R, dplyr: cumulative version of n_distinct

Question

I have a dataframe as follows. It is ordered by column time.

Input -

df = data.frame(time = 1:20,
            grp = sort(rep(1:5,4)),
             


        

Answer 1

Assuming stuff is ordered by time already, first define a cumulative distinct function:

dist_cum <- function(var)
  sapply(seq_along(var), function(x) length(unique(head(var, x))))

Then a base solution that uses ave to create groups (note, assumes var1 is factor), and then applies our function to each group:

transform(df, var2=ave(as.integer(var1), grp, FUN=dist_cum))

A data.table solution, basically doing the same thing:

library(data.table)
(data.table(df)[, var2:=dist_cum(var1), by=grp])

And dplyr, again, same thing:

library(dplyr)
df %>% group_by(grp) %>% mutate(var2=dist_cum(var1))

Answer 2

A dplyr solution inspired from @akrun's answer -

Ths logic is basically to set 1st occurrence of each unique values of var1 to 1 and rest to 0 for each group grp and then apply cumsum on it -

df = df %>%
  arrange(time) %>%
  group_by(grp,var1) %>%
  mutate(var_temp = ifelse(row_number()==1,1,0)) %>%
  group_by(grp) %>%
  mutate(var2 = cumsum(var_temp)) %>%
  select(-var_temp)

head(df,10)

Source: local data frame [10 x 4]
Groups: grp

   time grp var1 var2
1     1   1    A    1
2     2   1    B    2
3     3   1    A    2
4     4   1    B    2
5     5   2    A    1
6     6   2    B    2
7     7   2    A    2
8     8   2    B    2
9     9   3    A    1
10   10   3    B    2

Answer 3

Try:

Update

With your new dataset, an approach in base R

  df$var2 <-  unlist(lapply(split(df, df$grp),
              function(x) {x$var2 <-0
               indx <- match(unique(x$var1), x$var1)
               x$var2[indx] <- 1
               cumsum(x$var2) }))

  head(df,7)
  #   time grp var1 var2
  # 1    1   1    A    1
  # 2    2   1    B    2
  # 3    3   1    A    2
  # 4    4   1    B    2
  # 5    5   2    A    1
  # 6    6   2    B    2
  # 7    7   2    A    2

Answer 4

Here's another solution using data.table that's pretty quick.

Generic Function

cum_n_distinct <- function(x, na.include = TRUE){
  # Given a vector x, returns a corresponding vector y
  # where the ith element of y gives the number of unique
  # elements observed up to and including index i
  # if na.include = TRUE (default) NA is counted as an 
  # additional unique element, otherwise it's essentially ignored

  temp <- data.table(x, idx = seq_along(x))
  firsts <- temp[temp[, .I[1L], by = x]$V1]
  if(na.include == FALSE) firsts <- firsts[!is.na(x)]
  y <- rep(0, times = length(x))
  y[firsts$idx] <- 1
  y <- cumsum(y)

  return(y)
}

Example Use

cum_n_distinct(c(5,10,10,15,5))  # 1 2 2 3 3
cum_n_distinct(c(5,NA,10,15,5))  # 1 2 3 4 4
cum_n_distinct(c(5,NA,10,15,5), na.include = FALSE)  # 1 1 2 3 3

Solution To Your Question

d_out = df %>%
  arrange(time) %>%
  group_by(grp) %>%
  mutate(var2 = cum_n_distinct(var1))