# dashboards

created_on: 2017-08-16 11:16:26.975644
changed_on: 2017-08-16 13:10:43.087996
dashboard_title: World's Bank Data
slug: world_health
position_json:
  [
      {
          "col": 1, 
          "row": 0, 
          "size_x": 2, 
          "size_y": 2, 
          "slice_id": "4"
      }, 
      {
          "col": 1, 
          "row": 2, 
          "size_x": 2, 
          "size_y": 2, 
          "slice_id": "5"
      }, 
      {
          "col": 10, 
          "row": 0, 
          "size_x": 3, 
          "size_y": 7, 
          "slice_id": "6"
      }, 
      {
          "col": 1, 
          "row": 4, 
          "size_x": 6, 
          "size_y": 3, 
          "slice_id": "7"
      }, 
      {
          "col": 3, 
          "row": 0, 
          "size_x": 7, 
          "size_y": 4, 
          "slice_id": "8"
      }, 
      {
          "col": 5, 
          "row": 7, 
          "size_x": 8, 
          "size_y": 4, 
          "slice_id": "9"
      }, 
      {
          "col": 7, 
          "row": 4, 
          "size_x": 3, 
          "size_y": 3, 
          "slice_id": "10"
      }, 
      {
          "col": 1, 
          "row": 7, 
          "size_x": 4, 
          "size_y": 4, 
          "slice_id": "11"
      }, 
      {
          "col": 9, 
          "row": 11, 
          "size_x": 4, 
          "size_y": 4, 
          "slice_id": "12"
      }, 
      {
          "col": 1, 
          "row": 11, 
          "size_x": 8, 
          "size_y": 4, 
          "slice_id": "13"
      }
  ]

json_metadata:
  {
      "filter_immune_slices": [], 
      "expanded_slices": {}, 
      "filter_immune_slice_fields": {}
  }


created_on: 2017-08-16 11:16:28.942390
changed_on: 2017-08-16 11:16:28.942409
dashboard_title: Births
slug: births
position_json:
  [
      {
          "size_y": 4, 
          "size_x": 2, 
          "col": 9, 
          "slice_id": "15", 
          "row": 6
      }, 
      {
          "size_y": 4, 
          "size_x": 2, 
          "col": 11, 
          "slice_id": "16", 
          "row": 6
      }, 
      {
          "size_y": 2, 
          "size_x": 2, 
          "col": 1, 
          "slice_id": "17", 
          "row": 0
      }, 
      {
          "size_y": 2, 
          "size_x": 2, 
          "col": 3, 
          "slice_id": "18", 
          "row": 0
      }, 
      {
          "size_y": 3, 
          "size_x": 8, 
          "col": 5, 
          "slice_id": "19", 
          "row": 3
      }, 
      {
          "size_y": 4, 
          "size_x": 8, 
          "col": 1, 
          "slice_id": "20", 
          "row": 6
      }, 
      {
          "size_y": 3, 
          "size_x": 3, 
          "col": 10, 
          "slice_id": "21", 
          "row": 0
      }, 
      {
          "size_y": 3, 
          "size_x": 5, 
          "col": 5, 
          "slice_id": "22", 
          "row": 0
      }, 
      {
          "size_y": 4, 
          "size_x": 4, 
          "col": 1, 
          "slice_id": "23", 
          "row": 2
      }
  ]


created_on: 2017-08-16 11:16:37.493749
changed_on: 2017-08-16 11:16:37.493762
dashboard_title: Misc Charts
slug: misc_charts
position_json:
  [
      {
          "size_y": 4, 
          "size_x": 6, 
          "col": 1, 
          "slice_id": "1", 
          "row": 7
      }, 
      {
          "size_y": 5, 
          "size_x": 6, 
          "col": 1, 
          "slice_id": "2", 
          "row": 2
      }, 
      {
          "size_y": 4, 
          "size_x": 6, 
          "col": 7, 
          "slice_id": "3", 
          "row": 2
      }, 
      {
          "size_y": 2, 
          "size_x": 4, 
          "col": 9, 
          "slice_id": "14", 
          "row": 0
      }, 
      {
          "size_y": 5, 
          "size_x": 6, 
          "col": 7, 
          "slice_id": "27", 
          "row": 6
      }, 
      {
          "size_y": 2, 
          "size_x": 8, 
          "col": 1, 
          "slice_id": "28", 
          "row": 0
      }
  ]


# slices

created_on: 2017-08-16 11:16:02.513196
changed_on: 2017-08-16 11:16:02.513210
slice_name: Energy Sankey
datasource_type: table
viz_type: sankey
perm: [main].[energy_usage](id:1)
params:
  {
      "collapsed_fieldsets": "",
      "groupby": [
          "source",
          "target"
      ],
      "having": "",
      "metric": "sum__value",
      "row_limit": "5000",
      "slice_name": "Energy Sankey",
      "viz_type": "sankey",
      "where": ""
  }


created_on: 2017-08-16 11:16:02.522105
changed_on: 2017-08-16 11:16:02.522118
slice_name: Energy Force Layout
datasource_type: table
viz_type: directed_force
perm: [main].[energy_usage](id:1)
params:
  {
      "charge": "-500",
      "collapsed_fieldsets": "",
      "groupby": [
          "source",
          "target"
      ],
      "having": "",
      "link_length": "200",
      "metric": "sum__value",
      "row_limit": "5000",
      "slice_name": "Force",
      "viz_type": "directed_force",
      "where": ""
  }


created_on: 2017-08-16 11:16:02.532147
changed_on: 2017-08-16 11:16:02.532161
slice_name: Heatmap
datasource_type: table
viz_type: heatmap
perm: [main].[energy_usage](id:1)
params:
  {
      "all_columns_x": "source",
      "all_columns_y": "target",
      "canvas_image_rendering": "pixelated",
      "collapsed_fieldsets": "",
      "having": "",
      "linear_color_scheme": "blue_white_yellow",
      "metric": "sum__value",
      "normalize_across": "heatmap",
      "slice_name": "Heatmap",
      "viz_type": "heatmap",
      "where": "",
      "xscale_interval": "1",
      "yscale_interval": "1"
  }


created_on: 2017-08-16 11:16:26.825480
changed_on: 2017-08-16 11:16:26.825494
slice_name: Region Filter
datasource_type: table
viz_type: filter_box
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [
          "region", 
          "country_name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "2014-01-01", 
      "until": "2014-01-02", 
      "viz_type": "filter_box", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.843635
changed_on: 2017-08-16 11:16:26.843649
slice_name: World's Population
datasource_type: table
viz_type: big_number
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "over 10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "2000", 
      "until": "2014-01-02", 
      "viz_type": "big_number", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.853962
changed_on: 2017-08-16 11:16:26.853975
slice_name: Most Populated Countries
datasource_type: table
viz_type: table
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [
          "country_name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "2014-01-01", 
      "until": "2014-01-02", 
      "viz_type": "table", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.866438
changed_on: 2017-08-16 11:16:26.866451
slice_name: Growth Rate
datasource_type: table
viz_type: line
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [
          "country_name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "num_period_compare": "10", 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "1960-01-01", 
      "until": "2014-01-02", 
      "viz_type": "line", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.878589
changed_on: 2017-08-16 11:16:26.878602
slice_name: % Rural
datasource_type: table
viz_type: world_map
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_RUR_TOTL_ZS", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "num_period_compare": "10", 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "2014-01-01", 
      "until": "2014-01-02", 
      "viz_type": "world_map", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.887898
changed_on: 2017-08-16 11:16:26.887910
slice_name: Life Expectancy VS Rural %
datasource_type: table
viz_type: bubble
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_name", 
      "filters": [
          {
              "col": "country_code", 
              "op": "not in", 
              "val": [
                  "TCA", 
                  "MNP", 
                  "DMA", 
                  "MHL", 
                  "MCO", 
                  "SXM", 
                  "CYM", 
                  "TUV", 
                  "IMY", 
                  "KNA", 
                  "ASM", 
                  "ADO", 
                  "AMA", 
                  "PLW"
              ]
          }
      ], 
      "granularity": "year", 
      "groupby": [], 
      "limit": 0, 
      "markup_type": "markdown", 
      "max_bubble_size": "50", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "series": "region", 
      "show_bubbles": true, 
      "since": "2011-01-01", 
      "size": "sum__SP_POP_TOTL", 
      "until": "2011-01-02", 
      "viz_type": "bubble", 
      "where": "", 
      "x": "sum__SP_RUR_TOTL_ZS", 
      "y": "sum__SP_DYN_LE00_IN"
  }


