dplyr R组总数因因素而异
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【中文标题】dplyr R组总数因因素而异【英文标题】：dplyr R group totals differ between factors 【发布时间】：2015-01-22 18:42:14 【问题描述】：我整天都在尝试用 dplyr 总结 R 中的数据集，但似乎 a) 得到的结果不一致，或者 b) 已经停止思考！
我的数据集“resi_type”（见下文）捕获了一个城市的人口数据和垃圾产生率。我试图在三个层次上总结这些数字： 地理区域、选区和社会经济区域（低、中、高）。这些是按大小排列的，例如有 437 个独立领域属于三个社会经济类别之一；这些都只在六个选区之一中；并且六个选区位于四个 geo_zones 之一中。
dplyr 的速度和易用性给我留下了深刻的印象，并且一直在使用它来总结我的数据并添加一些计算值。例如，在选区级别汇总整个数据集：
constit_sum <-
resi_type %.%
group_by(constit, grouped_types) %.%
summarise(area = sum(area),
constit_pop = first(constit_pop),
wg_rate = first(per_capita)) %.%
mutate(prop = area/sum(area)*100) %.%
mutate(pop = (prop/100)*constit_pop) %.%
mutate(waste = (wg_rate*pop)/1000)
这为我制作了一张漂亮的桌子。如果对行求和，我的总人口为 939,370，总“废物”产量为 744.3238（吨）。
现在，在尝试生成县级摘要时，我一直在使用两段代码。当我跨行求和时，这个产生与上面相同的结果，例如总人口为 939,370，总“废物”产量为 744.3238（吨）：
county_sum <-
constit_sum %.%
group_by(grouped_types) %.%
summarise(area = sum(area),
waste = sum(waste))
但是，以下代码块是解决同一问题的一种略有不同的方法，会产生不同的结果，例如总人口为 939,370，总“废物”产量为 757.8447（吨）
county_sum2 <-
resi_type %.%
group_by(grouped_types) %.%
summarise(area = sum(area),
wg_rate = first(per_capita)) %.%
mutate(prop = area/sum(area)*100) %.%
mutate(pop = (prop/100)*939370) %.%
mutate(waste = (wg_rate*pop)/1000)
我似乎在绕圈子，因为我假设无论您如何分割数据，无论我进行何种级别的分析，产生的总浪费都应该是相同的？或者我的代码可能有问题？我真的是斗鸡眼了，我希望有人能帮我解释一下？！
希望……！
提前致谢
马蒂
数据集 = resi_type
structure(list(geo_zone = structure(c(2L, 2L, 2L, 2L, 2L, 2L,
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33759L, 31367L, 10659L, 16966L, 8623L, 35058L, 5673L, 68267L,
705371L, 949977L, 486446L, 294029L, 134848L, 302860L, 33036L,
53250L, 68122L, 62749L, 68404L, 73999L, 132052L, 276413L,
96435L, 93399L, 11964L, 63652L, 43107L, 41734L, 8942L, 106987L,
73466L, 15260L, 34230L, 26790L, 102071L, 66920L, 53821L,
41854L, 112277L, 35686L, 121283L, 442860L, 161307L, 268652L,
99335L, 132137L, 44353L, 12662L, 46855L, 82968L, 107930L,
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61246L, 50378L, 382692L, 226353L, 30415L, 140897L, 380308L,
278496L, 140738L, 90931L, 309373L, 675839L, 126616L, 28072L,
359436L, 265410L, 61959L, 14987L, 163470L, 63586L, 202991L,
17073L, 153207L, 10945L, 32625L, 6528L, 66156L, 41513L, 71556L,
59877L, 1672723L, 83184L, 127108L, 225371L, 7148L, 55633L,
23490L, 33440L, 31874L, 57616L, 141644L, 4041L, 16330L, 24755L,
21670L, 17487L, 18316L, 2185L, 57400L, 65359L, 2195L, 3315L,
1921L, 149230L, 27015L, 125189L, 8282L, 123498L, 43192L,
28750L, 53547L, 90610L, 111713L, 1389550L, 46932L, 65567L,
34963L, 157530L, 495876L, 35865L, 69871L, 33037L, 15238L,
187162L, 25614L, 222164L, 208159L, 35492L, 74961L, 119257L,
338205L, 157776L, 269244L, 7808L, 81022L, 117785L, 53097L,
88359L, 120146L, 25215L, 5604L, 44767L, 107211L, 48531L,
