Cumulative Count Paste - r

Question

I have this dataset:
ID Set Type Count
1 1 1 A NA
2 2 1 R NA
3 3 1 R NA
4 4 1 U NA
5 5 1 U NA
6 6 1 U NA
7 7 2 A NA
8 8 3 R NA
9 9 3 R NA
As dputs:
mystart <- structure(list(ID = 1:9, Set = c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
3L, 3L), Type = structure(c(1L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L
), .Label = c("A", "R", "U"), class = "factor"), Count = c(NA,
NA, NA, NA, NA, NA, NA, NA, NA)), .Names = c("ID", "Set", "Type",
"Count"), class = "data.frame", row.names = c(NA, -9L))
By using dplyr package how can I obtain this:
ID Set Type Count
1 1 1 A A1
2 2 1 R A1R1
3 3 1 R A1R2
4 4 1 U A1R2U1
5 5 1 U A1R2U2
6 6 1 U A1R2U3
7 7 2 A A1
8 8 3 R R1
9 9 3 R R2
Again dputs:
myend <- structure(list(ID = 1:9, Set = c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
3L, 3L), Type = structure(c(1L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L
), .Label = c("A", "R", "U"), class = "factor"), Count = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 1L, 7L, 8L), .Label = c("A1", "A1R1", "A1R2",
"A1R2U1", "A1R2U2", "A1R2U3", "R1", "R2"), class = "factor")), .Names = c("ID",
"Set", "Type", "Count"), class = "data.frame", row.names = c(NA,
-9L))
In short, I want to count the observations of the column "type" within column "set" and print this count(text) cumulatively.
Examining similar posts, I got closely to this:
myend <- structure(list(ID = 1:9, Set = c(1L, 1L, 1L, 1L, 1L, 1L, 2L,
3L, 3L), Type = structure(c(1L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L
), .Label = c("A", "R", "U"), class = "factor"), Count = c(1L,
1L, 2L, 1L, 2L, 3L, 1L, 1L, 2L)), .Names = c("ID", "Set", "Type",
"Count"), class = "data.frame", row.names = c(NA, -9L))
With the code:
library(dplyr)
myend <- read.table("mydata.txt", header=TRUE, fill=TRUE)
myend %>%
group_by(Set, Type) %>%
mutate(Count = seq(n())) %>%
ungroup(myend)
Thank you very much for your help,

Answer 1

Base R version :
aggregateGroup <- function(x){
vecs <- Reduce(f=function(a,b){ a[b] <- sum(a[b],1L,na.rm=TRUE); a },
init=integer(0),
as.character(x),
accumulate = TRUE)
# vecs is a list with something like this :
# [[1]]
# integer(0)
# [[2]]
# A
# 1
# [[3]]
# A R
# 1 1
# ...
# so we simply turn those vectors into characters using vapply and paste
# (excluding the first)
vapply(vecs,function(y) paste0(names(y),y,collapse=''),FUN.VALUE='')[-1]
}
split(mystart$Count,mystart$Set) <- lapply(split(mystart$Type,mystart$Set), aggregateGroup)
> mystart
ID Set Type Count
1 1 1 A A1
2 2 1 R A1R1
3 3 1 R A1R2
4 4 1 U A1R2U1
5 5 1 U A1R2U2
6 6 1 U A1R2U3
7 7 2 A A1
8 8 3 R R1
9 9 3 R R2

Answer 2

A dplyr version:
mystart %>%
group_by(Set) %>%
mutate(Count = paste0('A', cumsum(Type == 'A'),
'R', cumsum(Type == 'R'),
'U', cumsum(Type == 'U'))) %>%
ungroup()
Which yields
# A tibble: 9 x 4
ID Set Type Count
<int> <int> <chr> <chr>
1 1 1 A A1R0U0
2 2 1 R A1R1U0
3 3 1 R A1R2U0
4 4 1 U A1R2U1
5 5 1 U A1R2U2
6 6 1 U A1R2U3
7 7 2 A A1R0U0
8 8 3 R A0R1U0
9 9 3 R A0R2U0
If you want to omit the variables with count zero, you'd need to wrap a function around it like so
mygroup <- function(lst) {
name <- names(lst)
vectors <- lapply(seq_along(lst), function(i) {
x <- lst[[i]]
char <- name[i]
x <- ifelse(x == 0, "", paste0(char, x))
return(x)
})
return(do.call("paste0", vectors))
}
mystart %>%
group_by(Set) %>%
mutate(Count = mygroup(list(A = cumsum(Type == 'A'),
R = cumsum(Type == 'R'),
U = cumsum(Type == 'U')))) %>%
ungroup()
This yields
# A tibble: 9 x 4
ID Set Type Count
<int> <int> <chr> <chr>
1 1 1 A A1
2 2 1 R A1R1
3 3 1 R A1R2
4 4 1 U A1R2U1
5 5 1 U A1R2U2
6 6 1 U A1R2U3
7 7 2 A A1
8 8 3 R R1
9 9 3 R R2