created_on: 2017-08-16 11:16:26.897465
changed_on: 2017-08-16 11:16:26.897478
slice_name: Rural Breakdown
datasource_type: table
viz_type: sunburst
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [
          "region", 
          "country_name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_RUR_TOTL", 
      "show_bubbles": true, 
      "since": "2011-01-01", 
      "until": "2011-01-01", 
      "viz_type": "sunburst", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.906745
changed_on: 2017-08-16 11:16:26.906757
slice_name: World's Pop Growth
datasource_type: table
viz_type: area
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [
          "region"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "1960-01-01", 
      "until": "now", 
      "viz_type": "area", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.921134
changed_on: 2017-08-16 11:16:26.921147
slice_name: Box plot
datasource_type: table
viz_type: box_plot
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [
          "region"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "1960-01-01", 
      "until": "now", 
      "viz_type": "box_plot", 
      "where": "", 
      "whisker_options": "Min/max (no outliers)"
  }


created_on: 2017-08-16 11:16:26.931084
changed_on: 2017-08-16 11:16:26.931097
slice_name: Treemap
datasource_type: table
viz_type: treemap
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [
          "region", 
          "country_code"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "show_bubbles": true, 
      "since": "1960-01-01", 
      "until": "now", 
      "viz_type": "treemap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:26.943229
changed_on: 2017-08-16 11:16:26.943241
slice_name: Parallel Coordinates
datasource_type: table
viz_type: para
perm: [main].[wb_health_population](id:2)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "country_fieldtype": "cca3", 
      "entity": "country_code", 
      "granularity": "year", 
      "groupby": [], 
      "limit": 100, 
      "markup_type": "markdown", 
      "metric": "sum__SP_POP_TOTL", 
      "metrics": [
          "sum__SP_POP_TOTL", 
          "sum__SP_RUR_TOTL_ZS", 
          "sum__SH_DYN_AIDS"
      ], 
      "row_limit": 50000, 
      "secondary_metric": "sum__SP_POP_TOTL", 
      "series": "country_name", 
      "show_bubbles": true, 
      "since": "2011-01-01", 
      "until": "2011-01-01", 
      "viz_type": "para", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.810381
changed_on: 2017-08-16 11:16:28.810393
slice_name: Girls
datasource_type: table
viz_type: table
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "filters": [
          {
              "col": "gender", 
              "op": "in", 
              "val": [
                  "girl"
              ]
          }
      ], 
      "granularity": "ds", 
      "groupby": [
          "name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "table", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.819781
changed_on: 2017-08-16 11:16:28.819796
slice_name: Boys
datasource_type: table
viz_type: table
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "filters": [
          {
              "col": "gender", 
              "op": "in", 
              "val": [
                  "boy"
              ]
          }
      ], 
      "granularity": "ds", 
      "groupby": [
          "name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "table", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.830129
changed_on: 2017-08-16 11:16:28.830141
slice_name: Participants
datasource_type: table
viz_type: big_number
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "5", 
      "compare_suffix": "over 5Y", 
      "granularity": "ds", 
      "groupby": [], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50000, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "big_number", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.843728
changed_on: 2017-08-16 11:16:28.843740
slice_name: Genders
datasource_type: table
viz_type: pie
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "granularity": "ds", 
      "groupby": [
          "gender"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50000, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "pie", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.851695
changed_on: 2017-08-16 11:16:28.851708
slice_name: Genders by State
datasource_type: table
viz_type: dist_bar
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "filters": [
          {
              "col": "state", 
              "op": "not in", 
              "val": [
                  "other"
              ]
          }
      ], 
      "granularity": "ds", 
      "groupby": [
          "state"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__sum_girls", 
          "sum__sum_boys"
      ], 
      "row_limit": 50000, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "dist_bar", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.862081
changed_on: 2017-08-16 11:16:28.862094
slice_name: Trends
datasource_type: table
viz_type: line
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "granularity": "ds", 
      "groupby": [
          "name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "rich_tooltip": true, 
      "row_limit": 50000, 
      "show_legend": true, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "line", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.871514
changed_on: 2017-08-16 11:16:28.871526
slice_name: Average and Sum Trends
datasource_type: table
viz_type: dual_line
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "granularity": "ds", 
      "groupby": [], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "avg__num", 
      "metric_2": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50000, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "dual_line", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.881270
changed_on: 2017-08-16 11:16:28.881282
slice_name: Title
datasource_type: table
viz_type: markup
perm: [main].[birth_names](id:3)
params:
  {
      "code": "<div style=\"text-align:center\">\n    <h1>Birth Names Dashboard</h1>\n    <p>\n        The source dataset came from\n        <a href=\"https://github.com/hadley/babynames\">[here]</a>\n    </p>\n    <img src=\"/static/assets/images/babytux.jpg\">\n</div>\n", 
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "granularity": "ds", 
      "groupby": [], 
      "limit": "25", 
      "markup_type": "html", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50000, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "markup", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.890414
changed_on: 2017-08-16 11:16:28.890437
slice_name: Name Cloud
datasource_type: table
viz_type: word_cloud
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "granularity": "ds", 
      "groupby": [], 
      "limit": "100", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "rotation": "square", 
      "row_limit": 50000, 
      "series": "name", 
      "since": "100 years ago", 
      "size_from": "10", 
      "size_to": "70", 
      "until": "now", 
      "viz_type": "word_cloud", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.902538
changed_on: 2017-08-16 11:16:28.902551
slice_name: Pivot Table
datasource_type: table
viz_type: pivot_table
perm: [main].[birth_names](id:3)
params:
  {
      "columns": [
          "state"
      ], 
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "granularity": "ds", 
      "groupby": [
          "name"
      ], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50000, 
      "since": "100 years ago", 
      "until": "now", 
      "viz_type": "pivot_table", 
      "where": ""
  }


created_on: 2017-08-16 11:16:28.912137
changed_on: 2017-08-16 11:16:28.912149
slice_name: Number of Girls
datasource_type: table
viz_type: big_number_total
perm: [main].[birth_names](id:3)
params:
  {
      "compare_lag": "10", 
      "compare_suffix": "o10Y", 
      "filters": [
          {
              "col": "gender", 
              "op": "in", 
              "val": [
                  "girl"
              ]
          }
      ], 
      "granularity": "ds", 
      "groupby": [], 
      "limit": "25", 
      "markup_type": "markdown", 
      "metric": "sum__num", 
      "metrics": [
          "sum__num"
      ], 
      "row_limit": 50000, 
      "since": "100 years ago", 
      "subheader": "total female participants", 
      "until": "now", 
      "viz_type": "big_number_total", 
      "where": ""
  }


created_on: 2017-08-16 11:16:29.511041
changed_on: 2017-08-16 11:16:29.511055
slice_name: Calendar Heatmap
datasource_type: table
viz_type: cal_heatmap
perm: [main].[random_time_series](id:4)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:36.407435
changed_on: 2017-08-16 11:16:36.407447
slice_name: Mapbox Long/Lat
datasource_type: table
viz_type: mapbox
perm: [main].[long_lat](id:5)
params:
  {
      "all_columns": [
          "occupancy"
      ], 
      "all_columns_x": "LON", 
      "all_columns_y": "LAT", 
      "granularity": "day", 
      "mapbox_style": "mapbox://styles/mapbox/light-v9", 
      "row_limit": 500000, 
      "since": "2014-01-01", 
      "until": "now", 
      "viz_type": "mapbox", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.103112
changed_on: 2017-08-16 11:16:37.103125
slice_name: Birth in France by department in 2016
datasource_type: table
viz_type: country_map
perm: [main].[birth_france_by_region](id:6)
params:
  {
      "entity": "DEPT_ID", 
      "granularity": "", 
      "metric": "avg__2004", 
      "row_limit": 500000, 
      "since": "", 
      "until": "", 
      "viz_type": "country_map", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.379028
changed_on: 2017-08-16 11:16:37.379041
slice_name: Calendar Heatmap multiformat 0
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "ds", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.389797
changed_on: 2017-08-16 11:16:37.389810
slice_name: Calendar Heatmap multiformat 1
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "ds2", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.403785
changed_on: 2017-08-16 11:16:37.403797
slice_name: Calendar Heatmap multiformat 2
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "epoch_ms", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.415088
changed_on: 2017-08-16 11:16:37.415101
slice_name: Calendar Heatmap multiformat 3
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "epoch_s", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.427419
changed_on: 2017-08-16 11:16:37.427433
slice_name: Calendar Heatmap multiformat 4
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "string0", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.437931
changed_on: 2017-08-16 11:16:37.437943
slice_name: Calendar Heatmap multiformat 5
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "string1", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.450317
changed_on: 2017-08-16 11:16:37.450330
slice_name: Calendar Heatmap multiformat 6
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "string2", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


created_on: 2017-08-16 11:16:37.471365
changed_on: 2017-08-16 11:16:37.471378
slice_name: Calendar Heatmap multiformat 7
datasource_type: table
viz_type: cal_heatmap
perm: [main].[multiformat_time_series](id:7)
params:
  {
      "domain_granularity": "month", 
      "granularity": "day", 
      "granularity_sqla": "string3", 
      "metric": "count", 
      "row_limit": 50000, 
      "since": "1 year ago", 
      "subdomain_granularity": "day", 
      "until": "now", 
      "viz_type": "cal_heatmap", 
      "where": ""
  }


