using tidyverse; counting after and before change in value, within groups, generating new variables for each unique shift

Question

I am looking for a tidyverse-solution that can count occurrences of unique values of TF within groups, id in the data datatbl

Answer 1

I had a answer without data.table but it was not using dplyr. Here is my attempt using dplyr:

        #Remove the NAs 
dfr <-  df %>% filter(!is.na(TF)) %>% 
  # group by id
  group_by(id) %>% 
  # Calculate the rle on TF for each group
  do(., mrle = rle(.$TF)) %>% mutate(Total=sum(mrle$lengths)) %>%
  # Trasform the rle result in a data.frame counting the values after and before changes
  do( {
  t<- .$mrle
  #for each length generate the columns
  res <- as.data.frame(lapply(seq_along(t$lengths[-length(t$lengths)]), function(i) {

      #before change counts
      n1 <- t$lengths[i]
      #position  the counts
      if(i==1) {
        before <- 0
      } else {
        before <- sum(t$lengths[1:i-1])
      }

      #after change conts
      n2 <- t$lengths[i+1]

      if(i == (length(t$lengths)-1))
        after  <- 0
      else
        after <- .$Total - before - n1 - n2

      # assemble the column
      c(rep(NA,before),-n1:-1,1:n2, rep(NA,after))

    } ))

  colnames(res) <- paste0("PM", 1:ncol(res))
  #preserve the id
  cbind(id=.$id,res)

 })

#Join with the original data.frame
res <-  df %>% mutate(rn = row_number()) %>% filter(!is.na(TF)) %>% bind_cols(dfr) %>% right_join( df %>% mutate(rn = row_number()) ) %>% select(-rn, -id1)

#Verify
mapply(all.equal, dfa,res)
#  id   TF PM01 PM02 PM03 PM04 PM05 
#TRUE TRUE TRUE TRUE TRUE TRUE TRUE

Answer 2

Here is another tidyverse approach that uses dplyr, tidyr and zoo (used for its na.locf function) package:

Firstly, instead of dropping NAs in the TF column and then join back as all the other suggested approaches (including the data.table approach), I wrote a helper method here, that counts forward by chunks ignoring NAs;

forward_count <- function(v) {
    valid <- !is.na(v)
    valid_v <- v[valid]
    chunk_size = head(rle(valid_v)$lengths, -1)
    idx <- cumsum(chunk_size) + 1
    ones <- rep(1, length(valid_v))
    ones[idx] <- 1 - chunk_size
    v[valid] <- cumsum(ones)
    v
}

And it works as is required by count after the change:

v <- sample(c(NA, 0, 1), 15, replace = T)
v
# [1] NA NA NA  0  1 NA  1 NA  1  1  0  1  0  0  0
forward_count(v)
# [1] NA NA NA  1  1 NA  2 NA  3  4  1  1  1  2  3

Count before the change can be implemented by reverse the vector twice with this exact same function:

-rev(forward_count(rev(v)))
# [1] NA NA NA -1 -4 NA -3 NA -2 -1 -1 -1 -3 -2 -1

Now define the headers, count forward column as fd, count backward column as bd using dplyr package:

library(dplyr); library(tidyr); library(zoo);

tidy_method <- function(df) {
    df %>% 
        group_by(id) %>% 
        mutate(
            rle_id = cumsum(diff(na.locf(c(0, TF))) != 0),   # chunk id for constant TF
            PM_fd = if_else(                 # PM count after change headers
                rle_id == head(rle_id, 1), 
                "head", sprintf('PM%02d', rle_id)
            ), 
            PM_bd = if_else(                 # shift the header up as before change headers
                rle_id == tail(rle_id, 1), 
                "tail", sprintf('PM%02d', rle_id+1)
            ), 
            fd = forward_count(TF),             # after change count
            bd = -rev(forward_count(rev(TF))),  # before change count
            rn = seq_along(id)) %>%             # row number
        gather(key, value, PM_fd, PM_bd) %>%    # align headers with the count
        mutate(count_ = if_else(key == "PM_fd", fd, bd)) %>%
        select(-key) %>% spread(value, count_) %>%    # reshaper PM column as headers
        select(id, TF, rn, matches('PM')) %>%  # drop no longer needed columns
        arrange(id, rn) %>% select(-rn)
}

