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I am trying to permute a char vector a of variable length picking 3 elements every time, without repetition. Ordering counts only for the first element but doesn't for second and third (e.g. abc != bac != cab, but abc = acb and bca = bac). Each set of 3 permuted elements should be a row in a dataframe b.
A vector with letters a,b,c,d,e would result in this expected output:
abc
abd
abe
acd
ace
ade
bac
bad
bae
bcd
bce
bde
cab
cad
cae
cbd
cbe
cde
dab
dac
dae
dbc
dbe
dce
eab
eac
ead
ebc
ebd
ecd
Using 3 for loops I think I was able to achieve this output, but it is slow if the vector is long.
a = letters[1:5]
aL = length(a)
b <- data.frame(var1 = character(),
var2 = character(),
var3 = character(),
stringsAsFactors = FALSE)
# restricted permutations for moderation
pracma::tic()
for(i in 1:aL){
for(j in 1:(aL-1)){
for(k in (j+1):aL){
if(j != i & k != i) {
b <- rbind(b, data.frame(a[i], a[j], a[k])) }
}
}
}
pracma::toc()
#> elapsed time is 0.070000 seconds
b
#> a.i. a.j. a.k.
#> 1 a b c
#> 2 a b d
#> 3 a b e
#> 4 a c d
#> 5 a c e
#> 6 a d e
#> 7 b a c
#> 8 b a d
#> 9 b a e
#> 10 b c d
#> 11 b c e
#> 12 b d e
#> 13 c a b
#> 14 c a d
#> 15 c a e
#> 16 c b d
#> 17 c b e
#> 18 c d e
#> 19 d a b
#> 20 d a c
#> 21 d a e
#> 22 d b c
#> 23 d b e
#> 24 d c e
#> 25 e a b
#> 26 e a c
#> 27 e a d
#> 28 e b c
#> 29 e b d
#> 30 e c d
Created on 2019-07-17 by the reprex package (v0.2.1)
How can I achieve the same outcome in less time. Is recursion faster?
Any help is greatly appreciated. Thank you.
I propose the following solution:
a = letters[1:5]
A = t(combn(a,3)) # create all possible three-letter combinations,
# disregarding the order
Full = rbind(A, A[,3:1], A[,c(2,3,1)]) # put every of the elements of the
# differing combinations in first place once
Here's one option for your specific example:
library(gtools)
library(dplyr)
# example vector
vec = letters[1:5]
# vectorised function to rearrange elements (based on your restriction)
f = function(x1,x2,x3) paste0(c(x1, sort(c(x2,x3))), collapse = " ")
f = Vectorize(f)
permutations(length(vec), 3, vec) %>% # get permutations
data.frame(., stringsAsFactors = F) %>% # save as data frame
mutate(vec = f(X1,X2,X3)) %>% # apply function to each row
distinct(vec, .keep_all = T) # keep distinct vec values
# X1 X2 X3 vec
# 1 a b c a b c
# 2 a b d a b d
# 3 a b e a b e
# 4 a c d a c d
# 5 a c e a c e
# 6 a d e a d e
# 7 b a c b a c
# ...
Not clear if you want your output to be 3 separate columns with 1 element each, or one column with the vector, so I'm keeping both for you to choose from. You can keep columns {X1, X2, X3} or just vec.
The following is a straightforward rewrite of the triple for loop as a triple lapply loop.
t1 <- system.time({
for(i in 1:aL){
for(j in 1:(aL-1)){
for(k in (j+1):aL){
if(j != i & k != i) {
b <- rbind(b, data.frame(a[i], a[j], a[k])) }
}
}
}
})
t2 <- system.time({
d <- lapply(1:aL, function(i){
tmp <- lapply(1:(aL-1), function(j){
tmp <- lapply((j+1):aL, function(k){
if(j != i & k != i) c(a[i], a[j], a[k])
})
do.call(rbind, tmp)
})
do.call(rbind, tmp)
})
d <- do.call(rbind.data.frame, d)
names(d) <- paste("a", 1:3, sep = ".")
