In your Data Studio Dashboard, click the "+Create" button at the top left. Select "Data Source".
Select "BigQuery" from the list of Google Connectors.
If this is your first time connecting Data Studio to BigQuery, click the "Authorize" button to authorize the connection.
Select Custom Query -- Your billing project -- and paste in the SQL code below to add data for Washington, D.C. Then click the "add" button to connect. The process of connecting data will be done a total of 3 times if you want to include data from Maryland and Virginia in your dashboard. See the box at the bottom of the page for code for each state.
select
ct.state_fips_code,
ct.county_fips_code,
c.county_name,
ct.tract_ce,
ct.geo_id,
ct.tract_name,
ct.lsad_name,
ct.internal_point_lat,
ct.internal_point_lon,
ct.internal_point_geo,
ct.tract_geom,
acs.total_pop,
acs.households,
acs.male_pop,
acs.female_pop,
acs.median_age,
acs.median_income,
acs.income_per_capita,
acs.gini_index,
acs.owner_occupied_housing_units_median_value,
acs.median_rent,
acs.percent_income_spent_on_rent,
from `bigquery-public-data.geo_census_tracts.census_tracts_district_of_columbia` ct
left join `bigquery-public-data.geo_us_boundaries.counties` c
on (ct.state_fips_code || ct.county_fips_code) = c.geo_id
left join `bigquery-public-data.census_bureau_acs.censustract_2018_5yr` acs
on ct.geo_id = acs.geo_id
Verify the data fields being imported. Change the "province_state" datat type from "text" to "Geo--Region". Click "Create Report" once you have confirmed the data types.
select
ct.state_fips_code,
ct.county_fips_code,
c.county_name,
ct.tract_ce,
ct.geo_id,
ct.tract_name,
ct.lsad_name,
ct.internal_point_lat,
ct.internal_point_lon,
ct.internal_point_geo,
ct.tract_geom,
acs.total_pop,
acs.households,
acs.male_pop,
acs.female_pop,
acs.median_age,
acs.median_income,
acs.income_per_capita,
acs.gini_index,
acs.owner_occupied_housing_units_median_value,
acs.median_rent,
acs.percent_income_spent_on_rent,
from `bigquery-public-data.geo_census_tracts.census_tracts_district_of_columbia` ct
left join `bigquery-public-data.geo_us_boundaries.counties` c
on (ct.state_fips_code || ct.county_fips_code) = c.geo_id
left join `bigquery-public-data.census_bureau_acs.censustract_2018_5yr` acs
on ct.geo_id = acs.geo_id
select
ct.state_fips_code,
ct.county_fips_code,
c.county_name,
ct.tract_ce,
ct.geo_id,
ct.tract_name,
ct.lsad_name,
ct.internal_point_lat,
ct.internal_point_lon,
ct.internal_point_geo,
ct.tract_geom,
acs.total_pop,
acs.households,
acs.male_pop,
acs.female_pop,
acs.median_age,
acs.median_income,
acs.income_per_capita,
acs.gini_index,
acs.owner_occupied_housing_units_median_value,
acs.median_rent,
acs.percent_income_spent_on_rent,
from `bigquery-public-data.geo_census_tracts.census_tracts_virginia` ct
left join `bigquery-public-data.geo_us_boundaries.counties` c
on (ct.state_fips_code || ct.county_fips_code) = c.geo_id
left join `bigquery-public-data.census_bureau_acs.censustract_2018_5yr` acs
on ct.geo_id = acs.geo_id
select
ct.state_fips_code,
ct.county_fips_code,
c.county_name,
ct.tract_ce,
ct.geo_id,
ct.tract_name,
ct.lsad_name,
ct.internal_point_lat,
ct.internal_point_lon,
ct.internal_point_geo,
ct.tract_geom,
acs.total_pop,
acs.households,
acs.male_pop,
acs.female_pop,
acs.median_age,
acs.median_income,
acs.income_per_capita,
acs.gini_index,
acs.owner_occupied_housing_units_median_value,
acs.median_rent,
acs.percent_income_spent_on_rent,
from `bigquery-public-data.geo_census_tracts.census_tracts_maryland` ct
left join `bigquery-public-data.geo_us_boundaries.counties` c
on (ct.state_fips_code || ct.county_fips_code) = c.geo_id
left join `bigquery-public-data.census_bureau_acs.censustract_2018_5yr` acs
on ct.geo_id = acs.geo_id