Question

我一直在尝试使用dplyr在R中整理数据集，并且似乎要么a）得到不一致的结果，要么b）已经不再直接思考了！

我的数据集＆＃39; resi_type（见下文）捕获了一个城市的人口数据和垃圾产生率。我试图总结这些数字有三个层次：地理区域，选区和社会经济区域（低，中，高）。这些是大小顺序，例如有437个个别地区属于三个社会经济阶层之一;这些都只在六个选区中的一个;六个选区位于四个地理区域之一。

我对dplyr的速度和易用性印象深刻，并且一直使用它来总结我的数据并添加一些计算值。例如，总结选区一级的整个数据集：

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。如果我总结行数，我的总人口将达到939,370，并且总共会浪费掉＃39;产量744.3238（吨）。

现在，当我尝试在县级制作摘要时，我已经玩了两个代码块。当我在行之间求和时，这个产生与上面相同的结果，例如总人口939,370，总浪费＆＃39;产量744.3238（吨）：

county_sum <- 
  constit_sum %.%
  group_by(grouped_types) %.%
  summarise(area = sum(area),
            waste = sum(waste))

然而，以下代码块是解决相同问题的略微不同的方法，会产生不同的结果，例如：总人口939,370，总浪费＆＃39;产量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)

我似乎在四处走动，因为我认为无论你如何分割数据，无论我在进行什么级别的分析，产生的总废物应该是相同的？或者我的代码可能有问题？我真的走了十字架，我希望有人可以为我揭开这一点吗？！

希望......！

提前致谢

玛蒂

数据集= resi_type

    structure(list(geo_zone = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 
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    1921L, 149230L, 27015L, 125189L, 8282L, 123498L, 43192L, 
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    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, 
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    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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    0.89, 0.89, 0.89, 0.89, 0.89, 0.55, 0.89, 0.55, 0.55, 0.89, 
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    0.55, 0.55, 0.89, 0.89, 0.55, 0.89, 0.55, 0.55, 0.55, 0.55, 
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    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))

Answer 1

如果不加载要检查的数据，差异似乎是在第一个版本中，浪费被定义（在所有突变之后）：

area / sum(area) * constit_pop * wg_rate / 1000

而在第二个中：

area / sum(area) * 939370 * wg_rate / 1000

在第一个版本中，您似乎将一个百分比应用于不完整的人口，这可能是您获得较低数字的原因。

更新1

稍微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。由于您的区域比例总和为1，并且您希望应用该百分比来分配人口，您必须将其应用于总人口，而不是选区的总人口，小于或等于总数。这就是为什么你得到的答案少于757。

我建议在纸上绘制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，总结它应该是dplyr的轻而易举。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晰。

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)

现在你应该可以在你想要的任何轴上聚合：

> 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

dplyr R组总数因子不同

1 个答案:

更新1