})
all.equal(b, d)
#[1] "Names: 3 string mismatches"
rbind(t1, t2)
# user.self sys.self elapsed user.child sys.child
#t1 0.051 0 0.051 0 0
#t2 0.017 0 0.018 0 0
I have 3 columns
2 are the same length
1 is of a lesser length
here are the columns:
column1 <- letters[1:10]
column2 <- letters[1:15]
column3 <- letters[1:15]
I want all 3 columns to be joined together but have the missing 5 values in column1 to be NA?
What can i do to achieve this? a tibble?
You can change length of a vector
column1 <- letters[1:10]
column2 <- letters[1:15]
length(column1) <- length(column2)
Now
> column1
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" NA NA NA NA NA
We can wrap it in function
cbind_dif <- function(x = list()){
# Find max length
max_length <- max(unlist(lapply(x, length)))
# Set length of each vector as
res <- lapply(x, function(x){
length(x) <- max_length
return(x)
})
return(as.data.frame(res))
}
# Example usage:
> cbind_dif(list(column1 = column1, column2 = column2))
column1 column2
1 a a
2 b b
3 c c
4 d d
5 e e
6 f f
7 g g
8 h h
9 i i
10 j j
11 <NA> k
12 <NA> l
13 <NA> m
14 <NA> n
15 <NA> o
n <- max(length(column1), length(column2), length(column3))
data.frame(column1[1:n],column2[1:n],column3[1:n])
column1.1.n. column2.1.n. column3.1.n.
1 a a a
2 b b b
3 c c c
4 d d d
5 e e e
6 f f f
7 g g g
8 h h h
9 i i i
10 j j j
11 <NA> k k
12 <NA> l l
13 <NA> m m
14 <NA> n n
15 <NA> o o
Using cbind.fill from rowr package you can do it easily.
library(rowr)
new<- cbind.fill(column1,column2,column3)
I hope this helps
column1 <- letters[1:10]
column2 <- letters[1:15]
column3 <- letters[1:15]
tibble(a = c(column1, rep(NA, length(column2) - length(column1))), b = column2, c = column3)
# A tibble: 15 × 3
a b c
<chr> <chr> <chr>
1 a a a
2 b b b
3 c c c
4 d d d
5 e e e
6 f f f
7 g g g
8 h h h
9 i i i
10 j j j
11 NA k k
12 NA l l
13 NA m m
14 NA n n
15 NA o o
I am attempting to write a simulation that involves randomly re-assigning items to categories with some restrictions.
Lets say I have a collection of pebbles 1 to N distributed across buckets A through J:
set.seed(100)
df1 <- data.frame(pebble = 1:100,
bucket = sample(LETTERS[1:10], 100, T),
stringsAsFactors = F)
head(df1)
#> pebble bucket
#> 1 1 D
#> 2 2 C
#> 3 3 F
#> 4 4 A
#> 5 5 E
#> 6 6 E
I want to randomly re-assign pebbles to buckets. Without restrictions I could do it like so:
random.permutation.df1 <- data.frame(pebble = df1$pebble, bucket = sample(df1$bucket))
colSums(table(random.permutation.df1))
#> A B C D E F G H I J
#> 4 7 13 14 12 11 11 10 9 9
colSums(table(df1))
#> A B C D E F G H I J
#> 4 7 13 14 12 11 11 10 9 9
Importantly this re-assigns pebbles while ensuring that each bucket retains the same number (because we are sampling without replacement).
However, I have a set of restrictions such that certain pebbles cannot be assigned to certain buckets. I encode the restrictions in df2:
df2 <- data.frame(pebble = sample(1:100, 10),
bucket = sample(LETTERS[1:10], 10, T),
stringsAsFactors = F)
df2
#> pebble bucket
#> 1 33 I
#> 2 39 I
#> 3 5 A
#> 4 36 C
#> 5 55 J
#> 6 66 A
#> 7 92 J
#> 8 95 H
#> 9 2 C
#> 10 49 I
The logic here is that pebbles 33 and 39 cannot be placed in bucket I, or pebble 5 in bucket A, etc. I would like to permute which pebbles are in which bucket subject to these restrictions.
