如何解决在R中的嵌套函数中合并函数的输出
我有2个函数，每个函数提供不同的输出，我试图创建一个合并这些函数的2个输出的新函数，但是不断收到错误消息，指出未找到对象，我知道一旦出局在我的任何函数中（在常规函数内部），main函数都无法识别这些对象。我不知道如何在主要功能的全局环境中识别这些输出。这是代码
#############################################################################
#############################################################################
# 1. datasets
IDr= c(seq(1,5))
BTR=c("A","B","AB","O","O")
data_R=data.frame(IDr,BTR,A=c(0,1,rep(0,3)),B=c(0,3),1),C=c(0,rep(1,0),D=c(0,4)),E=c(1,stringsAsFactors=FALSE)
data_R
IDr BTR A B C D E
1 1 A 0 0 0 0 1
2 2 B 1 0 1 1 1
3 3 AB 0 0 1 1 0
4 4 O 0 0 1 1 1
5 5 O 0 1 0 1 0
IDd= c(seq(1,8))
BTD= c("A","O")
fg= c(rep(0.0025,each=2),rep(0.00125,rep(0.0011,rep(0.0015,each=2))
data_D=data.frame(IDd,BTD,A=c(rep(0,5),B=c(rep(0,6),C=c(rep(1,7),D=rep(1,8),E=c(rep(0,2),fg,stringsAsFactors=FALSE)
data_D
IDd BTD A B C D E fg
1 1 A 0 0 1 1 0 0.00250
2 2 B 0 0 1 1 0 0.00250
3 3 AB 0 0 1 1 0 0.00125
4 4 O 0 0 1 1 0 0.00125
5 5 AB 0 0 1 1 0 0.00110
6 6 AB 1 0 1 1 1 0.00110
7 7 O 1 1 1 1 1 0.00150
8 8 O 1 1 0 1 0 0.00150
############################################################################
############################################################################
# fist function
# calulate the frequency of repeated set (A:E) using fg
freq<- function(df,Vars,col.interest){
col.interest=as.data.frame(col.interest)
resultat1= df %>%
group_by(across(all_of(Vars))) %>%
dplyr::summarise(count = n(),frequency.epi = sum(fg),.groups = 'drop')
res=merge(resultat1,col.interest,all=TRUE)
res_final=cbind(df[1:2],res)
return(res_final)
}
dfreq= freq(data_D,colnames(data_D)[3:7],data_D[3:7])
dfreq
IDd BTD A B C D E count frequency.epi
1 1 A 0 0 1 1 0 5 0.0086
2 2 B 0 0 1 1 0 5 0.0086
3 3 AB 0 0 1 1 0 5 0.0086
4 4 O 0 0 1 1 0 5 0.0086
5 5 AB 0 0 1 1 0 5 0.0086
6 6 AB 1 0 1 1 1 1 0.0011
7 7 O 1 1 0 1 0 1 0.0015
8 8 O 1 1 1 1 1 1 0.0015
###############################################################
# the second function that was corrected by @MrFlic
mis.test = function(D,R,threshold) {
D = as.data.frame(D)
R = as.data.frame(R)
mismatch.i = function(i) {
dif = purrr::map2_df(D[-1],R[i,-1],`-`)
dif[dif<0] = 0
dif$mismatch=rowSums(dif)
dif = cbind(ID = D[1],IDr=R[i,1],dif)
dif = dif[which(dif$mismatch <= threshold),]
return(list=dif[c(1,2,ncol(dif))])
}
diff.mat = do.call(rbind,lapply(1:nrow(R),function(x) mismatch.i(x)))
diff.mat = as.data.frame(diff.mat)
return(diff.mat)
}
# if i want mis.test for 1 person
mis_one=mis.test(data_D[,c(1,3:7)],data_R[1,2)
mis_one
IDd IDr mismatch
1 1 1 2
2 2 1 2
3 3 1 2
4 4 1 2
5 5 1 2
# what i want to do in the main function is this step (for example using these exact outputs)
merge(mis_one,dfreq,by="IDd") # this was executed outside to show the expected output
# this is the output expected that i want if i run the main function
IDd IDr mismatch BTD A B C D E count frequency.epi
1 1 1 2 A 0 0 1 1 0 5 0.0086
2 2 1 2 B 0 0 1 1 0 5 0.0086
3 3 1 2 AB 0 0 1 1 0 5 0.0086
4 4 1 2 O 0 0 1 1 0 5 0.0086
5 5 1 2 AB 0 0 1 1 0 5 0.0086
test.merge=function(D,DF,threshold,col.interest){
R=as.data.frame(R)
D=as.data.frame(D)
DF=as.data.frame(DF)
col.interest=as.data.frame(col.interest)
