Hi I have a dataframe sleep_data where I am attempting to change Id values to user1:user33 based on groups. So where Id == 1503960366 change to user_1, Id == 1644430081 change to user_2, etc etc.
I am originally using this code, however would enjoy a more concise and clear code to update each user string as there are 33 different values and am looking for the most efficient way of achieving this.
sleep_data["Id"][sleep_data["Id"] == 1503960366 ] <- user_1
sleep_data["Id"][sleep_data["Id"] == 1644430081] <- user_2
structure(list(Id = c(1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1503960366, 1644430081, 1644430081, 1644430081, 1644430081, 1844505072, 1844505072, 1844505072, 1927972279, 1927972279, 1927972279, 1927972279, 1927972279, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035, 2026352035), SleepDay = c("4/12/2016 12:00:00 AM", "4/13/2016 12:00:00 AM", "4/15/2016 12:00:00 AM", "4/16/2016 12:00:00 AM", "4/17/2016 12:00:00 AM", "4/19/2016 12:00:00 AM", "4/20/2016 12:00:00 AM", "4/21/2016 12:00:00 AM", "4/23/2016 12:00:00 AM", "4/24/2016 12:00:00 AM", "4/25/2016 12:00:00 AM", "4/26/2016 12:00:00 AM", "4/28/2016 12:00:00 AM", "4/29/2016 12:00:00 AM", "4/30/2016 12:00:00 AM", "5/1/2016 12:00:00 AM", "5/2/2016 12:00:00 AM", "5/3/2016 12:00:00 AM", "5/5/2016 12:00:00 AM", "5/6/2016 12:00:00 AM", "5/7/2016 12:00:00 AM", "5/8/2016 12:00:00 AM", "5/9/2016 12:00:00 AM", "5/10/2016 12:00:00 AM", "5/11/2016 12:00:00 AM", "4/29/2016 12:00:00 AM", "4/30/2016 12:00:00 AM", "5/2/2016 12:00:00 AM", "5/8/2016 12:00:00 AM", "4/15/2016 12:00:00 AM", "4/30/2016 12:00:00 AM", "5/1/2016 12:00:00 AM", "4/12/2016 12:00:00 AM", "4/13/2016 12:00:00 AM", "4/15/2016 12:00:00 AM", "4/26/2016 12:00:00 AM", "4/28/2016 12:00:00 AM", "4/12/2016 12:00:00 AM", "4/13/2016 12:00:00 AM", "4/14/2016 12:00:00 AM", "4/15/2016 12:00:00 AM", "4/16/2016 12:00:00 AM", "4/17/2016 12:00:00 AM", "4/19/2016 12:00:00 AM", "4/20/2016 12:00:00 AM", "4/21/2016 12:00:00 AM", "4/22/2016 12:00:00 AM", "4/23/2016 12:00:00 AM", "4/24/2016 12:00:00 AM", "4/25/2016 12:00:00 AM"), TotalSleepRecords = c(1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), TotalMinutesAsleep = c(327L, 384L, 412L, 340L, 700L, 304L, 360L, 325L, 361L, 430L, 277L, 245L, 366L, 341L, 404L, 369L, 277L, 273L, 247L, 334L, 331L, 594L, 338L, 383L, 285L, 119L, 124L, 796L, 137L, 644L, 722L, 590L, 750L, 398L, 475L, 296L, 166L, 503L, 531L, 545L, 523L, 524L, 437L, 498L, 461L, 477L, 520L, 522L, 555L, 506L), TotalTimeInBed = c(346L, 407L, 442L, 367L, 712L, 320L, 377L, 364L, 384L, 449L, 323L, 274L, 393L, 354L, 425L, 396L, 309L, 296L, 264L, 367L, 349L, 611L, 342L, 403L, 306L, 127L, 142L, 961L, 154L, 961L, 961L, 961L, 775L, 422L, 499L, 315L, 178L, 546L, 565L, 568L, 573L, 567L, 498L, 540L, 510L, 514L, 545L, 554L, 591L, 531L)), row.names = c(NA, 50L), class = "data.frame")
Thank you
CodePudding user response：
This should do the job, unless you want specific Ids to have specific User_x.
