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是否有可能通过 12 岁或以上的州获得美国人口？我正在尝试使用该tidycensus软件包，但我不确定如何限制计数以添加年龄限制。

library(tidycensus)
library(tidyverse)
census_api_key("MYKEY")
pop90 <- get_acs(geography = "state", variables = "B01003_001", year = 1990)
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1 回答 1

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该特定变量的“宇宙”"B01003-001"是 TOTAL POPULATION，除此之外没有进一步细分，因此您无法从中获得 12 岁以上的年龄"B01003-001"，而只能获得您从中提取的整个州或县或地区的人口当时。

但是，您可以为您想要的表格提取和聚合数据框，使用和B01001后缀按年龄和性别提取人口，然后将它们相加。_001_049

或者

您可以像上面一样拉出整个人口并减去年龄（男性和女性都不在您的目标群体中，考虑到与其他生活相比，儿童年龄组的细分工作要少得多）

您将遇到困难的一件事是获得 12+，因为您要排除的最高分组是 10-14...这意味着您无法选择 12 岁以下

所有种族代码按性别划分的一般年龄：

    B01001_001                     Total: 
    B01001_002                      Male: 
    B01001_003        Male: Under 5 years 
    B01001_004         Male: 5 to 9 years 
    B01001_005       Male: 10 to 14 years 
    B01001_006       Male: 15 to 17 years 
    B01001_007      Male: 18 and 19 years 
    B01001_008             Male: 20 years 
    B01001_009             Male: 21 years 
    B01001_010       Male: 22 to 24 years 
    B01001_011       Male: 25 to 29 years 
    B01001_012       Male: 30 to 34 years 
    B01001_013       Male: 35 to 39 years 
    B01001_014       Male: 40 to 44 years 
    B01001_015       Male: 45 to 49 years 
    B01001_016       Male: 50 to 54 years 
    B01001_017       Male: 55 to 59 years 
    B01001_018      Male: 60 and 61 years 
    B01001_019       Male: 62 to 64 years 
    B01001_020      Male: 65 and 66 years 
    B01001_021       Male: 67 to 69 years 
    B01001_022       Male: 70 to 74 years 
    B01001_023       Male: 75 to 79 years 
    B01001_024       Male: 80 to 84 years 
    B01001_025    Male: 85 years and over 
    B01001_026                    Female: 
    B01001_027      Female: Under 5 years 
    B01001_028       Female: 5 to 9 years 
    B01001_029     Female: 10 to 14 years 
    B01001_030     Female: 15 to 17 years 
    B01001_031    Female: 18 and 19 years 
    B01001_032           Female: 20 years 
    B01001_033           Female: 21 years 
    B01001_034     Female: 22 to 24 years 
    B01001_035     Female: 25 to 29 years 
    B01001_036     Female: 30 to 34 years 
    B01001_037     Female: 35 to 39 years 
    B01001_038     Female: 40 to 44 years 
    B01001_039     Female: 45 to 49 years 
    B01001_040     Female: 50 to 54 years 
    B01001_041     Female: 55 to 59 years 
    B01001_042    Female: 60 and 61 years 
    B01001_044    Female: 65 and 66 years 
    B01001_045     Female: 67 to 69 years 
    B01001_046     Female: 70 to 74 years 
    B01001_047     Female: 75 to 79 years 
    B01001_048     Female: 80 to 84 years 
    B01001_049  Female: 85 years and over

因此，您将需要以某种方式调整模型或获取 PUMS 数据并根据自己的喜好进行汇总。

于 2017-08-23T20:26:13.757 回答