Hi,
I have two sets of data x.HHu and y.HHo, rows are IDs and columns are
individuals. I do not know in advance indv or HHid, both of them will be
captured from the data. As the y.HHo set updates, y.HHo set has better
information then x.HHu set. Thus I want a merge where right set
overwrites left set info based on HHid, i.e. to overwrite x.HHu set with
y.HHo set but keep any extra info from the x.HHu set that is not present
in y.HHo set.
HHids will be complete based on z.map, with the corresponding positions.
I am having trouble with the part after this line: ###
============================================+++++++++++++++++++++++++++
I am thinking that I am creating new columns "position"
"indv1" and
"indv2", but R is interpreting them as row information.
See the expected final table at the end. HHid is common, indv3 is from
x.HHu, and the rest position and indv1 and indv2 are from y.HHo
Any suggestions are appreciated.
Thank you in advance,
Aldi
x.HHu<- data.frame(
HHid = c( 'HH1', 'HH2', 'HH3',
'HH4', 'HH5', 'HH10')
, indv1 = c( 2, 0, 2 , 0, 2, 0)
, indv2 = c( 0, NA, 2, 2, 2, 2)
, ind3 = c( 0, 0, 0, 0, 0, 0)
)
### the HHo data will be the top set to overwrite any HHu data, when
they exist, thinking that HHo are better than HHu results
### when they are available
y.HHo<-data.frame(HHid=c('HH1', 'HH2','HH5',
'HH3', 'HH10')
, indv1 = c(2, 0, 2, 0, NA)
, indv2 = c(0, 2, 2, 1, 2)
)
z.map<-data.frame(HHid = c('HH1', 'HH2', 'HH3',
'HH4', 'HH5',
'HH6','HH8', 'HH7', 'HH9', 'HH10',
'HH11')
, position= c(10,20,30,42,55,66,81,75,92,101,111)
)
### see objects
x.HHu
y.HHo
z.map
### now sort the map by position, this sorted map will be used to sort
finally all data
z.map<-z.map[with(z.map, order(position)), ]
z.map
### First I introduce position to both sets so I can sort them in
advance by position.
x.HHu.map <-merge( z.map, x.HHu, by='HHid', all=T)
x.HHu.map<-x.HHu.map[with(x.HHu.map, order(position)), ]
x.HHu.map
y.HHo.map <-merge( z.map, y.HHo, by='HHid', all= T)
y.HHo.map<-y.HHo.map[with(y.HHo.map, order(position)), ]
y.HHo.map
### now merge HHu and HHo with the hope to overwrite the HHu set with
HHo wherever they overlap by column names.
zzz <- merge(x.HHu.map, y.HHo.map, by='HHid', all=T)
zzz
### find common variable names in two sets
commonNames <- names(x.impu.map)[which(colnames(x.impu.map) %in%
colnames(y.geno.map))]
## remove HHid wich is common for x and y, but work with the rest of columns
commonNames<-commonNames[-c(1)]
### ============================================+++++++++++++++++++++++++++
for(i in 1:length(commonNames)){
print(commonNames[i])
zzz$commonNames[i] <- NA
print(paste("zzz","$",commonNames[i],".y",sep=""))
zzz$commonNames[i] <-
zzz[,paste(commonNames[i],".y",sep="")]
### paste(zzz$commonNames[i],".x",sep='') <- NULL;
### paste(zzz$commonNames[i],".y",sep='') <- NULL;
}
zzz
The final expected set has to be: HHid is common, indv3 is from x.HHu,
and the rest position and indv1 and indv2 are from y.HHo
HHid position ind3 indv1 indv2
1 HH1 10 0 2 0
2 HH10 101 0 NA 2
3 HH11 111 NA NA NA
4 HH2 20 0 0 2
5 HH3 30 0 0 1
6 HH4 42 0 NA NA
7 HH5 55 0 2 2
8 HH6 66 NA NA NA
9 HH7 75 NA NA NA
10 HH8 81 NA NA NA
11 HH9 92 NA NA NA
--
[[alternative HTML version deleted]]
I think this does what you want:
## find idiv coloumns in x.HHu.map that don't exist in y.HHo.map
x.HHu.map <- x.HHu.map[
c("HHid",
"position",
names(x.HHu.map)[
!names(x.HHu.map)
%in% names(y.HHo.map)]
)]
## merge, adding extra column from x.HHu.map
zzz <- merge(y.HHo.map, x.HHu.map, by=c('HHid', 'position'),
all=T)
## order by HHid
zzz <- zzz[order(zzz$HHid),]
Best,
Ista
On Sun, Jul 12, 2015 at 10:45 AM, aldi <aldi at dsgmail.wustl.edu>
wrote:> Hi,
> I have two sets of data x.HHu and y.HHo, rows are IDs and columns are
> individuals. I do not know in advance indv or HHid, both of them will be
> captured from the data. As the y.HHo set updates, y.HHo set has better
> information then x.HHu set. Thus I want a merge where right set
> overwrites left set info based on HHid, i.e. to overwrite x.HHu set with
> y.HHo set but keep any extra info from the x.HHu set that is not present
> in y.HHo set.