# logs

action: explore_json
dttm: 2017-08-16 03:22:58.271794
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/misc_charts/
json:
  {"form_data": "{\"datasource\":\"1__table\",\"viz_type\":\"sankey\",\"slice_id\":1,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"7 days ago\",\"until\":\"now\",\"groupby\":[\"source\",\"target\"],\"metric\":\"sum__value\",\"row_limit\":\"5000\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "1", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 03:22:58.420546
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/misc_charts/
json:
  {"form_data": "{\"datasource\":\"7__table\",\"viz_type\":\"cal_heatmap\",\"slice_id\":36,\"granularity_sqla\":\"string3\",\"time_grain_sqla\":null,\"since\":\"1 year ago\",\"until\":\"now\",\"metric\":\"count\",\"domain_granularity\":\"month\",\"subdomain_granularity\":\"day\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "7", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 03:22:58.446610
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/misc_charts/
json:
  {"form_data": "{\"datasource\":\"6__table\",\"viz_type\":\"country_map\",\"slice_id\":28,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"\",\"until\":\"\",\"select_country\":\"France\",\"entity\":\"DEPT_ID\",\"metric\":\"avg__2004\",\"linear_color_scheme\":\"blue_white_yellow\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "6", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 03:22:58.916173
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/misc_charts/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"para\",\"slice_id\":14,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2011-01-01\",\"until\":\"2011-01-01\",\"series\":\"country_name\",\"metrics\":[\"sum__SP_POP_TOTL\",\"sum__SP_RUR_TOTL_ZS\",\"sum__SH_DYN_AIDS\"],\"secondary_metric\":\"sum__SP_POP_TOTL\",\"limit\":100,\"show_datatable\":false,\"include_series\":false,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 03:23:00.690592
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/misc_charts/
json:
  {"form_data": "{\"datasource\":\"1__table\",\"viz_type\":\"directed_force\",\"slice_id\":2,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"7 days ago\",\"until\":\"now\",\"groupby\":[\"source\",\"target\"],\"metric\":\"sum__value\",\"row_limit\":\"5000\",\"link_length\":\"200\",\"charge\":\"-500\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "1", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 03:23:02.156922
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/misc_charts/
json:
  {"form_data": "{\"datasource\":\"1__table\",\"viz_type\":\"heatmap\",\"slice_id\":3,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"7 days ago\",\"until\":\"now\",\"all_columns_x\":\"source\",\"all_columns_y\":\"target\",\"metric\":\"sum__value\",\"linear_color_scheme\":\"blue_white_yellow\",\"xscale_interval\":\"1\",\"yscale_interval\":\"1\",\"canvas_image_rendering\":\"pixelated\",\"normalize_across\":\"heatmap\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "1", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 03:23:05.079487
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/misc_charts/
json:
  {"form_data": "{\"datasource\":\"5__table\",\"viz_type\":\"mapbox\",\"slice_id\":27,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2014-01-01\",\"until\":\"now\",\"all_columns_x\":\"LON\",\"all_columns_y\":\"LAT\",\"clustering_radius\":\"60\",\"row_limit\":500000,\"groupby\":[],\"render_while_dragging\":true,\"point_radius\":\"Auto\",\"point_radius_unit\":\"Pixels\",\"mapbox_label\":[],\"pandas_aggfunc\":\"sum\",\"mapbox_style\":\"mapbox://styles/mapbox/light-v9\",\"global_opacity\":1,\"mapbox_color\":\"rgb(0, 122, 135)\",\"viewport_longitude\":-122.405293,\"viewport_latitude\":37.772123,\"viewport_zoom\":11,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "5", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:24.188763
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"world_map\",\"slice_id\":8,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2014-01-01\",\"until\":\"2014-01-02\",\"entity\":\"country_code\",\"country_fieldtype\":\"cca3\",\"metric\":\"sum__SP_RUR_TOTL_ZS\",\"show_bubbles\":true,\"secondary_metric\":\"sum__SP_POP_TOTL\",\"max_bubble_size\":\"25\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:24.318619
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"sunburst\",\"slice_id\":10,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2011-01-01\",\"until\":\"2011-01-01\",\"groupby\":[\"region\",\"country_name\"],\"metric\":\"sum__SP_POP_TOTL\",\"secondary_metric\":\"sum__SP_RUR_TOTL\",\"row_limit\":50000,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:25.253556
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"bubble\",\"slice_id\":9,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2011-01-01\",\"until\":\"2011-01-02\",\"series\":\"region\",\"entity\":\"country_name\",\"size\":\"sum__SP_POP_TOTL\",\"limit\":0,\"show_legend\":true,\"max_bubble_size\":\"50\",\"x\":\"sum__SP_RUR_TOTL_ZS\",\"x_axis_format\":\".3s\",\"x_axis_label\":\"\",\"x_log_scale\":false,\"y\":\"sum__SP_DYN_LE00_IN\",\"y_axis_format\":\".3s\",\"y_axis_label\":\"\",\"y_log_scale\":false,\"where\":\"\",\"having\":\"\",\"filters\":[{\"col\":\"country_code\",\"val\":[\"TCA\",\"MNP\",\"DMA\",\"MHL\",\"MCO\",\"SXM\",\"CYM\",\"TUV\",\"IMY\",\"KNA\",\"ASM\",\"ADO\",\"AMA\",\"PLW\"],\"op\":\"not in\"}],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:25.330245
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"big_number\",\"slice_id\":5,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2000\",\"until\":\"2014-01-02\",\"metric\":\"sum__SP_POP_TOTL\",\"compare_lag\":\"10\",\"compare_suffix\":\"over 10Y\",\"y_axis_format\":\".3s\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:25.598046
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"treemap\",\"slice_id\":13,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\",\"country_code\"],\"treemap_ratio\":1.618033988749895,\"number_format\":\".3s\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:26.832617
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"area\",\"slice_id\":11,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\"],\"limit\":\"25\",\"timeseries_limit_metric\":null,\"show_brush\":false,\"show_legend\":true,\"line_interpolation\":\"linear\",\"stacked_style\":\"stack\",\"rich_tooltip\":true,\"contribution\":false,\"show_controls\":false,\"x_axis_format\":\"smart_date\",\"x_axis_showminmax\":true,\"y_axis_format\":\".3s\",\"y_axis_bounds\":[null,null],\"y_log_scale\":false,\"rolling_type\":\"None\",\"time_compare\":null,\"num_period_compare\":\"\",\"period_ratio_type\":\"growth\",\"resample_how\":null,\"resample_rule\":null,\"resample_fillmethod\":null,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:26.848083
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"table\",\"slice_id\":6,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2014-01-01\",\"until\":\"2014-01-02\",\"groupby\":[\"country_name\"],\"metrics\":[\"sum__SP_POP_TOTL\"],\"include_time\":false,\"all_columns\":[],\"order_by_cols\":[],\"table_timestamp_format\":\"%Y-%m-%d %H:%M:%S\",\"row_limit\":50000,\"page_length\":0,\"include_search\":false,\"table_filter\":false,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:27.336391
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"line\",\"slice_id\":7,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"2014-01-02\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"country_name\"],\"limit\":\"25\",\"timeseries_limit_metric\":null,\"show_brush\":false,\"show_legend\":true,\"rich_tooltip\":true,\"show_markers\":false,\"x_axis_showminmax\":true,\"line_interpolation\":\"linear\",\"contribution\":false,\"x_axis_label\":\"\",\"x_axis_format\":\"smart_date\",\"y_axis_label\":\"\",\"y_axis_bounds\":[null,null],\"y_axis_format\":\".3s\",\"y_log_scale\":false,\"rolling_type\":\"None\",\"time_compare\":null,\"num_period_compare\":\"10\",\"period_ratio_type\":\"growth\",\"resample_how\":null,\"resample_rule\":null,\"resample_fillmethod\":null,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:27.413438
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"box_plot\",\"slice_id\":12,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\"],\"limit\":\"25\",\"whisker_options\":\"Min/max (no outliers)\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:06:28.261957
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"filter_box\",\"slice_id\":4,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2014-01-01\",\"until\":\"2014-01-02\",\"date_filter\":false,\"instant_filtering\":true,\"groupby\":[\"region\",\"country_name\"],\"metric\":\"sum__SP_POP_TOTL\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[]}", "datasource_id": "2", "datasource_type": "table"}


