My final post for thisthread! Since I first asked myquestion on Stack Overflow, I posted all the solutions along with my timingstudy there.
https://stackoverflow.com/questions/50807970/converting-a-list-of-data-frames-not-a-simple-rbind-second-row-to-new-columns/51129202#51129202 Thanks again toeveryone for their help. Ira On Saturday, June 30, 2018, 6:11:00 PM PDT, Jeff Newmiller <jdnew...@dcn.davis.ca.us> wrote: Your request is getting a bit complicated with so much re-hashing, but here are three solutions: base only, a bit of dplyr, and dplyr+tidyr: ######### # input data employees4List = list(data.frame(first1 = "Al", second1 = "Jones"), data.frame(first2 = c("Al2", "Barb"), second2 = c("Jones", "Smith")), data.frame(first3 = c("Al3", "Barbara", "Carol"), second3 = c("Jones", "Smith", "Adams")), data.frame(first4 = ("Al"), second4 = "Jones2")) employees4List #> [[1]] #> first1 second1 #> 1 Al Jones #> #> [[2]] #> first2 second2 #> 1 Al2 Jones #> 2 Barb Smith #> #> [[3]] #> first3 second3 #> 1 Al3 Jones #> 2 Barbara Smith #> 3 Carol Adams #> #> [[4]] #> first4 second4 #> 1 Al Jones2 # expected output df1 = data.frame(First1 = "Al", Second1 = "Jones", First2 = NA, Second2 = NA, First3 = NA, Second3 = NA, First4 = NA, Second4 = NA) df2 = data.frame(First1 = "Al2", Second1 = "Jones", First2 = "Barb", Second2 = "Smith", First3 = NA, Second3 = NA, First4 = NA, Second4 = NA) df3 = data.frame(First1 = "Al3", Second1 = "Jones", First2 = "Barbara", Second2 = "Smith", First3 = "Carol", Second3 = "Adams", First4 = NA, Second4 = NA) df4 = data.frame(First1 = "Al", Second1 = "Jones2", First2 = NA, Second2 = NA, First3 = NA, Second3 = NA, First4 = NA, Second4 = NA) listFinal = list(df1, df2, df3, df4) listFinal #> [[1]] #> First1 Second1 First2 Second2 First3 Second3 First4 Second4 #> 1 Al Jones NA NA NA NA NA NA #> #> [[2]] #> First1 Second1 First2 Second2 First3 Second3 First4 Second4 #> 1 Al2 Jones Barb Smith NA NA NA NA #> #> [[3]] #> First1 Second1 First2 Second2 First3 Second3 First4 Second4 #> 1 Al3 Jones Barbara Smith Carol Adams NA NA #> #> [[4]] #> First1 Second1 First2 Second2 First3 Second3 First4 Second4 #> 1 Al Jones2 NA NA NA NA NA NA myrename1 <- function( DF, m ) { # if a pair of columns is not present, raise an error stopifnot( 2 == length( DF ) ) n <- nrow( DF ) # use memory layout of elements of matrix # t() automatically converts to matrix (nrow=2) # matrix(,nrow=1) re-interprets the column-major output of t() # as a single row matrix result <- as.data.frame( matrix( t( DF ), nrow = 1 ) , stringsAsFactors = FALSE ) if ( n < m ) { result[ , seq( 2 * n + 1, 2 * m ) ] <- NA } setNames( result , sprintf( "%s%d" , c( "First", "Second" ) , rep( seq.int( m ), each = 2 ) ) ) } m <- max( unlist( lapply( employees4List, nrow ) ) ) listFinal1 <- lapply( employees4List, myrename1, m = m ) listFinal1 #> [[1]] #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al Jones NA NA NA NA #> #> [[2]] #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al2 Jones Barb Smith NA NA #> #> [[3]] #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al3 Jones Barbara Smith Carol Adams #> #> [[4]] #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al Jones2 NA NA NA NA result1 <- do.call( rbind, listFinal1 ) result1 #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al Jones <NA> <NA> <NA> <NA> #> 2 Al2 Jones Barb Smith <NA> <NA> #> 3 Al3 Jones Barbara Smith Carol Adams #> 4 Al Jones2 <NA> <NA> <NA> <NA> myrename2 <- function( DF ) { # if a pair of columns is not present, raise an error stopifnot( 2 == length( DF ) ) n <- nrow( DF ) # use memory layout of elements of matrix # t() automatically converts to matrix (nrow=2) # matrix(,nrow=1) re-interprets the column-major output of t() # as a single row matrix setNames( as.data.frame( matrix( t( DF ), nrow = 1 ) , stringsAsFactors = FALSE ) , sprintf( "%s%d" , c( "First", "Second" ) , rep( seq.int( n ), each = 2 ) ) ) } listFinal2 <- lapply( employees4List, myrename2 ) listFinal2 #> [[1]] #> First1 Second1 #> 1 Al Jones #> #> [[2]] #> First1 Second1 First2 Second2 #> 1 Al2 Jones Barb Smith #> #> [[3]] #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al3 Jones Barbara Smith Carol Adams #> #> [[4]] #> First1 Second1 #> 1 Al Jones2 result2 <- dplyr::bind_rows( listFinal2 ) result2 #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al Jones <NA> <NA> <NA> <NA> #> 2 Al2 Jones Barb Smith <NA> <NA> #> 3 Al3 Jones Barbara Smith Carol Adams #> 4 Al Jones2 <NA> <NA> <NA> <NA> library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union library(tidyr) myrename3 <- function( DF ) { # if a pair of columns is not present, raise an error stopifnot( 2 == length( DF ) ) names( DF ) <- c( "a", "b" ) m <- nrow( DF ) ( DF %>% mutate_all( as.character ) %>% mutate( rw = LETTERS[ seq.int( n() ) ] ) %>% gather( col, val, -rw ) %>% tidyr::unite( "labels", rw, col, sep="" ) %>% spread( labels, val ) %>% setNames( sprintf( "%s%d" , c( "First", "Second" ) , rep( seq.int( m ), each = 2 ) ) ) ) } listFinal3 <- lapply( employees4List, myrename3 ) listFinal3 #> [[1]] #> First1 Second1 #> 1 Al Jones #> #> [[2]] #> First1 Second1 First2 Second2 #> 1 Al2 Jones Barb Smith #> #> [[3]] #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al3 Jones Barbara Smith Carol Adams #> #> [[4]] #> First1 Second1 #> 1 Al Jones2 result3 <- dplyr::bind_rows( listFinal3 ) result3 #> First1 Second1 First2 Second2 First3 Second3 #> 1 Al Jones <NA> <NA> <NA> <NA> #> 2 Al2 Jones Barb Smith <NA> <NA> #> 3 Al3 Jones Barbara Smith Carol Adams #> 4 Al Jones2 <NA> <NA> <NA> <NA> #' Created on 2018-06-30 by the [reprex package](http://reprex.tidyverse.org) (v0.2.0). ######### On Sat, 30 Jun 2018, Ira Sharenow via R-help wrote: > I would like to thank everyone who helped me out. I have obtained some > offline help, so I would like to summarize all the information I have > received. > Before I summarize the thread, there is one loose end. > Initially I thought > library(dplyr) > dplyr::bind_rows(lapply(employees4List, function(x) > rbind.data.frame(c(t(x))))) > would work, but there were problems. > lapply(employees4List, function(x) rbind.data.frame(c(t(x)))) > spreads out the data frames converting the data frames from long to wide, but > it messes up the names. So one question I still have, is how can I > programmatically change all of the names? > After this initial step, the first data frame's names might be derived from > c("George", "Washington") > and the second data frame's names might be derived from > c("John", "Adams", "Thomas", "Jefferson") > What I want to change to the names to: > c("First1", "Second1") > and > c("First1", "Second1", "First2", "Second2") > I believe that will enable me to then go back and use bind_rows and complete > that method of solution: > Step 1: lapply(employees4List, function(x) rbind.data.frame(c(t(x)))) > Step 2: Clean up the names > Step 3: bind_rows > Immediately below is hopefully a clear and precise statement of the problem > and the proposed solution path. Then there are the various solutions. > # Starting list of data frames > employees4List = list(data.frame(first1 = "Al", second1 = "Jones"), > data.frame(first2 = c("Al2", "Barb"), second2 = > c("Jones", "Smith")), > data.frame(first3 = c("Al3", "Barbara", "Carol"), > second3 = c("Jones", "Smith", "Adams")), > data.frame(first4 = ("Al"), second4 = "Jones2")) > > employees4List > > > # Intermediate step that messes up the names but successfully converts from > long to wide > lapply(employees4List, function(x) rbind.data.frame(c(t(x)))) > > # The intermediate