> HHids will be complete based on z.map, with the corresponding positions.
> I am having trouble with the part after this line: ###
> ============================================+++++++++++++++++++++++++++
> I am thinking that I am creating new columns "position"
"indv1" and
> "indv2", but R is interpreting them as row information.
> See the expected final table at the end. HHid is common, indv3 is from
> x.HHu, and the rest position and indv1 and indv2 are from y.HHo
> Any suggestions are appreciated.
> Thank you in advance,
> Aldi
>
> x.HHu<- data.frame(
> HHid = c( 'HH1', 'HH2', 'HH3',
'HH4', 'HH5', 'HH10')
> , indv1 = c( 2, 0, 2 , 0, 2, 0)
> , indv2 = c( 0, NA, 2, 2, 2, 2)
> , ind3 = c( 0, 0, 0, 0, 0, 0)
> )
> ### the HHo data will be the top set to overwrite any HHu data, when
> they exist, thinking that HHo are better than HHu results
> ### when they are available
>
> y.HHo<-data.frame(HHid=c('HH1', 'HH2','HH5',
'HH3', 'HH10')
> , indv1 = c(2, 0, 2, 0, NA)
> , indv2 = c(0, 2, 2, 1, 2)
> )
>
> z.map<-data.frame(HHid = c('HH1', 'HH2', 'HH3',
'HH4', 'HH5',
> 'HH6','HH8', 'HH7', 'HH9', 'HH10',
'HH11')
> , position= c(10,20,30,42,55,66,81,75,92,101,111)
> )
> ### see objects
> x.HHu
> y.HHo
> z.map
> ### now sort the map by position, this sorted map will be used to sort
> finally all data
> z.map<-z.map[with(z.map, order(position)), ]
> z.map
>
> ### First I introduce position to both sets so I can sort them in
> advance by position.
> x.HHu.map <-merge( z.map, x.HHu, by='HHid', all=T)
> x.HHu.map<-x.HHu.map[with(x.HHu.map, order(position)), ]
> x.HHu.map
>
> y.HHo.map <-merge( z.map, y.HHo, by='HHid', all= T)
> y.HHo.map<-y.HHo.map[with(y.HHo.map, order(position)), ]
> y.HHo.map
>
> ### now merge HHu and HHo with the hope to overwrite the HHu set with
> HHo wherever they overlap by column names.
> zzz <- merge(x.HHu.map, y.HHo.map, by='HHid', all=T)
> zzz
> ### find common variable names in two sets
>
> commonNames <- names(x.impu.map)[which(colnames(x.impu.map) %in%
> colnames(y.geno.map))]
>
> ## remove HHid wich is common for x and y, but work with the rest of
columns
> commonNames<-commonNames[-c(1)]
>
> ### ============================================+++++++++++++++++++++++++++
> for(i in 1:length(commonNames)){
>
> print(commonNames[i])
> zzz$commonNames[i] <- NA
>
>
print(paste("zzz","$",commonNames[i],".y",sep=""))
>
> zzz$commonNames[i] <-
zzz[,paste(commonNames[i],".y",sep="")]
>
> ### paste(zzz$commonNames[i],".x",sep='') <- NULL;
> ### paste(zzz$commonNames[i],".y",sep='') <- NULL;
>
> }
> zzz
>
> The final expected set has to be: HHid is common, indv3 is from x.HHu,
> and the rest position and indv1 and indv2 are from y.HHo
> HHid position ind3 indv1 indv2
> 1 HH1 10 0 2 0
> 2 HH10 101 0 NA 2
> 3 HH11 111 NA NA NA
> 4 HH2 20 0 0 2
> 5 HH3 30 0 0 1
> 6 HH4 42 0 NA NA
> 7 HH5 55 0 2 2
> 8 HH6 66 NA NA NA
> 9 HH7 75 NA NA NA
> 10 HH8 81 NA NA NA
> 11 HH9 92 NA NA NA
>
> --
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at 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.
