[R] cumulative sum by group and under some criteria
arun
smartpink111 at yahoo.com
Sat Feb 2 04:17:49 CET 2013
Hi,
Saw your reply on Nabble:
#Your code:
library(zoo)
res1<- do.call(rbind,lapply(lapply(split(d,list(d$m1,d$n1)),function(x)
{x$cterm1_p0L[x$Qm<=c11]<- cumsum(x$term1_p0[x$Qm<=c11]);
x$cterm1_p0H[x$Qn<=c12]<- cumsum(x$term1_p0[x$Qn<=c12]);
x$cterm1_p1L[x$Qm<=c11]<- cumsum(x$term1_p1[x$Qm<=c11]);
x$cterm1_p1H[x$Qm<=c12]<- cumsum(x$term1_p1[x$Qn<=c12]); #Check this line Qm and Qn
x}),function(x) {x$cterm1_p0L<-na.locf(x$cterm1_p0L,na.rm=F);
x$cterm1_p0H<-na.locf(x$cterm1_p0H,na.rm=F);
x$cterm1_p1L<-na.locf(x$cterm1_p1L,na.rm=F);
x$cterm1_p1H<-na.locf(x$cterm1_p1H,na.rm=F);x}))
#should be:
colnames(d)<-c("m1","n1","x1","y1","Fmm", "Fnn", "Qm", "Qn", "term1_p0", "term1_p1")
res1<- do.call(rbind,lapply(lapply(split(d,list(d$m1,d$n1)),function(x) {x$cterm1_p0L[x$Qm<=c11]<- cumsum(x$term1_p0[x$Qm<=c11]);
x$cterm1_p0H[x$Qn<=c12]<- cumsum(x$term1_p0[x$Qn<=c12]);
x$cterm1_p1L[x$Qm<=c11]<- cumsum(x$term1_p1[x$Qm<= c11]);
x$cterm1_p1H[x$Qn<=c12]<- cumsum(x$term1_p1[x$Qn<= c12]);
x}),function(x) {x$cterm1_p0L<-na.locf(x$cterm1_p0L,na.rm=F);
x$cterm1_p0H<-na.locf(x$cterm1_p0H,na.rm=F);
x$cterm1_p1L<-na.locf(x$cterm1_p1L,na.rm=F);
x$cterm1_p1H<-na.locf(x$cterm1_p1H,na.rm=F);x}))
row.names(res1) <- 1:nrow(res1)
res1[,11:14][is.na(res1[,11:14])]<- 0
res1[,11:14][is.na(res1[,11:14])]<- 0
res1
# m1 n1 x1 y1 Fmm Fnn Qm Qn term1_p0 term1_p1 cterm1_p0L cterm1_p0H cterm1_p1L cterm1_p1H
#1 2 2 0 0 0.00 0.00 1.000 1.000 0.8145062500 0.40960 0.00000e+00 0.0000000000 0.00000 0.00000
#2 2 2 0 1 0.00 0.60 1.000 0.400 0.0857375000 0.20480 0.00000e+00 0.0000000000 0.00000 0.00000
#3 2 2 0 2 0.00 1.00 1.000 0.000 0.0022562500 0.02560 0.00000e+00 0.0022562500 0.00000 0.02560
#4 2 2 1 0 0.61 0.00 0.695 0.695 0.0857375000 0.20480 0.00000e+00 0.0022562500 0.00000 0.02560
#5 2 2 1 1 0.61 0.62 0.390 0.380 0.0090250000 0.10240 0.00000e+00 0.0022562500 0.00000 0.02560
#6 2 2 1 2 0.63 1.00 0.370 0.000 0.0002375000 0.01280 0.00000e+00 0.0024937500 0.00000 0.03840
#7 2 2 2 0 1.00 0.00 0.500 0.500 0.0022562500 0.02560 0.00000e+00 0.0024937500 0.00000 0.03840
#8 2 2 2 1 1.00 0.67 0.165 0.165 0.0002375000 0.01280 2.37500e-04 0.0027312500 0.01280 0.05120
#9 2 2 2 2 1.00 1.00 0.000 0.000 0.0000062500 0.00160 2.43750e-04 0.0027375000 0.01440 0.05280
#10 3 2 0 0 0.00 0.00 1.000 1.000 0.7737809375 0.32768 0.00000e+00 0.0000000000 0.00000 0.00000
#11 3 2 0 1 0.00 0.65 1.000 0.350 0.0814506250 0.16384 0.00000e+00 0.0000000000 0.00000 0.00000
#12 3 2 0 2 0.00 1.00 1.000 0.000 0.0021434375 0.02048 0.00000e+00 0.0021434375 0.00000 0.02048
#13 3 2 1 0 0.67 0.00 0.665 0.665 0.1221759375 0.24576 0.00000e+00 0.0021434375 0.00000 0.02048
#14 3 2 1 1 0.60 0.64 0.400 0.360 0.0128606250 0.12288 0.00000e+00 0.0021434375 0.00000 0.02048
#15 3 2 1 2 0.66 1.00 0.340 0.000 0.0003384375 0.01536 0.00000e+00 0.0024818750 0.00000 0.03584
#16 3 2 2 0 0.71 0.00 0.645 0.645 0.0064303125 0.06144 0.00000e+00 0.0024818750 0.00000 0.03584
#17 3 2 2 1 0.69 0.66 0.325 0.325 0.0006768750 0.03072 0.00000e+00 0.0024818750 0.00000 0.03584
#18 3 2 2 2 0.64 1.00 0.360 0.000 0.0000178125 0.00384 0.00000e+00 0.0024996875 0.00000 0.03968
#19 3 2 3 0 1.00 0.00 0.500 0.500 0.0001128125 0.00512 0.00000e+00 0.0024996875 0.00000 0.03968
