在具有许多列的数据集中传播单个 variable/column，聚合其他变量
Spreading a single variable/column in a dataset with many columns, aggregating the other variables
我有一个数据集如下（有关更广泛的示例，请参阅下面的编辑）：
DT <- structure(list(Geo_level = c(50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L
), State_Code = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), County_Code = c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), State = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Alabama", class = "factor"),
County = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Autauga County", class = "factor"),
year = c(1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L), Age_Group = c(0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 99L), Population = c(576L, 2445L, 3622L, 3744L,
3266L, 2344L, 2710L, 3033L, 4002L, 3678L, 3028L, 2610L, 2301L,
1904L, 1549L, 1153L, 835L, 516L, 435L, 43751L), Male_Population = c(263L,
1237L, 1875L, 1959L, 1679L, 1182L, 1277L, 1471L, 1941L, 1784L,
1501L, 1241L, 1085L, 931L, 711L, 498L, 330L, 172L, 115L,
21252L), Female_Population = c(313L, 1208L, 1747L, 1785L,
1587L, 1162L, 1433L, 1562L, 2061L, 1894L, 1527L, 1369L, 1216L,
973L, 838L, 655L, 505L, 344L, 320L, 22499L), WA_MALE = c(215L,
971L, 1494L, 1553L, 1279L, 915L, 1044L, 1252L, 1652L, 1484L,
1244L, 1062L, 934L, 808L, 620L, 426L, 271L, 146L, 86L, 17456L
), WA_FEMALE = c(248L, 954L, 1389L, 1382L, 1195L, 869L, 1120L,
1265L, 1706L, 1505L, 1224L, 1130L, 1034L, 838L, 693L, 535L,
408L, 271L, 253L, 18019L)), class = c("data.table", "data.frame"
), row.names = c(NA, -20L))
Geo_level State_Code County_Code State County year Age_Group Population Male_Population Female_Population WA_MALE WA_FEMALE
1: 50 1 1 Alabama Autauga County 1995 0 576 263 313 215 248
2: 50 1 1 Alabama Autauga County 1995 1 2445 1237 1208 971 954
3: 50 1 1 Alabama Autauga County 1995 2 3622 1875 1747 1494 1389
4: 50 1 1 Alabama Autauga County 1995 3 3744 1959 1785 1553 1382
5: 50 1 1 Alabama Autauga County 1995 4 3266 1679 1587 1279 1195
6: 50 1 1 Alabama Autauga County 1995 5 2344 1182 1162 915 869
7: 50 1 1 Alabama Autauga County 1995 6 2710 1277 1433 1044 1120
8: 50 1 1 Alabama Autauga County 1995 7 3033 1471 1562 1252 1265
9: 50 1 1 Alabama Autauga County 1995 8 4002 1941 2061 1652 1706
10: 50 1 1 Alabama Autauga County 1995 9 3678 1784 1894 1484 1505
11: 50 1 1 Alabama Autauga County 1995 10 3028 1501 1527 1244 1224
12: 50 1 1 Alabama Autauga County 1995 11 2610 1241 1369 1062 1130
13: 50 1 1 Alabama Autauga County 1995 12 2301 1085 1216 934 1034
14: 50 1 1 Alabama Autauga County 1995 13 1904 931 973 808 838
15: 50 1 1 Alabama Autauga County 1995 14 1549 711 838 620 693
16: 50 1 1 Alabama Autauga County 1995 15 1153 498 655 426 535
17: 50 1 1 Alabama Autauga County 1995 16 835 330 505 271 408
18: 50 1 1 Alabama Autauga County 1995 17 516 172 344 146 271
19: 50 1 1 Alabama Autauga County 1995 18 435 115 320 86 253
20: 50 1 1 Alabama Autauga County 1995 99 43751 21252 22499 17456 18019
我正在尝试用 dcast 传播 Age_Group 变量，但我不知道该怎么做。我一直遇到的问题之一是如何告诉 dcast 仅需要更改的列，而不是单独列出所有列。我也从来没有真正理解我的 value.var 应该是什么。
有没有人可以向我解释如何处理这个问题？
编辑：期望的结果是：
DT_desired_result <- structure(list(Geo_level = 50L, State_Code = 1L, County_Code = 1L,
State = structure(1L, .Label = "Alabama", class = "factor"),
County = structure(1L, .Label = "Autauga County", class = "factor"),
year = 1995L, Age_Group = 99L, Population = 43751L, Male_Population = 21252L,
Female_Population = 22499L, WA_MALE = 17456L, WA_FEMALE = 18019L,
V1 = 576L, V2 = 2445L, V3 = 3622L, V4 = 3744L, V5 = 3266L,
V6 = 2344L, V7 = 2710L, V8 = 3033L, V9 = 4002L, V10 = 3678L,
V11 = 3028L, V12 = 2610L, V13 = 2301L, V14 = 1904L, V15 = 1549L,
V16 = 1153L, V17 = 835L, V18 = 516L, V19 = 435L, V20 = 43751L), row.names = c(NA,
-1L), class = c("data.table", "data.frame")
其中V变量对应Population为Age_Group.