I get confused by your use of the position map table. If I follow your
description toward your desired result, I take a different route that
makes sense to me. Perhaps it will make sense to you as well. The key idea
is to make individual comparisons of the values for each combination of
HHid and indv, regardless of where they are in the original data frames.
Below are two different syntactic representations of my understanding of
your problem. They differ only in the approach taken to strip away syntax
clutter. I start by making the HHid identifiers character values in the
original data frames, because their respective factor levels in the two
data frames would not necessarily correspond.
x.HHu <- data.frame(
HHid = c( 'HH1', 'HH2', 'HH3', 'HH4',
'HH5', 'HH10' )
, indv1 = c( 2, 0, 2 , 0, 2, 0 )
, indv2 = c( 0, NA, 2, 2, 2, 2 )
, ind3 = c( 0, 0, 0, 0, 0, 0 )
, stringsAsFactors = FALSE # avoid creating HHid as a factor
)
y.HHo <- data.frame(
HHid=c( 'HH1', 'HH2','HH5', 'HH3',
'HH10' )
, indv1 = c( 2, 0, 2, 0, NA )
, indv2 = c( 0, 2, 2, 1, 2 )
, stringsAsFactors = FALSE
)
z.map <- data.frame(
HHid = c( 'HH1', 'HH2', 'HH3', 'HH4',
'HH5'
, 'HH6','HH8', 'HH7', 'HH9',
'HH10', 'HH11' )
, position= c( 10, 20, 30, 42, 55, 66, 81, 75, 92, 101, 111 )
, stringsAsFactors = FALSE
)
# reshape2 solution
library(reshape2)
x.HHu.long <- melt( x.HHu, "HHid", variable.name = "indv"
)
x.HHu.long$indv <- as.character( x.HHu.long$indv )
y.HHo.long <- melt( y.HHo, "HHid", variable.name = "indv"
)
y.HHo.long$indv <- as.character( y.HHo.long$indv )
xy.HH.long <- merge( x.HHu.long
, y.HHo.long
, by = c( "HHid", "indv" )
, all = TRUE )
xy.HH.long$value <- with( xy.HH.long
, ifelse( is.na( value.y )
, value.x
, value.y ) )
xy.HH0 <- dcast( xy.HH.long, HHid ~ indv )
xy.HH <- merge( xy.HH0, z.map, all=TRUE )
xy.HH <- xy.HH[ order( xy.HH$HHid ), ]
# compare xy.HH with zzz ... I think there is an error in zzz for
# HH10/indv1, because NA should not be considered more informative
# than 0...
# tidyr/dplyr solution
library(tidyr)
library(dplyr)
# define a common processing sequence
lengthen <- ( . # period is placeholder for data frame
%>% gather( indv, value, -HHid )
%>% mutate( indv = as.character( indv ) )
)
x.HHu.dlong <- x.HHu %>% lengthen
y.HHo.dlong <- y.HHo %>% lengthen
xy.HH.d <- ( x.HHu.dlong
%>% full_join( y.HHo.dlong, by= c( "HHid",
"indv" ) )
%>% transmute( HHid = HHid
, indv = indv
, value = ifelse( is.na( value.y )
, value.x
, value.y )
)
%>% spread( indv, value )
%>% full_join( z.map, by="HHid" )
%>% arrange( HHid )
%>% as.data.frame
)
On Sun, 12 Jul 2015, aldi wrote:
> Hi,
> I have two sets of data x.HHu and y.HHo, rows are IDs and columns are
> individuals. I do not know in advance indv or HHid, both of them will be
> captured from the data. As the y.HHo set updates, y.HHo set has better
> information then x.HHu set. Thus I want a merge where right set
> overwrites left set info based on HHid, i.e. to overwrite x.HHu set with
> y.HHo set but keep any extra info from the x.HHu set that is not present
> in y.HHo set.
> HHids will be complete based on z.map, with the corresponding positions.
> I am having trouble with the part after this line: ###
> ============================================+++++++++++++++++++++++++++
> I am thinking that I am creating new columns "position"
"indv1" and
> "indv2", but R is interpreting them as row information.
> See the expected final table at the end. HHid is common, indv3 is from
> x.HHu, and the rest position and indv1 and indv2 are from y.HHo
> Any suggestions are appreciated.
