我使用 GROUPBY 然后使用 SUMMARIZE 将总标签添加到数据框。 BUT 表示总水平的 % 数据是错误的

Question

我使用 GROUPBY 然后 SUM 然后 SUMMARIZE 将总标签添加到数据框。 BUT 表示总水平的 % 数据是错误的。因此，我想用具有正确结果的计算覆盖百分比变量 'percentage absent staff'。问题是它是一个长数据集，不可能手动完成。正在寻找好的解决方案，LOOP 或其他东西？

代码：

Date=c("01/09/2020","01/09/2020","01/09/2020","01/09/2020",
       "01/09/2020","01/09/2020","01/09/2020","01/09/2020",
       "02/09/2020","02/09/2020","02/09/2020","02/09/2020",
       "02/09/2020","02/09/2020","02/09/2020","02/09/2020")

Asset=c("Blue Hotel","Blue Hotel","Blue Hotel","Blue Hotel",
        "Green Hotel","Green Hotel","Green Hotel","Green Hotel",
        "Blue Hotel","Blue Hotel","Blue Hotel","Blue Hotel",
        "Green Hotel","Green Hotel","Green Hotel","Green Hotel")

Variable=c("hotel staff","bar staff","absent staff","percentage absent 
   staff",
           "hotel staff","bar staff","absent staff","percentage absent staff",
           "hotel staff","bar staff","absent staff","percentage absent staff",
           "hotel staff","bar staff","absent staff","percentage absent staff")
value=c(5,10,3,0.2,4,8,2,0.17,5,10,3,0.20,6,3,3,0.33)

df=data.frame(Date,Asset,Variable,value)

#to create totals
df2= df %>% 
  group_by(Date,Variable) %>%
  summarise(value = sum(as.numeric(value), na.rm=F)) %>% ungroup()

Answer 1

我不确定你想要什么计算，因为第一个“正确”的计算看起来像 absent_staff/(hotel_staff + bar_staff + absent_staff)第二个正确的计算看起来像 absent_staff/(hotel_staff + bar_staff)。但是，您可以根据自己的喜好制定以下解决方案。

df2= df %>% 
  group_by(Date,Variable) %>%
  summarise(value = sum(as.numeric(value), na.rm=F)) %>% 
  ungroup() %>% 
  group_by(Date) %>% 
  mutate(value = case_when(
           Variable == "percentage absent staff" ~ value[which(Variable == "absent staff")]/
                    sum(value[which(Variable %in% c("absent staff", "bar staff", "hotel staff"))]), 
           TRUE ~ value)
         )
df2
# # A tibble: 8 x 3
# # Groups:   Date [2]
#     Date       Variable                 value
#     <chr>      <chr>                    <dbl>
# 1 01/09/2020 absent staff             5    
# 2 01/09/2020 bar staff               18    
# 3 01/09/2020 hotel staff              9    
# 4 01/09/2020 percentage absent staff  0.156
# 5 02/09/2020 absent staff             6    
# 6 02/09/2020 bar staff               13    
# 7 02/09/2020 hotel staff             11    
# 8 02/09/2020 percentage absent staff  0.2

在上面，您按 Date 对汇总数据进行分组，然后用条件表达式替换值。当 Variable 等于 "percentage absent staff" 时，该值将是 "absent staff" 的值除以 "absent staff", "bar staff", "hotel staff" 的值之和。所以，如果你真的想要上面的第二个计算，你可以把 "absent staff" 留在这个向量之外。否则，value 将返回原来的值。

编辑

要回答评论中的问题，如果同一变量中还有其他驻留值 - Variable 具有相同的结构，您可以将它们重新添加为：

Date=c("01/09/2020","01/09/2020","01/09/2020","01/09/2020",
       "01/09/2020","01/09/2020","01/09/2020","01/09/2020",
       "02/09/2020","02/09/2020","02/09/2020","02/09/2020",
       "02/09/2020","02/09/2020","02/09/2020","02/09/2020")

Asset=c("Blue Hotel","Blue Hotel","Blue Hotel","Blue Hotel",
        "Green Hotel","Green Hotel","Green Hotel","Green Hotel",
        "Blue Hotel","Blue Hotel","Blue Hotel","Blue Hotel",
        "Green Hotel","Green Hotel","Green Hotel","Green Hotel")

Variable=c("hotel staff","bar staff","absent staff","percentage absent staff",
           "hotel staff","bar staff","absent staff","percentage absent staff",
           "hotel staff","bar staff","absent staff","percentage absent staff",
           "hotel staff","bar staff","absent staff","percentage absent staff")
value=c(5,10,3,0.2,4,8,2,0.17,5,10,3,0.20,6,3,3,0.33)

df=data.frame(Date,Asset,Variable,value)

#to create totals

dfr <- df
dfr$Variable <- gsub("staff", "residents", dfr$Variable)
dfr$value <- rpois(nrow(dfr), 25)
df <- bind_rows(df, dfr)
df[c(1:5, 17:21), ]


df2= df %>% 
  group_by(Date,Variable) %>%
  summarise(value = sum(as.numeric(value), na.rm=F)) %>% ungroup()



df2a= df2 %>% 
  group_by(Date,Variable) %>% 
  summarise(value = sum(as.numeric(value), na.rm=F)) %>% 
  ungroup() %>% 
  group_by(Date) %>% 
  mutate(value = case_when( Variable == "percentage absent staff" ~ value[which(Variable == "absent staff")]/ 
                              sum(value[which(Variable %in% c("absent staff", "bar staff", "hotel staff"))]),
                            Variable == "percentage absent residents" ~ value[which(Variable == "absent residents")]/ 
                              sum(value[which(Variable %in% c("absent residents", "bar residents", "hotel residents"))]),
                            TRUE ~ value) )

我使用 GROUPBY 然后使用 SUMMARIZE 将总标签添加到数据框。 BUT 表示总水平的 % 数据是错误的

I used GROUPBY then SUMMARISE to add a total label to dataframe. BUT means the % data on total level is wrong

r

loops

group-by

summarize