CODEWITHSUNDEEP
rcount
jhyeon
jhyeon

Reputation: 456

How do I count only previous value not using summarize in R?

This is my dataset.

num col1
1   SENSOR_01
2   SENSOR_01
3   SENSOR_01
4   SENSOR_05
5   SENSOR_05
6   SENSOR_05
7   NA
8   SENSOR_01
9   SENSOR_01
10  SENSOR_05
11  SENSOR_05

structure(list(num = 1:11, col1 = structure(c(1L, 1L, 1L, 2L, 2L, 2L, NA, 1L, 1L, 2L, 2L), .Label = c("SENSOR_01", "SENSOR_05" ), class = "factor"), count = c(3L, 3L, 3L, 3L, 3L, 3L, 0L, 2L, 2L, 2L, 2L)), class = "data.frame", row.names = c(NA, -11L))

I would like to count for only previous duplicated rows. In the row 1-3, there are sensor 3 repeatedly 3 times so count = 3. Here is my expected outcome. 

num col1    count
1   SENSOR_01   3
2   SENSOR_01   3
3   SENSOR_01   3
4   SENSOR_05   3
5   SENSOR_05   3
6   SENSOR_05   3
7   NA          1
8   SENSOR_01   2
9   SENSOR_01   2
10  SENSOR_05   2
11  SENSOR_05   2

Using dplyr, How can I make this outcome?

Upvotes: 1

Views: 89

Answers (3)

utubun
utubun

Reputation: 4520

Like an option, we can use order of variables (rownames in traditional data.frame). The idea is simple:

  • If within the group of identical sensor names, the distance between adjacent records is equal to 1 and the same is true in a global view, without grouping - set the flag for this record to zero or one otherwise;
  • Still within the group of identical sensor names, find cumulative sum of flags, which allows us to identify all subgroups of records appearing consequently in global data set;
  • Still within the group count the number of elements in each individual subgroup;
  • Repeat for each group of records.

In tidyverse:

dat %>%
  mutate(tmp = 1:n()) %>%
  group_by(col1) %>%
  add_count(tmp = cumsum(c(0, diff(tmp)) > 1)) %>%
  ungroup() %>%
  select(-tmp)


# # A tibble: 11 x 3
#      num col1          n
#    <int> <fct>     <int>
#  1     1 SENSOR_01     3
#  2     2 SENSOR_01     3
#  3     3 SENSOR_01     3
#  4     4 SENSOR_05     3
#  5     5 SENSOR_05     3
#  6     6 SENSOR_05     3
#  7     7 NA            1
#  8     8 SENSOR_01     2
#  9     9 SENSOR_01     2
# 10    10 SENSOR_05     2
# 11    11 SENSOR_05     2

Data:

dat <- structure(
  list(
    num = 1:11, 
    col1 = structure(
      c(1L, 1L, 1L, 2L, 2L, 2L, NA, 1L, 1L, 2L, 2L), 
      .Label = c("SENSOR_01", "SENSOR_05" ), 
      class = "factor")
    ), 
  class = "data.frame", 
  row.names = c(NA, -11L)
  )

Upvotes: 3

akrun
akrun

Reputation: 887108

We can use base R with rle to create the 'count' column

df$count <-  with(rle(df$col1), rep(lengths, lengths))    
df$count
#[1] 3 3 3 3 3 3 1 2 2 2 2

Or the dplyr implementation of the above

library(dplyr)
df %>% 
    mutate(count = with(rle(col1), rep(lengths, lengths)))

Or an option with tidyverse without including any other packages

library(dplyr)
df %>%
    group_by(grp = replace_na(col1, "VALUE"),
    grp = cumsum(grp != lag(grp, default = first(grp)))) %>% 
    mutate(count = n()) %>%
    ungroup %>%
    select(-grp)
# A tibble: 11 x 3
#     num col1      count
#   <int> <chr>     <int>
# 1     1 SENSOR_01     3
# 2     2 SENSOR_01     3
# 3     3 SENSOR_01     3
# 4     4 SENSOR_05     3
# 5     5 SENSOR_05     3
# 6     6 SENSOR_05     3
# 7     7 <NA>          1
# 8     8 SENSOR_01     2
# 9     9 SENSOR_01     2
#10    10 SENSOR_05     2
#11    11 SENSOR_05     2

data

df <- structure(list(num = 1:11, col1 = c("SENSOR_01", "SENSOR_01", 
"SENSOR_01", "SENSOR_05", "SENSOR_05", "SENSOR_05", NA, "SENSOR_01", 
"SENSOR_01", "SENSOR_05", "SENSOR_05")), 
class = "data.frame", row.names = c(NA, 
-11L))

Upvotes: 2

Ronak Shah
Ronak Shah

Reputation: 388982

We can use rleid to create groups and then count number of rows in each group.

library(dplyr)

df %>%
  group_by(group = data.table::rleid(col1)) %>%
  mutate(n = n()) %>%
  ungroup() %>%
  dplyr::select(-group)


# A tibble: 11 x 4
#     num col1      count     n
#   <int> <fct>     <int> <int>
# 1     1 SENSOR_01     3     3
# 2     2 SENSOR_01     3     3
# 3     3 SENSOR_01     3     3
# 4     4 SENSOR_05     3     3
# 5     5 SENSOR_05     3     3
# 6     6 SENSOR_05     3     3
# 7     7 NA            1     1
# 8     8 SENSOR_01     2     2
# 9     9 SENSOR_01     2     2
#10    10 SENSOR_05     2     2
#11    11 SENSOR_05     2     2

Keeping both the columns for comparison purposes.

Or using data.table

library(data.table)  
setDT(df)[, n := .N, by = rleid(col1)]

Upvotes: 4

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