> Thank you in advance,
> Aldi
>
> x.HHu<- data.frame(
> HHid = c( 'HH1', 'HH2', 'HH3',
'HH4', 'HH5', 'HH10')
> , indv1 = c( 2, 0, 2 , 0, 2, 0)
> , indv2 = c( 0, NA, 2, 2, 2, 2)
> , ind3 = c( 0, 0, 0, 0, 0, 0)
> )
> ### the HHo data will be the top set to overwrite any HHu data, when
> they exist, thinking that HHo are better than HHu results
> ### when they are available
>
> y.HHo<-data.frame(HHid=c('HH1', 'HH2','HH5',
'HH3', 'HH10')
> , indv1 = c(2, 0, 2, 0, NA)
> , indv2 = c(0, 2, 2, 1, 2)
> )
>
> z.map<-data.frame(HHid = c('HH1', 'HH2', 'HH3',
'HH4', 'HH5',
> 'HH6','HH8', 'HH7', 'HH9', 'HH10',
'HH11')
> , position= c(10,20,30,42,55,66,81,75,92,101,111)
> )
> ### see objects
> x.HHu
> y.HHo
> z.map
> ### now sort the map by position, this sorted map will be used to sort
> finally all data
> z.map<-z.map[with(z.map, order(position)), ]
> z.map
>
> ### First I introduce position to both sets so I can sort them in
> advance by position.
> x.HHu.map <-merge( z.map, x.HHu, by='HHid', all=T)
> x.HHu.map<-x.HHu.map[with(x.HHu.map, order(position)), ]
> x.HHu.map
>
> y.HHo.map <-merge( z.map, y.HHo, by='HHid', all= T)
> y.HHo.map<-y.HHo.map[with(y.HHo.map, order(position)), ]
> y.HHo.map
>
> ### now merge HHu and HHo with the hope to overwrite the HHu set with
> HHo wherever they overlap by column names.
> zzz <- merge(x.HHu.map, y.HHo.map, by='HHid', all=T)
> zzz
> ### find common variable names in two sets
>
> commonNames <- names(x.impu.map)[which(colnames(x.impu.map) %in%
> colnames(y.geno.map))]
>
> ## remove HHid wich is common for x and y, but work with the rest of
columns
> commonNames<-commonNames[-c(1)]
>
> ### ============================================+++++++++++++++++++++++++++
> for(i in 1:length(commonNames)){
>
> print(commonNames[i])
> zzz$commonNames[i] <- NA
>
>
print(paste("zzz","$",commonNames[i],".y",sep=""))
>
> zzz$commonNames[i] <-
zzz[,paste(commonNames[i],".y",sep="")]
>
> ### paste(zzz$commonNames[i],".x",sep='') <- NULL;
> ### paste(zzz$commonNames[i],".y",sep='') <- NULL;
>
> }
> zzz
>
> The final expected set has to be: HHid is common, indv3 is from x.HHu,
> and the rest position and indv1 and indv2 are from y.HHo
> HHid position ind3 indv1 indv2
> 1 HH1 10 0 2 0
> 2 HH10 101 0 NA 2
> 3 HH11 111 NA NA NA
> 4 HH2 20 0 0 2
> 5 HH3 30 0 0 1
> 6 HH4 42 0 NA NA
> 7 HH5 55 0 2 2
> 8 HH6 66 NA NA NA
> 9 HH7 75 NA NA NA
> 10 HH8 81 NA NA NA
> 11 HH9 92 NA NA NA
>
> --
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at 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:<jdnewmil at 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
Thank you Ista,
Your solution is smart, by sub-setting from x.HHu.map data only
"HHid",
"position" as indices (because they are unique) for the merge, and any
extra columns in x.HHu.map that are not present in y.HHo,map, thus when
the merge is done with option all=T, will work among the two sets of
data, without creating .x and .y when the variables are in common in two
sets.