Answer 3

One line solve with data.table
you gotta first do
require(data.table)
mystart <- as.data.table(mystart)
then just use one line
mystart[, .(Type,
count = paste0(
'A',
cumsum(Type == 'A'),
'R',
countR = cumsum(Type == 'R'),
'U',
countU = cumsum(Type == 'U')
)),
by = c('Set')]
first you want cumsum each type and paste them together by 'set'
cumsum(Type=='A') equals the count, since when Type==A, it's 1, otherwise it's 0.
you wanted to paste them into one column also. So, paste0() is good to use.
you still wanted the Type column, so I included Type in the line.
The output:
Set Type count
1: 1 A A1R0U0
2: 1 R A1R1U0
3: 1 R A1R2U0
4: 1 U A1R2U1
5: 1 U A1R2U2
6: 1 U A1R2U3
7: 2 A A1R0U0
8: 3 R A0R1U0
9: 3 R A0R2U0
Hope this helps.
btw, if you want count 0 ignored, you gotta design some if-esle clause yourself.
basically you want this: if cumsum(something) ==0, NULL, esle paste0('something', cumsum(something)), then you paste0() them together.
It's gonna get nasty, I'm not writing it. you get the idea

Answer 4

Here's a base solution.
We can paste raw letters toseq_along of letter groups to get the last 2 characters, then paste the result to the last element of the previous result, using Reduce.
On top of this we use ave to compute by group.
fun <- function(x,y) paste0(x[length(x)],y,seq_along(y))
mystart$Count <- ave(as.character(mystart$Type),mystart$Set,
FUN = function(x) unlist(Reduce(fun,split(x,x),init=NULL,accumulate = TRUE)))
# ID Set Type Count
# 1 1 1 A A1
# 2 2 1 R A1R1
# 3 3 1 R A1R2
# 4 4 1 U A1R2U1
# 5 5 1 U A1R2U2
# 6 6 1 U A1R2U3
# 7 7 2 A A1
# 8 8 3 R R1
# 9 9 3 R R2
Details
split(x,x) splits letters as shown here for first Set:
with(subset(mystart,Set==1),split(Type,Type))
# $A
# [1] "A"
#
# $R
# [1] "R" "R"
#
# $U
# [1] "U" "U" "U"
Then fun does this type of operations, helped by Reduce :
fun(NULL,"A") # [1] "A1"
fun("A1",c("R","R")) # [1] "A1R1" "A1R2"
fun(c("A1R1","A1R2"),c("U","U","U")) # [1] "A1R2U1" "A1R2U2" "A1R2U3"
Bonus solution
This other base solution, using rle and avoiding split gives the same output for given example (and whenever Type values are grouped in Sets), but not with mystart2 <- rbind(mystart,mystart) for instance.
fun2 <- function(x){
rle_ <- rle(x)
suffix <- paste0(x,sequence(rle_$length))
prefix <- unlist(mapply(rep,
lag(unlist(
Reduce(paste0,paste0(rle_$values,rle_$lengths),accumulate=TRUE)
),rle_$lengths[1]),
each=rle_$lengths))
prefix[is.na(prefix)] <- ""
paste0(prefix,suffix)
}
mystart$Count2 <-ave(as.character(mystart$Type), mystart$Set,FUN=fun2)

Answer 5

Many elegant solutions have been provided for the problem. Still I was looking for something dplyr way (without-cumsum on fixed types). The function is generic enough to handle additional values of Type.
A solution with help of a custom function as:
library(dplyr)
mystart %>% group_by(Set, Type) %>%
mutate(type_count = row_number()) %>%
mutate(TypeMod = paste0(Type,type_count)) %>%
group_by(Set) %>%
mutate(Count = cumCat(TypeMod, type_count)) %>%
select(-type_count, -TypeMod)
cumCat <- function(x, y){
retVal <- character(length(x))
prevVal = ""
lastGrpVal = ""
for ( i in seq_along(x)){
if(y[i]==1){
lastGrpVal = prevVal
}
retVal[i] = paste0(lastGrpVal,x[i])
prevVal = retVal[i]
}
retVal
}
# # Groups: Set [3]
# ID Set Type Count
# <int> <int> <fctr> <chr>
# 1 1 1 A A1
# 2 2 1 R A1R1
# 3 3 1 R A1R2
# 4 4 1 U A1R2U1
# 5 5 1 U A1R2U2
# 6 6 1 U A1R2U3
# 7 7 2 A A1
# 8 8 3 R R1
# 9 9 3 R R2