action: explore_json
dttm: 2017-08-16 05:07:26.447586
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
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action: explore_json
dttm: 2017-08-16 05:07:26.610283
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
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action: explore_json
dttm: 2017-08-16 05:07:26.836529
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
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action: explore_json
dttm: 2017-08-16 05:07:26.871633
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
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action: explore_json
dttm: 2017-08-16 05:07:27.134279
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
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action: explore_json
dttm: 2017-08-16 05:07:27.141339
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
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action: explore_json
dttm: 2017-08-16 05:07:27.484395
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
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action: explore_json
dttm: 2017-08-16 05:07:27.488313
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
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action: explore_json
dttm: 2017-08-16 05:07:27.650145
dt: 2017-08-16
referrer: http://39.108.119.12:8088/superset/dashboard/world_health/
json:
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action: explore_json
dttm: 2017-08-16 05:07:31.339033
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:31.353459
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:31.833864
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:32.016008
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 216 characters]


action: explore_json
dttm: 2017-08-16 05:07:32.119084
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 216 characters]


action: explore_json
dttm: 2017-08-16 05:07:32.309524
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 216 characters]


action: explore_json
dttm: 2017-08-16 05:07:32.629087
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 216 characters]


action: explore_json
dttm: 2017-08-16 05:07:32.680417
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 216 characters]


action: explore_json
dttm: 2017-08-16 05:07:32.841175
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 216 characters]


action: explore_json
dttm: 2017-08-16 05:07:34.525557
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:34.603693
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:34.880107
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:35.027508
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 162 characters]


action: explore_json
dttm: 2017-08-16 05:07:35.120762
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 162 characters]


action: explore_json
dttm: 2017-08-16 05:07:35.495798
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 162 characters]


action: explore_json
dttm: 2017-08-16 05:07:35.512014
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:35.838800
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:07:35.918341
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 162 characters]


action: explore_json
dttm: 2017-08-16 05:08:10.810306
dt: 2017-08-16
json:
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referrer:
  http://39.108.119.12:8088/superset/dashboard/world_health/?preselect_filters=%7B%0A%20%20%224%22%3A%20%7B%0A%20%20%20%20%22region%22%3A%20%5B%0A%20%20%20%20%20%20%22East%20Asia%20%26%20Pacific%22%0A%20%20%20%20%5D%0A%20%20%7D%0A%7D


action: explore_json
dttm: 2017-08-16 05:08:10.822753
dt: 2017-08-16
json:
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referrer:
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action: explore_json
dttm: 2017-08-16 05:08:11.096096
dt: 2017-08-16
json:
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referrer:
  http://39.108.119.12:8088/superset/dashboard/world_health/?preselect_filters=%7B%0A%20%20%224%22%3A%20%7B%0A%20%20%20%20%22region%22%3A%20%5B%0A%20%20%20%20%20%20%22East%20Asia%20%26%20Pacific%22%0A%20%20%20%20%5D%0A%20%20%7D%0A%7D


action: explore_json
dttm: 2017-08-16 05:08:11.253369
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 231 characters]


action: explore_json
dttm: 2017-08-16 05:08:11.361618
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"bubble\",\"slice_id\":9,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2011-01-01\",\"until\":\"2011-01-02\",\"series\":\"region\",\"entity\":\"country_name\",\"size\":\"sum__SP_POP_TOTL\",\"limit\":0,\"show_legend\":true,\"max_bubble_size\":\"50\",\"x\":\"sum__SP_RUR_TOTL_ZS\",\"x_axis_format\":\".3s\",\"x_axis_label\":\"\",\"x_log_scale\":false,\"y\":\"sum__SP_DYN_LE00_IN\",\"y_axis_format\":\".3s\",\"y_axis_label\":\"\",\"y_log_scale\":false,\"where\":\"\",\"having\":\"\",\"filters\":[{\"col\":\"country_code\",\"val\":[\"TCA\",\"MNP\",\"DMA\",\"MHL\",\"MCO\",\"SXM\",\"CYM\",\"TUV\",\"IMY\",\"KNA\",\"ASM\",\"ADO\",\"AMA\",\"PLW\"],\"op\":\"not in\"}],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": [\n      \"East Asia & Pacific\"\n    ]\n  }\n}"}

referrer:
[cell repeats an earlier one in this file: 231 characters]


action: explore_json
dttm: 2017-08-16 05:08:11.525054
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"sunburst\",\"slice_id\":10,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2011-01-01\",\"until\":\"2011-01-01\",\"groupby\":[\"region\",\"country_name\"],\"metric\":\"sum__SP_POP_TOTL\",\"secondary_metric\":\"sum__SP_RUR_TOTL\",\"row_limit\":50000,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": [\n      \"East Asia & Pacific\"\n    ]\n  }\n}"}

referrer:
[cell repeats an earlier one in this file: 231 characters]


action: explore_json
dttm: 2017-08-16 05:08:11.877407
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"area\",\"slice_id\":11,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\"],\"limit\":\"25\",\"timeseries_limit_metric\":null,\"show_brush\":false,\"show_legend\":true,\"line_interpolation\":\"linear\",\"stacked_style\":\"stack\",\"rich_tooltip\":true,\"contribution\":false,\"show_controls\":false,\"x_axis_format\":\"smart_date\",\"x_axis_showminmax\":true,\"y_axis_format\":\".3s\",\"y_axis_bounds\":[null,null],\"y_log_scale\":false,\"rolling_type\":\"None\",\"time_compare\":null,\"num_period_compare\":\"\",\"period_ratio_type\":\"growth\",\"resample_how\":null,\"resample_rule\":null,\"resample_fillmethod\":null,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": [\n      \"East Asia & Pacific\"\n    ]\n  }\n}"}

referrer:
[cell repeats an earlier one in this file: 231 characters]


action: explore_json
dttm: 2017-08-16 05:08:11.907099
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"box_plot\",\"slice_id\":12,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\"],\"limit\":\"25\",\"whisker_options\":\"Min/max (no outliers)\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": [\n      \"East Asia & Pacific\"\n    ]\n  }\n}"}

referrer:
[cell repeats an earlier one in this file: 231 characters]


action: explore_json
dttm: 2017-08-16 05:08:12.063104
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 231 characters]


action: explore_json
dttm: 2017-08-16 05:09:45.475938
dt: 2017-08-16
json:
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referrer:
[cell repeats an earlier one in this file: 162 characters]


action: explore_json
dttm: 2017-08-16 05:09:46.672969
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"box_plot\",\"slice_id\":12,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\"],\"limit\":\"25\",\"whisker_options\":\"Min/max (no outliers)\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
[cell repeats an earlier one in this file: 162 characters]


action: explore_json
dttm: 2017-08-16 05:09:47.388321
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"treemap\",\"slice_id\":13,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\",\"country_code\"],\"treemap_ratio\":1.618033988749895,\"number_format\":\".3s\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
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action: explore_json
dttm: 2017-08-16 05:09:47.451937
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"big_number\",\"slice_id\":5,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2000\",\"until\":\"2014-01-02\",\"metric\":\"sum__SP_POP_TOTL\",\"compare_lag\":\"10\",\"compare_suffix\":\"over 10Y\",\"y_axis_format\":\".3s\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
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action: explore_json
dttm: 2017-08-16 05:09:47.937485
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"world_map\",\"slice_id\":8,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2014-01-01\",\"until\":\"2014-01-02\",\"entity\":\"country_code\",\"country_fieldtype\":\"cca3\",\"metric\":\"sum__SP_RUR_TOTL_ZS\",\"show_bubbles\":true,\"secondary_metric\":\"sum__SP_POP_TOTL\",\"max_bubble_size\":\"25\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
[cell repeats an earlier one in this file: 162 characters]


