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Notes on Table Formatting Ideas
Currently the data is fairly long and the formatting is pretty raw. If you run this below, you'll get an output that looks like the following.
county.big.tbl <- psrccensus::get_acs_recs(geography='county',table.names=c('B03002'),years=c(2019), acs.type='acs1') write.table(county.big.tbl, "clipboard", sep="\t", row.names=FALSE)
(result is truncated because the table is big)
| "GEOID" | "name" | "state" | "variable" | "estimate" | "moe" | "label" | "concept" | "census_geography" | "acs_type" | "year" |
|---|---|---|---|---|---|---|---|---|---|---|
| "53033" | "King County" | "Washington" | "B03002_001" | 2252782 | NA | "Estimate!!Total:" | "HISPANIC OR LATINO ORIGIN BY RACE" | "County" | "acs1" | 2019 |
| "53033" | "King County" | "Washington" | "B03002_002" | 2030140 | NA | "Estimate!!Total:!!Not Hispanic or Latino:" | "HISPANIC OR LATINO ORIGIN BY RACE" | "County" | "acs1" | 2019 |
| "53033" | "King County" | "Washington" | "B03002_003" | 1302544 | 3208 | "Estimate!!Total:!!Not Hispanic or Latino:!!White alone" | "HISPANIC OR LATINO ORIGIN BY RACE" | "County" | "acs1" | 2019 |
| "53033" | "King County" | "Washington" | "B03002_004" | 147822 | 4678 | "Estimate!!Total:!!Not Hispanic or Latino:!!Black or African American alone" | "HISPANIC OR LATINO ORIGIN BY RACE" | "County" | "acs1" | 2019 |
| "53033" | "King County" | "Washington" | "B03002_005" | 13321 | 1990 | "Estimate!!Total:!!Not Hispanic or Latino:!!American Indian and Alaska Native alone" | "HISPANIC OR LATINO ORIGIN BY RACE" | "County" | "acs1" | 2019 |
| "53033" | "King County" | "Washington" | "B03002_006" | 424590 | 7085 | "Estimate!!Total:!!Not Hispanic or Latino:!!Asian alone" | "HISPANIC OR LATINO ORIGIN BY RACE" | "County" | "acs1" | 2019 |
What I think we should do is make tables that are really similar to our travel survey explorer. The user should be able to select whether they want shares, totals, share with margin of error, or total with margin of error. The function should download the data into xlsx in a similar way to the data explorer, and name the file by the year, the concept, and the geography scale. So one sheet of the data would look like this (taking an example from the data explorer).
| Sample county | African American | African American_MOE | Asian | Asian_MOE | Child | Child_MOE | Hispanic | Hispanic_MOE | Missing | Missing_MOE | Other | Other_MOE | White Only | White Only_MOE | Result Type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| King | 3.9% | +/-1.2% | 10.7% | +/-1.2% | 21.7% | +/-1.2% | 3.9% | +/-1.2% | 15.9% | +/-1.2% | 5.7% | +/-1.2% | 38.2% | +/-1.2% | Regional results |
| Kitsap | 2% | +/-5.7% | 2.1% | +/-5.7% | 21.7% | +/-5.7% | 2.6% | +/-5.7% | 2.4% | +/-5.7% | 4.5% | +/-5.7% | 64.5% | +/-5.7% | Regional results |
| Pierce | 8% | +/-3.4% | 3% | +/-3.4% | 23.1% | +/-3.4% | 4.5% | +/-3.4% | 2.8% | +/-3.4% | 9% | +/-3.4% | 49.6% | +/-3.4% | Regional results |
| Snohomish | 1.2% | +/-4% | 8.1% | +/-4% | 23.3% | +/-4% | 4.9% | +/-4% | 13.9% | +/-4% | 4.3% | +/-4% | 44.3% | +/-4% | Regional results |