With best wishes,
Aldi
> ## find indv columns in x.HHu.map that don't exist in y.HHo.map
> x.HHu.map <- x.HHu.map[
+ c("HHid",
+ "position",
+ names(x.HHu.map)[
+ !names(x.HHu.map)
+ %in% names(y.HHo.map)]
+ )]
> ## merge, adding extra column from x.HHu.map
> zzz <- merge(y.HHo.map, x.HHu.map, by=c('HHid',
'position'), all=T)
> ## order by HHid
> zzz <- zzz[order(zzz$HHid),]
> zzz
HHid position indv1 indv2 ind3
1 HH1 10 2 0 0
2 HH10 101 NA 2 0
3 HH11 111 NA NA NA
4 HH2 20 0 2 0
5 HH3 30 0 1 0
6 HH4 42 NA NA 0
7 HH5 55 2 2 0
8 HH6 66 NA NA NA
9 HH7 75 NA NA NA
10 HH8 81 NA NA NA
11 HH9 92 NA NA NA
On 7/12/2015 12:06 PM, Ista Zahn wrote:> I think this does what you want:
>
> ## find idiv coloumns in x.HHu.map that don't exist in y.HHo.map
> x.HHu.map <- x.HHu.map[
> c("HHid",
> "position",
> names(x.HHu.map)[
> !names(x.HHu.map)
> %in% names(y.HHo.map)]
> )]
> ## merge, adding extra column from x.HHu.map
> zzz <- merge(y.HHo.map, x.HHu.map, by=c('HHid',
'position'), all=T)
> ## order by HHid
> zzz <- zzz[order(zzz$HHid),]
>
> Best,
> Ista
>
> On Sun, Jul 12, 2015 at 10:45 AM, aldi <aldi at dsgmail.wustl.edu>
wrote:
>> Hi,
>> I have two sets of data x.HHu and y.HHo, rows are IDs and columns are
>> individuals. I do not know in advance indv or HHid, both of them will
be
>> captured from the data. As the y.HHo set updates, y.HHo set has better
>> information then x.HHu set. Thus I want a merge where right set
>> overwrites left set info based on HHid, i.e. to overwrite x.HHu set
with
>> y.HHo set but keep any extra info from the x.HHu set that is not
present
>> in y.HHo set.
>> HHids will be complete based on z.map, with the corresponding
positions.
>> I am having trouble with the part after this line: ###
>> ============================================+++++++++++++++++++++++++++
>> I am thinking that I am creating new columns "position"
"indv1" and
>> "indv2", but R is interpreting them as row information.
>> See the expected final table at the end. HHid is common, indv3 is from
>> x.HHu, and the rest position and indv1 and indv2 are from y.HHo
>> Any suggestions are appreciated.
>> Thank you in advance,
>> Aldi
>>
>> x.HHu<- data.frame(
>> HHid = c( 'HH1', 'HH2', 'HH3',
'HH4', 'HH5', 'HH10')
>> , indv1 = c( 2, 0, 2 , 0, 2, 0)
>> , indv2 = c( 0, NA, 2, 2, 2, 2)
>> , ind3 = c( 0, 0, 0, 0, 0, 0)
>> )
>> ### the HHo data will be the top set to overwrite any HHu data, when
>> they exist, thinking that HHo are better than HHu results
>> ### when they are available
>>
>> y.HHo<-data.frame(HHid=c('HH1', 'HH2','HH5',
'HH3', 'HH10')
>> , indv1 = c(2, 0, 2, 0, NA)
>> , indv2 = c(0, 2, 2, 1, 2)
>> )
>>
>> z.map<-data.frame(HHid = c('HH1', 'HH2',
'HH3', 'HH4', 'HH5',
>> 'HH6','HH8', 'HH7', 'HH9',
'HH10', 'HH11')
>> , position= c(10,20,30,42,55,66,81,75,92,101,111)
>> )
>> ### see objects
>> x.HHu
>> y.HHo
>> z.map
>> ### now sort the map by position, this sorted map will be used to sort
>> finally all data
>> z.map<-z.map[with(z.map, order(position)), ]
>> z.map
>>
>> ### First I introduce position to both sets so I can sort them in
>> advance by position.
>> x.HHu.map <-merge( z.map, x.HHu, by='HHid', all=T)
>> x.HHu.map<-x.HHu.map[with(x.HHu.map, order(position)), ]
>> x.HHu.map
>>
>> y.HHo.map <-merge( z.map, y.HHo, by='HHid', all= T)
>> y.HHo.map<-y.HHo.map[with(y.HHo.map, order(position)), ]
>> y.HHo.map
>>
>> ### now merge HHu and HHo with the hope to overwrite the HHu set with
>> HHo wherever they overlap by column names.