编辑 II，代码基于以下答案：
执行结果：
test <- DT %>%
group_by(State, year, County) %>%
pivot_wider(names_from = Age_Group, values_from = Population) %>%
fill(`0`:`99`) %>%
head(21)
基于实际数据
编辑 III：新 DPUT（见评论回答）
DTtwo <- structure(list(Geo_level = c(50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L), State_Code = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L), County_Code = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 45L, 45L, 45L, 45L, 45L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
45L), State = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Alabama",
"Wyoming"), class = "factor"), County = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("Autauga County", "Barbour County", "Weston County"
), class = "factor"), year = c(1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L), Age_Group = c(0L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 99L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 99L, 0L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 99L), Population = c(576L, 2445L,
3622L, 3744L, 3266L, 2344L, 2710L, 3033L, 4002L, 3678L, 3028L,
2610L, 2301L, 1904L, 1549L, 1153L, 835L, 516L, 435L, 43751L,
587L, 2604L, 3596L, 4016L, 3408L, 2505L, 2567L, 3165L, 3892L,
4072L, 3264L, 2823L, 2526L, 2067L, 1685L, 1253L, 890L, 546L,
443L, 45909L, 354L, 1434L, 2056L, 2156L, 2148L, 1929L, 2058L,
2008L, 2350L, 2185L, 1956L, 1970L, 1423L, 1141L, 1061L, 978L,
806L, 517L, 512L, 29042L, 364L, 1423L, 1891L, 2074L, 2062L, 1988L,
1873L, 2053L, 2207L, 2131L, 2045L, 1991L, 1632L, 1159L, 1034L,
978L, 753L, 535L, 460L, 28653L, 57L, 289L, 355L, 511L, 579L,
296L, 314L, 343L, 493L, 597L, 575L, 484L, 368L, 345L, 274L, 254L,
218L, 157L, 134L, 6643L, 62L, 288L, 355L, 442L, 553L, 350L, 280L,
355L, 385L, 577L, 595L, 514L, 429L, 337L, 302L, 236L, 239L, 143L,
136L, 6578L, 576L, 2445L, 3622L, 3744L, 3266L, 2344L, 2710L,
3033L, 4002L, 3678L, 3028L, 2610L, 2301L, 1904L, 1549L, 1153L,
835L, 516L, 435L, 43751L, 354L, 1434L, 2056L, 2156L, 2148L, 1929L,
2058L, 2008L, 2350L, 2185L, 1956L, 1970L, 1423L, 1141L, 1061L,
978L, 806L, 517L, 512L, 29042L, 57L, 289L, 355L, 511L, 579L,
296L, 314L, 343L, 493L, 597L, 575L, 484L, 368L, 345L, 274L, 254L,
218L, 157L, 134L, 6643L), Male_Population = c(263L, 1237L, 1875L,
1959L, 1679L, 1182L, 1277L, 1471L, 1941L, 1784L, 1501L, 1241L,
1085L, 931L, 711L, 498L, 330L, 172L, 115L, 21252L, 269L, 1237L,
1844L, 2135L, 1784L, 1241L, 1232L, 1536L, 1895L, 2011L, 1602L,
1388L, 1196L, 1025L, 784L, 530L, 357L, 175L, 121L, 22362L, 185L,
745L, 1075L, 1081L, 1117L, 1184L, 1229L, 1156L, 1309L, 1192L,
986L, 1002L, 696L, 534L, 493L, 400L, 288L, 179L, 123L, 14974L,
187L, 732L, 987L, 1028L, 1072L, 1200L, 1166L, 1213L, 1235L, 1149L,
1077L, 1031L, 801L, 551L, 484L, 407L, 306L, 174L, 120L, 14920L,
25L, 154L, 178L, 246L, 305L, 184L, 163L, 172L, 252L, 309L, 320L,
266L, 168L, 169L, 132L, 132L, 98L, 64L, 35L, 3372L, 29L, 144L,
184L, 221L, 293L, 208L, 144L, 200L, 191L, 305L, 319L, 288L, 212L,
156L, 152L, 108L, 110L, 60L, 40L, 3364L, 263L, 1237L, 1875L,
1959L, 1679L, 1182L, 1277L, 1471L, 1941L, 1784L, 1501L, 1241L,
1085L, 931L, 711L, 498L, 330L, 172L, 115L, 21252L, 185L, 745L,
1075L, 1081L, 1117L, 1184L, 1229L, 1156L, 1309L, 1192L, 986L,
1002L, 696L, 534L, 493L, 400L, 288L, 179L, 123L, 14974L, 25L,
154L, 178L, 246L, 305L, 184L, 163L, 172L, 252L, 309L, 320L, 266L,
168L, 169L, 132L, 132L, 98L, 64L, 35L, 3372L), Female_Population = c(313L,
1208L, 1747L, 1785L, 1587L, 1162L, 1433L, 1562L, 2061L, 1894L,
