I have a dataframe that looks like the following (dput at the end):
region type age_group year value
AO1 p 0 1990 12
AO1 p 5 1990 10
AO1 p 10 1990 8
AO1 p 15 1990 14
AO1 p 20 1990 19
...
AO1 p 80 1990 12
AO1 p 1 1990 0.54
AO1 p 2 1990 0.46
AO1 p 3 1990 1
where the last three lines express the percentage of males (1) and female (2) and total (3).
What I would like to do is to produce two more variables value.m and value.f by multiplying value by the correct percentage In this case, value.m would use 0.54 and value.f 0.46 for year 1990 in region AO1
dt$value.m <- dt %>%
group_by(region, type, age_num, year) %>%
mutate(value.m=value*???)
Any ideas?
dt <- structure(list(region = structure(c(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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L,
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, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 4L, 4L,
4L, 2L, 2L, 2L, 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, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 4L, 4L, 4L, 2L, 2L, 2L), .Label =
c("AO1", "AO11", "AO22", "AO3"), class = "factor"), age = structure(c(1L,
10L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 1L, 10L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 1L, 10L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 1L, 10L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 19L, 18L, 20L,
19L, 18L, 20L, 19L, 18L, 20L, 19L, 18L, 20L, 21L, 30L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
21L, 30L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 21L, 30L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 21L, 30L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 39L,
38L, 40L, 39L, 38L, 40L, 39L, 38L, 40L, 39L, 38L, 40L, 1L, 10L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L, 14L, 15L, 16L,
17L, 1L, 10L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 11L, 12L, 13L,
14L, 15L, 16L, 17L, 1L, 10L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
11L, 12L, 13L, 14L, 15L, 16L, 17L, 1L, 10L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 19L, 18L, 20L,
19L, 18L, 20L, 19L, 18L, 20L, 19L, 18L, 20L, 21L, 30L, 22L, 23L,
24L, 25L, 26L, 27L, 28L, 29L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
21L, 30L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 21L, 30L, 22L, 23L, 24L, 25L, 26L, 27L, 28L,
29L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 21L, 30L, 22L, 23L, 24L,
25L, 26L, 27L, 28L, 29L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 39L,
38L, 40L, 39L, 38L, 40L, 39L, 38L, 40L, 39L, 38L, 40L), .Label = c("c_0_4",
"c_10_14", "c_15_19", "c_20_24", "c_25_29", "c_30_34", "c_35_39",
"c_40_44", "c_45_49", "c_5_9", "c_50_54", "c_55_59", "c_60_64",
"c_65_69", "c_70_74", "c_75_79", "c_80+", "c_f", "c_m", "c_total_sex",
"p_0_4", "p_10_14", "p_15_19", "p_20_24", "p_25_29", "p_30_34",
"p_35_39", "p_40_44", "p_45_49", "p_5_9", "p_50_54", "p_55_59",
"p_60_64", "p_65_69", "p_70_74", "p_75_79", "p_80+", "p_f", "p_m",
"p_total_sex"), class = "factor"), age_num = c(0L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 0L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 0L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 0L, 5L, 10L, 15L,
20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L,
0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L,
65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L,
45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 0L, 5L, 10L, 15L, 20L,
25L, 30L, 35L, 40L, 45L, 50L, 55L, 60L, 65L, 70L, 75L, 80L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), year = c(2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L, 2006L,
2006L, 2006L, 2006L, 2006L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L,
2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L, 2007L), value
= c(79.6, 55.1, 44.6, 44.3,
26.8, 9.5, 7.2, 6.5, 5.6, 2.4, 0.6, 5.2, 7.6, 10.4, 12, 13.5,
13.5, 42.4, 23.1, 14.7, 12.5, 3.9, 1.4, 2.4, 5, 4.2, 7, 7.6,
10.2, 9.5, 11.1, 12.1, 13.8, 14.1, 30.5, 18.1, 14.6, 7.6, 1.4,
3.3, 4.1, 6.9, 8, 9.9, 9.8, 13.5, 13.1, 14.1, 14.2, 14.6, 14.6,
60.1, 52.1, 52.5, 64.1, 45.5, 26.9, 10.6, 7.7, 8.7, 0.4, 0.5,
4.1, 8.8, 9.9, 12.4, 13.3, 14, 216.8, 227.6, 459.7, 115.8, 112.3,
243.5, 85, 87.9, 188.2, 241.6, 253.9, 510.8, 0.2, 0.15, 0.13,
0.13, 0.09, 0.053, 0.05, 0.05, 0.04, 0.03, 0.03, 0.024, 0, 0.01,
0.016, 0, 0, 0.22, 0.15, 0.12, 0.11, 0.07, 0.05, 0.05, 0.04,
0.04, 0.03, 0.03, 0.02, 0.02, 0.02, 0.01, 0.01, 0, 0.2, 0.19,
0.15, 0.11, 0.07, 0.06, 0.06, 0.04, 0.04, 0.03, 0.03, 0.01, 0.01,
0.01, 0.01, 0, 0, 0.14, 0.13, 0.13, 0.15, 0.12, 0.08, 0.05, 0.04,
0.05, 0.03, 0.03, 0.02, 0.01, 0.01, 0.01, 0, 0, 0.49, 0.51, 1,
0.51, 0.49, 1, 0.49, 0.51, 1, 0.49, 0.51, 1, 241.9, 175.54, 146.5,
138.46, 108.14, 73.94, 66.58, 64.78, 58.9, 43.86, 49.1, 36.5,
33.38, 25.54, 21.66, 18.42, 18.58, 243.74, 163.86, 130.22, 121.42,
96.1, 80.3, 63.9, 55.02, 49.02, 41.78, 51.74, 35.22, 32.66, 25.78,
23.06, 18.66, 18.14, 152.5, 109.9, 93.34, 82.62, 61.7, 56.06,
44.38, 38.26, 33.02, 29.58, 30.86, 21.86, 21.18, 17.62, 17.86,
15.86, 15.58, 196.82, 175.74, 180.46, 182.3, 153.22, 118.18,
81.34, 70.46, 65.82, 47.7, 54.66, 38.54, 29.42, 25.58, 20.38,
18.18, 17.18, 547.58, 566.78, 1100.38, 519.1, 522.78, 1028.06,
310.54, 322.26, 618.82, 619.62, 647.02, 1252.66, 0.206, 0.15,
0.126, 0.122, 0.088, 0.052, 0.05, 0.05, 0.04, 0.03, 0.032, 0.02,
0.02, 0.01, 0.01, 0, 0.002, 0.222, 0.15, 0.118, 0.108, 0.074,
0.054, 0.05, 0.04, 0.038, 0.028, 0.032, 0.02, 0.02, 0.018, 0.01,
0.008, 0, 0.23, 0.158, 0.142, 0.11, 0.074, 0.064, 0.056, 0.04,
0.038, 0.028, 0.03, 0.012, 0.01, 0.01, 0.01, 0, 0, 0.144, 0.132,
0.134, 0.14, 0.118, 0.082, 0.054, 0.042, 0.046, 0.028, 0.032,
0.02, 0.01, 0.01, 0.008, 0, 0, 0.49, 0.51, 1, 0.57, 0.43, 1,
0.4, 0.6, 1, 0.3, 0.7, 1)), .Names = c("region", "age", "age_num",
"year", "value"), class = "data.frame", row.names = c(NA, -320L))