Data Hub - Economics Observatory

Welcome to the Economics Observatory Data Hub.

First, you can use the Data Explorer below to access our very own Application Programming Interface (ECO-API). This will allow you to get hold of the latest data from 24 countries, delivered directly from source.

Next, you can build your own chart using this live data. The Create tool allows you craft a data visualisation using the Vega-lite library – with no coding experience required. Just drop in your ECO-API link and watch the data appear.

Finally, please share your work. Anyone can post their graphs into the chart timeline using our Share tool. Users can also comment on other people’s ideas, and even save visualisations to their personal library.

To find out more about the Data Hub, click here. For a step-by-step guide on each tool, click here. And to learn more about our API, click here.

Explore

You need to create something first, before you can share. You can use your own data, just paste a link to the source here.

Create

Data

Variables

Appearance

Colour

Page

Canvas

CSV JSON

Code is being edited directly

Your visualisation:

The dataset you loaded:

Download

Your chart code in JSON format:

Edit Code

Data Source Notes

De Fraja et al, 2022a

This chart shows remote working before and after the pandemic by occupation.

The data is taken from De Fraja et al, 2022a.

This chart first appeared in an article by Jesse Matheson, Paul Mizen and James Rockey.

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "height": 300,
  "width": 270,
  "data": {
    "url": "https://raw.githubusercontent.com/RDeconomist/observatory/main/F45_C1b.csv"
  },
  "layer": [
    {
      "mark": {"type": "point", "size": 60, "color": "#e6224b", "filled": true},
      "encoding": {
        "x": {
          "field": "value2",
          "type": "quantitative",
          "title": "Remote working, actual 2019 (%)"
        },
        "y": {
          "field": "value1",
          "type": "quantitative",
          "title": "Remote working, employer expected 2022 (%)"
        },
        "tooltip": [
          {"field": "job", "type": "nominal", "title": "Occupation"},
          {
            "field": "value2",
            "type": "quantitative",
            "title": "Remote working, actual 2019 (%)"
          },
          {
            "field": "value1",
            "type": "quantitative",
            "title": "Remote working, employer expected 2022 (%)"
          }
        ]
      }
    },
    {
      "mark": {"type": "line", "color": "#122b39", "opacity": 0.6},
      "encoding": {
        "x": {"field": "value3", "type": "quantitative"},
        "y": {"field": "value4", "type": "quantitative"}
      }
    }
  ]
}

Data Source Notes

Office for National Statistics, author’s calculations

This chart shows industry composition in Scotland.

The data is taken from the Office for National Statistics, author’s calculations.

This chart first appeared in an article by Graeme Roy and Stuart McIntyre, published on 24 January 2022.