>> zzz <- merge(x.HHu.map, y.HHo.map, by='HHid', all=T)
>> zzz
>> ### find common variable names in two sets
>>
>> commonNames <- names(x.impu.map)[which(colnames(x.impu.map) %in%
>> colnames(y.geno.map))]
>>
>> ## remove HHid wich is common for x and y, but work with the rest of
columns
>> commonNames<-commonNames[-c(1)]
>>
>> ###
============================================+++++++++++++++++++++++++++
>> for(i in 1:length(commonNames)){
>>
>> print(commonNames[i])
>> zzz$commonNames[i] <- NA
>>
>>
print(paste("zzz","$",commonNames[i],".y",sep=""))
>>
>> zzz$commonNames[i] <-
zzz[,paste(commonNames[i],".y",sep="")]
>>
>> ### paste(zzz$commonNames[i],".x",sep='') <- NULL;
>> ### paste(zzz$commonNames[i],".y",sep='') <- NULL;
>>
>> }
>> zzz
>>
>> The final expected set has to be: HHid is common, indv3 is from x.HHu,
>> and the rest position and indv1 and indv2 are from y.HHo
>> HHid position ind3 indv1 indv2
>> 1 HH1 10 0 2 0
>> 2 HH10 101 0 NA 2
>> 3 HH11 111 NA NA NA
>> 4 HH2 20 0 0 2
>> 5 HH3 30 0 0 1
>> 6 HH4 42 0 NA NA
>> 7 HH5 55 0 2 2
>> 8 HH6 66 NA NA NA
>> 9 HH7 75 NA NA NA
>> 10 HH8 81 NA NA NA
>> 11 HH9 92 NA NA NA
>>
>> --
>>
>>
[[alternative HTML version deleted]]
Thank you Jeff,
Your solutions have two great aspects: a) you provide a different
approach by using reshape2 syntax / tidyr, and b) the concern that it is
better to update x.HHu.map with y.HHo.map, without overwriting x.HHu.map
with NA from y.HHo.map, thus keeping intact the old value(s). That is
the ideal situation, but I do not know exactly if the old value is
correct, which if it is not correct then it may create strata in the
data. Therefore I prefer better to overwrite any old column with the new
column when it exits, even with NA. In addition, you introduce the
safety stringAsFactors=F. The map is only for getting "position" based
on HHid, and at the end I plan sorting final set based on position.
While the reshape2 worked with no problems from the start, the other
solution with
# tidyr/dplyr solution
library(tidyr)
library(dplyr)
did not work correct in the start (it stopped R working and OS says R
have to close) in windows 8.1 with R 3.1.2, quite possible because
called libraries were compiled under R 3.1.3.
When installed the new version of R: 3.2.1, the second solution worked
also with no problem.
For whoever has written the libraries tidyr and dplyr, they produced
warnings for function name conflicts with base and stats:
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
Great solutions!
Thank you,
Aldi
> x.HHu <- data.frame(
+ HHid = c( 'HH1', 'HH2', 'HH3', 'HH4',
'HH5', 'HH10' )
+ , indv1 = c( 2, 0, 2 , 0, 2, 0 )
+ , indv2 = c( 0, NA, 2, 2, 2, 2 )
+ , ind3 = c( 0, 0, 0, 0, 0, 0 )
+ , stringsAsFactors = FALSE # avoid creating HHid as a factor
+ )
> y.HHo <- data.frame(
+ HHid=c( 'HH1', 'HH2','HH5', 'HH3',
'HH10' )
+ , indv1 = c( 2, 0, 2, 0, NA )
+ , indv2 = c( 0, 2, 2, 1, 2 )
+ , stringsAsFactors = FALSE
+ )
> z.map <- data.frame(
+ HHid = c( 'HH1', 'HH2', 'HH3', 'HH4',
'HH5'
+ , 'HH6','HH8', 'HH7', 'HH9',
'HH10', 'HH11' )
+ , position= c( 10, 20, 30, 42, 55, 66, 81, 75, 92, 101, 111 )
+ , stringsAsFactors = FALSE
+ )
> # reshape2 solution
> library(reshape2)
>
> x.HHu.long <- melt( x.HHu, "HHid", variable.name =
"indv" )
> x.HHu.long$indv <- as.character( x.HHu.long$indv )
> y.HHo.long <- melt( y.HHo, "HHid", variable.name =
"indv" )
> y.HHo.long$indv <- as.character( y.HHo.long$indv )
> xy.HH.long <- merge( x.HHu.long
+ , y.HHo.long
+ , by = c( "HHid", "indv" )
+ , all = TRUE )
> xy.HH.long$value <- with( xy.HH.long
+ , ifelse( is.na( value.y )
+ , value.x
+ , value.y ) )
> xy.HH0 <- dcast( xy.HH.long, HHid ~ indv )
> xy.HH <- merge( xy.HH0, z.map, all=TRUE )
> xy.HH <- xy.HH[ order( xy.HH$HHid ), ]
> # compare xy.HH with zzz ... I think there is an error in zzz for #
HH10/indv1, because NA should not be considered more informative
> # than 0...
