CODEWITHSUNDEEP
raggregate
Chudar
Chudar

Reputation: 394

Aggregating rows with same Ids and retaining only unique entries in R

I am a beginner in R. I have data frame in R as follows:

 Id          Values
A_0_d   Low_5524; Low_6412; Hi_50567
A_0_d   Low_5509; Low_6412; Low_6897; Hi_16021
A_0_d   Low_5524; Low_4930; Low_5886
B_1_d   Low_3697; Low_4519; Low_5524
C_3_d   Low_5576; Low_5581
C_3_d   Hi_30246
C_3_d   Low_5576; Hi_30246

I would like aggregate the data frame based on the Ids i.e. group all the values of same ID in a single row and retain only unique entries like follows:

A_0_d   Low_5524; Low_6412; Hi_50567; Low_5509; Low_6897; Hi_16021; Low_4930; Low_5886  
B_1_d   Low_3697; Low_4519; Low_5524
C_3_d   Low_5576; Low_5581; Hi_30246

Can I make use of aggregate function.Kindly guide me.

Upvotes: 3

Views: 672

Answers (3)

Veerendra Gadekar
Veerendra Gadekar

Reputation: 4472

Using aggregate you could try this

aggregate(Values ~ Id, df, function(x) paste(unique(x), collapse = '; '))

Upvotes: 3

Ronak Shah
Ronak Shah

Reputation: 389012

Using aggregate you can 

aggregate(Values~Id, df, function(x) unique(unlist(strsplit(x, ";"))))

#   Id                                                                         Values
#1  A_0_d Low_5524, Low_6412, Hi_50567, Low_5509, Low_6897, Hi_16021, Low_4930, Low_5886
#2 B_1_d                                                   Low_3697, Low_4519, Low_5524
#3 C_3_d                                                   Low_5576, Low_5581, Hi_30246

Upvotes: 2

akrun
akrun

Reputation: 887213

Convert the 'data.frame' to 'data.table' (setDT(df1)). Then, split the 'Values' by "; " after grouping by 'Ids', unlist the output, get the unique elements and paste it together

library(data.table)
setDT(df1)[, .(Values = paste(unique(unlist(strsplit(Values, "; "))), 
                                                 collapse="; ")), by = Id]
#   Id
#1: A_0_d
#2: B_1_d
#3: C_3_d
#                                                                           Values
#1: Low_5524; Low_6412; Hi_50567; Low_5509; Low_6897; Hi_16021; Low_4930; Low_5886
#2:                                                   Low_3697; Low_4519; Low_5524
#3:                                                   Low_5576; Low_5581; Hi_30246

Upvotes: 2

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