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r dplyr tidyverse

Answers (3)

March 17, 2026 Score: 7 Rep: 134,653 Quality: High Completeness: 60%

I would do a join. I assume you can do that with the tidyverse but I recommend using package data.table. This apporach is efficient and scales well to large datasets.

library(data.table)
DT
March 17, 2026 Score: 3 Rep: 106,187 Quality: Medium Completeness: 60%

Maybe you can use reframe + tibble along with casewhen

# library(tidyverse)
library(dplyr)
library(tibble)
diamonds %>%
  mutate(casewhen(
    color %in% c('E','I') ~ tibble(kpia = "A", kpib = 1.1),
    color %in% c('J','H') ~ tibble(kpia = "B", kpib = 1.2),
    TRUE ~ tibble(kpia = "C", kpib = 2)
  ))

and you will obtain

# A tibble: 53,940 × 12
   carat cut       color clarity depth table price     x     y     z kpia kpib
 1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43 A       1.1
 2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31 A       1.1
 3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31 A       1.1
 4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63 A       1.1
 5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75 B       1.2
 6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48 B       1.2
 7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47 A       1.1
 8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53 B       1.2
 9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49 A       1.1
10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39 B       1.2

ℹ 53,930 more rows
March 17, 2026 Score: 1 Rep: 273,704 Quality: Low Completeness: 50%

Assign strings containing both and then separate them.

library(dplyr)
library(tidyr)
BOD %>%
  mutate(
    kp = casewhen(
      Time == 1 ~ "A 1.1",
      Time == 2 ~ "B 1.2",
      .default = "C 2"
    )) %>%
  separate(kp, c("kp1", "kp2"), sep = " ", convert = TRUE)
  Time demand kp1 kp2


1    1    8.3   A 1.1


2    2   10.3   B 1.2


3    3   19.0   C 2.0


4    4   16.0   C 2.0


5    5   15.6   C 2.0


6    7   19.8   C 2.0

This variation also works:

library(dplyr)
BOD %>%
  mutate(
    casewhen(
      Time == 1 ~ "A 1.1",
      Time == 2 ~ "B 1.2",
      .default = "C 2"
    ) %>% read.table(text = ., col.names = c("kp1", "kp2"))
  )