Data Analytics Consultant, Instructor. Areas: Data Analytics, Geometry, Simulation, Visualization, Computer Vision, Robotics...
vl.markLine({size:3})
.data(temps_brasil_narrow)
.encode(
vl.x().fieldO("mês").sort(month_names),
vl.y().fieldQ("temp").scale({domain: [12, 28]}),
vl.color().fieldN("Cidade")
)
.title("Temperatura")
.width(500)
.height(300)
.render()
temps_brasil = aq.loadCSV(url_gs)
temps_brasil
.view({height:240})
temps_brasil_colnames=temps_brasil.columnNames()
month_names = temps_brasil_colnames.slice(1)
temps_brasil
.fold(month_names,{as:['mês','temp']})
.view({height:240})
temps_brasil
.fold(aq.range(1,12),{as:['mês','temp']}) // nomeia as novas colunas)
.view({height:240})
temps_brasil
.fold(aq.not(0),{as:['mês','temp']})
.view({height:240})
typeof(temps_brasil
.fold(aq.not(0),{as:['mês','temp']})
.columnArray('temp')[0])
typeof(temps_brasil
.fold(aq.not(0),{as:['mês','temp']})
.columnArray('mês')[0])
viewof temps_brasil_narrow = temps_brasil
.fold(aq.not(0),{as:['mês','temp']})
//.derive({temp: d => +d.temp}) // '+' em js converte string para numérico
.view({height:240})
temps_brasil_narrow
.groupby('Cidade')
.rollup({ temp_media: op.mean('temp'), temp_minima: op.min('temp') } )
.orderby(aq.desc('temp_media'))
.view(10)
temps_brasil_narrow.view({height:240})
temps_brasil_narrow
.groupby('Cidade')
// sort: mantém as colunas nas ordens vistas na tabela narrow original
.pivot('mês','temp',{sort:false})
.view({height:240})
db_weather= Object.defineProperties(new SQLiteDatabaseClient(FileAttachment("weather.sqlite").arrayBuffer()),
{name: {value: "weather", enumerable: true}});
metadata=db_weather.query('pragma table_info(weather)')
col_names = metadata.map(row => row.name)
db_weather.queryRow('select count(*) as cnt from weather')
db_weather.query('select * from weather limit 10')
db_weather.query('select distinct location from weather')
db_weather.query('select location, count(*) as cnt from weather group by location')
weather_table=db_weather.query('select * from weather');
weather_arquero = aq.from(weather_table);
parseFloat("1.234")
+"1.2345"
precipitations_in_seattle = weather_arquero
.filter(d=>d.location==="Seattle")
// converte string numerico com '+'
.derive({precipitation: d=> +d.precipitation})
.columnArray('precipitation')
five_nums(precipitations_in_seattle)
weather_arquero
.filter(d=>d.location==="Seattle")
// converte para float
.derive({precipitation: d=> +d.precipitation})
.rollup({
linhas: op.count(),
min: op.min('precipitation'),
q1: op.quantile('precipitation', 0.25),
median: op.median('precipitation'),
q3: op.quantile('precipitation', 0.75),
max: op.max('precipitation')
})
.view()
weather_arquero
.groupby('weather')
.count()
.derive({total:op.sum('count')})
.derive({pct: d=>100*d.count/d.total })
.orderby(aq.desc('pct'))
.view()
weather_arquero_date_precipitation=weather_arquero
.select('location','date','precipitation')
// traduz nome das colunas
.rename({location:'cidade',date:'dia',precipitation:'precipitação'})
// muda o formato de strng para data e numérico
.derive({
dia: d=>op.parse_date(d.dia),
precipitação: d=> +d.precipitação
})
vl.markLine()
.data(weather_arquero_date_precipitation)
.encode(
vl.x().fieldT("dia").axis({title:"data"}),
vl.y().fieldQ("precipitação").scale({type: tipo_de_eixo_y==="linear" ? 'linear':'sqrt'}),
vl.color().fieldN("cidade")
)
.title("Precipitacao")
.width(500)
.render()
vl.markBoxplot()
.data(weather_arquero_date_precipitation)
.encode(
vl.x().fieldQ("precipitação").scale({type: tipo_de_eixo_y==="linear" ? 'linear':'sqrt'}),
vl.y().fieldN("cidade"),
vl.color().fieldN("cidade")
)
.title("Precipitacao")
.width(500)
.height(100)
.render()
vl.data(weather_arquero_date_precipitation)
.transform([{
window: [
{
field: "precipitação",
op: "mean",
as: "média_móvel"
}
],
frame: [-30, 30] // janela de 60 dias
}])
.encode(
vl.x().fieldT("dia").axis({title:"data"}),
vl.y().fieldQ("precipitação").scale({type: tipo_de_eixo_y==="linear" ? 'linear':'sqrt'})
)
.layer(
vl.markLine({"opacity": 0.3})
.encode(
vl.color().fieldN("cidade").scale({ range: ["red", "green"] })
),
vl.markLine({size:2})
.encode(
vl.y().fieldQ("média_móvel"),
vl.color().fieldN("cidade")
)
)
.title("Precipitação")
.width(500)
.render()
weather_arquero_date_precipitation.columnNames()
viewof weather_arquero_precip_bin = weather_arquero_date_precipitation
.groupby({
precip_bin: aq.bin('precipitação', {step:5}),
})
.count()
.orderby('precip_bin')
.view({height:240})
vl.markBar({color: "green", stroke:"black",width:20})
.data(weather_arquero_precip_bin)
.encode(
vl.x().fieldQ('precip_bin').scale({domain: [-2.5, 120]}),
vl.y().fieldQ('count').scale({type: 'log'})
)
.title("Histograma de precipitação em Seattle")
.width(800)
.render()
viewof temps_ny = weather_arquero
.filter(d=>d.location==="New York")
.rename({date:'data',precipitation:'precip'})
// converte precipit numerico
.derive({data: d=>op.parse_date(d.data),
precip: d=> +d.precip})
.view({height:240})
viewof temps_ny_narrow = temps_ny
.select('data','temp_min','temp_max')
.fold(['temp_min','temp_max'])
.view({height:240})
vl.markLine()
.data(temps_ny_narrow)
.encode(
vl.x().fieldT("data").axis({title:"data"}),
vl.y().fieldQ("value"),
vl.color().fieldN("key")
)
.title("Temp Min & Max em NY")
.width(500)
.render()
viewof covid_ano_semana = aq
.fromCSV(await FileAttachment("covid_ano_semana.csv").text())
.view({height:240})
vl.markLine()
.data(covid_ano_semana_fold)
.encode(
vl.x().fieldO("ano_semana"), //
vl.y().fieldQ("vacinados"),
vl.color().fieldN("país")
)
.title("Temperatura")
.width(500)
.height(300)
.render()
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