有没有更有效的方法来处理在 R 数据帧中重复的事实？

Question

我有一个看起来像这样的数据框：

ID <- c(1,1,1,2,2,2,2,3,3,3,3)
Fact <- c(233,233,233,50,50,50,50,15,15,15,15)
Overall_Category <- c("Purchaser","Purchaser","Purchaser","Car","Car","Car","Car","Car","Car","Car","Car")
Descriptor <- c("Country", "Gender", "Eyes", "Color", "Financed", "Type", "Transmission", "Color", "Financed", "Type", "Transmission")
Members <- c("America", "Male", "Brown", "Red", "Yes", "Sedan", "Manual", "Blue","No", "Van", "Automatic")

df <- data.frame(ID, Fact, Overall_Category, Descriptor, Members)

数据框维度的工作方式如下：

• 总会有一个 ID/key 唯一地和唯一地标识提交的事实
• 给定事实总是有一个维度来定义提交的事实所属的 Total_Category。
• 大多数时候——但并非总是如此——“描述符”会有一个维度，
• 如果有一个“描述”尺寸对于一个给定的事实，就会有另一个“成员”的尺寸，以示“描述”中可能的成员。

问题在于，根据应用于给定事实的维度数量，针对给定 ID 重复提交的单个事实。 我想要的是一种根据其 ID 仅显示一次事实的方法，并将适用的维度存储在该单个 ID 上。

我通过这样做实现了它：

df1 <- pivot_wider(df, 
id_cols = ID,
names_from = c(Overall_Category, Descriptor, Members),
names_prefix = "zzzz",
values_from = Fact,
names_sep = "-",
names_repair = "unique")

ColumnNames <- df1 %>% select(matches("zzzz")) %>% colnames()


df2 <- df1 %>% mutate(mean_sel = rowMeans(select(., ColumnNames), na.rm = T))
df3 <- df2 %>% mutate_at(ColumnNames, function(x) ifelse(!is.na(x), deparse(substitute(x)), NA))
df3 <- df3 %>% unite('Descriptor', ColumnNames, na.rm = T, sep = "_")
df3 <- df3 %>% mutate_at("Descriptor", str_replace_all, "zzzz", "")

但是由于pivot_wide，它似乎不能很好地扩展具有多个维度的事实，并且通常看起来不是一种非常有效的方法。

有一个更好的方法吗？

Answer 1

我认为你想要带有sep和collapse参数的简单paste

library(dplyr, warn.conflicts = F)

df %>% group_by(ID, Fact) %>%
  summarise(Descriptor = paste(paste(Overall_Category, Descriptor, Members, sep = '-'), collapse = '_'), .groups = 'drop')

# A tibble: 3 x 3
     ID  Fact Descriptor                                                            
  <dbl> <dbl> <chr>                                                                 
1     1   233 Purchaser-Country-America_Purchaser-Gender-Male_Purchaser-Eyes-Brown  
2     2    50 Car-Color-Red_Car-Financed-Yes_Car-Type-Sedan_Car-Transmission-Manual 
3     3    15 Car-Color-Blue_Car-Financed-No_Car-Type-Van_Car-Transmission-Automatic

Answer 2

您可以unite的列和每个ID它们组合在一起，并采取平均的Fact值。

library(dplyr)
library(tidyr)

df %>%
  unite(Descriptor, Overall_Category:Members, sep = '-', na.rm = TRUE) %>%
  group_by(ID) %>%
  summarise(Descriptor = paste0(Descriptor, collapse = '_'), 
            mean_sel = mean(Fact, na.rm = TRUE))

#     ID Descriptor                                               mean_sel
#  <dbl> <chr>                                                       <dbl>
#1     1 Purchaser-Country-America_Purchaser-Gender-Male_Purchas…      233
#2     2 Car-Color-Red_Car-Financed-Yes_Car-Type-Sedan_Car-Trans…       50
#3     3 Car-Color-Blue_Car-Financed-No_Car-Type-Van_Car-Transmi…       15

Answer 3

str_c一个选项

library(dplyr)
library(stringr)
df %>%
   group_by(ID, Fact) %>%
   summarise(Descriptor = str_c(Overall_Category, Descriptor, Members, sep= "-", collapse="_"), .groups = 'drop')