action: explore_json
dttm: 2017-08-16 05:09:48.238670
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"table\",\"slice_id\":6,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2014-01-01\",\"until\":\"2014-01-02\",\"groupby\":[\"country_name\"],\"metrics\":[\"sum__SP_POP_TOTL\"],\"include_time\":false,\"all_columns\":[],\"order_by_cols\":[],\"table_timestamp_format\":\"%Y-%m-%d %H:%M:%S\",\"row_limit\":50000,\"page_length\":0,\"include_search\":false,\"table_filter\":false,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
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action: explore_json
dttm: 2017-08-16 05:09:48.695761
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"filter_box\",\"slice_id\":4,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2014-01-01\",\"until\":\"2014-01-02\",\"date_filter\":false,\"instant_filtering\":true,\"groupby\":[\"region\",\"country_name\"],\"metric\":\"sum__SP_POP_TOTL\",\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
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action: explore_json
dttm: 2017-08-16 05:09:48.699598
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"sunburst\",\"slice_id\":10,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2011-01-01\",\"until\":\"2011-01-01\",\"groupby\":[\"region\",\"country_name\"],\"metric\":\"sum__SP_POP_TOTL\",\"secondary_metric\":\"sum__SP_RUR_TOTL\",\"row_limit\":50000,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
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action: explore_json
dttm: 2017-08-16 05:09:49.460334
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"area\",\"slice_id\":11,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"1960-01-01\",\"until\":\"now\",\"metrics\":[\"sum__SP_POP_TOTL\"],\"groupby\":[\"region\"],\"limit\":\"25\",\"timeseries_limit_metric\":null,\"show_brush\":false,\"show_legend\":true,\"line_interpolation\":\"linear\",\"stacked_style\":\"stack\",\"rich_tooltip\":true,\"contribution\":false,\"show_controls\":false,\"x_axis_format\":\"smart_date\",\"x_axis_showminmax\":true,\"y_axis_format\":\".3s\",\"y_axis_bounds\":[null,null],\"y_log_scale\":false,\"rolling_type\":\"None\",\"time_compare\":null,\"num_period_compare\":\"\",\"period_ratio_type\":\"growth\",\"resample_how\":null,\"resample_rule\":null,\"resample_fillmethod\":null,\"where\":\"\",\"having\":\"\",\"filters\":[],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
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action: explore_json
dttm: 2017-08-16 05:09:49.569638
dt: 2017-08-16
json:
  {"form_data": "{\"datasource\":\"2__table\",\"viz_type\":\"bubble\",\"slice_id\":9,\"granularity_sqla\":null,\"time_grain_sqla\":null,\"since\":\"2011-01-01\",\"until\":\"2011-01-02\",\"series\":\"region\",\"entity\":\"country_name\",\"size\":\"sum__SP_POP_TOTL\",\"limit\":0,\"show_legend\":true,\"max_bubble_size\":\"50\",\"x\":\"sum__SP_RUR_TOTL_ZS\",\"x_axis_format\":\".3s\",\"x_axis_label\":\"\",\"x_log_scale\":false,\"y\":\"sum__SP_DYN_LE00_IN\",\"y_axis_format\":\".3s\",\"y_axis_label\":\"\",\"y_log_scale\":false,\"where\":\"\",\"having\":\"\",\"filters\":[{\"col\":\"country_code\",\"val\":[\"TCA\",\"MNP\",\"DMA\",\"MHL\",\"MCO\",\"SXM\",\"CYM\",\"TUV\",\"IMY\",\"KNA\",\"ASM\",\"ADO\",\"AMA\",\"PLW\"],\"op\":\"not in\"}],\"extra_filters\":[{\"col\":\"region\",\"op\":\"in\",\"val\":[]}]}", "datasource_id": "2", "force": "true", "datasource_type": "table", "preselect_filters": "{\n  \"4\": {\n    \"region\": []\n  }\n}"}

referrer:
[cell repeats an earlier one in this file: 162 characters]


# css_templates

created_on: 2017-08-16 11:16:02.351631
changed_on: 2017-08-16 11:16:02.351653
template_name: Flat
css:
  .gridster div.widget {
      transition: background-color 0.5s ease;
      background-color: #FAFAFA;
      border: 1px solid #CCC;
      box-shadow: none;
      border-radius: 0px;
  }
  .gridster div.widget:hover {
      border: 1px solid #000;
      background-color: #EAEAEA;
  }
  .navbar {
      transition: opacity 0.5s ease;
      opacity: 0.05;
  }
  .navbar:hover {
      opacity: 1;
  }
  .chart-header .header{
      font-weight: normal;
      font-size: 12px;
  }
  /*
  var bnbColors = [
      //rausch    hackb      kazan      babu      lima        beach     tirol
      '#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
      '#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
      '#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
   ];
  */


created_on: 2017-08-16 11:16:02.358877
changed_on: 2017-08-16 11:16:02.358890
template_name: Courier Black
css:
  .gridster div.widget {
      transition: background-color 0.5s ease;
      background-color: #EEE;
      border: 2px solid #444;
      border-radius: 15px;
      box-shadow: none;
  }
  h2 {
      color: white;
      font-size: 52px;
  }
  .navbar {
      box-shadow: none;
  }
  .gridster div.widget:hover {
      border: 2px solid #000;
      background-color: #EAEAEA;
  }
  .navbar {
      transition: opacity 0.5s ease;
      opacity: 0.05;
  }
  .navbar:hover {
      opacity: 1;
  }
  .chart-header .header{
      font-weight: normal;
      font-size: 12px;
  }
  .nvd3 text {
      font-size: 12px;
      font-family: inherit;
  }
  body{
      background: #000;
      font-family: Courier, Monaco, monospace;;
  }
  /*
  var bnbColors = [
      //rausch    hackb      kazan      babu      lima        beach     tirol
      '#ff5a5f', '#7b0051', '#007A87', '#00d1c1', '#8ce071', '#ffb400', '#b4a76c',
      '#ff8083', '#cc0086', '#00a1b3', '#00ffeb', '#bbedab', '#ffd266', '#cbc29a',
      '#ff3339', '#ff1ab1', '#005c66', '#00b3a5', '#55d12e', '#b37e00', '#988b4e',
   ];
  */


# tables

created_on: 2017-08-16 11:16:12.209892
changed_on: 2017-08-16 11:16:12.210642
table_name: wb_health_population
main_dttm_col: year
perm: [main].[wb_health_population](id:2)
description:
  This data was downloaded from the
  [World's Health Organization's website](http://data.worldbank.org/data-catalog/health-nutrition-and-population-statistics)

  Here's the script that was used to massage the data:

      DIR = ""
      df_country = pd.read_csv(DIR + '/HNP_Country.csv')
      df_country.columns = ['country_code'] + list(df_country.columns[1:])
      df_country = df_country[['country_code', 'Region']]
      df_country.columns = ['country_code', 'region']

      df = pd.read_csv(DIR + '/HNP_Data.csv')
      del df['Unnamed: 60']
      df.columns = ['country_name', 'country_code'] + list(df.columns[2:])
      ndf = df.merge(df_country, how='inner')

      dims = ('country_name', 'country_code', 'region')
      vv = [str(i) for i in range(1960, 2015)]
      mdf = pd.melt(ndf, id_vars=dims + ('Indicator Code',), value_vars=vv)
      mdf['year'] = mdf.variable + '-01-01'
      dims = dims + ('year',)

      pdf = mdf.pivot_table(values='value', columns='Indicator Code', index=dims)
      pdf = pdf.reset_index()
      pdf.to_csv(DIR + '/countries.csv')
      pdf.to_json(DIR + '/countries.json', orient='records')

  Here's the description of the metrics available:

