问题描述:

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)),

var1 = rep(c('A','B'),10)

)

head(df,10)

time grp var1

1 1 1 A

2 2 1 B

3 3 1 A

4 4 1 B

5 5 2 A

6 6 2 B

7 7 2 A

8 8 2 B

9 9 3 A

10 10 3 B

I want to create another variable var2 which computes no of distinct var1 values so far i.e. until that point in time for each group grp . This is a little different from what I'd get if I were to use n_distinct.

Expected output -

 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

I want to create a function say cum_n_distinct for this and use it as -

d_out = df %>%

arrange(time) %>%

group_by(grp) %>%

mutate(var2 = cum_n_distinct(var1))

网友答案:

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))
网友答案:

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
网友答案:

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
相关阅读:
Top