Is there a more efficient way to handle facts which are duplicating in an R dataframe?

Question

I have a dataframe which looks like this:

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)

The dataframes dimensions work like this:

• There will always be an ID/key which singularly and uniquely identifies a submitted fact
• There will always be a dimension for a given fact defining the Overall_Category of which a submitted fact belongs.
• Most of the time - but not always - there will be a dimension for a "Descriptor",
• If there is a "Descriptor" dimension for a given fact, there will be another "Members" dimension to show possible members within "Descriptor".

The problem is that a single submitted fact is duplicated for a given ID based on how many dimensions apply to the given fact. What I'd like is a way to show the fact only once, based on its ID, and have the applicable dimensions stored against that single ID.

I've achieved it by doing this:

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", "")

But it seems like it wouldn't scale well for facts with many dimensions due to the pivot_wide, and in general doesn't seem like a very efficient approach.

Is there a better way to do this?

Answer 1

I think you want simple paste with sep and collapse arguments

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

You can unite the columns and for each ID combine them together and take average of Fact values.

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

An option with str_c

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