  Series | Code Indicator Name
  --- | ---
  NY.GNP.PCAP.CD | GNI per capita, Atlas method (current US$)
  SE.ADT.1524.LT.FM.ZS | Literacy rate, youth (ages 15-24), gender parity index (GPI)
  SE.ADT.1524.LT.MA.ZS | Literacy rate, youth male (% of males ages 15-24)
  SE.ADT.1524.LT.ZS | Literacy rate, youth total (% of people ages 15-24)
  SE.ADT.LITR.FE.ZS | Literacy rate, adult female (% of females ages 15 and above)
  SE.ADT.LITR.MA.ZS | Literacy rate, adult male (% of males ages 15 and above)
  SE.ADT.LITR.ZS | Literacy rate, adult total (% of people ages 15 and above)
  SE.ENR.ORPH | Ratio of school attendance of orphans to school attendance of non-orphans ages 10-14
  SE.PRM.CMPT.FE.ZS | Primary completion rate, female (% of relevant age group)
  SE.PRM.CMPT.MA.ZS | Primary completion rate, male (% of relevant age group)
  SE.PRM.CMPT.ZS | Primary completion rate, total (% of relevant age group)
  SE.PRM.ENRR | School enrollment, primary (% gross)
  SE.PRM.ENRR.FE | School enrollment, primary, female (% gross)
  SE.PRM.ENRR.MA | School enrollment, primary, male (% gross)
  SE.PRM.NENR | School enrollment, primary (% net)
  SE.PRM.NENR.FE | School enrollment, primary, female (% net)
  SE.PRM.NENR.MA | School enrollment, primary, male (% net)
  SE.SEC.ENRR | School enrollment, secondary (% gross)
  SE.SEC.ENRR.FE | School enrollment, secondary, female (% gross)
  SE.SEC.ENRR.MA | School enrollment, secondary, male (% gross)
  SE.SEC.NENR | School enrollment, secondary (% net)
  SE.SEC.NENR.FE | School enrollment, secondary, female (% net)
  SE.SEC.NENR.MA | School enrollment, secondary, male (% net)
  SE.TER.ENRR | School enrollment, tertiary (% gross)
  SE.TER.ENRR.FE | School enrollment, tertiary, female (% gross)
  SE.XPD.TOTL.GD.ZS | Government expenditure on education, total (% of GDP)
  SH.ANM.CHLD.ZS | Prevalence of anemia among children (% of children under 5)
  SH.ANM.NPRG.ZS | Prevalence of anemia among non-pregnant women (% of women ages 15-49)
  SH.CON.1524.FE.ZS | Condom use, population ages 15-24, female (% of females ages 15-24)
  SH.CON.1524.MA.ZS | Condom use, population ages 15-24, male (% of males ages 15-24)
  SH.CON.AIDS.FE.ZS | Condom use at last high-risk sex, adult female (% ages 15-49)
  SH.CON.AIDS.MA.ZS | Condom use at last high-risk sex, adult male (% ages 15-49)
  SH.DTH.COMM.ZS | Cause of death, by communicable diseases and maternal, prenatal and nutrition conditions (% of total)
  SH.DTH.IMRT | Number of infant deaths
  SH.DTH.INJR.ZS | Cause of death, by injury (% of total)
  SH.DTH.MORT | Number of under-five deaths
  SH.DTH.NCOM.ZS | Cause of death, by non-communicable diseases (% of total)
  SH.DTH.NMRT | Number of neonatal deaths
  SH.DYN.AIDS | Adults (ages 15+) living with HIV
  SH.DYN.AIDS.DH | AIDS estimated deaths (UNAIDS estimates)
  SH.DYN.AIDS.FE.ZS | Women's share of population ages 15+ living with HIV (%)
  SH.DYN.AIDS.ZS | Prevalence of HIV, total (% of population ages 15-49)
  SH.DYN.MORT | Mortality rate, under-5 (per 1,000 live births)
  SH.DYN.MORT.FE | Mortality rate, under-5, female (per 1,000 live births)
  SH.DYN.MORT.MA | Mortality rate, under-5, male (per 1,000 live births)
  SH.DYN.NMRT | Mortality rate, neonatal (per 1,000 live births)
  SH.FPL.SATI.ZS | Met need for contraception (% of married women ages 15-49)
  SH.H2O.SAFE.RU.ZS | Improved water source, rural (% of rural population with access)
  SH.H2O.SAFE.UR.ZS | Improved water source, urban (% of urban population with access)
  SH.H2O.SAFE.ZS | Improved water source (% of population with access)
  SH.HIV.0014 | Children (0-14) living with HIV
  SH.HIV.1524.FE.ZS | Prevalence of HIV, female (% ages 15-24)
  SH.HIV.1524.KW.FE.ZS | Comprehensive correct knowledge of HIV/AIDS, ages 15-24, female (2 prevent ways and reject 3 misconceptions)
  SH.HIV.1524.KW.MA.ZS | Comprehensive correct knowledge of HIV/AIDS, ages 15-24, male (2 prevent ways and reject 3 misconceptions)
  SH.HIV.1524.MA.ZS | Prevalence of HIV, male (% ages 15-24)
  SH.HIV.ARTC.ZS | Antiretroviral therapy coverage (% of people living with HIV)
  SH.HIV.KNOW.FE.ZS | % of females ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions)
  SH.HIV.KNOW.MA.ZS | % of males ages 15-49 having comprehensive correct knowledge about HIV (2 prevent ways and reject 3 misconceptions)
  SH.HIV.ORPH | Children orphaned by HIV/AIDS
  SH.HIV.TOTL | Adults (ages 15+) and children (0-14 years) living with HIV
  SH.IMM.HEPB | Immunization, HepB3 (% of one-year-old children)
  SH.IMM.HIB3 | Immunization, Hib3 (% of children ages 12-23 months)
  SH.IMM.IBCG | Immunization, BCG (% of one-year-old children)
  SH.IMM.IDPT | Immunization, DPT (% of children ages 12-23 months)
  SH.IMM.MEAS | Immunization, measles (% of children ages 12-23 months)
  SH.IMM.POL3 | Immunization, Pol3 (% of one-year-old children)
  SH.MED.BEDS.ZS | Hospital beds (per 1,000 people)
  SH.MED.CMHW.P3 | Community health workers (per 1,000 people)
  SH.MED.NUMW.P3 | Nurses and midwives (per 1,000 people)
  SH.MED.PHYS.ZS | Physicians (per 1,000 people)
  SH.MLR.NETS.ZS | Use of insecticide-treated bed nets (% of under-5 population)
  SH.MLR.PREG.ZS | Use of any antimalarial drug (% of pregnant women)
  SH.MLR.SPF2.ZS | Use of Intermittent Preventive Treatment of malaria, 2+ doses of SP/Fansidar (% of pregnant women)
  SH.MLR.TRET.ZS | Children with fever receiving antimalarial drugs (% of children under age 5 with fever)
  SH.MMR.DTHS | Number of maternal deaths
  SH.MMR.LEVE | Number of weeks of maternity leave
  SH.MMR.RISK | Lifetime risk of maternal death (1 in: rate varies by country)
  SH.MMR.RISK.ZS | Lifetime risk of maternal death (%)
  SH.MMR.WAGE.ZS | Maternal leave benefits (% of wages paid in covered period)
  SH.PRG.ANEM | Prevalence of anemia among pregnant women (%)
  SH.PRG.ARTC.ZS | Antiretroviral therapy coverage (% of pregnant women living with HIV)
  SH.PRG.SYPH.ZS | Prevalence of syphilis (% of women attending antenatal care)
  SH.PRV.SMOK.FE | Smoking prevalence, females (% of adults)
  SH.PRV.SMOK.MA | Smoking prevalence, males (% of adults)
  SH.STA.ACSN | Improved sanitation facilities (% of population with access)
  SH.STA.ACSN.RU | Improved sanitation facilities, rural (% of rural population with access)
  SH.STA.ACSN.UR | Improved sanitation facilities, urban (% of urban population with access)
  SH.STA.ANV4.ZS | Pregnant women receiving prenatal care of at least four visits (% of pregnant women)
  SH.STA.ANVC.ZS | Pregnant women receiving prenatal care (%)
  SH.STA.ARIC.ZS | ARI treatment (% of children under 5 taken to a health provider)
  SH.STA.BFED.ZS | Exclusive breastfeeding (% of children under 6 months)
  SH.STA.BRTC.ZS | Births attended by skilled health staff (% of total)
  SH.STA.BRTW.ZS | Low-birthweight babies (% of births)
  SH.STA.DIAB.ZS | Diabetes prevalence (% of population ages 20 to 79)
  SH.STA.IYCF.ZS | Infant and young child feeding practices, all 3 IYCF (% children ages 6-23 months)
  SH.STA.MALN.FE.ZS | Prevalence of underweight, weight for age, female (% of children under 5)
  SH.STA.MALN.MA.ZS | Prevalence of underweight, weight for age, male (% of children under 5)
  SH.STA.MALN.ZS | Prevalence of underweight, weight for age (% of children under 5)
  SH.STA.MALR | Malaria cases reported
  SH.STA.MMRT | Maternal mortality ratio (modeled estimate, per 100,000 live births)