list should likely look like this listFinal > df1 = data.frame(First1 = "Al", Second1 = "Jones", First2 = NA, Second2 = NA, > First3 = NA, Second3 = NA, > First4 = NA, Second4 = NA) > df2 = data.frame(First1 = "Al2", Second1 = "Jones", First2 = "Barb", Second2 > = "Smith", > First3 = NA, Second3 = NA, First4 = NA, Second4 = NA) > > df3 = data.frame(First1 = "Al3", Second1 = "Jones", First2 = "Barbara", > Second2 = "Smith", > First3 = "Carol", Second3 = "Adams", First4 = NA, Second4 = > NA) > df4 = data.frame(First1 = "Al", Second1 = "Jones2", First2 = NA, Second2 = > NA, First3 = NA, Second3 = NA, > First4 = NA, Second4 = NA) > listFinal = list(df1, df2, df3, df4) > listFinal > > # Requested data frame (except that the columns are not just character but > some are factor or even logical) > dplyr::bind_rows(listFinal) > Sarah Goslee solved the problem using base R. > Given > employees4List = list( > data.frame(first1 = ("Al"), second1 = "Jones"), > data.frame(first2 = c("Al2", "Barb"), second2 = c("Jones2", "Smith")), > data.frame(first3 = c("Al3", "Barbara", "Carol"), second3 = c("Jones3", > "Smith", > "Adams")), > data.frame(first4 = ("Al"), second4 = "Jones2")) > > This function produces the solution in the requested structure. > dfbycol <- function(x) { > x <- lapply(x, function(y)as.vector(t(as.matrix(y)))) > x <- lapply(x, function(y){length(y) <- max(sapply(x, length)); y}) > x <- do.call(rbind, x) > x <- data.frame(x, stringsAsFactors=FALSE) > colnames(x) <- paste0(c("first", "last"), rep(seq(1, ncol(x)/2), each=2)) > x > } > dfbycol(employees4List) > Offline, Jeff Newmiller and Bert Gunter provided alternative approaches to > the problem as well as other advice. Their solutions meet the "tidy" > criterion. > Bert suggested this online. > ## list of two data frames with different column names and numbers of rows: > zz <-list(one = data.frame(f=1:3,g=letters[2:4]), two = data.frame(a = 5:9,b > = letters[11:15])) > ## create common column names and bind them up: > do.call(rbind,lapply(zz,function(x){ names(x) <- c("first","last"); x})) > This and the next suggestion by Jeff produced useful solutions but not in the > requested form. > library(dplyr) > # note that these data frames all have character columns > # rather than factors, due to the as.is option when the > # data are read in. > DF1 <- read.table( text = > "First Last > George Washington > ", header=TRUE, as.is = TRUE ) > # dput looks ugly but is actually much more practical for > # providing R data on the mailing list... here is an example > dput( DF1 ) > #> structure(list(First = "George", Last = "Washington") > #>, .Names = c("First", > #> "Last"), class = "data.frame", row.names = c(NA, -1L)) > > DF2 <- read.table( text = > "Start End > John Adams > Thomas Jefferson > ", header = TRUE, as.is = TRUE ) > > DFL <- list( DF1, DF2 ) > > # DFNames is a set of unique identifiers > DFL1 <- data_frame( .DFNames = sprintf( "DF%d", 1:2 ) > , data = DFL > ) > > DFL2 <- ( DFL1 > %>% mutate( data = lapply( data > , function( DF ) { > DF[[ ".PK" ]] <- seq.int( nrow( DF )) > gather( DF, ".Col", "value", -.PK ) > } > ) > ) > %>% unnest > %>% spread( .Col, value ) > ) > DFL2 > During the discussion, useful links were recommended > [1] https://www.jstatsoft.org/article/view/v059i10 Hadley on tidy data > [2] http://r4ds.had.co.nz/relational-data.html#keys Hadley on relational data > [3] > https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example > How to make a great reproducible example > http://adv-r.had.co.nz/Functionals.html Improving lapply and related > skills > Thanks again to everyone! > Ira > > > > > On Friday, June 29, 2018, 7:47:13 PM PDT, Jeff Newmiller ><jdnew...