Faster looping in r - r

Question

I have two data frames Test and User.
Test has 100 000 rows while User has 1 400 000 rows. I want to extract specific vectors from User data frame and merge this with Test data frame. Ex I want Income and Cat for every row in Test from User. Rows in Test is with repeated elements and I want any one value from User file. I want to keep the test file without removing duplicates.
Ex for Name A Income is 100 , Cat is M & L. Since M occurs first I need M.
> Test
Name Income Cat
A
B
C
D
...
User Cat Income
A M 100
B M 320
C U 400
D L 900
A L 100
..
I used for loop but takes lot of time. I do not want to use merge function.
for (i in 1:nrow(Test)
{
{ Test[i,"Cat"]<-User[which(User$Name==Test[i,"Name"]),"Cat"][1]}
{ Test[i,"Income"]<-User[which(User$Name==Test[i,"Name"]),"Income"][1]}}
I used merge as well but the overall count for Test file is more than 100k rows. It is appending extra elements.
I want a faster way to do by avoiding for loop and merge. Can someone suggest any apply family functions.

Answer 1

You can use match to find the first matching row (then vectorize the copying):
# Setup the data
User=data.frame(User=c('A','B','C','D','A'),Cat=c('M','M','U','L','L'),
Income=c(100,320,400,900,100))
Test=data.frame(Name=c('A','B','C','D'))
Test$Income<-NA
Test$Cat<-NA
> Test
Name Income Cat
1 A NA NA
2 B NA NA
3 C NA NA
4 D NA NA
## Copy only the first match to from User to Test
Test[,c("Income","Cat")]<-User[match(Test$Name,User$User),c("Income","Cat")]
> Test
Name Income Cat
1 A 100 M
2 B 320 M
3 C 400 U
4 D 900 L

Answer 2

Using dplyr package you can do something like this:
library(dplyr)
df %>% group_by(Name) %>% slice(1)
For your example, you get:
Original data frame:
df
Name Cat Income
1 A M 100
2 B M 320
3 C U 400
4 D L 900
5 A L 100
Picking first occurrence:
df %>% group_by(Name) %>% slice(1)
Source: local data frame [4 x 3]
Groups: Name [4]
Name Cat Income
(chr) (chr) (int)
1 A M 100
2 B M 320
3 C U 400
4 D L 900