1527L, 1369L, 1216L, 973L, 838L, 655L, 505L, 344L, 320L, 22499L,
318L, 1367L, 1752L, 1881L, 1624L, 1264L, 1335L, 1629L, 1997L,
2061L, 1662L, 1435L, 1330L, 1042L, 901L, 723L, 533L, 371L, 322L,
23547L, 169L, 689L, 981L, 1075L, 1031L, 745L, 829L, 852L, 1041L,
993L, 970L, 968L, 727L, 607L, 568L, 578L, 518L, 338L, 389L, 14068L,
177L, 691L, 904L, 1046L, 990L, 788L, 707L, 840L, 972L, 982L,
968L, 960L, 831L, 608L, 550L, 571L, 447L, 361L, 340L, 13733L,
32L, 135L, 177L, 265L, 274L, 112L, 151L, 171L, 241L, 288L, 255L,
218L, 200L, 176L, 142L, 122L, 120L, 93L, 99L, 3271L, 33L, 144L,
171L, 221L, 260L, 142L, 136L, 155L, 194L, 272L, 276L, 226L, 217L,
181L, 150L, 128L, 129L, 83L, 96L, 3214L, 313L, 1208L, 1747L,
1785L, 1587L, 1162L, 1433L, 1562L, 2061L, 1894L, 1527L, 1369L,
1216L, 973L, 838L, 655L, 505L, 344L, 320L, 22499L, 169L, 689L,
981L, 1075L, 1031L, 745L, 829L, 852L, 1041L, 993L, 970L, 968L,
727L, 607L, 568L, 578L, 518L, 338L, 389L, 14068L, 32L, 135L,
177L, 265L, 274L, 112L, 151L, 171L, 241L, 288L, 255L, 218L, 200L,
176L, 142L, 122L, 120L, 93L, 99L, 3271L), WA_MALE = c(215L, 971L,
1494L, 1553L, 1279L, 915L, 1044L, 1252L, 1652L, 1484L, 1244L,
1062L, 934L, 808L, 620L, 426L, 271L, 146L, 86L, 17456L, 207L,
945L, 1456L, 1674L, 1351L, 957L, 1003L, 1285L, 1608L, 1674L,
1314L, 1165L, 1034L, 883L, 681L, 465L, 309L, 143L, 91L, 18245L,
85L, 335L, 472L, 455L, 481L, 539L, 614L, 596L, 684L, 650L, 545L,
615L, 472L, 384L, 353L, 290L, 198L, 104L, 60L, 7932L, 98L, 325L,
407L, 439L, 470L, 508L, 569L, 611L, 623L, 606L, 595L, 604L, 531L,
375L, 360L, 288L, 215L, 107L, 68L, 7799L, 24L, 146L, 172L, 236L,
292L, 175L, 161L, 163L, 245L, 304L, 314L, 263L, 163L, 168L, 129L,
132L, 96L, 63L, 35L, 3281L, 28L, 136L, 176L, 210L, 282L, 198L,
142L, 189L, 184L, 299L, 313L, 285L, 207L, 155L, 149L, 108L, 109L,
59L, 40L, 3269L, 215L, 971L, 1494L, 1553L, 1279L, 915L, 1044L,
1252L, 1652L, 1484L, 1244L, 1062L, 934L, 808L, 620L, 426L, 271L,
146L, 86L, 17456L, 85L, 335L, 472L, 455L, 481L, 539L, 614L, 596L,
684L, 650L, 545L, 615L, 472L, 384L, 353L, 290L, 198L, 104L, 60L,
7932L, 24L, 146L, 172L, 236L, 292L, 175L, 161L, 163L, 245L, 304L,
314L, 263L, 163L, 168L, 129L, 132L, 96L, 63L, 35L, 3281L), WA_FEMALE = c(248L,
954L, 1389L, 1382L, 1195L, 869L, 1120L, 1265L, 1706L, 1505L,
1224L, 1130L, 1034L, 838L, 693L, 535L, 408L, 271L, 253L, 18019L,
262L, 1059L, 1346L, 1493L, 1218L, 971L, 1001L, 1313L, 1648L,
1655L, 1323L, 1163L, 1121L, 906L, 757L, 606L, 425L, 293L, 264L,
18824L, 77L, 270L, 397L, 469L, 452L, 327L, 400L, 418L, 518L,
522L, 509L, 555L, 460L, 401L, 360L, 325L, 345L, 215L, 219L, 7239L,
82L, 291L, 364L, 431L, 417L, 345L, 347L, 416L, 464L, 495L, 513L,
524L, 507L, 397L, 348L, 349L, 265L, 233L, 207L, 6995L, 31L, 127L,
170L, 257L, 264L, 105L, 145L, 167L, 232L, 281L, 252L, 211L, 194L,
172L, 138L, 122L, 120L, 93L, 99L, 3180L, 32L, 136L, 164L, 211L,
251L, 134L, 130L, 151L, 185L, 265L, 273L, 219L, 211L, 178L, 147L,
128L, 128L, 83L, 96L, 3122L, 248L, 954L, 1389L, 1382L, 1195L,
869L, 1120L, 1265L, 1706L, 1505L, 1224L, 1130L, 1034L, 838L,
693L, 535L, 408L, 271L, 253L, 18019L, 77L, 270L, 397L, 469L,
452L, 327L, 400L, 418L, 518L, 522L, 509L, 555L, 460L, 401L, 360L,
325L, 345L, 215L, 219L, 7239L, 31L, 127L, 170L, 257L, 264L, 105L,
145L, 167L, 232L, 281L, 252L, 211L, 194L, 172L, 138L, 122L, 120L,
93L, 99L, 3180L)), row.names = c(NA, -180L), class = c("data.table",
"data.frame"))
DT %>% group_by(County, State, year) %>% slice(1:4) %>% head(16)