library(tidyverse)
Data %>%
dplyr::mutate(User = str_c("user_",
Data %>%
dplyr::group_by(Id) %>%
dplyr::group_indices())) %>%
dplyr::relocate(User,.after = Id)
Output:
Id User SleepDay TotalSleepRecords TotalMinutesAsleep TotalTimeInBed
1 1503960366 user_1 4/12/2016 12:00:00 AM 1 327 346
2 1503960366 user_1 4/13/2016 12:00:00 AM 2 384 407
3 1503960366 user_1 4/15/2016 12:00:00 AM 1 412 442
4 1503960366 user_1 4/16/2016 12:00:00 AM 2 340 367
5 1503960366 user_1 4/17/2016 12:00:00 AM 1 700 712
6 1503960366 user_1 4/19/2016 12:00:00 AM 1 304 320
7 1503960366 user_1 4/20/2016 12:00:00 AM 1 360 377
8 1503960366 user_1 4/21/2016 12:00:00 AM 1 325 364
9 1503960366 user_1 4/23/2016 12:00:00 AM 1 361 384
10 1503960366 user_1 4/24/2016 12:00:00 AM 1 430 449
11 1503960366 user_1 4/25/2016 12:00:00 AM 1 277 323
12 1503960366 user_1 4/26/2016 12:00:00 AM 1 245 274
13 1503960366 user_1 4/28/2016 12:00:00 AM 1 366 393
14 1503960366 user_1 4/29/2016 12:00:00 AM 1 341 354
15 1503960366 user_1 4/30/2016 12:00:00 AM 1 404 425
16 1503960366 user_1 5/1/2016 12:00:00 AM 1 369 396
17 1503960366 user_1 5/2/2016 12:00:00 AM 1 277 309
18 1503960366 user_1 5/3/2016 12:00:00 AM 1 273 296
19 1503960366 user_1 5/5/2016 12:00:00 AM 1 247 264
20 1503960366 user_1 5/6/2016 12:00:00 AM 1 334 367
21 1503960366 user_1 5/7/2016 12:00:00 AM 1 331 349
22 1503960366 user_1 5/8/2016 12:00:00 AM 1 594 611
23 1503960366 user_1 5/9/2016 12:00:00 AM 1 338 342
24 1503960366 user_1 5/10/2016 12:00:00 AM 1 383 403
25 1503960366 user_1 5/11/2016 12:00:00 AM 1 285 306
26 1644430081 user_2 4/29/2016 12:00:00 AM 1 119 127
27 1644430081 user_2 4/30/2016 12:00:00 AM 1 124 142
28 1644430081 user_2 5/2/2016 12:00:00 AM 1 796 961
29 1644430081 user_2 5/8/2016 12:00:00 AM 1 137 154
30 1844505072 user_3 4/15/2016 12:00:00 AM 1 644 961
31 1844505072 user_3 4/30/2016 12:00:00 AM 1 722 961
32 1844505072 user_3 5/1/2016 12:00:00 AM 1 590 961
33 1927972279 user_4 4/12/2016 12:00:00 AM 3 750 775
34 1927972279 user_4 4/13/2016 12:00:00 AM 1 398 422
35 1927972279 user_4 4/15/2016 12:00:00 AM 2 475 499
36 1927972279 user_4 4/26/2016 12:00:00 AM 1 296 315
37 1927972279 user_4 4/28/2016 12:00:00 AM 1 166 178
38 2026352035 user_5 4/12/2016 12:00:00 AM 1 503 546
39 2026352035 user_5 4/13/2016 12:00:00 AM 1 531 565
40 2026352035 user_5 4/14/2016 12:00:00 AM 1 545 568
41 2026352035 user_5 4/15/2016 12:00:00 AM 1 523 573
42 2026352035 user_5 4/16/2016 12:00:00 AM 1 524 567
43 2026352035 user_5 4/17/2016 12:00:00 AM 1 437 498
44 2026352035 user_5 4/19/2016 12:00:00 AM 1 498 540
45 2026352035 user_5 4/20/2016 12:00:00 AM 1 461 510
46 2026352035 user_5 4/21/2016 12:00:00 AM 1 477 514
47 2026352035 user_5 4/22/2016 12:00:00 AM 1 520 545
48 2026352035 user_5 4/23/2016 12:00:00 AM 1 522 554
49 2026352035 user_5 4/24/2016 12:00:00 AM 1 555 591
50 2026352035 user_5 4/25/2016 12:00:00 AM 1 506 531
CodePudding user response：
Using data.table:
library(data.table)
Users <- c('user_1', 'user_3', 'user_4', 'user_5', 'user_7')
Id <- c(1503960366, 1644430081, 1844505072, 1927972279, 2026352035)
users <- data.table(Users, Id)
setDT(sleepdata)[users, User:=i.Users, on=.(Id)]
##
# validate
#
sleepdata[, .N, by=.(Id, User)]
## Id User N
## 1: 1503960366 user_1 25
## 2: 1644430081 user_3 4
## 3: 1844505072 user_4 3
## 4: 1927972279 user_5 5
## 5: 2026352035 user_7 13