# remark1: Here i kNow i repeated the same arguments because i did not kNow what to set in order to do the calculation
freq.epi<- function( Vars,col.interest){
resultat1= DF %>%
group_by(across(all_of(Vars))) %>%
dplyr::summarise(count = n(),.groups = 'drop')
res=merge(resultat1,all=TRUE)
res_final=cbind(DF[1:2],res)
return(res_final)
}
# same as remark1 for the arguments
mis.test = function(D,threshold) {
D = as.data.frame(D)
R = as.data.frame(R)
mismatch.i = function(i) {
dif = purrr::map2_df(D[-1],`-`)
dif[dif<0] = 0
dif$mismatch=rowSums(dif)
dif = cbind(ID = D[1],dif)
dif = dif[which(dif$mismatch <= threshold),]
return(list=dif[c(1,ncol(dif))])
}
diff.mat = do.call(rbind,function(x) mismatch.i(x)))
diff.mat = as.data.frame(diff.mat)
return(diff.mat)
}
# i dont kNow how to make diff.mat and res_final visible for test.merge
# i am trying to merge the two outputs res_final and diff.mat by the IDd
tab=merge(diff.mat,res_final,by="IDd")
return(tab)
}
test.merge(data_D[,data_D,data_D[3:7])
# Error in merge(diff.mat,by = "IDd") :
# object 'diff.mat' not found
我不知道在主函数中是否还有其他方法可以使用函数的输出。预先感谢您的帮助
解决方法
为什么要将所有功能混合在一起？我建议将它们分开并写test.merge以仅合并2个输出中的数据。
freq<- function(df,Vars,col.interest){
col.interest=as.data.frame(col.interest)
resultat1= df %>%
group_by(across(all_of(Vars))) %>%
dplyr::summarise(count = n(),frequency.epi = sum(fg),.groups = 'drop')
res=merge(resultat1,col.interest,all=TRUE)
res_final=cbind(df[1:2],res)
return(res_final)
}
mis.test = function(D,R,threshold) {
D = as.data.frame(D)
R = as.data.frame(R)
mismatch.i = function(i) {
dif = purrr::map2_df(D[-1],R[i,-1],`-`)
dif[dif<0] = 0
dif$mismatch=rowSums(dif)
dif = cbind(ID = D[1],IDr=R[i,1],dif)
dif = dif[which(dif$mismatch <= threshold),]
return(list=dif[c(1,2,ncol(dif))])
}
diff.mat = do.call(rbind,lapply(1:nrow(R),function(x) mismatch.i(x)))
diff.mat = as.data.frame(diff.mat)
return(diff.mat)
}
test.merge = function(x,y) {
merge(x,y,by="IDd")
}
test.merge(mis.test(data_D[,c(1,3:7)],data_R[1,2),freq(data_D,colnames(data_D)[3:7],data_D[3:7]))
# IDd IDr mismatch BTD A B C D E count frequency.epi
#1 1 1 2 A 0 0 1 1 0 5 0.0086
#2 2 1 2 B 0 0 1 1 0 5 0.0086
#3 3 1 2 AB 0 0 1 1 0 5 0.0086
#4 4 1 2 O 0 0 1 1 0 5 0.0086
#5 5 1 2 AB 0 0 1 1 0 5 0.0086
这是您原始代码的修复程序。
test.merge=function(D,threshold,DF,col.interest){
R=as.data.frame(R)
D=as.data.frame(D)
DF=as.data.frame(DF)
col.interest=as.data.frame(col.interest)
freq.epi<- function(DF,col.interest){
resultat1= DF %>%
group_by(across(all_of(Vars))) %>%
dplyr::summarise(count = n(),.groups = 'drop')
res=merge(resultat1,all=TRUE)
res_final=cbind(DF[1:2],res)
return(res_final)
}
# same as remark1 for the arguments
mis.test = function(D,threshold) {
D = as.data.frame(D)
R = as.data.frame(R)
mismatch.i = function(i) {
dif = purrr::map2_df(D[-1],`-`)
dif[dif<0] = 0
dif$mismatch=rowSums(dif)
dif = cbind(ID = D[1],dif)
dif = dif[which(dif$mismatch <= threshold),]
return(list=dif[c(1,ncol(dif))])
}
diff.mat = do.call(rbind,function(x) mismatch.i(x)))
diff.mat = as.data.frame(diff.mat)
return(diff.mat)
}
tab=merge(mis.test(D,threshold),freq.epi(DF,col.interest),by="IDd")
return(tab)
}
test.merge(data_D[,data_D,data_D[3:7])
我确信可以以更好的方式对其进行优化和编写（如第1部分中所述），但是由于我不了解此处的大图，因此将其留给OP。
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