Timing compared with the data.table method:

Define the data.table method as:

dt_method <- function(df) {
    tmp_dt <- setDT(df)[, rn := .I][!is.na(TF)][, rl := rleid(TF), by = id][
        , c("up", "dn") := .(seq_len(.N), -rev(seq_len(.N))), by = .(id, rl)][]

    res_dt <- tmp_dt[tmp_dt[, seq_len(max(rl) - 1L), by = .(id)], on = .(id), allow.cartesian = TRUE][
        rl == V1, PM := dn][rl == V1 + 1L, PM := up][
            , dcast(.SD, id + TF + rn ~ sprintf("PM%02d", V1), value.var = "PM")][
                df, on = .(rn, id, TF)][, -"rn"]
    res_dt
}

Data: A medium sized data by repeating the sample data frame 200 times:

df_test <- bind_rows(rep(list(df), 200))

microbenchmark::microbenchmark(dt_method(df_test), tidy_method(df_test), times = 10)
#Unit: milliseconds
#                 expr       min        lq      mean    median        uq       max neval
#   dt_method(df_test) 2321.5852 2439.8393 2490.8583 2456.1118 2557.4423 2834.2399    10
# tidy_method(df_test)  402.3624  412.2838  437.0801  414.5655  418.6564  540.9667    10

Order the data.table method result by id and convert all column data types to numeric; the results from data.table approach and tidyverse are identical:

identical(
    as.data.frame(dt_method(df_test)[order(id), lapply(.SD, as.numeric)]), 
    as.data.frame(tidy_method(df_test))
)
# [1] TRUE

Answer 3

Update with a bit optimized data.table function:

Should probably go to the old question, but maybe this will trigger some further optimization.

To keep things flowing I have played a bit with the data.table function and get down to about twice of the execution time of the tidyverse version - the bottleneck is the dcast() function, see the screenshot from profvis below:

dt_method <- function(dt_test) {
  tmp_dt <- dt_test[, rn := .I][!is.na(TF)][, rl := rleid(TF), by = id][
    , c("up", "dn") := .(seq_len(.N), -rev(seq_len(.N))), by = .(id, rl)][, ':='(
      rl_PM = sprintf("PM%02d", rl),
      United = paste(id, TF, rn, sep = '_')
    )]

  res_dt <- tmp_dt[, .(sprintf("PM%02d", seq_len(max(rl) - 1L)), seq_len(max(rl) - 1L)), by = .(id)] %>% 
    tmp_dt[., on = .(id), allow.cartesian = TRUE] %>%  
    .[rl == V2, PM := dn] %>%
    .[rl == V2 + 1L, PM := up] %>%
    dcast(., United ~ V1, value.var = "PM") %>%
    .[, c('id', 'TF', 'rn') := lapply(tstrsplit(United, '_'), as.numeric)] %>%
    .[dt_test, on = .(rn, id, TF)] %>% .[, -c('rn', 'United')]
  res_dt
}

Pipes were needed to deal with some odd errors, but I still consider them allowed even for data.table.

Microbenchmark results:

Unit: milliseconds
                 expr      min       lq      mean    median        uq       max neval
   dt_method(dt_test) 868.1491 932.8076 1048.5077 1029.9609 1078.0735 1518.0327    10
 tidy_method(df_test) 478.6824 515.5639  557.9644  565.9422  585.3143  622.1093    10

And identical() with fixed order of columns:

identical(
  dt_method(dt_test)[order(id), lapply(.SD, as.numeric)] %>% setcolorder(c('id', 'TF', setdiff(names(.), c('id', 'TF')))) %>% as.data.frame(),
  as.data.frame(tidy_method(df_test))
)

profvis timings:

Old part:

Using Uwe's answer as a base:

(Disclaimer: I am not using dplyr too much, treated this as an exercise for myself, so it is for sure not dplyr-optimal, see e.g. dcast.)

