For more complicated examples, the (relatively new) array2DF() function is also useful:
> with(data, tapply(count, Date, mean)) |> array2DF() Var1 Value 1 2024-03-23 5.416667 2 2024-03-24 5.500000 3 2024-03-25 6.000000 4 2024-03-26 4.476190 5 2024-03-27 6.538462 6 2024-03-28 5.200000 or > tapply(data, ~ Date, with, mean(count)) |> array2DF(responseName = "count") Date count 1 2024-03-23 5.416667 2 2024-03-24 5.500000 3 2024-03-25 6.000000 4 2024-03-26 4.476190 5 2024-03-27 6.538462 6 2024-03-28 5.200000 Best, -Deepayan On Wed, 27 Mar 2024 at 13:15, Rui Barradas <ruipbarra...@sapo.pt> wrote: > > Às 04:30 de 27/03/2024, Ogbos Okike escreveu: > > Warm greetings to you all. > > > > Using the tapply function below: > > data<-read.table("FD1month",col.names = c("Dates","count")) > > x=data$count > > f<-factor(data$Dates) > > AB<- tapply(x,f,mean) > > > > > > I made a simple calculation. The result, stored in AB, is of the form > > below. But an effort to write AB to a file as a data frame fails. When I > > use the write table, it only produces the count column and strip of the > > first column (date). > > > > 2005-11-01 2005-12-01 2006-01-01 2006-02-01 2006-03-01 2006-04-01 > > 2006-05-01 > > -4.106887 -4.259154 -5.836090 -4.756757 -4.118011 -4.487942 > > -4.430705 > > 2006-06-01 2006-07-01 2006-08-01 2006-09-01 2006-10-01 2006-11-01 > > 2006-12-01 > > -3.856727 -6.067103 -6.418767 -4.383031 -3.985805 -4.768196 > > -10.072579 > > 2007-01-01 2007-02-01 2007-03-01 2007-04-01 2007-05-01 2007-06-01 > > 2007-07-01 > > -5.342338 -4.653128 -4.325094 -4.525373 -4.574783 -3.915600 > > -4.127980 > > 2007-08-01 2007-09-01 2007-10-01 2007-11-01 2007-12-01 2008-01-01 > > 2008-02-01 > > -3.952150 -4.033518 -4.532878 -4.522941 -4.485693 -3.922155 > > -4.183578 > > 2008-03-01 2008-04-01 2008-05-01 2008-06-01 2008-07-01 2008-08-01 > > 2008-09-01 > > -4.336969 -3.813306 -4.296579 -4.575095 -4.036036 -4.727994 > > -4.347428 > > 2008-10-01 2008-11-01 2008-12-01 > > -4.029918 -4.260326 -4.454224 > > > > But the normal format I wish to display only appears on the terminal, > > leading me to copy it and paste into a text file. That is, when I enter AB > > on the terminal, it returns a format in the form: > > > > 008-02-01 -4.183578 > > 2008-03-01 -4.336969 > > 2008-04-01 -3.813306 > > 2008-05-01 -4.296579 > > 2008-06-01 -4.575095 > > 2008-07-01 -4.036036 > > 2008-08-01 -4.727994 > > 2008-09-01 -4.347428 > > 2008-10-01 -4.029918 > > 2008-11-01 -4.260326 > > 2008-12-01 -4.454224 > > > > Now, my question: How do I write out two columns displayed by AB on the > > terminal to a file? > > > > I have tried using AB<-data.frame(AB) but it doesn't work either. > > > > Many thanks for your time. > > Ogbos > > > > [[alternative HTML version deleted]] > > > > ______________________________________________ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > 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. > Hello, > > The main trick is to pipe to as.data.frame. But the result will have one > column only, you must assign the dates from the df's row names. > I also include an aggregate solution. > > > > # create a test data set > set.seed(2024) > data <- data.frame( > Date = sample(seq(Sys.Date() - 5, Sys.Date(), by = "1 days"), 100L, > TRUE), > count = sample(10L, 100L, TRUE) > ) > > # coerce tapply's result to class "data.frame" > res <- with(data, tapply(count, Date, mean)) |> as.data.frame() > # assign a dates column from the row names > res$Date <- row.names(res) > # cosmetics > names(res)[2:1] <- names(data) > # note that the row names are still tapply's names vector > # and that the columns order is not Date/count. Both are fixed > # after the calculations. > res > #> count Date > #> 2024-03-22 5.416667 2024-03-22 > #> 2024-03-23 5.500000 2024-03-23 > #> 2024-03-24 6.000000 2024-03-24 > #> 2024-03-25 4.476190 2024-03-25 > #> 2024-03-26 6.538462 2024-03-26 > #> 2024-03-27 5.200000 2024-03-27 > > # fix the columns' order > res <- res[2:1] > > > > # better all in one instruction > aggregate(count ~ Date, data, mean) > #> Date count > #> 1 2024-03-22 5.416667 > #> 2 2024-03-23 5.500000 > #> 3 2024-03-24 6.000000 > #> 4 2024-03-25 4.476190 > #> 5 2024-03-26 6.538462 > #> 6 2024-03-27 5.200000 > > > > Also, > I'm glad to help as always but Ogbos, you have been an R-Help > contributor for quite a while, please post data in dput format. Given > the problem the output of the following is more than enough. > > > dput(head(data, 20L)) > > > Hope this helps, > > Rui Barradas > > > -- > Este e-mail foi analisado pelo software antivírus AVG para verificar a > presença de vírus. > www.avg.com > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > 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. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see 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.