  SH.STA.MMRT.NE | Maternal mortality ratio (national estimate, per 100,000 live births)
  SH.STA.ORCF.ZS | Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)
  SH.STA.ORTH | Diarrhea treatment (% of children under 5 who received ORS packet)
  SH.STA.OW15.FE.ZS | Prevalence of overweight, female (% of female adults)
  SH.STA.OW15.MA.ZS | Prevalence of overweight, male (% of male adults)
  SH.STA.OW15.ZS | Prevalence of overweight (% of adults)
  SH.STA.OWGH.FE.ZS | Prevalence of overweight, weight for height, female (% of children under 5)
  SH.STA.OWGH.MA.ZS | Prevalence of overweight, weight for height, male (% of children under 5)
  SH.STA.OWGH.ZS | Prevalence of overweight, weight for height (% of children under 5)
  SH.STA.PNVC.ZS | Postnatal care coverage (% mothers)
  SH.STA.STNT.FE.ZS | Prevalence of stunting, height for age, female (% of children under 5)
  SH.STA.STNT.MA.ZS | Prevalence of stunting, height for age, male (% of children under 5)
  SH.STA.STNT.ZS | Prevalence of stunting, height for age (% of children under 5)
  SH.STA.WAST.FE.ZS | Prevalence of wasting, weight for height, female (% of children under 5)
  SH.STA.WAST.MA.ZS | Prevalence of wasting, weight for height, male (% of children under 5)
  SH.STA.WAST.ZS | Prevalence of wasting, weight for height (% of children under 5)
  SH.SVR.WAST.FE.ZS | Prevalence of severe wasting, weight for height, female (% of children under 5)
  SH.SVR.WAST.MA.ZS | Prevalence of severe wasting, weight for height, male (% of children under 5)
  SH.SVR.WAST.ZS | Prevalence of severe wasting, weight for height (% of children under 5)
  SH.TBS.CURE.ZS | Tuberculosis treatment success rate (% of new cases)
  SH.TBS.DTEC.ZS | Tuberculosis case detection rate (%, all forms)
  SH.TBS.INCD | Incidence of tuberculosis (per 100,000 people)
  SH.TBS.MORT | Tuberculosis death rate (per 100,000 people)
  SH.TBS.PREV | Prevalence of tuberculosis (per 100,000 population)
  SH.VAC.TTNS.ZS | Newborns protected against tetanus (%)
  SH.XPD.EXTR.ZS | External resources for health (% of total expenditure on health)
  SH.XPD.OOPC.TO.ZS | Out-of-pocket health expenditure (% of total expenditure on health)
  SH.XPD.OOPC.ZS | Out-of-pocket health expenditure (% of private expenditure on health)
  SH.XPD.PCAP | Health expenditure per capita (current US$)
  SH.XPD.PCAP.PP.KD | Health expenditure per capita, PPP (constant 2011 international $)
  SH.XPD.PRIV | Health expenditure, private (% of total health expenditure)
  SH.XPD.PRIV.ZS | Health expenditure, private (% of GDP)
  SH.XPD.PUBL | Health expenditure, public (% of total health expenditure)
  SH.XPD.PUBL.GX.ZS | Health expenditure, public (% of government expenditure)
  SH.XPD.PUBL.ZS | Health expenditure, public (% of GDP)
  SH.XPD.TOTL.CD | Health expenditure, total (current US$)
  SH.XPD.TOTL.ZS | Health expenditure, total (% of GDP)
  SI.POV.NAHC | Poverty headcount ratio at national poverty lines (% of population)
  SI.POV.RUHC | Rural poverty headcount ratio at national poverty lines (% of rural population)
  SI.POV.URHC | Urban poverty headcount ratio at national poverty lines (% of urban population)
  SL.EMP.INSV.FE.ZS | Share of women in wage employment in the nonagricultural sector (% of total nonagricultural employment)
  SL.TLF.TOTL.FE.ZS | Labor force, female (% of total labor force)
  SL.TLF.TOTL.IN | Labor force, total
  SL.UEM.TOTL.FE.ZS | Unemployment, female (% of female labor force) (modeled ILO estimate)
  SL.UEM.TOTL.MA.ZS | Unemployment, male (% of male labor force) (modeled ILO estimate)
  SL.UEM.TOTL.ZS | Unemployment, total (% of total labor force) (modeled ILO estimate)
  SM.POP.NETM | Net migration
  SN.ITK.DEFC | Number of people who are undernourished
  SN.ITK.DEFC.ZS | Prevalence of undernourishment (% of population)
  SN.ITK.SALT.ZS | Consumption of iodized salt (% of households)
  SN.ITK.VITA.ZS | Vitamin A supplementation coverage rate (% of children ages 6-59 months)
  SP.ADO.TFRT | Adolescent fertility rate (births per 1,000 women ages 15-19)
  SP.DYN.AMRT.FE | Mortality rate, adult, female (per 1,000 female adults)
  SP.DYN.AMRT.MA | Mortality rate, adult, male (per 1,000 male adults)
  SP.DYN.CBRT.IN | Birth rate, crude (per 1,000 people)
  SP.DYN.CDRT.IN | Death rate, crude (per 1,000 people)
  SP.DYN.CONU.ZS | Contraceptive prevalence (% of women ages 15-49)
  SP.DYN.IMRT.FE.IN | Mortality rate, infant, female (per 1,000 live births)
  SP.DYN.IMRT.IN | Mortality rate, infant (per 1,000 live births)
  SP.DYN.IMRT.MA.IN | Mortality rate, infant, male (per 1,000 live births)
  SP.DYN.LE00.FE.IN | Life expectancy at birth, female (years)
  SP.DYN.LE00.IN | Life expectancy at birth, total (years)
  SP.DYN.LE00.MA.IN | Life expectancy at birth, male (years)
  SP.DYN.SMAM.FE | Mean age at first marriage, female
  SP.DYN.SMAM.MA | Mean age at first marriage, male
  SP.DYN.TFRT.IN | Fertility rate, total (births per woman)
  SP.DYN.TO65.FE.ZS | Survival to age 65, female (% of cohort)
  SP.DYN.TO65.MA.ZS | Survival to age 65, male (% of cohort)
  SP.DYN.WFRT | Wanted fertility rate (births per woman)
  SP.HOU.FEMA.ZS | Female headed households (% of households with a female head)
  SP.MTR.1519.ZS | Teenage mothers (% of women ages 15-19 who have had children or are currently pregnant)
  SP.POP.0004.FE | Population ages 0-4, female
  SP.POP.0004.FE.5Y | Population ages 0-4, female (% of female population)
  SP.POP.0004.MA | Population ages 0-4, male
  SP.POP.0004.MA.5Y | Population ages 0-4, male (% of male population)
  SP.POP.0014.FE.ZS | Population ages 0-14, female (% of total)
  SP.POP.0014.MA.ZS | Population ages 0-14, male (% of total)
  SP.POP.0014.TO | Population ages 0-14, total
  SP.POP.0014.TO.ZS | Population ages 0-14 (% of total)
  SP.POP.0509.FE | Population ages 5-9, female
  SP.POP.0509.FE.5Y | Population ages 5-9, female (% of female population)
  SP.POP.0509.MA | Population ages 5-9, male
  SP.POP.0509.MA.5Y | Population ages 5-9, male (% of male population)
  SP.POP.1014.FE | Population ages 10-14, female
  SP.POP.1014.FE.5Y | Population ages 10-14, female (% of female population)
  SP.POP.1014.MA | Population ages 10-14, male
  SP.POP.1014.MA.5Y | Population ages 10-14, male (% of male population)
  SP.POP.1519.FE | Population ages 15-19, female
  SP.POP.1519.FE.5Y | Population ages 15-19, female (% of female population)
  SP.POP.1519.MA | Population ages 15-19, male
  SP.POP.1519.MA.5Y | Population ages 15-19, male (% of male population)
  SP.POP.1564.FE.ZS | Population ages 15-64, female (% of total)
  SP.POP.1564.MA.ZS | Population ages 15-64, male (% of total)
  SP.POP.1564.TO | Population ages 15-64, total
  SP.POP.1564.TO.ZS | Population ages 15-64 (% of total)
  SP.POP.2024.FE | Population ages 20-24, female
  SP.POP.2024.FE.5Y | Population ages 20-24, female (% of female population)
  SP.POP.2024.MA | Population ages 20-24, male
  SP.POP.2024.MA.5Y | Population ages 20-24, male (% of male population)
  SP.POP.2529.FE | Population ages 25-29, female
  SP.POP.2529.FE.5Y | Population ages 25-29, female (% of female population)
  SP.POP.2529.MA | Population ages 25-29, male
  SP.POP.2529.MA.5Y | Population ages 25-29, male (% of male population)
  SP.POP.3034.FE | Population ages 30-34, female
  SP.POP.3034.FE.5Y | Population ages 30-34, female (% of female population)
  SP.POP.3034.MA | Population ages 30-34, male