{
  "config": {
    "view": {"continuousWidth": 270, "continuousHeight": 300, "stroke": null}
  },
  "layer": [
    {
      "mark": {"type": "bar", "color": "#F4134D", "opacity": 0.9},
      "encoding": {
        "color": {"type": "nominal", "field": "Industry", "legend": null},
        "x": {"type": "nominal", "axis": null, "field": "Industry"},
        "y": {
          "type": "quantitative",
          "axis": {
            "domainColor": "#676A86",
            "gridOpacity": 0.3,
            "labelColor": "#676A86",
            "tickColor": "#676A86",
            "title": [
              "Location quotient (LQ)",
              "Industry share of employment in Scotland",
              "is the same as it is in the UK, when LQ=1"
            ],
            "titleAlign": "left",
            "titleAngle": 0,
            "titleBaseline": "bottom",
            "titleColor": "#676A86",
            "titleX": 5,
            "titleY": 5
          },
          "field": "LQ",
          "sort": []
        }
      }
    },
    {
      "mark": {"type": "text", "align": "left", "angle": 270, "dx": 5},
      "encoding": {
        "color": {"type": "nominal", "field": "Industry", "legend": null},
        "text": {"type": "nominal", "field": "Industry"},
        "x": {"type": "nominal", "axis": null, "field": "Industry"},
        "y": {
          "type": "quantitative",
          "axis": {
            "domainColor": "#676A86",
            "gridOpacity": 0.3,
            "labelColor": "#676A86",
            "tickColor": "#676A86",
            "title": [
              "Location quotient (LQ)",
              "Industry share of employment in Scotland",
              "is the same as it is in the UK, when LQ=1"
            ],
            "titleAlign": "left",
            "titleAngle": 0,
            "titleBaseline": "bottom",
            "titleColor": "#676A86",
            "titleX": 5,
            "titleY": 5
          },
          "field": "LQ",
          "sort": []
        }
      },
      "transform": [{"filter": "datum.Industry!='Mining and quarrying'"}]
    },
    {
      "mark": {
        "type": "text",
        "align": "right",
        "angle": 270,
        "color": "white",
        "dx": -5
      },
      "encoding": {
        "text": {"type": "nominal", "field": "Industry"},
        "x": {"type": "nominal", "field": "Industry"},
        "y": {"type": "quantitative", "field": "LQ"}
      },
      "transform": [{"filter": "datum.Industry=='Mining and quarrying'"}]
    },
    {
      "mark": {
        "type": "line",
        "color": "#676A86",
        "strokeDash": [5, 5],
        "strokeWidth": 1
      },
      "encoding": {
        "x": {"type": "nominal", "field": "Industry"},
        "y": {"type": "quantitative", "field": "UK"}
      }
    }
  ],
  "data": {
    "url": "https://raw.githubusercontent.com/EconomicsObservatory/ECOvisualisations/main/articles/scottish-independence-what-are-the-big-economic-questions/data/fig6a_industry_mix.csv"
  },
  "height": 300,
  "title": "",
  "width": 400,
  "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json"
}

@Finn McEvoy

This week's #ChartOfTheWeek examines how adults in the UK spend their time, using data from the ONS's Time Use Survey. Throughout the week, there are distinct periods of personal care, which includes eating and drinking. On the weekend, adults tend to wake up later and start watching television earlier. 47.5 per cent of adults are watching TV at 9:20pm on an average weekend. Visit our Data Hub to explore other charts and create your own https://buff.ly/3mkZ2je.

{
    "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
    "config": {"style": {"cell": {"stroke": "transparent"}}},
      "title": {
      "font": "Circular Std",
      "text": "Value-added Tax Rates",
      "subtitle": [
        "Includes equivalent taxes, 2024",
        "Source: PwC",
        ""
      ],
      "anchor": "start",
      "fontSize": 16,
      "subtitleFont": "Arial",
      "subtitleFontWeight": "lighter"
    },
    "width": 500,
    "height": 300,
    "data": {
      "url": "https://raw.githubusercontent.com/jhellingsdata/RADataHub/main/e4e/ch8_Trends/data/chart8_x_europe.topojson",
      "format": {"type": "topojson", "feature": "europe"}
    },
    "transform": [
      {
        "filter": "datum.id != 'RU' && datum.id != 'IL' && datum.id != 'GE'"
      },
      {
        "lookup": "id",
        "from" : {
          "data": {
            "url": "https://raw.githubusercontent.com/jhellingsdata/RADataHub/main/Chart%20Packs/VAT/pwc_rates_fixed.csv"
          },
          "key": "id",
          "fields": ["value"]
        }
      }
    ],
    "mark": {
      "type": "geoshape",
      "strokeWidth": 0.3,
      "stroke": "rgba(0,0,0,0.3)"
    },
    "encoding": {
      "color": {
        "legend": {
          "title":null,
          "format": "%"
        },
      "field":"value",
                  "scale": {
              "range": [
                "rgb(111, 181, 219)",
                "rgb(73, 191, 161)",
                "rgb(230, 189, 81)",
                "rgb(235, 61, 75)"
              ],
              "domain": [0.07, 0.27]
            },
      "type": "quantitative"
      },
      "tooltip": [
        {"field" : "id"},
        {"field": "value"}
      ]
    }
  }