# A tibble: 16 x 55
# Groups: County, State, year [4]
Geo_level State_Code County_Code State County year Age_Group Population Male_Population Female_Populati~ WA_MALE WA_FEMALE BA_MALE BA_FEMALE IA_MALE IA_FEMALE AA_MALE
<int> <int> <int> <fct> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 50 1 1 Alab~ Autau~ 1995 0 576 263 313 215 248 43 60 0 2 3
2 50 1 1 Alab~ Autau~ 1995 1 2445 1237 1208 971 954 245 227 2 4 3
3 50 1 1 Alab~ Autau~ 1995 2 3622 1875 1747 1494 1389 352 327 9 6 4
4 50 1 1 Alab~ Autau~ 1995 3 3744 1959 1785 1553 1382 375 364 9 11 1
5 50 1 1 Alab~ Autau~ 2000 0 586 267 319 217 252 44 60 0 2 3
6 50 1 1 Alab~ Autau~ 2000 1 2443 1236 1207 969 955 243 226 2 4 3
7 50 1 1 Alab~ Autau~ 2000 2 3619 1874 1745 1494 1389 351 326 9 6 4
8 50 1 1 Alab~ Autau~ 2000 3 3776 1976 1800 1565 1393 379 367 9 11 1
9 50 1 1 Alab~ Autau~ 2001 0 621 303 318 244 251 51 58 0 2 2
10 50 1 1 Alab~ Autau~ 2001 1 2499 1223 1276 956 986 240 256 2 4 4
11 50 1 1 Alab~ Autau~ 2001 2 3541 1807 1734 1425 1347 349 352 9 6 6
12 50 1 1 Alab~ Autau~ 2001 3 3896 2028 1868 1598 1468 390 360 12 11 6
13 50 1 1 Alab~ Autau~ 2002 0 587 269 318 207 262 55 49 0 1 1
14 50 1 1 Alab~ Autau~ 2002 1 2604 1237 1367 945 1059 259 262 2 4 5
15 50 1 1 Alab~ Autau~ 2002 2 3596 1844 1752 1456 1346 353 364 9 6 7
16 50 1 1 Alab~ Autau~ 2002 3 4016 2135 1881 1674 1493 417 346 10 11 8
# ... with 38 more variables: AA_FEMALE <int>, NA_MALE <int>, NA_FEMALE <int>, TOM_MALE <int>, TOM_FEMALE <int>, NH_MALE <int>, NH_FEMALE <int>, NHWA_MALE <int>,
# NHWA_FEMALE <int>, NHBA_MALE <int>, NHBA_FEMALE <int>, NHIA_MALE <int>, NHIA_FEMALE <int>, NHAA_MALE <int>, NHAA_FEMALE <int>, NHNA_MALE <int>, NHNA_FEMALE <int>,
# NHTOM_MALE <int>, NHTOM_FEMALE <int>, H_MALE <int>, H_FEMALE <int>, HWA_MALE <int>, HWA_FEMALE <int>, HBA_MALE <int>, HBA_FEMALE <int>, HIA_MALE <int>,
# HIA_FEMALE <int>, HAA_MALE <int>, HAA_FEMALE <int>, HNA_MALE <int>, HNA_FEMALE <int>, HTOM_MALE <int>, HTOM_FEMALE <int>, Female_perc <dbl>, Male_perc <dbl>,
# orig_year <int>, orig_age_grp <int>, orig_pop <int>
你可以试试
DTtwo %>%
select(State, County, year, Age_Group, Population) %>%
pivot_wider(names_from = Age_Group, values_from = Population, values_fn = unique) %>%
rename(Population = `99`)
# A tibble: 6 x 23
State County year `0` `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` `12` `13` `14` `15` `16` `17` `18` Population
<fct> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 Alaba~ Autauga ~ 1995 576 2445 3622 3744 3266 2344 2710 3033 4002 3678 3028 2610 2301 1904 1549 1153 835 516 435 43751
2 Alaba~ Autauga ~ 2002 587 2604 3596 4016 3408 2505 2567 3165 3892 4072 3264 2823 2526 2067 1685 1253 890 546 443 45909
3 Alaba~ Barbour ~ 1995 354 1434 2056 2156 2148 1929 2058 2008 2350 2185 1956 1970 1423 1141 1061 978 806 517 512 29042
4 Alaba~ Barbour ~ 2002 364 1423 1891 2074 2062 1988 1873 2053 2207 2131 2045 1991 1632 1159 1034 978 753 535 460 28653
5 Wyomi~ Weston C~ 1995 57 289 355 511 579 296 314 343 493 597 575 484 368 345 274 254 218 157 134 6643
6 Wyomi~ Weston C~ 2002 62 288 355 442 553 350 280 355 385 577 595 514 429 337 302 236 239 143 136 6578
您可以使用 left_join 和 by = c(State, County, year)
添加此输出
我有一个数据集如下（有关更广泛的示例，请参阅下面的编辑）：
DT <- structure(list(Geo_level = c(50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L
