R reshape by day, month, year [duplicate] - r

Question

This question already has answers here:
How to reshape data from long to wide format
(14 answers)
Closed 5 years ago.
I have a simple table in the following format:
Date val
2005-01-01 15
2005-01-02 18
2005-01-03 20
...
And am trying to reshape it to the following "wide" column format:
Year Month day1 day2 day3 day4 ...day31
2005 01 day1val day2val day3val day4val ...day31val
2005 02 day1val day2val day3val day4val ...day31val
I've successfully split the date column into three separate d,m,y columns using
dates_separated <- data.frame(year = as.numeric(format(input_df$DATE, format = "%Y")),
month = as.numeric(format(input_df$DATE, format = "%m")),
day = as.numeric(format(input_df$DATE, format = "%d")))
output_df <- cbind(input_df, dates_sep)
I'm trying to use the reshape function to get this done, but am finding my output could be more complicated than it can handle. Is there another function I should be using here?
Edit: I don't believe this was a duplicate of what was suggested. markdly's answer below did exactly what I needed. Thanks!

Answer 1

For the sake of completeness, here is a solution using the dcast() function.
OP's input_df consists only of two columns Date and val. So, let's create a full year of sample data by
set.seed(1234L)
input_df <- data.frame(Date = as.Date("2005-01-01") + 0:364,
val = sample(100:999, 365L, TRUE))
The dcast() function is available from the reshape2 and the data.table packages. Here, data.table is used because of its handy year(), month(), and mday() functions:
library(data.table)
dcast(input_df, year(Date) + month(Date) ~ mday(Date))
year(Date) month(Date) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
1 2005 1 202 660 648 661 874 676 108 309 699 562 724 590 354 931 363 853 357 340 268 309 384 372 243 135 296 829 573 923 848 141 510
2 2005 2 338 374 556 262 783 281 332 992 826 598 681 380 659 396 551 709 536 319 788 166 378 745 554 237 553 544 776 257 NA NA NA
3 2005 3 863 878 137 385 112 315 735 377 557 146 608 209 903 113 804 180 567 445 163 388 701 933 524 228 589 276 908 450 379 244 906
4 2005 4 249 910 220 218 194 560 370 124 378 767 131 608 352 283 220 393 239 216 491 134 741 190 955 209 297 921 951 351 211 817 NA
5 2005 5 769 924 995 948 537 355 326 552 547 386 966 670 214 480 922 521 917 637 668 882 552 985 391 533 421 664 767 609 982 619 495
6 2005 6 305 173 865 311 989 641 998 438 599 486 618 489 302 176 673 487 165 822 392 781 625 737 484 409 783 481 604 204 372 530 NA
7 2005 7 410 640 168 960 119 857 669 379 768 675 993 215 894 829 839 851 759 984 675 694 575 385 791 573 759 376 463 283 987 609 352
8 2005 8 266 782 610 938 674 730 531 865 480 128 332 401 220 549 821 403 558 544 817 610 196 826 610 291 774 376 540 990 481 319 295
9 2005 9 720 982 529 796 616 969 817 578 636 337 351 158 606 336 102 630 568 860 126 639 341 208 190 773 114 144 772 421 783 438 NA
10 2005 10 819 123 555 839 590 340 410 432 486 926 805 764 352 511 358 726 838 689 472 956 318 647 782 724 203 672 378 417 982 584 499
11 2005 11 954 507 271 992 593 791 922 713 466 466 231 277 272 467 413 851 278 875 457 237 405 430 484 267 692 928 760 894 958 275 NA
12 2005 12 525 447 436 125 936 469 960 344 565 980 432 379 130 700 928 140 281 769 217 737 998 949 633 758 538 791 102 602 514 396 851
To prettify the result, Year and Month can be computed in advance:
dcast(setDT(input_df)[, Year := year(Date)][, Month := month(Date)],
Year + Month ~ sprintf("day%02i", mday(Date)), value.var = "val")
Year Month day01 day02 day03 day04 day05 day06 day07 day08 day09 day10 day11 day12 day13 day14 day15 day16 day17 day18 day19 day20 ...
1: 2005 1 202 660 648 661 874 676 108 309 699 562 724 590 354 931 363 853 357 340 268 309
2: 2005 2 338 374 556 262 783 281 332 992 826 598 681 380 659 396 551 709 536 319 788 166
3: 2005 3 863 878 137 385 112 315 735 377 557 146 608 209 903 113 804 180 567 445 163 388
4: 2005 4 249 910 220 218 194 560 370 124 378 767 131 608 352 283 220 393 239 216 491 134
5: 2005 5 769 924 995 948 537 355 326 552 547 386 966 670 214 480 922 521 917 637 668 882
6: 2005 6 305 173 865 311 989 641 998 438 599 486 618 489 302 176 673 487 165 822 392 781
7: 2005 7 410 640 168 960 119 857 669 379 768 675 993 215 894 829 839 851 759 984 675 694
8: 2005 8 266 782 610 938 674 730 531 865 480 128 332 401 220 549 821 403 558 544 817 610
9: 2005 9 720 982 529 796 616 969 817 578 636 337 351 158 606 336 102 630 568 860 126 639
10: 2005 10 819 123 555 839 590 340 410 432 486 926 805 764 352 511 358 726 838 689 472 956
11: 2005 11 954 507 271 992 593 791 922 713 466 466 231 277 272 467 413 851 278 875 457 237
12: 2005 12 525 447 436 125 936 469 960 344 565 980 432 379 130 700 928 140 281 769 217 737
Note that here sprintf("Day%02i", mday(Date)) is used to keep the columns ordered. Using paste0("day", day) as in markdly's answer, the columns would be in the wrong order:
day1 day10 day11 day12 day13 day14 day15 day16 day17 day18 day19 day2 day20 ...

Answer 2

If you can add actual data to your question it really helps others to post answers. For example, here's some data for 5 days in each month in 2015:
set.seed(123)
df <- expand.grid(year = 2015, month = 1:12, day = 1:5)
df$val <- sample.int(1000, nrow(df))
head(df)
#> year month day val
#> 1 2015 1 1 288
#> 2 2015 2 1 788
#> 3 2015 3 1 409
#> 4 2015 4 1 881
#> 5 2015 5 1 937
#> 6 2015 6 1 46
This can be converted to the desired format using tidyr::spread:
library(dplyr)
library(tidyr)
df %>%
mutate(day = paste0("day", day)) %>%
spread(day, val)
#> year month day1 day2 day3 day4 day5
#> 1 2015 1 288 670 640 732 254
#> 2 2015 2 788 566 691 209 816
#> 3 2015 3 409 102 530 307 44
#> 4 2015 4 881 993 579 223 420
#> 5 2015 5 937 243 282 138 758
#> 6 2015 6 46 42 143 398 116
#> 7 2015 7 525 323 935 397 531
#> 8 2015 8 887 996 875 353 196
#> 9 2015 9 548 872 669 146 121
#> 10 2015 10 453 679 770 133 711
#> 11 2015 11 948 627 24 961 844
#> 12 2015 12 449 972 462 445 957