[R] Conditionally replace multiple rows in a dataframe
Bert Gunter
gunter.berton at gene.com
Thu Nov 20 13:04:09 CET 2014
No comment on whether Jeff's solution is correct or not, but for
setting elements to NA, perhaps preferable, as it invokes the NA
method for data frames -- see ?NA -- is:
is,na(DF[ , c("value1","value2") ]) <- !DF$ToKeep
(I would welcome comments from cogniscenti as to whether this is
actually preferable and why or why not)
Cheers,
Bert
Bert Gunter
Genentech Nonclinical Biostatistics
(650) 467-7374
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
Clifford Stoll
On Wed, Nov 19, 2014 at 11:23 PM, Jeff Newmiller
<jdnewmil at dcn.davis.ca.us> wrote:
> Hi. Your failure to post in plain text has nearly rendered your example code unusable. Please post in plain text, not HTML. Also, the ToKeep attribute was most of your example, yet was completely irrelevant.
>
> I recommend avoiding the variable name "df" as it is easily confused with the base function of that name.
>
> Assuming the data frame is in variable DF...
>
> DF[ !DF$ToKeep, c("value1","value2") ] <- NA
>
> should do it.
> ---------------------------------------------------------------------------
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> Sent from my phone. Please excuse my brevity.
>
> On November 19, 2014 8:03:28 PM PST, "Thiago V. dos Santos" <thi_veloso at yahoo.com.br> wrote:
>>Hi programming fellows,
>>Please consider the following data frame:
>>df <- structure(list(date = structure(c(1251350100.288,
>>1251351900, 1251353699.712, 1251355500.288, 1251357300,
>>1251359099.712), class = c("POSIXct", "POSIXt")), mix.ratio.csi =
>>c(442.78316237477, 436.757082063885, 425.742872761246,
>>395.770804307671, 386.758335309866, 392.115887652156), mix.ratio.licor
>>= c(447.141491945547, 441.319548211994,
>>430.854166343173, 402.232640566763, 393.683007533694,
>>398.388336602215), ToKeep = c(FALSE, FALSE, TRUE, TRUE, TRUE, TRUE)),
>>.Names = c("date", "value1", "value2", "ToKeep"), index =
>>structure(integer(0), ToKeep = c(1L, 2L, 8L, 52L, 53L, 54L, 55L, 85L,
>>86L, 87L, 88L, 89L, 92L, 93L, 94L, 95L, 96L, 97L, 98L, 99L, 100L, 102L,
>>103L, 105L, 106L, 192L, 193L, 220L, 223L, 225L, 228L, 229L, 260L, 263L,
>>264L, 265L, 266L, 267L, 305L, 306L, 307L, 308L, 309L, 310L, 311L, 312L,
>>313L, 314L, 315L, 352L, 353L, 354L, 375L, 376L, 378L, 379L, 380L, 383L,
>>411L, 412L, 413L, 414L, 415L, 416L, 418L, 419L, 445L, 453L, 463L,
>>464L, 465L, 466L, 467L, 468L, 497L, 504L, 547L, 548L, 549L, 586L,
>>589L, 630L, 631L, 632L, 633L, 634L, 635L, 636L, 644L, 645L, 646L,
>>647L, 648L, 649L, 650L, 651L, 674L, 675L, 676L, 677L, 678L, 682L,
>>687L, 690L, 691L, 724L, 725L, 726L, 727L, 728L, 729L, 730L, 731L,
>>732L, 733L, 734L, 735L, 736L, 739L, 740L, 741L, 742L, 768L, 771L,
>>772L, 773L, 774L, 775L, 776L, 777L, 778L, 779L, 3L, 4L, 5L, 6L, 7L, 9L,
>>10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L,
>>24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L,
>>38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L,
>>56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L,
>>70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L, 83L,
>>84L, 90L, 91L, 101L, 104L, 107L, 108L, 109L, 110L, 111L, 112L, 113L,
>>114L, 115L, 116L, 117L, 118L, 119L, 120L, 121L, 122L, 123L, 124L, 125L,
>>126L, 127L, 128L, 129L, 130L, 131L, 132L, 133L, 134L, 135L, 136L, 137L,
>>138L, 139L, 140L, 141L, 142L, 143L, 144L, 145L, 146L, 147L, 148L, 149L,
>>150L, 151L, 152L, 153L, 154L, 155L, 156L, 157L, 158L, 159L, 160L, 161L,
>>162L, 163L, 164L, 165L, 166L, 167L, 168L, 169L, 170L, 171L, 172L,
>>173L, 174L, 175L, 176L, 177L, 178L, 179L, 180L, 181L, 182L, 183L,
>>184L, 185L, 186L, 187L, 188L, 189L, 190L, 191L, 194L, 195L, 196L,
>>197L, 198L, 199L, 200L, 201L, 202L, 203L, 204L, 205L, 206L, 207L,
>>208L, 209L, 210L, 211L, 212L, 213L, 214L, 215L, 216L, 217L, 218L,
>>219L, 221L, 222L, 224L, 226L, 227L, 230L, 231L, 232L, 233L, 234L,
>>235L, 236L, 237L, 238L, 239L, 240L, 241L, 242L, 243L, 244L, 245L,