), State_Code = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), County_Code = c(1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L), State = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Alabama", class = "factor"),
County = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Autauga County", class = "factor"),
year = c(1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L), Age_Group = c(0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 18L, 99L), Population = c(576L, 2445L, 3622L, 3744L,
3266L, 2344L, 2710L, 3033L, 4002L, 3678L, 3028L, 2610L, 2301L,
1904L, 1549L, 1153L, 835L, 516L, 435L, 43751L), Male_Population = c(263L,
1237L, 1875L, 1959L, 1679L, 1182L, 1277L, 1471L, 1941L, 1784L,
1501L, 1241L, 1085L, 931L, 711L, 498L, 330L, 172L, 115L,
21252L), Female_Population = c(313L, 1208L, 1747L, 1785L,
1587L, 1162L, 1433L, 1562L, 2061L, 1894L, 1527L, 1369L, 1216L,
973L, 838L, 655L, 505L, 344L, 320L, 22499L), WA_MALE = c(215L,
971L, 1494L, 1553L, 1279L, 915L, 1044L, 1252L, 1652L, 1484L,
1244L, 1062L, 934L, 808L, 620L, 426L, 271L, 146L, 86L, 17456L
), WA_FEMALE = c(248L, 954L, 1389L, 1382L, 1195L, 869L, 1120L,
1265L, 1706L, 1505L, 1224L, 1130L, 1034L, 838L, 693L, 535L,
408L, 271L, 253L, 18019L)), class = c("data.table", "data.frame"
), row.names = c(NA, -20L))
Geo_level State_Code County_Code State County year Age_Group Population Male_Population Female_Population WA_MALE WA_FEMALE
1: 50 1 1 Alabama Autauga County 1995 0 576 263 313 215 248
2: 50 1 1 Alabama Autauga County 1995 1 2445 1237 1208 971 954
3: 50 1 1 Alabama Autauga County 1995 2 3622 1875 1747 1494 1389
4: 50 1 1 Alabama Autauga County 1995 3 3744 1959 1785 1553 1382
5: 50 1 1 Alabama Autauga County 1995 4 3266 1679 1587 1279 1195
6: 50 1 1 Alabama Autauga County 1995 5 2344 1182 1162 915 869
7: 50 1 1 Alabama Autauga County 1995 6 2710 1277 1433 1044 1120
8: 50 1 1 Alabama Autauga County 1995 7 3033 1471 1562 1252 1265
9: 50 1 1 Alabama Autauga County 1995 8 4002 1941 2061 1652 1706
10: 50 1 1 Alabama Autauga County 1995 9 3678 1784 1894 1484 1505
11: 50 1 1 Alabama Autauga County 1995 10 3028 1501 1527 1244 1224
12: 50 1 1 Alabama Autauga County 1995 11 2610 1241 1369 1062 1130
13: 50 1 1 Alabama Autauga County 1995 12 2301 1085 1216 934 1034
14: 50 1 1 Alabama Autauga County 1995 13 1904 931 973 808 838
15: 50 1 1 Alabama Autauga County 1995 14 1549 711 838 620 693
16: 50 1 1 Alabama Autauga County 1995 15 1153 498 655 426 535
17: 50 1 1 Alabama Autauga County 1995 16 835 330 505 271 408
18: 50 1 1 Alabama Autauga County 1995 17 516 172 344 146 271
19: 50 1 1 Alabama Autauga County 1995 18 435 115 320 86 253
20: 50 1 1 Alabama Autauga County 1995 99 43751 21252 22499 17456 18019
我正在尝试用 dcast 传播 Age_Group 变量，但我不知道该怎么做。我一直遇到的问题之一是如何告诉 dcast 仅需要更改的列，而不是单独列出所有列。我也从来没有真正理解我的 value.var 应该是什么。
有没有人可以向我解释如何处理这个问题？
编辑：期望的结果是：
DT_desired_result <- structure(list(Geo_level = 50L, State_Code = 1L, County_Code = 1L,
State = structure(1L, .Label = "Alabama", class = "factor"),
County = structure(1L, .Label = "Autauga County", class = "factor"),
year = 1995L, Age_Group = 99L, Population = 43751L, Male_Population = 21252L,
Female_Population = 22499L, WA_MALE = 17456L, WA_FEMALE = 18019L,
V1 = 576L, V2 = 2445L, V3 = 3622L, V4 = 3744L, V5 = 3266L,
V6 = 2344L, V7 = 2710L, V8 = 3033L, V9 = 4002L, V10 = 3678L,
V11 = 3028L, V12 = 2610L, V13 = 2301L, V14 = 1904L, V15 = 1549L,
V16 = 1153L, V17 = 835L, V18 = 516L, V19 = 435L, V20 = 43751L), row.names = c(NA,
-1L), class = c("data.table", "data.frame")
其中V变量对应Population为Age_Group.