  SP.POP.3034.MA.5Y | Population ages 30-34, male (% of male population)
  SP.POP.3539.FE | Population ages 35-39, female
  SP.POP.3539.FE.5Y | Population ages 35-39, female (% of female population)
  SP.POP.3539.MA | Population ages 35-39, male
  SP.POP.3539.MA.5Y | Population ages 35-39, male (% of male population)
  SP.POP.4044.FE | Population ages 40-44, female
  SP.POP.4044.FE.5Y | Population ages 40-44, female (% of female population)
  SP.POP.4044.MA | Population ages 40-44, male
  SP.POP.4044.MA.5Y | Population ages 40-44, male (% of male population)
  SP.POP.4549.FE | Population ages 45-49, female
  SP.POP.4549.FE.5Y | Population ages 45-49, female (% of female population)
  SP.POP.4549.MA | Population ages 45-49, male
  SP.POP.4549.MA.5Y | Population ages 45-49, male (% of male population)
  SP.POP.5054.FE | Population ages 50-54, female
  SP.POP.5054.FE.5Y | Population ages 50-54, female (% of female population)
  SP.POP.5054.MA | Population ages 50-54, male
  SP.POP.5054.MA.5Y | Population ages 50-54, male (% of male population)
  SP.POP.5559.FE | Population ages 55-59, female
  SP.POP.5559.FE.5Y | Population ages 55-59, female (% of female population)
  SP.POP.5559.MA | Population ages 55-59, male
  SP.POP.5559.MA.5Y | Population ages 55-59, male (% of male population)
  SP.POP.6064.FE | Population ages 60-64, female
  SP.POP.6064.FE.5Y | Population ages 60-64, female (% of female population)
  SP.POP.6064.MA | Population ages 60-64, male
  SP.POP.6064.MA.5Y | Population ages 60-64, male (% of male population)
  SP.POP.6569.FE | Population ages 65-69, female
  SP.POP.6569.FE.5Y | Population ages 65-69, female (% of female population)
  SP.POP.6569.MA | Population ages 65-69, male
  SP.POP.6569.MA.5Y | Population ages 65-69, male (% of male population)
  SP.POP.65UP.FE.ZS | Population ages 65 and above, female (% of total)
  SP.POP.65UP.MA.ZS | Population ages 65 and above, male (% of total)
  SP.POP.65UP.TO | Population ages 65 and above, total
  SP.POP.65UP.TO.ZS | Population ages 65 and above (% of total)
  SP.POP.7074.FE | Population ages 70-74, female
  SP.POP.7074.FE.5Y | Population ages 70-74, female (% of female population)
  SP.POP.7074.MA | Population ages 70-74, male
  SP.POP.7074.MA.5Y | Population ages 70-74, male (% of male population)
  SP.POP.7579.FE | Population ages 75-79, female
  SP.POP.7579.FE.5Y | Population ages 75-79, female (% of female population)
  SP.POP.7579.MA | Population ages 75-79, male
  SP.POP.7579.MA.5Y | Population ages 75-79, male (% of male population)
  SP.POP.80UP.FE | Population ages 80 and above, female
  SP.POP.80UP.FE.5Y | Population ages 80 and above, female (% of female population)
  SP.POP.80UP.MA | Population ages 80 and above, male
  SP.POP.80UP.MA.5Y | Population ages 80 and above, male (% of male population)
  SP.POP.AG00.FE.IN | Age population, age 0, female, interpolated
  SP.POP.AG00.MA.IN | Age population, age 0, male, interpolated
  SP.POP.AG01.FE.IN | Age population, age 01, female, interpolated
  SP.POP.AG01.MA.IN | Age population, age 01, male, interpolated
  SP.POP.AG02.FE.IN | Age population, age 02, female, interpolated
  SP.POP.AG02.MA.IN | Age population, age 02, male, interpolated
  SP.POP.AG03.FE.IN | Age population, age 03, female, interpolated
  SP.POP.AG03.MA.IN | Age population, age 03, male, interpolated
  SP.POP.AG04.FE.IN | Age population, age 04, female, interpolated
  SP.POP.AG04.MA.IN | Age population, age 04, male, interpolated
  SP.POP.AG05.FE.IN | Age population, age 05, female, interpolated
  SP.POP.AG05.MA.IN | Age population, age 05, male, interpolated
  SP.POP.AG06.FE.IN | Age population, age 06, female, interpolated
  SP.POP.AG06.MA.IN | Age population, age 06, male, interpolated
  SP.POP.AG07.FE.IN | Age population, age 07, female, interpolated
  SP.POP.AG07.MA.IN | Age population, age 07, male, interpolated
  SP.POP.AG08.FE.IN | Age population, age 08, female, interpolated
  SP.POP.AG08.MA.IN | Age population, age 08, male, interpolated
  SP.POP.AG09.FE.IN | Age population, age 09, female, interpolated
  SP.POP.AG09.MA.IN | Age population, age 09, male, interpolated
  SP.POP.AG10.FE.IN | Age population, age 10, female, interpolated
  SP.POP.AG10.MA.IN | Age population, age 10, male
  SP.POP.AG11.FE.IN | Age population, age 11, female, interpolated
  SP.POP.AG11.MA.IN | Age population, age 11, male
  SP.POP.AG12.FE.IN | Age population, age 12, female, interpolated
  SP.POP.AG12.MA.IN | Age population, age 12, male
  SP.POP.AG13.FE.IN | Age population, age 13, female, interpolated
  SP.POP.AG13.MA.IN | Age population, age 13, male
  SP.POP.AG14.FE.IN | Age population, age 14, female, interpolated
  SP.POP.AG14.MA.IN | Age population, age 14, male
  SP.POP.AG15.FE.IN | Age population, age 15, female, interpolated
  SP.POP.AG15.MA.IN | Age population, age 15, male, interpolated
  SP.POP.AG16.FE.IN | Age population, age 16, female, interpolated
  SP.POP.AG16.MA.IN | Age population, age 16, male, interpolated
  SP.POP.AG17.FE.IN | Age population, age 17, female, interpolated
  SP.POP.AG17.MA.IN | Age population, age 17, male, interpolated
  SP.POP.AG18.FE.IN | Age population, age 18, female, interpolated
  SP.POP.AG18.MA.IN | Age population, age 18, male, interpolated
  SP.POP.AG19.FE.IN | Age population, age 19, female, interpolated
  SP.POP.AG19.MA.IN | Age population, age 19, male, interpolated
  SP.POP.AG20.FE.IN | Age population, age 20, female, interpolated
  SP.POP.AG20.MA.IN | Age population, age 20, male, interpolated
  SP.POP.AG21.FE.IN | Age population, age 21, female, interpolated
  SP.POP.AG21.MA.IN | Age population, age 21, male, interpolated
  SP.POP.AG22.FE.IN | Age population, age 22, female, interpolated
  SP.POP.AG22.MA.IN | Age population, age 22, male, interpolated
  SP.POP.AG23.FE.IN | Age population, age 23, female, interpolated
  SP.POP.AG23.MA.IN | Age population, age 23, male, interpolated
  SP.POP.AG24.FE.IN | Age population, age 24, female, interpolated
  SP.POP.AG24.MA.IN | Age population, age 24, male, interpolated
  SP.POP.AG25.FE.IN | Age population, age 25, female, interpolated
  SP.POP.AG25.MA.IN | Age population, age 25, male, interpolated
  SP.POP.BRTH.MF | Sex ratio at birth (male births per female births)
  SP.POP.DPND | Age dependency ratio (% of working-age population)
  SP.POP.DPND.OL | Age dependency ratio, old (% of working-age population)
  SP.POP.DPND.YG | Age dependency ratio, young (% of working-age population)
  SP.POP.GROW | Population growth (annual %)
  SP.POP.TOTL | Population, total
  SP.POP.TOTL.FE.IN | Population, female
  SP.POP.TOTL.FE.ZS | Population, female (% of total)
  SP.POP.TOTL.MA.IN | Population, male
  SP.POP.TOTL.MA.ZS | Population, male (% of total)
  SP.REG.BRTH.RU.ZS | Completeness of birth registration, rural (%)
  SP.REG.BRTH.UR.ZS | Completeness of birth registration, urban (%)
  SP.REG.BRTH.ZS | Completeness of birth registration (%)
  SP.REG.DTHS.ZS | Completeness of death registration with cause-of-death information (%)
  SP.RUR.TOTL | Rural population
  SP.RUR.TOTL.ZG | Rural population growth (annual %)
  SP.RUR.TOTL.ZS | Rural population (% of total population)
  SP.URB.GROW | Urban population growth (annual %)
  SP.URB.TOTL | Urban population
  SP.URB.TOTL.IN.ZS | Urban population (% of total)
  SP.UWT.TFRT | Unmet need for contraception (% of married women ages 15-49)