So far, I've thought of tackling it in a loop as below, but this does not result in buckets retaining the same number of pebbles:
perms <- character(0)
cnt <- 1
for (p in df1$pebble) {
perms[cnt] <- sample(df1$bucket[!df1$bucket %in% df2$bucket[df2$pebble==p]], 1)
cnt <- cnt + 1
}
table(perms)
#> perms
#> A B C D E F G H I J
#> 6 7 12 22 15 1 14 7 7 9
I then tried sampling positions, and then removing that position from the available buckets and the available remaining positions. This is also not working, and I suspect it is because I am sampling my way into branches of the tree that do not yield solutions.
set.seed(42)
perms <- character(0)
cnt <- 1
ids <- 1:nrow(df1)
bckts <- df1$bucket
for (p in df1$pebble) {
id <- sample(ids[!bckts %in% df2$bucket[df2$pebble==p]], 1)
perms[cnt] <- bckts[id]
bckts <- bckts[-id]
ids <- ids[ids!=id]
cnt <- cnt + 1
}
table(perms)
#> perms
#> A B C D E F G J
#> 1 1 4 1 2 1 2 2
Any thoughts or advice much appreciated (and apologies for the length).
EDIT:
I foolishly forgot to clarify that I was previously solving this by just resampling until I got a draw that didn't violate any of the conditions in df2, but I now have many conditions such that this would make my code take too long to run. I am still up for trying to force it if I could figure out a way to make forcing it faster.
I have a solution (I managed to write it in base R, but the data.table solution is easier to understand and write:
random.permutation.df2 <- data.frame(pebble = df1$pebble, bucket = rep(NA,length(df1$pebble)))
for(bucket in unique(df1$bucket)){
N <- length( random.permutation.df2$bucket[is.na(random.permutation.df2$bucket) &
!random.permutation.df2$pebble %in% df2$pebble[df2$bucket == bucket] ] )
random.permutation.df2$bucket[is.na(random.permutation.df2$bucket) &
!random.permutation.df2$pebble %in% df2$pebble[df2$bucket == bucket] ] <-
sample(c(rep(bucket,sum(df1$bucket == bucket)),rep(NA,N-sum(df1$bucket == bucket))))
}
The idea is to sample the authorised peeble for each bucket: those that are not in df2, and those that are not already filled. You sample then a vector of the good length, choosing between NAs (for the following buckets values) and the value in the loop, and voilà.
Now easier to read with data.table
library(data.table)
random.permutation.df2 <- setDT(random.permutation.df2)
df2 <- setDT(df2)
for( bucketi in unique(df1$bucket)){
random.permutation.df2[is.na(bucket) & !pebble %in% df2[bucket == bucketi, pebble],
bucket := sample(c(rep(bucketi,sum(df1$bucket == bucket)),rep(NA,.N-sum(df1$bucket == bucket))))]
}
it has the two conditions
> colSums(table(df1))
A B C D E F G H I J
4 7 13 14 12 11 11 10 9 9
> colSums(table(random.permutation.df2))
A B C D E F G H I J
4 7 13 14 12 11 11 10 9 9
To verify that there isn't any contradiction with df2
> df2
pebble bucket
1: 37 D
2: 95 H
3: 90 C
4: 80 C
5: 31 D
6: 84 G
7: 76 I
8: 57 H
9: 7 E
10: 39 A
> random.permutation.df2[pebble %in% df2$pebble,.(pebble,bucket)]
pebble bucket
1: 7 D
2: 31 H
3: 37 J
4: 39 F
5: 57 B
6: 76 E
7: 80 F
8: 84 B
9: 90 H
10: 95 D
Here a brute force approach where one simply tries long enough until a valid solution is found:
set.seed(123)
df1 <- data.frame(pebble = 1:100,
bucket = sample(LETTERS[1:10], 100, T),
stringsAsFactors = F)
df2 <- data.frame(pebble = sample(1:100, 10),
bucket = sample(LETTERS[1:10], 10, T),
stringsAsFactors = F)
random.permutation.df1 <- data.frame(pebble = df1$pebble, bucket = sample(df1$bucket))
Random permutation does not match the condition, so try new ones:
merge(random.permutation.df1, df2)
#> pebble bucket
#> 1 60 J
while(TRUE) {
random.permutation.df1 <- data.frame(pebble = df1$pebble, bucket = sample(df1$bucket))
if(nrow(merge(random.permutation.df1, df2)) == 0)
break;
}
New permutation matches the condition:
merge(random.permutation.df1, df2)
#> [1] pebble bucket
#> <0 Zeilen> (oder row.names mit Länge 0)
colSums(table(random.permutation.df1))
#> A B C D E F G H I J
#> 7 12 11 9 14 7 11 11 11 7
colSums(table(df1))
#> A B C D E F G H I J
#> 7 12 11 9 14 7 11 11 11 7
I have a dataframe, say
df = data.frame(x = c("a","a","b","b","b","c","d","t","c","b","t","c","t","a","a","b","d","t","t","c"),
y = c(2,4,5,2,6,2,4,5,2,6,2,4,5,2,6,2,4,5,2,6))
I want to remove only those rows in which one or multiple ts are directly in between a d and a c, in all other cases I want to retain the cases. So for this example, I would like to remove the ts on row 8, 18 and 19, but keep the others. I have over thousands of cases so doing this manually would be a true horror. Any help is very much appreciated.