编辑 II，代码基于以下答案：
执行结果：
test <- DT %>%
group_by(State, year, County) %>%
pivot_wider(names_from = Age_Group, values_from = Population) %>%
fill(`0`:`99`) %>%
head(21)
基于实际数据
编辑 III：新 DPUT（见评论回答）
DTtwo <- structure(list(Geo_level = c(50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L, 50L,
50L, 50L, 50L, 50L), State_Code = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L,
56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L, 56L), County_Code = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 45L, 45L, 45L, 45L, 45L, 45L,
45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L, 45L,
45L), State = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Alabama",
"Wyoming"), class = "factor"), County = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L), .Label = c("Autauga County", "Barbour County", "Weston County"
), class = "factor"), year = c(1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L,
2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 2002L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L, 1995L,
1995L, 1995L, 1995L, 1995L), Age_Group = c(0L, 1L, 2L, 3L, 4L,
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L,
99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 99L, 0L,
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,
15L, 16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L,
9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 99L, 0L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L,
16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 99L, 0L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L,
18L, 99L, 0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L,
13L, 14L, 15L, 16L, 17L, 18L, 99L), Population = c(576L, 2445L,
3622L, 3744L, 3266L, 2344L, 2710L, 3033L, 4002L, 3678L, 3028L,
2610L, 2301L, 1904L, 1549L, 1153L, 835L, 516L, 435L, 43751L,
587L, 2604L, 3596L, 4016L, 3408L, 2505L, 2567L, 3165L, 3892L,
4072L, 3264L, 2823L, 2526L, 2067L, 1685L, 1253L, 890L, 546L,
443L, 45909L, 354L, 1434L, 2056L, 2156L, 2148L, 1929L, 2058L,
2008L, 2350L, 2185L, 1956L, 1970L, 1423L, 1141L, 1061L, 978L,
806L, 517L, 512L, 29042L, 364L, 1423L, 1891L, 2074L, 2062L, 1988L,
1873L, 2053L, 2207L, 2131L, 2045L, 1991L, 1632L, 1159L, 1034L,
978L, 753L, 535L, 460L, 28653L, 57L, 289L, 355L, 511L, 579L,
296L, 314L, 343L, 493L, 597L, 575L, 484L, 368L, 345L, 274L, 254L,
218L, 157L, 134L, 6643L, 62L, 288L, 355L, 442L, 553L, 350L, 280L,
355L, 385L, 577L, 595L, 514L, 429L, 337L, 302L, 236L, 239L, 143L,
136L, 6578L, 576L, 2445L, 3622L, 3744L, 3266L, 2344L, 2710L,
3033L, 4002L, 3678L, 3028L, 2610L, 2301L, 1904L, 1549L, 1153L,
835L, 516L, 435L, 43751L, 354L, 1434L, 2056L, 2156L, 2148L, 1929L,
2058L, 2008L, 2350L, 2185L, 1956L, 1970L, 1423L, 1141L, 1061L,
978L, 806L, 517L, 512L, 29042L, 57L, 289L, 355L, 511L, 579L,
296L, 314L, 343L, 493L, 597L, 575L, 484L, 368L, 345L, 274L, 254L,
218L, 157L, 134L, 6643L), Male_Population = c(263L, 1237L, 1875L,
1959L, 1679L, 1182L, 1277L, 1471L, 1941L, 1784L, 1501L, 1241L,
1085L, 931L, 711L, 498L, 330L, 172L, 115L, 21252L, 269L, 1237L,
1844L, 2135L, 1784L, 1241L, 1232L, 1536L, 1895L, 2011L, 1602L,
1388L, 1196L, 1025L, 784L, 530L, 357L, 175L, 121L, 22362L, 185L,
745L, 1075L, 1081L, 1117L, 1184L, 1229L, 1156L, 1309L, 1192L,
986L, 1002L, 696L, 534L, 493L, 400L, 288L, 179L, 123L, 14974L,
187L, 732L, 987L, 1028L, 1072L, 1200L, 1166L, 1213L, 1235L, 1149L,
1077L, 1031L, 801L, 551L, 484L, 407L, 306L, 174L, 120L, 14920L,
25L, 154L, 178L, 246L, 305L, 184L, 163L, 172L, 252L, 309L, 320L,
266L, 168L, 169L, 132L, 132L, 98L, 64L, 35L, 3372L, 29L, 144L,