62563L, 30239L, 39535L, 18375L, 50473L, 118626L, 6583L, 25521L,
83484L, 37988L, 142569L, 60286L, 29201L, 21472L, 45229L,
218326L, 136081L, 72108L, 44545L, 419200L, 118830L, 293110L,
28082L, 34409L, 28544L, 70845L, 883447L, 50206L, 501740L,
292555L, 11381L, 760137L, 46906L, 45658L, 12068L, 31536L,
26448L, 66957L, 60306L, 63473L, 92440L, 33038L, 40690L, 4358L,
15420L, 35874L, 241844L, 103774L, 231520L, 55938L, 4980L,
15129L, 71766L, 15052L, 51907L, 58131L, 11222L, 84234L, 18250L,
55818L, 354058L, 426951L, 78515L, 69888L, 61814L, 63906L,
10022L, 12016L, 17379L, 41985L, 52158L, 26534L, 6521L, 24839L,
52534L, 6259L, 12217L, 27762L, 21291L, 13541L, 49876L, 10837L,
452375L, 71490L, 51393L, 138363L, 141327L, 22491L, 14763L,
24576L, 49290L, 103754L, 10742L, 85254L, 13606L, 3300L, 18141L,
16879L, 35271L, 82284L, 695830L, 4878L, 18671L, 2561L, 21473L,
31871L, 5064L, 37972L, 11353L, 13481L, 60994L, 10852L, 4068L,
15985L, 1380L, 90067L, 182569L, 111214L, 373818L, 29520L,
92995L, 15263L, 51544L, 14380L, 62169L, 31736L, 33060L, 34099L,
22353L, 3371L, 4004L, 130913L, 6064L, 35123L, 30165L, 32239L,
27727L, 65874L, 17705L, 15342L, 27021L, 32604L, 48038L, 43721L,
21798L, 23312L, 44106L, 34939L, 16593L, 5387L, 12524L, 88162L,
18039L, 475786L, 5763L, 8036L, 79110L, 9284L, 38040L, 63066L,
4939L, 5665L, 47305L, 47126L, 32030L, 10339L, 33543L, 6560L,
119694L, 45586L, 62892L, 56458L, 20836L, 38241L, 132671L,
4101L, 3229L, 32904L, 6779L, 21641L, 95824L, 182038L, 33146L,
11859L, 17811L, 19589L, 28067L, 92553L, 17485L, 25332L, 39660L,
4862L, 13969L, 18905L, 37109L, 37983L, 21651L), per_capita = c(0.55,
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0.55, 0.55, 0.55, 0.55, 0.55, 1.33, 1.33, 1.33, 1.33, 1.33,
1.33, 1.33, 1.33, 1.33, 0.55, 1.33, 1.33, 1.33, 1.33, 1.33,
0.55, 0.55, 0.55, 0.89, 0.55, 0.55, 0.55, 0.55, 0.55, 0.55,
0.55, 1.33, 0.55, 0.55, 0.89, 0.55, 0.55, 0.55, 0.55, 0.89,
0.55, 0.55, 0.55, 0.55, 1.33, 1.33, 1.33, 0.55, 0.55, 0.55,
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1.33, 0.55, 0.55, 0.55, 0.55, 0.55, 0.55, 1.33, 0.55, 0.55,
0.55, 0.55, 0.89, 0.89, 1.33, 1.33, 0.55, 0.55, 0.55, 0.55,
0.89, 0.89, 1.33, 0.89, 0.89, 1.33, 1.33, 1.33, 1.33, 1.33,
1.33, 1.33, 1.33, 1.33, 1.33, 0.89, 0.89, 0.89, 0.55, 0.55,
0.55, 0.89, 0.55, 0.55, 0.89, 0.89, 0.89, 0.89, 0.55, 0.55,
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0.55, 0.55, 0.55, 0.89, 0.55, 0.55, 0.55, 0.55, 0.89, 0.55,
0.55, 0.55, 0.89, 0.55, 0.55, 0.55, 0.89, 1.33, 0.89, 0.89,
0.89, 0.89, 0.89, 0.89, 0.89, 0.55, 0.89, 0.55, 0.55, 0.89,
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0.55, 0.89, 0.89, 0.55, 0.89, 0.55, 0.55, 0.55, 0.55, 0.55,
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0.55, 0.89, 0.55, 0.55, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89,
