How to Number/Label Data-Table by Group-Number from Group_By

How to number/label data-table by group-number from group_by?

Updated answer

get_group_number = function(){
    i = 0
    function(){
        i <<- i+1
        i
    }
}
group_number = get_group_number()
df %>% group_by(u,v) %>% mutate(label = group_number())

You can also consider the following slightly unreadable version

group_number = (function(){i = 0; function() i <<- i+1 })()
df %>% group_by(u,v) %>% mutate(label = group_number())

---

using iterators package

library(iterators)

counter = icount()
df %>% group_by(u,v) %>% mutate(label = nextElem(counter))

How to give numbers to each group of a dataframe with dplyr::group_by?

Use mutate to add a column which is just a numeric form of from as a factor:

df %>% mutate(group_no = as.integer(factor(from)))

#   from dest group_no
# 1    a    b        1
# 2    a    c        1
# 3    b    d        2

Note group_by isn't necessary here, unless you're using it for other purposes. If you want to group by the new column for use later, you can use group_by instead of mutate to add the column.

How to label a double grouped data frame with a group number with group_indices in dplyr?

We can use dense_rank.

library(dplyr)

db2 <- db %>%
  group_by(ID) %>%
  mutate(rank = dense_rank(date)) %>%
  ungroup()
db2
# # A tibble: 10 x 3
#      ID date        rank
#   <dbl> <date>     <int>
#  1    1. 2001-01-01     1
#  2    1. 2001-01-01     1
#  3    1. 2001-01-01     1
#  4    1. 2001-01-03     2
#  5    1. 2001-01-03     2
#  6    2. 2011-01-01     3
#  7    2. 2011-01-01     3
#  8    2. 2010-03-12     2
#  9    2. 2010-03-12     2
# 10    2. 2001-01-01     1

Numbering of groups in dplyr?

Using as.numeric will do the trick.

S <- rep(letters[1:12],each=6)
R = sort(replicate(9, sample(5000:6000,4)))
df <- data.frame(R,S)

result <- df %>% mutate(label = as.numeric(S)) %>% group_by(S)

result
Source: local data frame [72 x 3]
Groups: S

      R S label
1  5018 a     1
2  5042 a     1
3  5055 a     1
4  5066 a     1
5  5081 a     1
6  5133 a     1
7  5149 b     2
8  5191 b     2
9  5197 b     2
10 5248 b     2
..  ... .   ...

R: add a dplyr group label as a number

I think in this case something as simple as :

df %>%
  mutate(group_no = as.integer(name))

will work

# A tibble: 20 x 4
# Groups:   id [2]
   id    name     val group_no
   <fct> <fct>  <dbl>    <int>
 1 a     N1    0.647         1
 2 a     N1    0.530         1
 3 a     N1    0.245         1
 4 a     N2    0.693         2
 5 a     N2    0.478         2
 6 a     N2    0.861         2
 7 a     N3    0.821         3
 8 a     N3    0.0995        3
 9 a     N3    0.662         3
10 b     N1    0.553         1
11 b     N1    0.0233        1
12 b     N1    0.519         1
13 b     N2    0.783         2
14 b     N2    0.789         2
15 b     N2    0.477         2
16 b     N2    0.438         2
17 b     N2    0.407         2
18 b     N3    0.732         3
19 b     N3    0.0707        3
20 b     N3    0.316         3

Numbering rows within groups in a data frame

Use ave, ddply, dplyr or data.table:

df$num <- ave(df$val, df$cat, FUN = seq_along)

or:

library(plyr)
ddply(df, .(cat), mutate, id = seq_along(val))

or:

library(dplyr)
df %>% group_by(cat) %>% mutate(id = row_number())

or (the most memory efficient, as it assigns by reference within DT):

library(data.table)
DT <- data.table(df)

DT[, id := seq_len(.N), by = cat]
DT[, id := rowid(cat)]

Enumerate groups within groups in a data.table

Try

library(data.table)
dt[, id := rleid(cl), by=gr]
dt
#    gr cl id
# 1:  a  a  1
# 2:  a  a  1
# 3:  a  a  1
# 4:  a  b  2
# 5:  a  b  2
# 6:  a  b  2
# 7:  b  c  1
# 8:  b  c  1
# 9:  b  c  1
#10:  b  d  2
#11:  b  d  2
#12:  b  d  2

R grouping data by numeric numbers in a column

We can use tidvyerse to do a group by operation. Create a group of ranges with cut, summarise the frequency count based on the cut and the 'variants', then paste them together in summarise

library(dplyr)
patient1 %>% 
   group_by(group = cut(position, breaks = c(-Inf, seq(1, 100, 
       by = 10))), variants) %>%
   summarise(n = n()) %>%
   summarise(tally = paste(n, variants, collapse=' ', sep=""))

NOTE: Another option is findInterval which does similar option as cut but without the labels as it will output numeric index

R - Group by variable and then assign a unique ID

dplyr has a group_indices function for creating unique group IDs

library(dplyr)
data <- data.frame(personal_id = c("111-111-111", "999-999-999", "222-222-222", "111-111-111"),
                       gender = c("M", "F", "M", "M"),
                       temperature = c(99.6, 98.2, 97.8, 95.5))

data$group_id <- data %>% group_indices(personal_id) 
data <- data %>% select(-personal_id)

data
  gender temperature group_id
1      M        99.6        1
2      F        98.2        3
3      M        97.8        2
4      M        95.5        1

Or within the same pipeline (https://github.com/tidyverse/dplyr/issues/2160):

data %>% 
    mutate(group_id = group_indices(., personal_id))

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