CODEWITHSUNDEEP
r
Alegría
Alegría

Reputation: 169

Group by sum specific column in R

df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))

I want to group_by sum quantity is about 500(ballpark) , When count close about 500 put the same group,like below

enter image description here

Any help would be appreciated.

Updated Because the condition need to change, I reset the threshold to 250, I summarize to find the max total value for each group, and then, How could I change the the total of group6 < 200 , into group5. I think about using ifelse but can't work successfully.

set.seed(123)

df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))

df$group=cumsum(c(1,ifelse(diff(cumsum(df$quantity)%% 250) < 0,1,0)))
df$total=ave(df$quantity,df$group,FUN=cumsum)
df %>% group_by(group) %>% summarise(max = max(total, na.rm=TRUE))

# A tibble: 6 × 2
  group   max
  <dbl> <int>
1     1   238
2     2   254
3     3   256
4     4   246
5     5   237
6     6   101

I want get like

> df
   items quantity price group total
1      O       36   393     1    36
2      S       78   376     1   114
3      N       81   562     1   195
4      C       43   140     1   238
5      J       76   530     2    76
6      R       15   189     2    91
7      V       32   415     2   123
8      K        7   322     2   130
9      E        9   627     2   139
10     T       41   215     2   180
11     N       74   705     2   254
12     V       23   873     3    23
13     Y       27   846     3    50
14     Z       60   555     3   110
15     E       53   697     3   163
16     S       93   953     3   256
17     Y       86   138     4    86
18     Y       88   258     4   174
19     I       38   851     4   212
20     C       34   308     4   246
21     H       69   473     5    69
22     Z       72   917     5   141
23     G       96   133     5   237
24     J       63   615     5   300
25     I       13   112     5   376
26     S       25   168     5   477

Thank you for any helping all the time.

Upvotes: 4

Views: 123

Answers (3)

Rui Barradas
Rui Barradas

Reputation: 76402

Here is a base R solution. The groups break after the cumulative sum passes a threshold. The output of aggregate shows that all cumulative sums are above thres except for the last one.

set.seed(2022)
df <- data.frame(items=sample(LETTERS,replace= T),
                 quantity=sample(1:100,26,replace=FALSE),
                 price=sample(100:1000,26,replace=FALSE))

f <- function(x, thres) {
  grp <- integer(length(x))
  run <- 0
  current_grp <- 0L
  for(i in seq_along(x)) {
    run <- run + x[i]
    grp[i] <- current_grp
    if(run > thres) {
      current_grp <- current_grp + 1L
      run <- 0
    }
  }
  grp
}

thres <- 500

group <- f(df$quantity, thres)
aggregate(quantity ~ group, df, sum)
#>   group quantity
#> 1     0      552
#> 2     1      513
#> 3     2      214

ave(df$quantity, group, FUN = cumsum)
#>  [1]  70 133 155 224 235 327 347 409 481 484 552  29  95 129 224 263 294 377 433
#> [20] 434 453 513  50  91 182 214

Created on 2022-09-06 by the reprex package (v2.0.1)

Edit

To assign groups and total quantities to the data can be done as follows.

df$group <- f(df$quantity, thres)
df$total_quantity <- ave(df$quantity, df$group, FUN = cumsum)
head(df)
#>   items quantity price group total_quantity
#> 1     D       70   731     0             70
#> 2     S       63   516     0            133
#> 3     N       22   710     0            155
#> 4     W       69   829     0            224
#> 5     K       11   887     0            235
#> 6     D       92   317     0            327

Created on 2022-09-06 by the reprex package (v2.0.1)

Edit 2

To assign only the total quantity per group use sum instead of cumsum.

df$total_quantity <- ave(df$quantity, df$group, FUN = sum)

Upvotes: 1

Darren Tsai
Darren Tsai

Reputation: 35554

You could use Reduce(..., accumulate = TRUE) to find where the first cumulative quantity >= 500.

set.seed(123)
df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))

library(dplyr)

df %>%
  group_by(group = lag(cumsum(Reduce(\(x, y) {
    z <- x + y
    if(z < 500) z else 0
  }, quantity, accumulate = TRUE) == 0) + 1, default = 1)) %>%
  mutate(total = sum(quantity)) %>%
  ungroup()

# A tibble: 26 × 5
   items quantity price group total
   <chr>    <int> <int> <dbl> <int>
 1 O           36   393     1   515
 2 S           78   376     1   515
 3 N           81   562     1   515
 4 C           43   140     1   515
 5 J           76   530     1   515
 6 R           15   189     1   515
 7 V           32   415     1   515
 8 K            7   322     1   515
 9 E            9   627     1   515
10 T           41   215     1   515
11 N           74   705     1   515
12 V           23   873     1   515
13 Y           27   846     2   548
14 Z           60   555     2   548
15 E           53   697     2   548
16 S           93   953     2   548
17 Y           86   138     2   548
18 Y           88   258     2   548
19 I           38   851     2   548
20 C           34   308     2   548
21 H           69   473     2   548
22 Z           72   917     3   269
23 G           96   133     3   269
24 J           63   615     3   269
25 I           13   112     3   269
26 S           25   168     3   269

Upvotes: 1

user2974951
user2974951

Reputation: 10375

Base R

set.seed(123)

df <- data.frame(items=sample(LETTERS,replace= T),quantity=sample(1:100,26,replace=FALSE),price=sample(100:1000,26,replace=FALSE))

df$group=cumsum(c(1,ifelse(diff(cumsum(df$quantity)%%500)<0,1,0)))
df$total=ave(df$quantity,df$group,FUN=cumsum)

   items quantity price group total
1      O       36   393     1    36
2      S       78   376     1   114
3      N       81   562     1   195
4      C       43   140     1   238
5      J       76   530     1   314
6      R       15   189     1   329
7      V       32   415     1   361
8      K        7   322     1   368
9      E        9   627     1   377
10     T       41   215     1   418
11     N       74   705     1   492
12     V       23   873     2    23
13     Y       27   846     2    50
14     Z       60   555     2   110
15     E       53   697     2   163
16     S       93   953     2   256
17     Y       86   138     2   342
18     Y       88   258     2   430
19     I       38   851     2   468
20     C       34   308     2   502
21     H       69   473     3    69
22     Z       72   917     3   141
23     G       96   133     3   237
24     J       63   615     3   300
25     I       13   112     3   313
26     S       25   168     3   338

Upvotes: 1

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