>>246L, 247L, 248L, 249L, 250L, 251L, 252L, 253L, 254L, 255L, 256L,
>>257L, 258L, 259L, 261L, 262L, 268L, 269L, 270L, 271L, 272L, 273L,
>>274L, 275L, 276L, 277L, 278L, 279L, 280L, 281L, 282L, 283L, 284L,
>>285L, 286L, 287L, 288L, 289L, 290L, 291L, 292L, 293L, 294L, 295L,
>>296L, 297L, 298L, 299L, 300L, 301L, 302L, 303L, 304L, 316L, 317L,
>>318L, 319L, 320L, 321L, 322L, 323L, 324L, 325L, 326L, 327L, 328L,
>>329L, 330L, 331L, 332L, 333L, 334L, 335L, 336L, 337L, 338L, 339L,
>>340L, 341L, 342L, 343L, 344L, 345L, 346L, 347L, 348L, 349L, 350L,
>>351L, 355L, 356L, 357L, 358L, 359L, 360L, 361L, 362L, 363L, 364L,
>>365L, 366L, 367L, 368L, 369L, 370L, 371L, 372L, 373L, 374L, 377L,
>>381L, 382L, 384L, 385L, 386L, 387L, 388L, 389L, 390L, 391L, 392L,
>>393L, 394L, 395L, 396L, 397L, 398L, 399L, 400L, 401L, 402L, 403L,
>>404L, 405L, 406L, 407L, 408L, 409L, 410L, 417L, 420L, 421L, 422L,
>>423L, 424L, 425L, 426L, 427L, 428L, 429L, 430L, 431L, 432L, 433L,
>>434L, 435L, 436L, 437L, 438L, 439L, 440L, 441L, 442L, 443L, 444L,
>>446L, 447L, 448L, 449L, 450L, 451L, 452L, 454L, 455L, 456L, 457L,
>>458L, 459L, 460L, 461L, 462L, 469L, 470L, 471L, 472L, 473L, 474L,
>>475L, 476L, 477L, 478L, 479L, 480L, 481L, 482L, 483L, 484L, 485L,
>>486L, 487L, 488L, 489L, 490L, 491L, 492L, 493L, 494L, 495L, 496L,
>>498L, 499L, 500L, 501L, 502L, 503L, 505L, 506L, 507L, 508L, 509L,
>>510L, 511L, 512L, 513L, 514L, 515L, 516L, 517L, 518L, 519L, 520L,
>>521L, 522L, 523L, 524L, 525L, 526L, 527L, 528L, 529L, 530L, 531L,
>>532L, 533L, 534L, 535L, 536L, 537L, 538L, 539L, 540L, 541L, 542L,
>>543L, 544L, 545L, 546L, 550L, 551L, 552L, 553L, 554L, 555L, 556L,
>>557L, 558L, 559L, 560L, 561L, 562L, 563L, 564L, 565L, 566L, 567L,
>>568L, 569L, 570L, 571L, 572L, 573L, 574L, 575L, 576L, 577L, 578L,
>>579L, 580L, 581L, 582L, 583L, 584L, 585L, 587L, 588L, 590L, 591L,
>>592L, 593L, 594L, 595L, 596L, 597L, 598L, 599L, 600L, 601L, 602L,
>>603L, 604L, 605L, 606L, 607L, 608L, 609L, 610L, 611L, 612L, 613L,
>>614L, 615L, 616L, 617L, 618L, 619L, 620L, 621L, 622L, 623L, 624L,
>>625L, 626L, 627L, 628L, 629L, 637L, 638L, 639L, 640L, 641L, 642L,
>>643L, 652L, 653L, 654L, 655L, 656L, 657L, 658L, 659L, 660L, 661L,
>>662L, 663L, 664L, 665L, 666L, 667L, 668L, 669L, 670L, 671L, 672L,
>>673L, 679L, 680L, 681L, 683L, 684L, 685L, 686L, 688L, 689L, 692L,
>>693L, 694L, 695L, 696L, 697L, 698L, 699L, 700L, 701L, 702L, 703L,
>>704L, 705L, 706L, 707L, 708L, 709L, 710L, 711L, 712L, 713L, 714L,
>>715L, 716L, 717L, 718L, 719L, 720L, 721L, 722L, 723L, 737L, 738L,
>>743L, 744L, 745L, 746L, 747L, 748L, 749L, 750L, 751L, 752L, 753L,
>>754L, 755L, 756L, 757L, 758L, 759L, 760L, 761L, 762L, 763L, 764L,
>>765L, 766L, 767L, 769L, 770L, 780L, 781L, 782L, 783L, 784L, 785L,
>>786L, 787L, 788L, 789L)), row.names = c(NA, 6L), class = "data.frame")
>>I need to create a new data.frame with the following structure:
>>1) if column 'ToKeep' is TRUE, then columns 'date', 'value1' and
>>'value2' remain the same;
>>2) if column 'ToKeep' is FALSE, then columns 'value1' e 'value2'
>>receive NA (and 'date' remains the same).
>>I have been trying to use ifelse so far, but still haven't found the
>>right indexing procedure:
>>df[, c(2,3)] <- lapply(df[, 4], function(x) ifelse(x == FALSE, NA, x))
>>Any suggestion? Greetings,
>>--
>>Thiago V. dos Santos
>>PhD student
>>Land and Atmospheric Science
>>University of Minnesota
>>http://www.laas.umn.edu/CurrentStudents/MeettheStudents/ThiagodosSantos/index.htm
>>Phone: (612) 323 9898
>> [[alternative HTML version deleted]]
>>
>>______________________________________________
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>>https://stat.ethz.ch/mailman/listinfo/r-help
>>PLEASE do read the posting guide
>>http://www.R-project.org/posting-guide.html
>>and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
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> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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