R根据参考表计算样本权重和加权聚合

【问题标题】：R Calculate sample weights and weighted aggregation based on reference tableR根据参考表计算样本权重和加权聚合
【发布时间】：2019-02-20 15:34:28
【问题描述】：

进行调查后，我以数据框的形式收集了结果。这是实际数据框外观的可重现版本。

library(dplyr)
library(tidyr)
df=data.frame(ID=c("1101","1102","1103","1104",
               "1105","1106","1107","1108",
               "1109","1110","1111","1112",
               "1113","1114","1115","1116",
               "1117","1118","1119","1120",
               "1121","1122","1123","1124",
               "1125","1126","1127","1128",
               "1129","1130","1131","1132",
               "1133","1134","1135","1136",
               "1137","1138","1139","1140",
               "1141","1142","1143","1144",
               "1145","1146","1147","1148",
               "1149","1150","1151","1152",
               "1153","1154","1155","1156"),
          Country=c("US","UK","Canada","Mexico",
                    "India","US","Peru","China",
                    "US","UK","Canada","Mexico",
                    "Portugal","India","Portugal","Mexico",
                    "Peru","India","Canada","Mexico",
                    "India","UK","India","Canada",
                    "US","UK","China","India",
                    "US","Mexico","Canada","Mexico",
                    "Canada","China","Canada","Canada",
                    "China","China","India","Mexico",
                    "Portugal","Portugal","Portugal","Portugal",
                    "UK","UK","UK","Peru",
                    "Peru","Mexico","US","US",
                    "Peru","Mexico","Peru","Mexico"),
          Gender=c("Male","Male","Male","Female",
                    "Female","Female","Male","Female",
                    "Female","Female","Male","Female",
                    "Male","Male","Female","Female",
                    "Female","Male","Female","Female",
                    "Female","Female","Male","Female",
                    "Male","Female","Male","Female",
                    "Female","Male","Female","Female",
                    "Male","Male","Male","Female",
                    "Male","Male","Female","Female",
                    "Male","Female","Male","Female",
                    "Male","Female","Male","Female",
                    "Male","Female","Male","Female",
                    "Male","Male","Male","Male"),
          Age=c("<25","25-35","25-35","36-45",
                ">55",">55","25-35",">55",
                "<25","25-35","25-35","36-45",
                "25-35","25-35","25-35","36-45",
                ">55","36-45","46-55","36-45",
                ">55","46-55","25-35","46-55",
                "<25","46-55","25-35","46-55",
                "25-35","25-35","46-55","36-45",
                "<25","<25",">55","36-45",
                "36-45","46-55","<25","<25",
                "<25",">55","36-45","46-55",
                "<25",">55","36-45","46-55",
                "36-45",">55","36-45","46-55",
                "<25","46-55","<25","46-55"),
          Score_Q1=c(4,4,3,2,
                  1,1,4,2,
                  1,1,1,2,
                  2,1,4,3,
                  4,3,1,1,
                  1,2,1,1,
                  1,4,1,4,
                  3,4,3,3,
                  1,3,3,1,
                  1,1,2,1,
                  1,2,1,2,
                  1,1,1,1,
                  2,2,2,2,
                  1,2,3,4),
          Score_Q2=c(1,4,1,1,
                     1,2,1,1,
                     1,4,4,4,
                     2,1,1,3,
                     4,3,1,1,
                     1,3,3,3,
                     2,4,1,2,
                     4,4,4,4,
                     1,1,1,1,
                     1,2,3,4,
                     4,4,2,1,
                     1,2,3,2,
                     1,2,1,2,
                     4,3,2,1))

数据框可以拆分为以下部分-

1) ID：受访者 ID

2) 国家：受访者的原籍国

3) 性别：受访者的性别

4) 年龄：受访者年龄

5) Score_Q1：第一季度的满意度得分，从1（非常满意）到4（非常不满意）。

6) Score_Q2：第二季度的满意度得分，从1（非常满意）到4（非常不满意）。

首先进行一些数据清理-

#convert to factor
df$Country=as.factor(df$Country)
df$Gender=as.factor(df$Gender)
df$Age=as.factor(df$Age)