@grizzly

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "title": {
    "text": "Gun deaths and income - US states",
    "subtitle": [
      "Median income (x), firearm deaths per 100k population (y), prison rate per 100 pop. (bubble size)",
      ""
    ],
    "subtitleFontStyle": "italic",
    "subtitleFontSize": 10,
    "anchor": "start",
    "color": "black"
  },
  "description": "Various US measures state by state",
  "data": {
    "url": "https://raw.githubusercontent.com/EconomicsObservatory/courses/main/datasets/advanced/usa_socioEconomic.csv",
    "format": {
      "type": "csv"
    }
  },
  "mark": {
    "type": "circle",
    "size": 150,
    "color": "rgb(0,67,167)",
    "opacity": 0.6
  },
  "height": 300,
  "width": 436,
  "transform": [
    {
      "calculate": "datum.PrisonRate * 1000",
      "as": "PrisonRatePer100k"
    }
  ],
  "encoding": {
    "x": {
      "field": "medIncome",
      "type": "quantitative",
      "scale": {
        "domain": [
          40000,
          80000
        ]
      },
      "axis": {
        "title": "Median Income"
      }
    },
    "y": {
      "field": "FirearmDeaths",
      "type": "quantitative",
      "axis": {
        "title": "Firearm Deaths"
      }
    },
    "color": {
      "field": "GeographicDivision",
      "type": "ordinal",
      "scale": {
        "scheme": "set1"
      },
      "legend": {
        "title": null
      }
    },
    "size": {
      "field": "PrisonRate",
      "type": "quantitative",
      "title": "Prison Rate",
      "scale": {
        "range": [
          5,
          300
        ]
      }
    },
    "tooltip": [
      {
        "field": "State"
      },
      {
        "field": "medIncome",
        "format": "$,",
        "title": "Median Income"
      },
      {
        "field": "FirearmDeaths",
        "format": ",",
        "title": "Firearm Deaths"
      },
      {
        "field": "ImprisonmentRate",
        "format": ",",
        "title": "Prison Rate"
      }
    ]
  },
  "autosize": {
    "type": "fit",
    "contains": "padding"
  }
}

@Finn McEvoy

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "config": {
    "font": "Circular Std",
    "mark": {"line": {"interpolate": "monotone"}},
    "view": {"stroke": "transparent", "width": 400, "height": 300},
    "title": {"anchor": "start", "subtitleColor": "#676A86"},
    "range": {
      "category": [
        "#36B7B4",
        "#E6224B",
        "#F4C245",
        "#0063AF",
        "#00A767",
        "#179FDB",
        "#EB5C2E"
      ],
      "diverging": ["#E6224B", "#E54753", "#C9C9C9", "#179FDB", "#122B39"],
      "heatmap": ["#C9C9C9", "#179FDB", "#0063AF", "#122B39"],
      "ordinal": ["#00A767", "#36B7B4", "#179FDB", "#0063AF", "#243B5A"]
    },
    "axisX": {
      "domainColor": "#676A86",
      "domainOpacity": 0.5,
      "grid": false,
      "labelAngle": 0,
      "labelColor": "#676A86",
      "labelOpacity": 0.9,
      "orient": "bottom",
      "tickColor": "#676A86",
      "tickCount": 10,
      "tickOpacity": 0.5,
      "titleAlign": "center",
      "titleAnchor": "middle",
      "titleColor": "#676A86",
      "titleFontSize": 12,
      "titleOpacity": 0.8,
      "titleY": -15
    },
    "axisY": {
      "domainColor": "#676A86",
      "domainOpacity": 0.5,
      "gridColor": "#676A86",
      "gridDash": [1, 5],
      "gridOpacity": 0.5,
      "labelColor": "#676A86",
      "labelOpacity": 0.9,
      "labelPadding": 5,
      "tickColor": "#676A86",
      "tickCount": 8,
      "tickOpacity": 0.5,
      "ticks": false,
      "titleAlign": "left",
      "titleAngle": 0,
      "titleBaseline": "bottom",
      "titleColor": "#676A86",
      "titleFontSize": 12,
      "titleOpacity": 0.8,
      "titleX": 0,
      "titleY": -7
    }
  },
  "title": {
    "text": "Literacy in India",
    "subtitle": ["Literacy Rate by District, 2011 Census", ""],
    "fontSize": 14,
    "anchor": "start"
  },
  "width": "container",
  "height": 300,
  "data": {
    "url": "https://raw.githubusercontent.com/EconomicsObservatory/courses/main/datasets/advanced/india_districts.topojson",
    "format": {"type": "topojson", "feature": "india-districts-727"}
  },
  "transform": [
    {"calculate": "toNumber(datum.properties.dt_code)", "as": "id"},
    {
      "lookup": "id",
      "from": {
        "data": {
          "url": "https://raw.githubusercontent.com/EconomicsObservatory/courses/main/datasets/advanced/indian_literacy_rates_wide.csv"
        },
        "key": "district_id",
        "fields": [
          "male_rate",
          "female_rate",
          "total_rate",
          "difference_rate",
          "district_name"
        ]
      }
    }
  ],
  "projection": {"type": "naturalEarth1"},
  "layer": [
    {
      "mark": {"type": "geoshape"},
      "encoding": {"color": {"value": "rgb(200,200,200)"}}
    },
    {
      "mark": {"type": "geoshape"},
      "encoding": {
        "color": {
          "field": "total_rate",
          "scale": {"scheme": "redyellowgreen", "domain": [0.3, 1]},
          "legend": {"title": null, "format": ".0%"},
          "type": "quantitative"
        },
        "tooltip": [
          {"field": "district_name", "title": "Name"},
          {"field": "male_rate", "title": "Male", "format": ".0%"},
          {"field": "female_rate", "title": "Female", "format": ".0%"},
          {"field": "total_rate", "title": "Total", "format": ".0%"}
        ]
      }
    }
  ],
  "autosize": {"type": "pad", "contains": "padding", "resize": true}
}