0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89,
0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89,
0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.55, 0.55,
0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89,
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0.89, 0.89, 0.89, 0.55, 0.89, 0.89, 0.55, 0.89, 0.89, 0.89,
0.55, 0.89, 0.55, 0.89, 0.89, 0.89, 0.55, 0.55, 1.33, 0.89,
0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89,
1.33, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89, 0.89,
0.55, 0.89, 0.89, 0.89, 0.89, 0.89)), .Names = c("geo_zone",
"constit", "constit_pop", "grouped_types", "area", "per_capita"
), class = "data.frame", row.names = c(NA, -437L))
【问题讨论】：
您使用的是什么版本的 dplyr？也许您应该更新到最新的 CRAN 版本。我问是因为您使用的是已弃用的%.%（由%>% 替换）。
【参考方案1】：
没有加载数据进行检查，差异似乎是在第一个版本中，废物被定义为（在所有突变之后）：
area / sum(area) * constit_pop * wg_rate / 1000
而在第二个是：
area / sum(area) * 939370 * wg_rate / 1000
在第一个版本中，您似乎将百分比应用于不完整的总体，这可能是您得到较低数字的原因。
更新 1
在玩了一下你的数据之后，我相信 757 的数字更准确。您将area 值视为实际大小，并使用它来划分位置。由于您的面积比例总和为 1，并且您希望将该百分比应用于分配人口，因此您必须将其应用于 total 人口，而不是选区的总人口，后者小于或等于总数。这就是为什么您得到的答案少于 757。
我的建议是在纸上绘制出您将如何根据您拥有的数据计算某个区域的总浪费，并使用它来创建一列实际（或预期）浪费。一旦你有每个区域的浪费，用dplyr总结应该是轻而易举的事。如果我正确理解您的问题，它可能类似于以下内容。可能有更优雅的方法可以做到这一点，但我希望以下内容应该相对清楚。
new_resi <- tbl_df(resi_type)
total_pop <- group_by(new_resi, constit_pop) %>% summarize() %>% sum()
new_resi <- new_resi %>% mutate(prop = area / sum(area),
area_pop = total_pop * prop, waste = per_capita * area_pop / 1000)
现在您应该能够在您想要的任何轴上进行聚合：
> sum(new_resi$waste)
[1] 757.8447
> new_resi %>% group_by(constit) %>% summarize(sum(waste))
Source: local data frame [6 x 2]
constit sum(waste)
1 changamwe 43.77569
2 jomvu 58.32272
3 kisauni 177.14872
4 likoni 122.57831
5 mvita 102.38562
6 nyali 253.63362
> new_resi %>% group_by(geo_zone) %>% summarize(sum(waste))
Source: local data frame [4 x 2]
geo_zone sum(waste)
1 Mainland North 430.7823
2 Mainland South 122.5783
3 Mainland West 102.0984
4 Mombasa Island 102.3856
> 430.7823+122.5783+102.0984+102.3856
[1] 757.8446
> new_resi %>% group_by(grouped_types) %>% summarize(sum(waste))
Source: local data frame [3 x 2]
grouped_types sum(waste)
1 low 276.8951
2 medium 199.9059
3 high 281.0437
【讨论】：
嘿嘿，我用%.%;好像没问题但是@Avraham 你的评论引起了共鸣。我将百分比应用于不完整的人口。我知道总人口，而且我知道各个选区的人口总和。我不知道的是每个选区中每个社会经济群体的人口 - 因此必须根据每个选区中每个社会经济类型所覆盖的面积比例来计算它。我目前看不到这个，你能告诉我如何确保这些总和正确吗？谢谢 @marty_carea 和 per_capita 究竟代表什么？ area 是公鸡的实际大小，还是像美国的 zip code 这样的标识符？ per_capita 是每人每年产生的垃圾吨数吗？
是的 area 是该区域的物理大小（m2），per_capita 是每人/每天产生的废物的重量，例如0.55公斤
@marty_c 这就是我的想法。请查看答案中的更新。
哇，非常感谢！正是我所追求的，我非常感谢您的投入。欢呼以上是关于dplyr R组总数因因素而异的主要内容，如果未能解决你的问题，请参考以下文章
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