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
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David Winsemius
Alameda, CA, USA
'Any technology distinguishable from magic is insufficiently advanced.'
-Gehm's Corollary to Clarke's Third Law
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