@dcn.davis.ca.us> wrote: > > Code below... > > a) Just because something can be done with dplyr does not mean that is the > best way to do it. A solution in the hand is worth two on the Internet, > and dplyr is not always the fastest method anyway. > > b) I highly recommend that you read Hadley Wickham's paper on tidy data > [1]. Also, having a group of one or more columns at all times that > uniquely identify where the data came from is a "key" to success [2]. > > c) Please read and follow one of the various online documents about making > reproducible examples in R (e.g. [3]). HTML formatting is really a pain > (at best... at worst, it corrupts your code) on a plain-text-only list > (you have read the Posting Guide, right?). Consider my example below as a > model for you to follow in the future, and make sure to set your email > program to send plain text. (Obviously your examples don't have to achieve > success... but they should bring us up to speed with where you are having > troubles IN R.) > > [1] https://www.jstatsoft.org/article/view/v059i10 > [2] http://r4ds.had.co.nz/relational-data.html#keys > [3] > https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example > > ---- > library(dplyr) > #> > #> Attaching package: 'dplyr' > #> The following objects are masked from 'package:stats': > #> > #> filter, lag > #> The following objects are masked from 'package:base': > #> > #> intersect, setdiff, setequal, union > library(tidyr) > > # note that these data frames all have character columns > # rather than factors, due to the as.is option when the > # data are read in. > DF1 <- read.table( text = > "First Last > George Washington > ", header=TRUE, as.is = TRUE ) > > # dput looks ugly but is actually much more practical for > # providing R data on the mailing list... here is an example > dput( DF1 ) > #> structure(list(First = "George", Last = "Washington") > #>, .Names = c("First", > #> "Last"), class = "data.frame", row.names = c(NA, -1L)) > > DF2 <- read.table( text = > "Start End > John Adams > Thomas Jefferson > ", header = TRUE, as.is = TRUE ) > > DFL <- list( DF1, DF2 ) > > # DFNames is a set of unique identifiers > DFL1 <- data_frame( .DFNames = sprintf( "DF%d", 1:2 ) > , data = DFL > ) > > DFL2 <- ( DFL1 > %>% mutate( data = lapply( data > , function( DF ) { > DF[[ ".PK" ]] <- seq.int( nrow( DF )) > gather( DF, ".Col", "value", -.PK ) > } > ) > ) > %>% unnest > %>% spread( .Col, value ) > ) > DFL2 > #> # A tibble: 3 x 6 > #> .DFNames .PK End First Last Start > #> <chr> <int> <chr> <chr> <chr> <chr> > #> 1 DF1 1 <NA> George Washington <NA> > #> 2 DF2 1 Adams <NA> <NA> John > #> 3 DF2 2 Jefferson <NA> <NA> Thomas > > #' Created on 2018-06-29 by the [reprex package](http://reprex.tidyverse.org) > (v0.2.0). > ---- > > On Sat, 30 Jun 2018, Ira Sharenow via R-help wrote: > >> >> Sarah and David, >> >> Thank you for your responses.I will try and be clearer. >> >> Base R solution: Sarah?smethod worked perfectly >> >> Is there a dplyrsolution? >> >> START: list of dataframes >> >> FINISH: one data frame >> >> DETAILS: The initiallist of data frames might have hundreds or a few >> thousand data frames. Everydata frame will have two columns. The first >> column will represent first names.The second column will represent last >> names. The column names are notconsistent. Data frames will most likely have >> from one to five rows. >> >> SUGGESTED STRATEGY:Convert the n by 2 data frames to 1 by 2n data frames. >> Then somehow do an rbindeven though the number of columns differ from data >> frame to data frame. >> >> EXAMPLE: List with twodata frames >> >> # DF1 >> >> First Last >> >> George Washington >> >> >> >> # DF2 >> >> Start End >> >> John Adams >> >> Thomas Jefferson >> >> >> >> # End Result. One dataframe >> >> First1 Second1 First2 Second2 >> >> George Washington NA NA >> >> John Adams Thomas Jefferson >> >> >> >> DISCUSSION: As mentionedI posted something on Stack Overflow. Unfortunately, >> my example was not generalenough and so the suggested solutions worked on >> the easy case which I provided butnot when the names were different. >> >> The suggested solution was: >> >> library(dplyr) >> >> bind_rows(lapply(employees4List,function(x) rbind.data.frame(c(t(x))))) >> >> >> >> On this site I pointedout that the inner function: lapply(employees4List, >> function(x) rbind.data.frame(c(t(x)))) >> >> For each data frame correctlyspread the multiple rows into 1 by 2ndata >> frames. However, the column names were derived from the values and were >> amess. This caused a problem with bind_rows. >> >> I felt that if I knewhow to change all the names of all of the data frames >> that were created afterlapply, then I could then use bind_rows. So if >> someone knows how to change allof the names at this intermediate stage, I >> hope that person will provide thesolution. >> >> In the end a 1 by 2 data frame would have namesFirst1 Second1. A 1 by >> 4 data framewould have names First1 Second1 First2 >> Second2. >> >> Ira >> >> >> On Friday, June 29, 2018, 12:49:18 PM PDT, David Winsemius >> <dwinsem...@comcast.net> wrote: >> >> >>> On Jun 29, 2018, at 7:28 AM, Sarah Goslee <sarah.gos...@gmail.com> wrote: >>> >>> Hi, >>> >>> It isn't super clear to me what you're after. >> >> Agree. >> >> Had a different read of ht erequest. Thought the request was for a first >> step that "harmonized" the names of the columns and then used >> `dplyr::bind_rows`: >> >> library(dplyr) >> newList <- lapply( employees4List, 'names<-', names(employees4List[[1]]) ) >> bind_rows(newList) >> >> #--------- >> >> first1 second1 >> 1 Al Jones >> 2 Al2 Jones >> 3 Barb Smith >> 4 Al3 Jones >> 5 Barbara Smith >> 6 Carol Adams >> 7 Al Jones2 >> >> Might want to wrap suppressWarnings around the right side of that assignment >> since there were many warnings regarding incongruent factor levels. >> >> -- >> David. >>> Is this what you intend? >>> >>>> dfbycol(employees4BList) >>> first1 last1 first2 last2 first3 last3 >>> 1 Al Jones <NA> <NA> <NA> <NA> >>> 2 Al Jones Barb Smith <NA> <NA> >>> 3 Al Jones Barb Smith Carol Adams >>> 4 Al Jones <NA> <NA> <NA> <NA> >>>> >>>> dfbycol(employees4List) >>> first1 last1 first2 last2 first3 last3 >>> 1 Al Jones <NA> <NA> <NA> <NA> >>> 2 Al2 Jones Barb Smith <NA> <NA> >>> 3 Al3 Jones Barbara Smith Carol Adams >>> 4 Al Jones2 <NA> <NA> <NA> <NA> >>> >>> >>> If so: >>> >>> employees4BList = list( >>> data.frame(first1 = "Al", second1 = "Jones"), >>> data.frame(first1 = c("Al", "Barb"), second1 = c("Jones", "Smith")), >>> data.frame(first1 = c("Al", "Barb", "Carol"), second1 = c("Jones", >>> "Smith", "Adams")), >>> data.frame(first1 = ("Al"), second1 = "Jones")) >>> >>> employees4List = list( >>> data.frame(first1 = ("Al"), second1 = "Jones"), >>> data.frame(first2 = c("Al2", "Barb"), second2 = c("Jones", "Smith")), >>> data.frame(first3 = c("Al3", "Barbara", "Carol"), second3 = c("Jones", >>> "Smith", "Adams")), >>> data.frame(first4 = ("Al"), second4 = "Jones2")) >>> >>> ### >>> >>> dfbycol <- function(x) { >>> x <- lapply(x, function(y)as.vector(t(as.matrix(y)))) >>> x <- lapply(x, function(y){length(y) <- max(sapply(x, length)); y}) >>> x <- do.call(rbind, x) >>> x <- data.frame(x, stringsAsFactors=FALSE) >>> colnames(x) <- paste0(c("first", "last"), rep(seq(1, ncol(x)/2), each=2)) >>> x >>> } >>> >>> ### >>> >>> dfbycol(employees4BList) >>> >>> dfbycol(employees4List) >>> >>> On Fri, Jun 29, 2018 at 2:36 AM, Ira Sharenow via R-help >>> <r-help@r-project.org> wrote: >>>> I have a list of data frames which I would like to combine into one data >>>> frame doing something like rbind. I wish to combine in column order and >>>> not