现在我在我的数据集中检查年龄和性别的比率 -

Country 的性别

#1) Gender by Country
split_gender=df %>% select(Country,Gender) %>%
  group_by(Gender,Country) %>%
  summarise(n=n()) %>%
  ungroup() %>%
  select(Country,Gender,n) %>%
  group_by(Country,add=TRUE) %>%
  spread(Country,n)

split_gender=data.frame(apply(split_gender, 2, as.numeric))
split_gender_sample=as.data.frame(sweep(split_gender,2,colSums(split_gender),`/`))
split_gender_sample[1,1]="Female"
split_gender_sample[2,1]="Male"

AgeCountry

#2) Age by Country
split_age=df %>% select(Country,Age) %>%
  group_by(Age,Country) %>%
  summarise(n=n()) %>%
  ungroup() %>%
  select(Country,Age,n) %>%
  group_by(Country,add=TRUE) %>%
  spread(Country,n)

split_age=data.frame(apply(split_age, 2, as.numeric))
split_age[is.na(split_age)] <- 0
split_age_sample=as.data.frame(sweep(split_age,2,colSums(split_age),`/`))
split_age_sample[1,1]="<25"
split_age_sample[2,1]=">55"
split_age_sample[3,1]="25-35"
split_age_sample[4,1]="36-45"
split_age_sample[5,1]="46-55"

#Clean up unwanted dataframes
rm(list=c('split_age','split_gender'))

以上两个步骤给了我两个数据框 - split_age_sample & split_gender_sample。这些数据框包含我的 56 名受访者按国家/地区划分的年龄和性别的样本比率。

我的目标：根据人口统计计算抽样权重

为了使我的数据框更能代表现实，我想根据国家/地区的年龄和性别官方人口比率为我的受访者分配权重 .

这些是我为我所调查的国家/地区找到的官方人口比率。

#Gender by Country
split_gender_official=data.frame(Gender=c("Female","Male"),
                                 Canada=c(0.4,0.6),
                                 China=c(0.3,0.7),
                                 India=c(0.3,0.7),
                                 Mexico=c(0.5,0.5),
                                 Peru=c(0.6,0.4),
                                 Portugal=c(0.5,0.5),
                                 UK=c(0.4,0.6),
                                 US=c(0.4,0.6))
#Age by Country
split_age_official=data.frame(Age=c("<25",">55","25-35","36-45","46-55"),
                                 Canada=c(0.1,0.3,0.3,0.2,0.1),
                                 China=c(0.3,0.05,0.35,0.1,0.2),
                                 India=c(0.5,0.05,0.35,0.05,0.05),
                                 Mexico=c(0.2,0.3,0.2,0.1,0.2),
                                 Peru=c(0.1,0.3,0.2,0.2,0.2),
                                 Portugal=c(0.2,0.1,0.05,0.05,0.6),
                                 UK=c(0.2,0.3,0.1,0.3,0.1),
                                 US=c(0.2,0.3,0.1,0.3,0.1))

期望的输出

根据我的样本比率以及年龄和性别的官方人口比率，我想在一个名为 weights 的单独列中为我的受访者分配权重。

目前我无法弄清楚如何进行此计算。

然后，一旦计算了权重，我想使用weights 列来总结分数。聚合看起来像这样（除了计算中的权重）-

示例：英国的加权汇总分数

#Calculate weighted overall scores by Country & Gender: example UK
weighted_aggregated_scores_gender=df %>%
  select(-Age) %>%
  group_by(Country,Gender) %>%
  filter(Country=='UK') %>%
  summarise(Q1_KPI=round(sum(Score_Q1 %in% c(1,2)/n()),2),
            Q2_KPI=round(sum(Score_Q2 %in% c(1,2)/n()),2))

