我想在人口普查区域级别汇总的数据合并到邮政编码(zcta5(。 每个 zcta5 包含多个人口普查区域,并给出百分比区域权重。 数据结构如下:
df1 <- structure(list(ZCTA5 = c(98110L, 98110L, 98110L, 98110L, 98310L,
98310L, 98310L, 98310L, 98310L, 98310L, 98310L), ctfips = c(53035090700,
53035090800, 53035090900, 53035091000, 53035080101, 53035080102,
53035080200, 53035080300, 53035080400, 53035091800, 53035091900
), ZAREAPCT = c(22.08, 27.38, 10.39, 40.15, 11.34, 11.88, 11.13,
8.39, 29.96, 15.77, 11.53)), row.names = c(NA, -11L), class = c("tbl_df",
"tbl", "data.frame"))
ZCTA5 ctfips ZAREAPCT
<int> <dbl> <dbl>
1 98110 53035090700. 22.1
2 98110 53035090800. 27.4
3 98110 53035090900. 10.4
4 98110 53035091000. 40.2
5 98310 53035080101. 11.3
6 98310 53035080102. 11.9
7 98310 53035080200. 11.1
8 98310 53035080300. 8.39
9 98310 53035080400. 30.0
10 98310 53035091800. 15.8
11 98310 53035091900. 11.5
df2 <- structure(list(date = structure(c(13149, 13149, 13149, 13149,
13149, 13149, 13149, 13149, 13149, 13149, 13149), class = "Date"),
ctfips = c(53035080101, 53035080102, 53035080200, 53035080300,
3035080400, 53035090700, 53035090800, 53035090900, 53035091000,
53035091800, 53035091900), DS_PM_pred = c(5.293963, 5.25517,
5.289735, 5.318018, 5.245346, 5.071309, 5.170838, 5.099778,
5.181464, 5.202728, 5.23456)), row.names = c(NA, -11L), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), vars = "ctfips", drop = TRUE, indices = list(
0L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), group_sizes = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), biggest_group_size = 1L, labels = structure(list(
ctfips = c(53035080101, 53035080102, 53035080200, 53035080300,
53035080400, 53035090700, 53035090800, 53035090900, 53035091000,
53035091800, 53035091900)), row.names = c(NA, -11L), class = "data.frame", vars = "ctfips", drop = TRUE))
date ctfips DS_PM_pred
<date> <dbl> <dbl>
1 2006-01-01 53035080101. 5.29
2 2006-01-01 53035080102. 5.26
3 2006-01-01 53035080200. 5.29
4 2006-01-01 53035080300. 5.32
5 2006-01-01 53035080400. 5.25
6 2006-01-01 53035090700. 5.07
7 2006-01-01 53035090800. 5.17
8 2006-01-01 53035090900. 5.10
9 2006-01-01 53035091000. 5.18
10 2006-01-01 53035091800. 5.20
11 2006-01-01 53035091900. 5.23
检查 df1,每个邮政编码 ZCTA5 与多个人口普查区域 (ctfips( 重叠,面积权重百分比为 ZAREAPCT。 在此示例中,有两个唯一的ZCTA5(98110和98310(。 第一个包含 4 个人口普查区域,第二个包含 7 个。
df2 包含每个人口普查区域 (ctfips( 和我想聚合到 ZCTA5 的变量。(DS_DM_Pred(。
我正在寻找的输出如下所示:
ZCTA5 date DS_DM_Pred_weighted
98110 2006-01-01 5.14981
98310 2006-01-01 5.250558
其中,在每个 ZCTA5 的人口普查区域上计算的加权平均值为: 5.14 = 5.07*(0.221( + 5.17*(0.274( + 5.10*(0.10(4 + 5.18*(0.402(
我似乎无法理解有效解决这个问题的最佳方式。
我在df2
的dput
代码中遇到了错误,但这里有代码可能会让您走上正确的轨道 -
library(dplyr)
inner_join(df1, df2, by = "ctfips") %>%
group_by(ZCTA5, date) %>%
summarise(DS_DM_Pred_weighted = weighted.mean(DS_PM_pred, ZAREAPCT/100))