One option would be to use rle to get runs of the same string and then you can use an sapply to check forward/backward and return all the positions you want to drop:
rle_vals <- rle(as.character(df$x))
drop <- unlist(sapply(2:length(rle_vals$values), #loop over values
function(i, vals, lengths) {
if(vals[i] == "t" & vals[i-1] == "d" & vals[i+1] == "c"){#Check if value is "t", previous is "d" and next is "c"
(sum(lengths[1:i-1]) + 1):sum(lengths[1:i]) #Get row #s
}
},vals = rle_vals$values, lengths = rle_vals$lengths))
drop
#[1] 8 18 19
df[-drop,]
# x y
#1 a 2
#2 a 4
#3 b 5
#4 b 2
#5 b 6
#6 c 2
#7 d 4
#9 c 2
#10 b 6
#11 t 2
#12 c 4
#13 t 5
#14 a 2
#15 a 6
#16 b 2
#17 d 4
#20 c 6
This also works, by collapsing to a string, identifying groups of t's between d and c (or c and d - not sure whether you wanted this option as well), then working out where they are and removing the rows as appropriate.
df = data.frame(x=c("a","a","b","b","b","c","d","t","c","b","t","c","t","a","a","b","d","t","t","c"),
y=c(2,4,5,2,6,2,4,5,2,6,2,4,5,2,6,2,4,5,2,6),stringsAsFactors = FALSE)
dfs <- paste0(df$x,collapse="") #collapse to a string
dfs2 <- do.call(rbind,lapply(list(gregexpr("dt+c",dfs),gregexpr("ct+d",dfs)),
function(L) data.frame(x=L[[1]],y=attr(L[[1]],"match.length"))))
dfs2 <- dfs2[dfs2$x>0,] #remove any -1 values (if string not found)
drop <- unlist(mapply(function(a,b) (a+1):(a+b-2),dfs2$x,dfs2$y))
df2 <- df[-drop,]
Here is another solution with base R:
df = data.frame(x = c("a","a","b","b","b","c","d","t","c","b","t","c","t","a","a","b","d","t","t","c"),
y = c(2,4,5,2,6,2,4,5,2,6,2,4,5,2,6,2,4,5,2,6))
#
s <- paste0(df$x, collapse="")
L <- c(NA, NA)
while (TRUE) {
r <- regexec("dt+c", s)[[1]]
if (r[1]==-1) break
L <- rbind(L, c(pos=r[1]+1, length=attr(r, "match.length")-2))
s <- sub("d(t+)c", "x\\1x", s)
}
L <- L[-1,]
drop <- unlist(apply(L,1, function(x) seq(from=x[1], len=x[2])))
df[-drop, ]
# > drop
# 8 18 19
# > df[-drop, ]
# x y
# 1 a 2
# 2 a 4
# 3 b 5
# 4 b 2
# 5 b 6
# 6 c 2
# 7 d 4
# 9 c 2
# 10 b 6
# 11 t 2
# 12 c 4
# 13 t 5
# 14 a 2
# 15 a 6
# 16 b 2
# 17 d 4
# 20 c 6
With gregexpr() it is shorter:
s <- paste0(df$x, collapse="")
g <- gregexpr("dt+c", s)[[1]]
L <- data.frame(pos=g+1, length=attr(g, "match.length")-2)
drop <- unlist(apply(L,1, function(x) seq(from=x[1], len=x[2])))
df[-drop, ]
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
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
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
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