@grizzly

@denes

This is a short description of the chart that has been pinned to the homepage

{
  "$schema": "https://vega.github.io/schema/vega-lite/v5.json",
  "data": {
    "url": "https://raw.githubusercontent.com/jhellingsdata/RADataHub/main/e4e/ch4_Money/data/chart4_8_ny_ch_prices.csv"
  },
  "config": {"view": {"stroke": "transparent"}, "axisY": {"minExtent": 40}},
  "title": {
    "text": "Inflation in Chicago & New York",
    "subtitle": [
      "Change in CPI, yearly (left) and cumulative (right) ",
      "Source: FRED",
      " "
    ],
    "fontSize": 16,
    "subtitleFontSize": 12,
    "subtitleFontStyle": "italic"
  },
  "concat": [
    {
      "encoding": {
        "x": {
          "field": "Date",
          "type": "temporal",
          "axis": {
            "ticks": false,
            "labelPadding": 6,
            "title": null,
            "grid": false,
            "gridOpacity": 0.3,
            "labelFontSize": 11
          }
        }
      },
      "height": 300,
      "width": 400,
      "transform": [
        {"filter": "year(datum.Date) > 1941"},
        {
          "calculate": "(datum.Series == 'NY_Change' | datum.Series == 'Chicago_Change') ? datum.Value / 100 : datum.Value ",
          "as": "Value"
        }
      ],
      "layer": [
        {
          "data": {"values": [{"can be ": "anything (compare rule)"}]},
          "mark": {"type": "rule", "opacity": 0.6},
          "transform": [
            {"calculate": "datetime(toNumber(compareYear),1,1)", "as": "Date"}
          ]
        },
        {
          "mark": {"type": "rect", "color": "black", "opacity": 0.05},
          "data": {"values": [{"can be ": "anything (area chart layer)"}]},
          "transform": [
            {"calculate": "datetime(toNumber(compareYear),1,1)", "as": "Date"},
            {"calculate": "toNumber(hover.Date)", "as": "Date_End"}
          ],
          "encoding": {
            "y": null,
            "x": {"field": "Date"},
            "x2": {"field": "Date_End"}
          }
        },
        {
          "data": {"values": [{"can be ": "anything"}]},
          "transform": [{"calculate": "toNumber(hover.Date)", "as": "Date"}],
          "mark": {
            "type": "text",
            "text": {"expr": "year(toNumber(hover.Date))"},
            "dx": {"expr": " year(toNumber(hover.Date))>2004 ? -4 : 4 "},
            "fontWeight": "bold",
            "opacity": 0.5,
            "size": 15,
            "baseline": "line-top",
            "align": {
              "expr": " year(toNumber(hover.Date))>2004 ? 'right' : 'left' "
            }
          },
          "encoding": {"x": {"field": "Date"}, "y": {"value": 0}}
        },
        {
          "transform": [
            {"filter": "datum.Date == hover.Date"},
            {
              "calculate": " { 'NY_Change':'NYC', 'Chicago_Change':'Chicago'}[datum.Series] + ': ' +format(datum.Value, '.1%') ",
              "as": "text"
            },
            {
              "calculate": "{'NY_Change':0.213,'Chicago_Change':0.225}[datum.Series]",
              "as": "Value"
            }
          ],
          "mark": {
            "type": "text",
            "text": {"expr": "year(toNumber(hover.Date))"},
            "dx": {"expr": " year(toNumber(hover.Date))>2004 ? -4 : 4 "},
            "fontWeight": "normal",
            "opacity": 0.7,
            "size": 12,
            "baseline": "line-top",
            "align": {
