合并相同的列组合

Question

我有一个看起来像这样的表：

在表格的下方，Target.Country中的国家/地区会重复Source.Country，因此会重复这些组合，但会有不同的数字，总和和方法。是否可能在组合相同时，将剩余的列加在一起并添加一列以查找平均值？

例如：

Source.Country Target.Country number   sum_intensity   mean_intensity
North Korea     South Korea    26492     10674.9         0.402
South Korea     North Korea    34912     53848.3         1.542

成为：

Source.Country Target.Country  number  sum_intensity mean_intensity  Average 
North Korea     South Korea     61404   64523.2         1.944         1.05

任何帮助都会很棒！

Answer 1

@Axeman在评论中提出的类似解决方案：

library(purrr)
library(dplyr)
df=data.frame(Source.Country=c('North Korea', 'South Korea'), 
              Target.Country=c('South Korea', 'North Korea'),
              number=c(26492, 34912),
              sum_intensity=c(10674.9, 53848.3),
              mean_intensity=c(0.402, 1.542))

df %>% mutate(grp = purrr::map2_chr(Source.Country, Target.Country, ~paste(sort(c(as.character(.x), as.character(.y))), collapse=' '))) %>% 
    group_by(grp) %>% 
    summarise(number = sum(number), 
    sum_intensity = sum(sum_intensity), 
    mean_intensity = sum(mean_intensity), 
    average = sum_intensity/number)

# # A tibble: 1 x 5
#   grp                     number sum_intensity mean_intensity average
#   <chr>                    <dbl>         <dbl>          <dbl>   <dbl>
# 1 North Korea South Korea 61404.        64523.           1.94    1.05

一些小调整：

• 在collapse命令
中确实需要paste
• 需要as.character以防止国家/地区名称被强制转换为整数
• mean_intensity无法用作摘要中的输出，然后作为输入，但无论如何number不平衡时，平均值的平均值并没有多大意义。我只是从总和中重新计算了平均值

Answer 2

我增加了数据框以检查代码是否正常工作

df1<-rbind(c( "North Korea ","South Korea" ,       26492 ,    10674.9 ,        
0.402), c(   "South Korea",  "North Korea"  ,  34912  ,   53848.3   ,      1.542),
c( "Canada ","South Korea" ,       26492 ,    10674.9 ,        0.402),
c(   "South Korea",  "Canada"  ,  34912  ,   53848.3   ,      1.542))
colnames(df1)<-c("Source.Country",  "Target.Country",  "number",  "sum_intensity",  
"mean_intensity")
df1<-data.frame(df1)

df1$number<-as.numeric(as.character(df1$number))
df1$sum_intensity<-as.numeric(as.character(df1$sum_intensity))
df1$mean_intensity<-as.numeric(as.character(df1$mean_intensity))

df1$Countries<-apply(cbind(df1$Source.Country, df1$Target.Country), 1, function(x) 
paste(sort(x), collapse=" "))

#
library(reshape)
m1 <- aggregate(df1$number~df1$Countries,data=df1,FUN=mean)
m2 <- aggregate(df1$sum_intensity~df1$Countries,data=df1,FUN=mean)
m3 <- aggregate(df1$mean_intensity~df1$Countries,data=df1,FUN=mean)

mvtab <- merge(rename(m1,c(y="number")),
               rename(m2,c(y="sum_intensity")))

mtab2<-merge(mvtab, rename(m3,c(y="mean_intensity")))