如果我能在权重计算及其在随后的加权聚合步骤中的使用方面获得任何帮助，我将不胜感激。

【问题讨论】：

    标签： r dplyr sample weighted


    【解决方案1】：

    不确定它是否正是您要找的东西，但这是我发现的。您需要将国家权重与您的数据框合并，然后您可以计算 KPI。

    > # Reshape national weights

> Nombres <- cbind.data.frame("Country" = colnames(split_gender_official)[colnames(split_gender_official) != "Gender"],
+                              "time" = 1:length(colnames(split_gender_official)[colnames(split_gender_official) != "Gender"]))
> Nombres$Country <- as.character(Nombres$Country)
> 
> split_gender_official_resh <- reshape(split_gender_official, direction = "long", varying = Nombres$Country, v.names = "Weights_gend")
> split_age_official_resh <- reshape(split_age_official, direction = "long", varying = Nombres$Country, v.names = "Weights_age")
> 
> split_gender_official_resh$id <- NULL
> split_age_official_resh$id <- NULL
> 
> split_gender_official_resh <- merge(split_gender_official_resh, Nombres, by = "time", all.x = TRUE)
> split_age_official_resh <- merge(split_age_official_resh, Nombres, by = "time", all.x = TRUE)
> 
> split_gender_official_resh$time <- NULL
> split_age_official_resh$time <- NULL

> # Merge weights with df

> df <- merge(df, split_gender_official_resh, by = c("Gender", "Country"), all.x = TRUE)
> df <- merge(df, split_age_official_resh, by = c("Age", "Country"), all.x = TRUE)
> 

> # Print tables
>
> # Without weights
>
> prop.table(table(df$Gender, df$Country), 2)

            Canada     China     India    Mexico      Peru  Portugal        UK        US
  Female 0.5000000 0.2000000 0.5714286 0.7000000 0.3333333 0.5000000 0.5714286 0.5714286
  Male   0.5000000 0.8000000 0.4285714 0.3000000 0.6666667 0.5000000 0.4285714 0.4285714
> prop.table(table(df$Age, df$Country), 2)

           Canada     China     India    Mexico      Peru  Portugal        UK        US
  <25   0.1250000 0.2000000 0.1428571 0.1000000 0.3333333 0.1666667 0.1428571 0.4285714
  >55   0.1250000 0.2000000 0.2857143 0.1000000 0.1666667 0.1666667 0.1428571 0.1428571
  25-35 0.2500000 0.2000000 0.2857143 0.1000000 0.1666667 0.3333333 0.2857143 0.1428571
  36-45 0.1250000 0.2000000 0.1428571 0.5000000 0.1666667 0.1666667 0.1428571 0.1428571
  46-55 0.3750000 0.2000000 0.1428571 0.2000000 0.1666667 0.1666667 0.2857143 0.1428571
> 
> # With weights
> prop.table(xtabs(Weights_gend ~ Gender + Country, df), 2)
        Country
Gender       Canada      China      India     Mexico       Peru   Portugal         UK         US
  Female 0.40000000 0.09677419 0.36363636 0.70000000 0.42857143 0.50000000 0.47058824 0.47058824
  Male   0.60000000 0.90322581 0.63636364 0.30000000 0.57142857 0.50000000 0.52941176 0.52941176
> prop.table(xtabs(Weights_age ~ Gender + Country, df), 2)
        Country
Gender      Canada     China     India    Mexico      Peru  Portugal        UK        US
  Female 0.3333333 0.0500000 0.4642857 0.6250000 0.4545455 0.7142857 0.5000000 0.5000000
  Male   0.6666667 0.9500000 0.5357143 0.3750000 0.5454545 0.2857143 0.5000000 0.5000000
> 
> #  Means with weights and scores
> tapply(df$Score_Q1 * df$Weights_gend, list(df$Gender, df$Country), mean)
       Canada China    India   Mexico Peru  Portugal  UK  US
Female    0.6  0.60 0.600000 1.000000  1.5 1.3333333 0.8 0.7
Male      1.2  1.05 1.166667 1.666667  1.0 0.6666667 1.2 1.4
> tapply(df$Score_Q1 * df$Weights_age, list(df$Age, df$Country), mean)
         Canada China India Mexico Peru Portugal   UK  US
<25   0.1000000  0.90  1.00   0.20  0.2     0.20 0.20 0.4
>55   0.9000000  0.10  0.05   0.60  1.2     0.20 0.30 0.3
25-35 0.6000000  0.35  0.35   0.80  0.8     0.15 0.25 0.3
36-45 0.2000000  0.10  0.15   0.22  0.4     0.05 0.30 0.6
46-55 0.1666667  0.20  0.20   0.60  0.2     1.20 0.30 0.2
>

    希望对你有帮助。

    【讨论】：

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