              "expr": " year(toNumber(hover.Date))>2004 ? 'right' : 'left' "
            }
          },
          "encoding": {
            "text": {"field": "text"},
            "x": {"field": "Date"},
            "y": {
              "field": "Value",
              "type": "quantitative",
              "axis": {
                "format": "%",
                "ticks": false,
                "labelPadding": 5,
                "title": null,
                "labelFontSize": 11
              }
            },
            "color": {
              "condition": [
                {
                  "test": "datum.Series == 'Chicago_Change'",
                  "value": "rgb(231,145,67)"
                },
                {
                  "test": "datum.Series == 'NY_Change'",
                  "value": "rgb(92,124,167)"
                }
              ]
            }
          }
        },
        {
          "transform": [
            {
              "filter": "datum.Series == 'NY_Change' | datum.Series == 'Chicago_Change'"
            },
            {"calculate": "year(datum.Date)", "as": "year"},
            {
              "calculate": "{'NY_Change': 'New York', 'Chicago_Change':'Chicago'}[datum.Series]",
              "as": "Series"
            }
          ],
          "encoding": {
            "color": {
              "field": "Series",
              "type": "nominal",
              "scale": {"domain": ["New York", "Chicago"]},
              "legend": {
                "orient": "top",
                "title": null,
                "offset": 10,
                "labelFontSize": 11
              }
            },
            "y": {
              "field": "Value",
              "type": "quantitative",
              "axis": {
                "ticks": false,
                "labelPadding": 5,
                "title": null,
                "labelFontSize": 11
              }
            }
          },
          "layer": [
            {"mark": {"type": "line", "strokeWidth": 1}},
            {
              "transform": [{"filter": {"param": "hover", "empty": false}}],
              "mark": "circle"
            }
          ]
        },
        {
          "transform": [
            {"pivot": "Series", "value": "Value", "groupby": ["Date"]},
            {"calculate": "year(datum.Date)", "as": "year"},
            {"filter": "year(datum.Date) > compareYear"}
          ],
          "mark": "rule",
          "encoding": {
            "opacity": {
              "condition": {"value": 0.4, "param": "hover", "empty": false},
              "value": 0
            }
          },
          "params": [
            {
              "name": "hover",
              "value": {"Date": [1682899200000]},
              "select": {
                "type": "point",
                "fields": ["Date"],
                "nearest": true,
                "on": "mouseover",
                "clear": "none"
              }
            }
          ]
        }
      ]
    }
  ],
  "params": [
    {
      "name": "compareYear",
      "value": 2000,
      "bind": {
        "input": "range",
        "min": 1941,
        "name": "Compare to Year",
        "max": 2022,
        "step": 1
      }
    }
  ],
  "width": 400
}

@grizzly

@JHellings

Record highs to record lows, UK pandemic redundancies spike past levels seen in the 2008 financial crisis, yet show a much sharper recovery to go below pre-pandemic levels.

@grizzly