Hcfyk

I have a little nut to crack.

I have a data.frame like this:

   group criterium

1      A        NA

2      A      TRUE

3      A      TRUE

4      A      TRUE

5      A     FALSE

6      A     FALSE

7      A      TRUE

8      A      TRUE

9      A     FALSE

10     A      TRUE

11     A      TRUE

12     A      TRUE

13     B        NA

14     B     FALSE

15     B      TRUE

16     B      TRUE

17     B      TRUE

18     B     FALSE



structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 

1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", 

"B"), class = "factor"), criterium = c(NA, TRUE, TRUE, TRUE, 

FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, NA, FALSE, 

TRUE, TRUE, TRUE, FALSE)), class = "data.frame", row.names = c(NA, 

-18L))

And I want to rank the groups of TRUE in column criterium in ascending order while disregarding the FALSEand NA. The goal is to have a unique group identifier inside each group of group.

So the result should look like:

    group criterium goal

1      A        NA   NA

2      A      TRUE    1

3      A      TRUE    1

4      A      TRUE    1

5      A     FALSE   NA

6      A     FALSE   NA

7      A      TRUE    2

8      A      TRUE    2

9      A     FALSE   NA

10     A      TRUE    3

11     A      TRUE    3

12     A      TRUE    3

13     B        NA   NA

14     B     FALSE   NA

15     B      TRUE    1

16     B      TRUE    1

17     B      TRUE    1

18     B     FALSE   NA

I'm sure there is a relatively easy way to do this, I just can't think of one. I experimented with dense_rank() and other window functions of dplyr, but to no avail.

Thanks for the help!

Another data.table approach:

library(data.table)

setDT(dt)

dt[, cr := rleid(criterium)][

    (criterium), goal := rleid(cr), by=.(group)]

Maybe I have over-complicated this but one way with dplyr is

library(dplyr)



df %>%

  mutate(temp = replace(criterium, is.na(criterium), FALSE), 

         temp1 = cumsum(!temp)) %>%

   group_by(temp1) %>%

   mutate(goal =  +(row_number() == which.max(temp) & any(temp))) %>%

   group_by(group) %>%

   mutate(goal = ifelse(temp, cumsum(goal), NA)) %>%

   select(-temp, -temp1)



#  group criterium  goal

#   <fct> <lgl>     <int>

# 1 A     NA           NA

# 2 A     TRUE          1

# 3 A     TRUE          1

# 4 A     TRUE          1

# 5 A     FALSE        NA

# 6 A     FALSE        NA

# 7 A     TRUE          2

# 8 A     TRUE          2

# 9 A     FALSE        NA

#10 A     TRUE          3

#11 A     TRUE          3

#12 A     TRUE          3

#13 B     NA           NA

#14 B     FALSE        NA

#15 B     TRUE          1

#16 B     TRUE          1

#17 B     TRUE          1

#18 B     FALSE        NA

We first replace NAs in criterium column to FALSE and take cumulative sum over the negation of it (temp1). We group_by temp1 and assign 1 to every first TRUE value in the group. Finally grouping by group we take a cumulative sum for TRUE values or return NA for FALSE and NA values.

A pure Base R solution, we can create a custom function via rle, and use it per group, i.e.

f1 <- function(x) {

    x[is.na(x)] <- FALSE

    rle1 <- rle(x)

    y <- rle1$values

    rle1$values[!y] <- 0

    rle1$values[y] <- cumsum(rle1$values[y])

    return(inverse.rle(rle1))

}





do.call(rbind, 

     lapply(split(df, df$group), function(i){i$goal <- f1(i$criterium); 

                                             i$goal <- replace(i$goal, is.na(i$criterium)|!i$criterium, NA); 

    i}))

Of course, If you want you can apply it via dplyr, i.e.

library(dplyr)



df %>% 

 group_by(group) %>% 

 mutate(goal = f1(criterium), 

        goal = replace(goal, is.na(criterium)|!criterium, NA))

which gives,

# A tibble: 18 x 3

# Groups:   group [2]

   group criterium  goal

   <fct> <lgl>     <dbl>

 1 A     NA           NA

 2 A     TRUE          1

 3 A     TRUE          1

 4 A     TRUE          1

 5 A     FALSE        NA

 6 A     FALSE        NA

 7 A     TRUE          2

 8 A     TRUE          2

 9 A     FALSE        NA

10 A     TRUE          3

11 A     TRUE          3

12 A     TRUE          3

13 B     NA           NA

14 B     FALSE        NA

15 B     TRUE          1

16 B     TRUE          1

17 B     TRUE          1

18 B     FALSE        NA

A data.table option using rle

library(data.table)

DT <- as.data.table(dat)

DT[, goal := {

  r <- rle(replace(criterium, is.na(criterium), FALSE))

  r$values <- with(r, cumsum(values) * values)          

  out <- inverse.rle(r)                                 

  replace(out, out == 0, NA)

}, by = group]

DT

#    group criterium goal

# 1:     A        NA   NA

# 2:     A      TRUE    1

# 3:     A      TRUE    1

# 4:     A      TRUE    1

# 5:     A     FALSE   NA

# 6:     A     FALSE   NA

# 7:     A      TRUE    2

# 8:     A      TRUE    2

# 9:     A     FALSE   NA

#10:     A      TRUE    3

#11:     A      TRUE    3

#12:     A      TRUE    3

#13:     B        NA   NA

#14:     B     FALSE   NA

#15:     B      TRUE    1

#16:     B      TRUE    1

#17:     B      TRUE    1

#18:     B     FALSE   NA

step by step

When we call r <- rle(replace(criterium, is.na(criterium), FALSE)) we get an object of class rle

r

#Run Length Encoding

#  lengths: int [1:9] 1 3 2 2 1 3 2 3 1

#  values : logi [1:9] FALSE TRUE FALSE TRUE FALSE TRUE ...

We manipulate the values compenent in the following way

r$values <- with(r, cumsum(values) * values)

r

#Run Length Encoding

#  lengths: int [1:9] 1 3 2 2 1 3 2 3 1

#  values : int [1:9] 0 1 0 2 0 3 0 4 0 

That is, we replaced TRUEs with the cumulative sum of values and set the FALSEs to 0. Now inverse.rle returns a vector in which values will repeated lenghts times

out <- inverse.rle(r)

out

# [1] 0 1 1 1 0 0 2 2 0 3 3 3 0 0 4 4 4 0 

This is almost what OP wants but we need to replace the 0s with NA

replace(out, out == 0, NA)

This is done for each group.

data

dat <- structure(list(group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 

1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", 

"B"), class = "factor"), criterium = c(NA, TRUE, TRUE, TRUE, 

FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, NA, FALSE, 

TRUE, TRUE, TRUE, FALSE)), class = "data.frame", row.names = c(NA, 

-18L))