Getting the maximum common words in R - r

Question

I have data of the form:
ID A1 A2 A3 ... A100
1 john max karl ... kevin
2 kevin bosy lary ... rosy
3 karl lary bosy ... hale
.
.
.
10000 isha john lewis ... dave
I want to get one ID for each ID such that both of them have maximum number of common attributes(A1,A2,..A100)
How can I do this in R ?
Edit: Let's call the output a MatchId:
ID MatchId
1 70
2 4000
.
.
10000 3000

Answer 1

I think this gets what you're looking for:
library(dplyr)
# make up some data
set.seed(1492)
rbind_all(lapply(1:15, function(i) {
x <- cbind.data.frame(stringsAsFactors=FALSE, i, t(sample(LETTERS, 10)))
colnames(x) <- c("ID", sprintf("A%d", 1:10))
x
})) -> dat
print(dat)
## Source: local data frame [15 x 11]
##
## ID A1 A2 A3 A4 A5 A6 A7 A8 A9 A10
## 1 1 H F E C B A R J Z N
## 2 2 Q P E M L Z C G V Y
## 3 3 Q J D N B T L K G Z
## 4 4 D Y U F V O I C A W
## 5 5 T Z D I J F R C B S
## 6 6 Q D H U P V O E R N
## 7 7 C L I M E K N S X Z
## 8 8 M J S E N O F Y X I
## 9 9 R H V N M T Q X L S
## 10 10 Q H L Y B W S M P X
## 11 11 M N J K B G S X V R
## 12 12 W X A H Y D N T Q I
## 13 13 K H V J D X Q W A U
## 14 14 M U F H S T W Z O N
## 15 15 G B U Y E L A Q W O
# get commons
rbind_all(lapply(1:15, function(i) {
rbind_all(lapply(setdiff(1:15, i), function(j) {
data.frame(id1=i,
id2=j,
common=length(intersect(c(t(dat[i, 2:11])),
c(t(dat[j, 2:11])))))
}))
})) -> commons
commons %>%
group_by(id1) %>%
top_n(1, common) %>%
filter(row_number()==1) %>%
select(ID=id1, MatchId=id2)
## Source: local data frame [15 x 2]
## Groups: ID
##
## ID MatchId
## 1 1 5
## 2 2 7
## 3 3 5
## 4 4 12
## 5 5 1
## 6 6 9
## 7 7 8
## 8 8 7
## 9 9 10
## 10 10 9
## 11 11 9
## 12 12 13
## 13 13 12
## 14 14 8
## 15 15 2

Answer 2

Using similar data as provided by #hrbrmstr
set.seed(1492)
dat <- do.call(rbind, lapply(1:15, function(i) {
x <- cbind.data.frame(stringsAsFactors=FALSE, i, t(sample(LETTERS, 10)))
colnames(x) <- c("ID", sprintf("A%d", 1:10))
x
}))
You could achieve the same using base R only
Res <- sapply(seq_len(nrow(dat)),
function(x) apply(dat[-1], 1,
function(y) length(intersect(dat[x, -1], y))))
diag(Res) <- -1
cbind(dat[1], MatchId = max.col(Res, ties.method = "first"))
# ID MatchId
# 1 1 5
# 2 2 7
# 3 3 5
# 4 4 12
# 5 5 1
# 6 6 9
# 7 7 8
# 8 8 7
# 9 9 10
# 10 10 9
# 11 11 9
# 12 12 13
# 13 13 12
# 14 14 8
# 15 15 2

Answer 3

If I understand correctly, the requirement is to obtain the maximum number of common attributes for each ID.
Frequency tables can be obtained using table() and recursively in lapply(), assuming that ID column is unique - slight modification is necessary if not (unique(df$ID) rather than df$ID in lapply()). The maximum frequencies can be taken and, if there is a tie, only the first one is chosen. Finally they are combined by do.call().
df <- read.table(header = T, text = "
ID A1 A2 A3 A100
1 john max karl kevin
2 kevin bosy lary rosy
3 karl lary bosy hale
10000 isha john lewis dave")
do.call(rbind, lapply(df$ID, function(x) {
tbl <- table(unlist(df[df$ID == x, 2:ncol(df)]))
data.frame(ID = x, MatchId = tbl[tbl == max(tbl)][1])
}))
# ID MatchId
#john 1 1
#kevin 2 1
#karl 3 1
#isha 10000 1