我在提交示例数据时犯了一个错误。它在两种不同的方面不够普遍。
列名的变化可能不一致，数据的变化可能比指示的要大得多。
然后我在 r-help 上问了我的问题。那里以多种方式回答了这个问题。
以下是其他人根据我的时序研究创建的解决方案。
# input data (list of data frames and data frames may have multiple rows)
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 (list of data frames with each just one row)
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
# Expected final step, except that all columns should be character
# Just one data frame
dplyr::bind_rows(listFinal)
sapply(dplyr::bind_rows(listFinal), class)
# Solution 1 using base R by Sarah Goslee
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(listFinal)
##########
# Solution 2 by Jeff Newmiller (Base R)
myrename2 <- 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 ) ) )
listFinal2 <- lapply( employees4List, myrename2, m = m )
listFinal2
result2 <- do.call( rbind, listFinal2 )
result2
##########
# Solution 3 by Jeff Newmiller (uses dplyr)
myrename3 <- 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 )
)
)
}
listFinal3 <- lapply( employees4List, myrename3 )
listFinal3
result3 <- dplyr::bind_rows( listFinal3 )
result3
# Solution 4 by Jeff Newmiller (uses dplyr and tidyr)
library(dplyr)
library(tidyr)
myrename4 <- 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 )
)
)
)
}
listFinal4 <- lapply( employees4List, myrename3)
listFinal4
result4 <- dplyr::bind_rows(listFinal4)
result4
#####
# Timing
# Create a large dataset
firsts = c("Al", "Barb", "Carol")
seconds = c("Washington", "Adams", "Jefferson" )
numReplications = 10000
set.seed(2018)
# Create data frames
sim_list1 = replicate(n = numReplications,
expr = {data.frame(first = base::sample(x = firsts, size = 1, replace = TRUE),
second = base::sample(x = seconds, size = 1, replace = TRUE))},
simplify = F)
sim_list2 = replicate(n = numReplications,
expr = {data.frame(first = base::sample(x = firsts, size = 2, replace = TRUE),
second = base::sample(x = seconds, size = 2, replace = TRUE))},
simplify = F)
sim_list3 = replicate(n = numReplications,
expr = {data.frame(first = base::sample(x = firsts, size = 3, replace = TRUE),
second = base::sample(x = seconds, size = 3, replace = TRUE))},
simplify = F)
# Create list
employeesList = c(sim_list1, sim_list2, sim_list3)
# Method 1
system.time(res1 <- dfbycol(employeesList))
# > system.time(dfbycol(employeesList))
# user system elapsed
# 757.87 0.18 758.62
# res1
rm(res1)
#####
# Method 2
system.time(m <- max( unlist( lapply( employeesList, nrow ) ) ))
# user system elapsed
# 0.22 0.00 0.22
system.time(listFinal2 <- lapply( employeesList, myrename2, m = m ) )
listFinal2
# user system elapsed
# 16.16 0.01 16.18
system.time(result2 <- do.call( rbind, listFinal2 ) )
# result2
# user system elapsed
# 3.96 0.00 3.96
rm(listFinal2)
rm(result2)
#####
# Method 3
system.time(listFinal3 <- lapply( employeesList, myrename3))
# user system elapsed
# 7.33 0.00 7.33
listFinal3
system.time(result3 <- dplyr::bind_rows( listFinal3 ))
# user system elapsed
# 0.17 0.00 0.17
rm(listFinal3)
rm(result3)
#####
# Method 4
system.time(listFinal4 <- lapply( employeesList, myrename4) )
# user system elapsed
# 400.05 0.04 400.24
listFinal4
system.time(result4 <- dplyr::bind_rows( listFinal4 ) )
# user system elapsed
# 0.17 0.00 0.17
# result4