by names. However, there are issues. >>>> >>>> The number of columns is not the same for each data frame. This is an >>>> intermediate step to a problem and the number of columns could be >>>> 2,4,6,8,or10. There might be a few thousand data frames. Another problem >>>> is that the names of the columns produced by the first step are garbage. >>>> >>>> Below is a method that I obtained by asking a question on stack >>>> overflow. Unfortunately, my example was not general enough. The code >>>> below works for the simple case where the names of the people are >>>> consistent. It does not work when the names are realistically not the same. >>>> >>>> https://stackoverflow.com/questions/50807970/converting-a-list-of-data-frames-not-a-simple-rbind-second-row-to-new-columns/50809432#50809432 >>>> >>>> >>>> Please note that the lapply step sets things up except for the column >>>> name issue. If I could figure out a way to change the column names, then >>>> the bind_rows step will, I believe, work. >>>> >>>> So I really have two questions. How to change all column names of all >>>> the data frames and then how to solve the original problem. >>>> >>>> # The non general case works fine. It produces one data frame and I can >>>> then change the column names to >>>> >>>> # c("first1", "last1","first2", "last2","first3", "last3",) >>>> >>>> #Non general easy case >>>> >>>> employees4BList = list(data.frame(first1 = "Al", second1 = "Jones"), >>>> >>>> data.frame(first1 = c("Al", "Barb"), second1 = c("Jones", "Smith")), >>>> >>>> data.frame(first1 = c("Al", "Barb", "Carol"), second1 = c("Jones", >>>> "Smith", "Adams")), >>>> >>>> data.frame(first1 = ("Al"), second1 = "Jones")) >>>> >>>> employees4BList >>>> >>>> bind_rows(lapply(employees4BList, function(x) rbind.data.frame(c(t(x))))) >>>> >>>> # This produces a nice list of data frames, except for the names >>>> >>>> lapply(employees4BList, function(x) rbind.data.frame(c(t(x)))) >>>> >>>> # This list is a disaster. I am looking for a solution that works in >>>> this case. >>>> >>>> employees4List = list(data.frame(first1 = ("Al"), second1 = "Jones"), >>>> >>>> data.frame(first2 = c("Al2", "Barb"), second2 = c("Jones", "Smith")), >>>> >>>> data.frame(first3 = c("Al3", "Barbara", "Carol"), second3 = c("Jones", >>>> "Smith", "Adams")), >>>> >>>> data.frame(first4 = ("Al"), second4 = "Jones2")) >>>> >>>> bind_rows(lapply(employees4List, function(x) rbind.data.frame(c(t(x))))) >>>> >>>> Thanks. >>>> >>>> Ira >>>> >>> >>> -- >>> Sarah Goslee >>> http://www.functionaldiversity.org >>> >>> ______________________________________________ >>> 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. >> >> David Winsemius >> Alameda, CA, USA >> >> 'Any technology distinguishable from magic is insufficiently advanced.' >> -Gehm's Corollary to Clarke's Third Law >> >> >> >> >> >> [[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. >> > > --------------------------------------------------------------------------- > Jeff Newmiller The ..... ..... Go Live... > DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. Live Go... > Live: OO#.. Dead: OO#.. Playing > Research Engineer (Solar/Batteries O.O#. #.O#. with > /Software/Embedded Controllers) .OO#. .OO#. rocks...1k > --------------------------------------------------------------------------- > [[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. > --------------------------------------------------------------------------- Jeff Newmiller The ..... ..... Go Live... DCN:<jdnew...@dcn.davis.ca.us> Basics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/Batteries O.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --------------------------------------------------------------------------- [[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.
