totals from the dataset.
})
return no3h
}
viewof search = Search(nitrateData)
viewof res = Table(search,
{columns: ["geography_name", "animal",'year','no3_kg_per_yr','geography_type',' '],
layout:'auto',
align: { 'animal':'center','year':'center','no3_kg_per_yr':'right'},
width: { 'geography_name':'150px','animal':'120px','year':'90px','no3_kg_per_yr':'120px' },
})
// filter by one region
no3_Canterbury = no3.filter( d => {
if (d.geography_name=='Canterbury') return d
})
no3 = nitrateData.filter( d => {
d.year=''+d.year // fix the auto format make the year a string
if (d.no3_kg_per_yr==-1) d.no3_kg_per_yr =0 // fix up the NA -1 data to read zero as some regions used 0 and others NA
return d
})
viewof canSearch = Search(no3_Canterbury, {label: "NO3 levels in Canterbury NZ."})
canNO3 = Table(canSearch,
{columns: ["year", "geography_name", "animal",'no3_kg_per_yr',' ','some padding to move the data left',''],
// layout:'auto',
align: { 'animal':'left','year':'center','geography_name':'left','no3_kg_per_yr':'right'},
}
)
nitrateData = FileAttachment("nitrate_leaching_fixs.csv").csv({typed: true})
import {Table, Search} from "@observablehq/inputs"
d3 = require('d3')
_ = require('lodash')
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Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
