CODEWITHSUNDEEP
rdataframematch
DisUsr1023
DisUsr1023

Reputation: 21

Replacing values in a column where there is a match

I'm new to R programming and I'm stuck on the example below.

Basically I have two data sets:

dataset1: 

ID       Category        
1        CatZZ         
2        CatVV         
3        CatAA  
4        CatQQ

dataset2: 

ID  Category  
1   Cat600  
3   Cat611

I'm trying to overwrite the 'category' values in dataset1 with the 'category' values in dataset2 where there is an ID match between the two data sets.

So the outcome would look something like this:

dataset1: 

ID  Category    
1   Cat600  
2   CatVV  
3   Cat611  
4   CatQQ

Upvotes: 2

Views: 130

Answers (5)

moodymudskipper
moodymudskipper

Reputation: 47310

You can stack df2 on top of df1 and keep the first instance for each ID.

With tidyverse that would be :

library(tidyverse)
bind_rows(df2,df1) %>%
  group_by(ID) %>%
  slice(1) %>%
  ungroup()

# # A tibble: 4 x 2
#      ID Category
#   <int>    <chr>
# 1     1   Cat600
# 2     2    CatVV
# 3     3   Cat611
# 4     4    CatQQ

Or a base version (which reorders the rows) :

subset(rbind(df2,df1), !duplicated(ID))
#   ID Category
# 1  1   Cat600
# 2  3   Cat611
# 4  2    CatVV
# 6  4    CatQQ

Upvotes: 1

markus
markus

Reputation: 26343

A base R approach that uses match 

df1$Category[match(df2$ID, df1$ID)] <- df2$Category
df1
#  ID Category
#1  1   Cat600
#2  2    CatVV
#3  3   Cat611
#4  4    CatQQ

data

df1 <- structure(list(ID = 1:4, Category = c("CatZZ", "CatVV", "CatAA", 
"CatQQ")), .Names = c("ID", "Category"), class = "data.frame", row.names = c(NA, 
-4L))

df2 <- structure(list(ID = c(1L, 3L), Category = c("Cat600", "Cat611"
)), .Names = c("ID", "Category"), class = "data.frame", row.names = c(NA, 
-2L))

Upvotes: 2

tushaR
tushaR

Reputation: 3116

Using a combination of base R match function and data.table's set function:

matchinds = na.omit(match(dataset1$ID,dataset2$ID)) # this will give index of dataset2$ID where values of dataset1$ID were equal to values of dataset2$ID

set(x=dataset1,i=matchinds,j="Category",value=dataset2$category[matchinds])  #this will set values at matching indexes in dataset1 Category column equal to Category column matching index values in dataset2

Upvotes: 2

ira
ira

Reputation: 2644

Another option is to use data.table package.

Using the same setup as @tmfmnk in his answer:

Construct the sample data set:

df1 <- read.table(text = "ID       Category        
1        CatZZ         
2        CatVV         
3        CatAA  
4        CatQQ", header = TRUE, stringsAsFactors = FALSE)

df2 <- read.table(text = "ID  Category  
1   Cat600  
                  3   Cat611", header = TRUE, stringsAsFactors = FALSE)

Load the data.table package and convert dataframes to data tables:

library(data.table)
df1 <- data.table(df1)
df2 <- data.table(df2)

Perform a left join

(take all values from df1, where ID matches with df2, and add there the category from df2, then create a new column combining info from df1 and df2)

a <- df2[df1, on = "ID"][, a := ifelse(is.na(Category), i.Category, Category)]

There is a nice question and answer on data.table joins here: Left join using data.table

Also, to get exactly the result you asked for, you can do:

a <- df2[df1, on = "ID"][, list(ID, Category = ifelse(is.na(Category), i.Category, Category))]

Upvotes: 2

tmfmnk
tmfmnk

Reputation: 39858

In tidyverse you can do:

df1 %>%
 left_join(df2, by = c("ID" = "ID")) %>% #Merging the two dfs on ID
 mutate(Category = if_else(!is.na(Category.y), Category.y, Category.x)) %>% #If there is a match, taking the value from df2, otherwise from df1
 select(ID, Category) #Deleting the redundant variables

  ID Category
1  1   Cat600
2  2    CatVV
3  3   Cat611
4  4    CatQQ

Or:

df1 %>%
 left_join(df2, by = c("ID" = "ID")) %>% #Merging the two dfs on ID
 gather(var, val, -ID) %>% #Transforming the data from wide to long format
 arrange(ID) %>% #Arranging by ID
 group_by(ID) %>% #Grouping by ID
 mutate(Category = if_else(!is.na(nth(val, 2)), nth(val, 2), first(val))) %>% #If non-NA, taking the value from df2, otherwise from df1
 spread(var, val) %>% #Returning the data to wide format
 select(ID, Category) #Removing the redundant variables 

     ID Category
  <int> <chr>   
1     1 Cat600  
2     2 CatVV   
3     3 Cat611  
4     4 CatQQ

Sample data:

df1 <- read.table(text = "ID       Category        
1        CatZZ         
2        CatVV         
3        CatAA  
4        CatQQ", header = TRUE, stringsAsFactors = FALSE)

df2 <- read.table(text = "ID  Category  
1   Cat600  
                  3   Cat611", header = TRUE, stringsAsFactors = FALSE)

Upvotes: 3

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