### solution with reshape2
> xy.HH
HHid ind3 indv1 indv2 position
1 HH1 0 2 0 10
2 HH10 0 0 2 101
3 HH11 NA NA NA 111
4 HH2 0 0 2 20
5 HH3 0 0 1 30
6 HH4 0 0 2 42
7 HH5 0 2 2 55
8 HH6 NA NA NA 66
9 HH7 NA NA NA 75
10 HH8 NA NA NA 81
11 HH9 NA NA NA 92
### Solution with tidyr:
> xy.HH.d
HHid ind3 indv1 indv2 position
1 HH1 0 2 0 10
2 HH10 0 0 2 101
3 HH11 NA NA NA 111
4 HH2 0 0 2 20
5 HH3 0 0 1 30
6 HH4 0 0 2 42
7 HH5 0 2 2 55
8 HH6 NA NA NA 66
9 HH7 NA NA NA 75
10 HH8 NA NA NA 81
11 HH9 NA NA NA 92
On 7/12/2015 1:35 PM, Jeff Newmiller wrote:> I get confused by your use of the position map table. If I follow your
> description toward your desired result, I take a different route that
> makes sense to me. Perhaps it will make sense to you as well. The key
> idea is to make individual comparisons of the values for each
> combination of HHid and indv, regardless of where they are in the
> original data frames.
>
> Below are two different syntactic representations of my understanding
> of your problem. They differ only in the approach taken to strip away
> syntax clutter. I start by making the HHid identifiers character
> values in the original data frames, because their respective factor
> levels in the two data frames would not necessarily correspond.
>
> x.HHu <- data.frame(
> HHid = c( 'HH1', 'HH2', 'HH3', 'HH4',
'HH5', 'HH10' )
> , indv1 = c( 2, 0, 2 , 0, 2, 0 )
> , indv2 = c( 0, NA, 2, 2, 2, 2 )
> , ind3 = c( 0, 0, 0, 0, 0, 0 )
> , stringsAsFactors = FALSE # avoid creating HHid as a factor
> )
> y.HHo <- data.frame(
> HHid=c( 'HH1', 'HH2','HH5', 'HH3',
'HH10' )
> , indv1 = c( 2, 0, 2, 0, NA )
> , indv2 = c( 0, 2, 2, 1, 2 )
> , stringsAsFactors = FALSE
> )
> z.map <- data.frame(
> HHid = c( 'HH1', 'HH2', 'HH3', 'HH4',
'HH5'
> , 'HH6','HH8', 'HH7', 'HH9',
'HH10', 'HH11' )
> , position= c( 10, 20, 30, 42, 55, 66, 81, 75, 92, 101, 111 )
> , stringsAsFactors = FALSE
> )
>
>
> # reshape2 solution
> library(reshape2)
>
> x.HHu.long <- melt( x.HHu, "HHid", variable.name =
"indv" )
> x.HHu.long$indv <- as.character( x.HHu.long$indv )
> y.HHo.long <- melt( y.HHo, "HHid", variable.name =
"indv" )
> y.HHo.long$indv <- as.character( y.HHo.long$indv )
> xy.HH.long <- merge( x.HHu.long
> , y.HHo.long
> , by = c( "HHid", "indv" )
> , all = TRUE )
> xy.HH.long$value <- with( xy.HH.long
> , ifelse( is.na( value.y )
> , value.x
> , value.y ) )
> xy.HH0 <- dcast( xy.HH.long, HHid ~ indv )
> xy.HH <- merge( xy.HH0, z.map, all=TRUE )
> xy.HH <- xy.HH[ order( xy.HH$HHid ), ]
> # compare xy.HH with zzz ... I think there is an error in zzz for #
> HH10/indv1, because NA should not be considered more informative
> # than 0...
>
> # tidyr/dplyr solution
> library(tidyr)
> library(dplyr)
>
> # define a common processing sequence
> lengthen <- ( . # period is placeholder for data frame
> %>% gather( indv, value, -HHid )
> %>% mutate( indv = as.character( indv ) )
> )
> x.HHu.dlong <- x.HHu %>% lengthen
> y.HHo.dlong <- y.HHo %>% lengthen
> xy.HH.d <- ( x.HHu.dlong
> %>% full_join( y.HHo.dlong, by= c( "HHid",
"indv" ) )
> %>% transmute( HHid = HHid
> , indv = indv
> , value = ifelse( is.na( value.y )
> , value.x
> , value.y )
> )
> %>% spread( indv, value )
> %>% full_join( z.map, by="HHid" )
> %>% arrange( HHid )
> %>% as.data.frame
> )
>
> On Sun, 12 Jul 2015, aldi wrote:
>
>> Hi,
>> I have two sets of data x.HHu and y.HHo, rows are IDs and columns are
>> individuals. I do not know in advance indv or HHid, both of them will
be
>> captured from the data. As the y.HHo set updates, y.HHo set has better
>> information then x.HHu set. Thus I want a merge where right set
>> overwrites left set info based on HHid, i.e. to overwrite x.HHu set
with
>> y.HHo set but keep any extra info from the x.HHu set that is not
present
>> in y.HHo set.