184L, 221L, 293L, 208L, 144L, 200L, 191L, 305L, 319L, 288L, 212L,
156L, 152L, 108L, 110L, 60L, 40L, 3364L, 263L, 1237L, 1875L,
1959L, 1679L, 1182L, 1277L, 1471L, 1941L, 1784L, 1501L, 1241L,
1085L, 931L, 711L, 498L, 330L, 172L, 115L, 21252L, 185L, 745L,
1075L, 1081L, 1117L, 1184L, 1229L, 1156L, 1309L, 1192L, 986L,
1002L, 696L, 534L, 493L, 400L, 288L, 179L, 123L, 14974L, 25L,
154L, 178L, 246L, 305L, 184L, 163L, 172L, 252L, 309L, 320L, 266L,
168L, 169L, 132L, 132L, 98L, 64L, 35L, 3372L), Female_Population = c(313L,
1208L, 1747L, 1785L, 1587L, 1162L, 1433L, 1562L, 2061L, 1894L,
1527L, 1369L, 1216L, 973L, 838L, 655L, 505L, 344L, 320L, 22499L,
318L, 1367L, 1752L, 1881L, 1624L, 1264L, 1335L, 1629L, 1997L,
2061L, 1662L, 1435L, 1330L, 1042L, 901L, 723L, 533L, 371L, 322L,
23547L, 169L, 689L, 981L, 1075L, 1031L, 745L, 829L, 852L, 1041L,
993L, 970L, 968L, 727L, 607L, 568L, 578L, 518L, 338L, 389L, 14068L,
177L, 691L, 904L, 1046L, 990L, 788L, 707L, 840L, 972L, 982L,
968L, 960L, 831L, 608L, 550L, 571L, 447L, 361L, 340L, 13733L,
32L, 135L, 177L, 265L, 274L, 112L, 151L, 171L, 241L, 288L, 255L,
218L, 200L, 176L, 142L, 122L, 120L, 93L, 99L, 3271L, 33L, 144L,
171L, 221L, 260L, 142L, 136L, 155L, 194L, 272L, 276L, 226L, 217L,
181L, 150L, 128L, 129L, 83L, 96L, 3214L, 313L, 1208L, 1747L,
1785L, 1587L, 1162L, 1433L, 1562L, 2061L, 1894L, 1527L, 1369L,
1216L, 973L, 838L, 655L, 505L, 344L, 320L, 22499L, 169L, 689L,
981L, 1075L, 1031L, 745L, 829L, 852L, 1041L, 993L, 970L, 968L,
727L, 607L, 568L, 578L, 518L, 338L, 389L, 14068L, 32L, 135L,
177L, 265L, 274L, 112L, 151L, 171L, 241L, 288L, 255L, 218L, 200L,
176L, 142L, 122L, 120L, 93L, 99L, 3271L), WA_MALE = c(215L, 971L,
1494L, 1553L, 1279L, 915L, 1044L, 1252L, 1652L, 1484L, 1244L,
1062L, 934L, 808L, 620L, 426L, 271L, 146L, 86L, 17456L, 207L,
945L, 1456L, 1674L, 1351L, 957L, 1003L, 1285L, 1608L, 1674L,
1314L, 1165L, 1034L, 883L, 681L, 465L, 309L, 143L, 91L, 18245L,
85L, 335L, 472L, 455L, 481L, 539L, 614L, 596L, 684L, 650L, 545L,
615L, 472L, 384L, 353L, 290L, 198L, 104L, 60L, 7932L, 98L, 325L,
407L, 439L, 470L, 508L, 569L, 611L, 623L, 606L, 595L, 604L, 531L,
375L, 360L, 288L, 215L, 107L, 68L, 7799L, 24L, 146L, 172L, 236L,
292L, 175L, 161L, 163L, 245L, 304L, 314L, 263L, 163L, 168L, 129L,
132L, 96L, 63L, 35L, 3281L, 28L, 136L, 176L, 210L, 282L, 198L,
142L, 189L, 184L, 299L, 313L, 285L, 207L, 155L, 149L, 108L, 109L,
59L, 40L, 3269L, 215L, 971L, 1494L, 1553L, 1279L, 915L, 1044L,
1252L, 1652L, 1484L, 1244L, 1062L, 934L, 808L, 620L, 426L, 271L,
146L, 86L, 17456L, 85L, 335L, 472L, 455L, 481L, 539L, 614L, 596L,
684L, 650L, 545L, 615L, 472L, 384L, 353L, 290L, 198L, 104L, 60L,
7932L, 24L, 146L, 172L, 236L, 292L, 175L, 161L, 163L, 245L, 304L,
314L, 263L, 163L, 168L, 129L, 132L, 96L, 63L, 35L, 3281L), WA_FEMALE = c(248L,
954L, 1389L, 1382L, 1195L, 869L, 1120L, 1265L, 1706L, 1505L,
1224L, 1130L, 1034L, 838L, 693L, 535L, 408L, 271L, 253L, 18019L,
262L, 1059L, 1346L, 1493L, 1218L, 971L, 1001L, 1313L, 1648L,
1655L, 1323L, 1163L, 1121L, 906L, 757L, 606L, 425L, 293L, 264L,
18824L, 77L, 270L, 397L, 469L, 452L, 327L, 400L, 418L, 518L,
522L, 509L, 555L, 460L, 401L, 360L, 325L, 345L, 215L, 219L, 7239L,
82L, 291L, 364L, 431L, 417L, 345L, 347L, 416L, 464L, 495L, 513L,
524L, 507L, 397L, 348L, 349L, 265L, 233L, 207L, 6995L, 31L, 127L,
170L, 257L, 264L, 105L, 145L, 167L, 232L, 281L, 252L, 211L, 194L,
172L, 138L, 122L, 120L, 93L, 99L, 3180L, 32L, 136L, 164L, 211L,
251L, 134L, 130L, 151L, 185L, 265L, 273L, 219L, 211L, 178L, 147L,
128L, 128L, 83L, 96L, 3122L, 248L, 954L, 1389L, 1382L, 1195L,
869L, 1120L, 1265L, 1706L, 1505L, 1224L, 1130L, 1034L, 838L,
