Results

This section allows you to explore the overall trend for multiple areas simultaneously. Average trends have been calculated for practices with all records within the main built-up area.

Results by Built-up Areas with CAZ

viewof caztype = Inputs.select(caztypes, {value: "CAZ D", label: "Type"})

cazfiltered = transpose(cazdata).filter(function(record) {
return caztype.includes(record.type) ;
})

Plot.plot({
  y: {domain: [0,70],
    grid: true,
    label: "SABA ratio(%)",
    transform: (f) => (f*100)
  },
  x: {
  transform: (x) => d3.timeParse("%Y-%m-%d")(x),
  interval: "month"
  },
  color: {legend: true},
  marks: [
    Plot.ruleY([0]),
    Plot.lineY(cazfiltered,
    {x: "date",
    y: "calc_value",
    stroke: "name",
    marker: "dot",
    strokeOpacity: 0.3}),
    Plot.linearRegressionY(cazfiltered,
    {x: "date",
    y: "calc_value",
    stroke: "name"})
    ]
})

Linar trends (Avg % Change per month)

Trends in each NHS region

Select the start month to calculate the linear trend

viewof month_min = Inputs.range(
  [0, 60],
  {value: 28, step: 1, label: "Start month:"}
)

Select the NHS regions of interest (max 6):

viewof names = Inputs.select(lstNames, {multiple: 6, label: "NHS Regions",value: ["NHS BRADFORD DISTRICT AND CRAVEN","NHS LIVERPOOL"]})

rfiltered = transpose(mydata).filter(function(record) {
return month_min < record.month && names.includes(record.name) ;
})

Plot.plot({
  y: {domain: [0,70],
    grid: true,
    label: "SABA ratio(%)",
    transform: (f) => (f*100)
  },
  x: {
  transform: (x) => d3.timeParse("%Y-%m-%d")(x),
  interval: "month"
  },
  color: {legend: true},
  marks: [
    Plot.ruleY([0]),
    Plot.lineY(transpose(mydata),
    {x: "date",
    y: "calc_value",
    stroke: "#aaa7ad",
    z: "name",
    strokeOpacity: 0.2}),
    Plot.lineY(rfiltered,
    {x: "date",
    y: "calc_value",
    stroke: "name",
    marker: "dot",
    strokeOpacity: 0.6}),
    Plot.linearRegressionY(rfiltered,
    {x: "date",
    y: "calc_value",
    stroke: "name"})
    ]
})