>> HHids will be complete based on z.map, with the corresponding
positions.
>> I am having trouble with the part after this line: ###
>> ============================================+++++++++++++++++++++++++++
>> I am thinking that I am creating new columns "position"
"indv1" and
>> "indv2", but R is interpreting them as row information.
>> See the expected final table at the end. HHid is common, indv3 is from
>> x.HHu, and the rest position and indv1 and indv2 are from y.HHo
>> Any suggestions are appreciated.
>> Thank you in advance,
>> Aldi
>>
>> x.HHu<- data.frame(
>> HHid = c( 'HH1', 'HH2', 'HH3',
'HH4', 'HH5', 'HH10')
>> , indv1 = c( 2, 0, 2 , 0, 2, 0)
>> , indv2 = c( 0, NA, 2, 2, 2, 2)
>> , ind3 = c( 0, 0, 0, 0, 0, 0)
>> )
>> ### the HHo data will be the top set to overwrite any HHu data, when
>> they exist, thinking that HHo are better than HHu results
>> ### when they are available
>>
>> y.HHo<-data.frame(HHid=c('HH1', 'HH2','HH5',
'HH3', 'HH10')
>> , indv1 = c(2, 0, 2, 0, NA)
>> , indv2 = c(0, 2, 2, 1, 2)
>> )
>>
>> z.map<-data.frame(HHid = c('HH1', 'HH2',
'HH3', 'HH4', 'HH5',
>> 'HH6','HH8', 'HH7', 'HH9',
'HH10', 'HH11')
>> , position= c(10,20,30,42,55,66,81,75,92,101,111)
>> )
>> ### see objects
>> x.HHu
>> y.HHo
>> z.map
>> ### now sort the map by position, this sorted map will be used to sort
>> finally all data
>> z.map<-z.map[with(z.map, order(position)), ]
>> z.map
>>
>> ### First I introduce position to both sets so I can sort them in
>> advance by position.
>> x.HHu.map <-merge( z.map, x.HHu, by='HHid', all=T)
>> x.HHu.map<-x.HHu.map[with(x.HHu.map, order(position)), ]
>> x.HHu.map
>>
>> y.HHo.map <-merge( z.map, y.HHo, by='HHid', all= T)
>> y.HHo.map<-y.HHo.map[with(y.HHo.map, order(position)), ]
>> y.HHo.map
>>
>> ### now merge HHu and HHo with the hope to overwrite the HHu set with
>> HHo wherever they overlap by column names.
>> zzz <- merge(x.HHu.map, y.HHo.map, by='HHid', all=T)
>> zzz
>> ### find common variable names in two sets
>>
>> commonNames <- names(x.impu.map)[which(colnames(x.impu.map) %in%
>> colnames(y.geno.map))]
>>
>> ## remove HHid wich is common for x and y, but work with the rest of
>> columns
>> commonNames<-commonNames[-c(1)]
>>
>> ###
>> ============================================+++++++++++++++++++++++++++
>> for(i in 1:length(commonNames)){
>>
>> print(commonNames[i])
>> zzz$commonNames[i] <- NA
>>
>>
print(paste("zzz","$",commonNames[i],".y",sep=""))
>>
>> zzz$commonNames[i] <-
zzz[,paste(commonNames[i],".y",sep="")]
>>
>> ### paste(zzz$commonNames[i],".x",sep='') <- NULL;
>> ### paste(zzz$commonNames[i],".y",sep='') <- NULL;
>>
>> }
>> zzz
>>
>> The final expected set has to be: HHid is common, indv3 is from x.HHu,
>> and the rest position and indv1 and indv2 are from y.HHo
>> HHid position ind3 indv1 indv2
>> 1 HH1 10 0 2 0
>> 2 HH10 101 0 NA 2
>> 3 HH11 111 NA NA NA
>> 4 HH2 20 0 0 2
>> 5 HH3 30 0 0 1
>> 6 HH4 42 0 NA NA
>> 7 HH5 55 0 2 2
>> 8 HH6 66 NA NA NA
>> 9 HH7 75 NA NA NA
>> 10 HH8 81 NA NA NA
>> 11 HH9 92 NA NA NA
>>
>> --
>>
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