693L, 535L, 408L, 271L, 253L, 18019L, 77L, 270L, 397L, 469L,
452L, 327L, 400L, 418L, 518L, 522L, 509L, 555L, 460L, 401L, 360L,
325L, 345L, 215L, 219L, 7239L, 31L, 127L, 170L, 257L, 264L, 105L,
145L, 167L, 232L, 281L, 252L, 211L, 194L, 172L, 138L, 122L, 120L,
93L, 99L, 3180L)), row.names = c(NA, -180L), class = c("data.table",
"data.frame"))
DT %>% group_by(County, State, year) %>% slice(1:4) %>% head(16)
# A tibble: 16 x 55
# Groups: County, State, year [4]
Geo_level State_Code County_Code State County year Age_Group Population Male_Population Female_Populati~ WA_MALE WA_FEMALE BA_MALE BA_FEMALE IA_MALE IA_FEMALE AA_MALE
<int> <int> <int> <fct> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 50 1 1 Alab~ Autau~ 1995 0 576 263 313 215 248 43 60 0 2 3
2 50 1 1 Alab~ Autau~ 1995 1 2445 1237 1208 971 954 245 227 2 4 3
3 50 1 1 Alab~ Autau~ 1995 2 3622 1875 1747 1494 1389 352 327 9 6 4
4 50 1 1 Alab~ Autau~ 1995 3 3744 1959 1785 1553 1382 375 364 9 11 1
5 50 1 1 Alab~ Autau~ 2000 0 586 267 319 217 252 44 60 0 2 3
6 50 1 1 Alab~ Autau~ 2000 1 2443 1236 1207 969 955 243 226 2 4 3
7 50 1 1 Alab~ Autau~ 2000 2 3619 1874 1745 1494 1389 351 326 9 6 4
8 50 1 1 Alab~ Autau~ 2000 3 3776 1976 1800 1565 1393 379 367 9 11 1
9 50 1 1 Alab~ Autau~ 2001 0 621 303 318 244 251 51 58 0 2 2
10 50 1 1 Alab~ Autau~ 2001 1 2499 1223 1276 956 986 240 256 2 4 4
11 50 1 1 Alab~ Autau~ 2001 2 3541 1807 1734 1425 1347 349 352 9 6 6
12 50 1 1 Alab~ Autau~ 2001 3 3896 2028 1868 1598 1468 390 360 12 11 6
13 50 1 1 Alab~ Autau~ 2002 0 587 269 318 207 262 55 49 0 1 1
14 50 1 1 Alab~ Autau~ 2002 1 2604 1237 1367 945 1059 259 262 2 4 5
15 50 1 1 Alab~ Autau~ 2002 2 3596 1844 1752 1456 1346 353 364 9 6 7
16 50 1 1 Alab~ Autau~ 2002 3 4016 2135 1881 1674 1493 417 346 10 11 8
# ... with 38 more variables: AA_FEMALE <int>, NA_MALE <int>, NA_FEMALE <int>, TOM_MALE <int>, TOM_FEMALE <int>, NH_MALE <int>, NH_FEMALE <int>, NHWA_MALE <int>,
# NHWA_FEMALE <int>, NHBA_MALE <int>, NHBA_FEMALE <int>, NHIA_MALE <int>, NHIA_FEMALE <int>, NHAA_MALE <int>, NHAA_FEMALE <int>, NHNA_MALE <int>, NHNA_FEMALE <int>,
# NHTOM_MALE <int>, NHTOM_FEMALE <int>, H_MALE <int>, H_FEMALE <int>, HWA_MALE <int>, HWA_FEMALE <int>, HBA_MALE <int>, HBA_FEMALE <int>, HIA_MALE <int>,
# HIA_FEMALE <int>, HAA_MALE <int>, HAA_FEMALE <int>, HNA_MALE <int>, HNA_FEMALE <int>, HTOM_MALE <int>, HTOM_FEMALE <int>, Female_perc <dbl>, Male_perc <dbl>,
# orig_year <int>, orig_age_grp <int>, orig_pop <int>
你可以试试
DTtwo %>%
select(State, County, year, Age_Group, Population) %>%
pivot_wider(names_from = Age_Group, values_from = Population, values_fn = unique) %>%
rename(Population = `99`)
# A tibble: 6 x 23
State County year `0` `1` `2` `3` `4` `5` `6` `7` `8` `9` `10` `11` `12` `13` `14` `15` `16` `17` `18` Population
<fct> <fct> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
1 Alaba~ Autauga ~ 1995 576 2445 3622 3744 3266 2344 2710 3033 4002 3678 3028 2610 2301 1904 1549 1153 835 516 435 43751
2 Alaba~ Autauga ~ 2002 587 2604 3596 4016 3408 2505 2567 3165 3892 4072 3264 2823 2526 2067 1685 1253 890 546 443 45909
3 Alaba~ Barbour ~ 1995 354 1434 2056 2156 2148 1929 2058 2008 2350 2185 1956 1970 1423 1141 1061 978 806 517 512 29042
4 Alaba~ Barbour ~ 2002 364 1423 1891 2074 2062 1988 1873 2053 2207 2131 2045 1991 1632 1159 1034 978 753 535 460 28653
5 Wyomi~ Weston C~ 1995 57 289 355 511 579 296 314 343 493 597 575 484 368 345 274 254 218 157 134 6643
6 Wyomi~ Weston C~ 2002 62 288 355 442 553 350 280 355 385 577 595 514 429 337 302 236 239 143 136 6578
您可以使用 left_join 和 by = c(State, County, year)