library(data.table)
library(magrittr)
library(dplyr)
library(tibble)

df <- tibble(id = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 
                    1, 1, 1, 1,7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7),
             TF = c(NA, 0, NA, 0, 0, 1, 1, 1, NA, 0, 0, NA, 0, 0, 0,
                    1, 1, 1, NA, NA, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1))

dfa <- tibble(id = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
                     1, 1, 1, 1, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7),
              TF = c(NA, 0, NA, 0, 0, 1, 1, 1, NA, 0, 0, NA, 0, 0,
                     0, 1, 1, 1, NA, NA, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1),
              PM01 = c(NA, -3, NA, -2, -1, 1, 2, 3, NA, NA, NA, NA, -3, -2, -1,
                       1, 2, 3, NA, NA, -2, -1, 1, NA, NA, NA, NA, NA, NA, NA),
              PM02 = c(NA, NA, NA, NA, NA, -3, -2, -1, NA, 1, 2, NA, NA, NA, NA,
                       NA, NA, NA, NA, NA, NA, NA, -1, 1, 2, NA, NA, NA, NA, NA),
              PM03 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
                       NA, NA, NA, NA, NA, NA, NA, NA, -2, -1, 1, NA, NA, NA, NA),
              PM04 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
                       NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -1, 1, NA, NA, NA),
              PM05 = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
                       NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -1, 1, 2, 3))

tmp_dt <- setDT(df)[, rn := .I][!is.na(TF)][, rl := rleid(TF), by = id][
  , c("up", "dn") := .(seq_len(.N), -rev(seq_len(.N))), by = .(id, rl)][]

res_dt <- tmp_dt[tmp_dt[, seq_len(max(rl) - 1L), by = .(id)], on = .(id), allow.cartesian = TRUE][
  rl == V1, PM := dn][rl == V1 + 1L, PM := up][
    , dcast(.SD, id + TF + rn ~ sprintf("PM%02d", V1), value.var = "PM")][
      df, on = .(rn, id, TF)][, -"rn"]
res_dt

all.equal(res_dt, as.data.table(dfa))

As much tidyverse-sque as possible:

tmp_dplyr <- df %>%
  # create row id column (required for final join to get NA rows back in)
  mutate(rn = row_number()) %>%
  # ignore NA rows 
  filter(complete.cases(.)) %>%
  # number streaks of unique values within each group
  group_by(id) %>%
  mutate(rl = rleid(TF)) %>%
  # create ascending and descending counts for each streak
  # this is done once to avoid repeatedly creation of counts for each PM 
  # (slight performance gain)
  group_by(id, rl) %>%
  mutate(
    up = seq_len(n()),
    dn = -rev(seq_len(n()))
  )

res_dplyr <- tmp_dplyr %>%
  ## Replicating tmp[tmp[, seq_len(max(rl) - 1L), by = .(id)], on = .(id), allow.cartesian = TRUE]
  group_by(id) %>%
  ## Part below can for sure be optimized for code length, it's just too early now...
  transmute(rl = max(rl)) %>% # Cannot transmute id directly
  unique() %>%
  ungroup() %>%
  slice(rep(1:n(), times = rl - 1L)) %>%
  group_by(id) %>%
  transmute(V1 = seq_len(max(rl) - 1L)) %>%
  ungroup() %>%
  right_join(tmp_dplyr, by = 'id') %>%
  ## End or replicating tmp[tmp[, seq_len(max(rl) - 1L), by = .(id)], on = .(id), allow.cartesian = TRUE]
  ## Copy descending counts to rows before the switch and ascending counts to rows after the switch
  mutate(
    PM = ifelse(rl == V1, dn, NA),
    PM = ifelse(rl == V1 + 1L, up, PM)
  ) %>%
  ## This is very not tidyverse-sque, but I don't get the gather/spread ...
  dcast(id + TF + rn ~ sprintf("PM%02d", V1), value.var = "PM") %>%
  full_join(df, by = c('rn', 'id', 'TF')) %>%
  select(-rn)

all.equal( ## Using data.table all.equal
  res_dplyr[do.call(order, res_dplyr),] %>% as.data.table(),
  res_dt[do.call(order, res_dt),]
)