#20 3 2 3 1 1.00 0.74 0.130 0.130 0.0000118750 0.00256 1.18750e-05 0.0025115625 0.00256 0.04224
#21 3 2 3 2 1.00 1.00 0.000 0.000 0.0000003125 0.00032 1.21875e-05 0.0025118750 0.00288 0.04256
#22 2 3 0 0 0.00 0.00 1.000 1.000 0.7737809375 0.32768 0.00000e+00 0.0000000000 0.00000 0.00000
#23 2 3 0 1 0.00 0.60 1.000 0.400 0.1221759375 0.24576 0.00000e+00 0.0000000000 0.00000 0.00000
#24 2 3 0 2 0.00 0.65 1.000 0.350 0.0064303125 0.06144 0.00000e+00 0.0000000000 0.00000 0.00000
#25 2 3 0 3 0.00 1.00 1.000 0.000 0.0001128125 0.00512 0.00000e+00 0.0001128125 0.00000 0.00512
#26 2 3 1 0 0.77 0.00 0.615 0.615 0.0814506250 0.16384 0.00000e+00 0.0001128125 0.00000 0.00512
#27 2 3 1 1 0.60 0.62 0.400 0.380 0.0128606250 0.12288 0.00000e+00 0.0001128125 0.00000 0.00512
#28 2 3 1 2 0.61 0.72 0.390 0.280 0.0006768750 0.03072 0.00000e+00 0.0001128125 0.00000 0.00512
#29 2 3 1 3 0.65 1.00 0.350 0.000 0.0000118750 0.00256 0.00000e+00 0.0001246875 0.00000 0.00768
#30 2 3 2 0 1.00 0.00 0.500 0.500 0.0021434375 0.02048 0.00000e+00 0.0001246875 0.00000 0.00768
#31 2 3 2 1 1.00 0.58 0.210 0.210 0.0003384375 0.01536 0.00000e+00 0.0001246875 0.00000 0.00768
#32 2 3 2 2 1.00 0.60 0.200 0.200 0.0000178125 0.00384 1.78125e-05 0.0001425000 0.00384 0.01152
#33 2 3 2 3 1.00 1.00 0.000 0.000 0.0000003125 0.00032 1.81250e-05 0.0001428125 0.00416 0.01184
A.K.
----- Original Message -----
From: Zjoanna <Zjoanna2013 at gmail.com>
To: r-help at r-project.org
Cc:
Sent: Friday, February 1, 2013 12:19 PM
Subject: Re: [R] cumulative sum by group and under some criteria
Thank you very much for your reply. Your code work well with this example.
I modified a little to fit my real data, I got an error massage.
Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) :
Group length is 0 but data length > 0
On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] <
ml-node+s789695n4657196h87 at n4.nabble.com> wrote:
> Hi,
> Try this:
> colnames(d)<-c("m1","n1","x1","y1","p11","p12")
> library(zoo)
> res1<- do.call(rbind,lapply(lapply(split(d,list(d$m1,d$n1)),function(x)
> {x$cp11[x$x1>1]<- cumsum(x$p11[x$x1>1]);x$cp12[x$y1>1]<-
> cumsum(x$p12[x$y1>1]);x}),function(x)
> {x$cp11<-na.locf(x$cp11,na.rm=F);x$cp12<- na.locf(x$cp12,na.rm=F);x}))
> #there would be a warning here as one of the list element is NULL. The,
> warning is okay
> row.names(res1)<- 1:nrow(res1)
> res1[,7:8][is.na(res1[,7:8])]<- 0
> res1
> # m1 n1 x1 y1 p11 p12 cp11 cp12
> #1 2 2 0 0 0.00 0.00 0.00 0.00
> #2 2 2 0 1 0.00 0.50 0.00 0.00
> #3 2 2 0 2 0.00 1.00 0.00 1.00
> #4 2 2 1 0 0.50 0.00 0.00 1.00
> #5 2 2 1 1 0.50 0.50 0.00 1.00
> #6 2 2 1 2 0.50 1.00 0.00 2.00
> #7 2 2 2 0 1.00 0.00 1.00 2.00
> #8 2 2 2 1 1.00 0.50 2.00 2.00
> #9 2 2 2 2 1.00 1.00 3.00 3.00
> #10 3 2 0 0 0.00 0.00 0.00 0.00
> #11 3 2 0 1 0.00 0.50 0.00 0.00
> #12 3 2 0 2 0.00 1.00 0.00 1.00
> #13 3 2 1 0 0.33 0.00 0.00 1.00
> #14 3 2 1 1 0.33 0.50 0.00 1.00
> #15 3 2 1 2 0.33 1.00 0.00 2.00
> #16 3 2 2 0 0.67 0.00 0.67 2.00
> #17 3 2 2 1 0.67 0.50 1.34 2.00
> #18 3 2 2 2 0.67 1.00 2.01 3.00
> #19 3 2 3 0 1.00 0.00 3.01 3.00
> #20 3 2 3 1 1.00 0.50 4.01 3.00
> #21 3 2 3 2 1.00 1.00 5.01 4.00
> #22 2 3 0 0 0.00 0.00 0.00 0.00
> #23 2 3 0 1 0.00 0.33 0.00 0.00
> #24 2 3 0 2 0.00 0.67 0.00 0.67
> #25 2 3 0 3 0.00 1.00 0.00 1.67
> #26 2 3 1 0 0.50 0.00 0.00 1.67
> #27 2 3 1 1 0.50 0.33 0.00 1.67
> #28 2 3 1 2 0.50 0.67 0.00 2.34
> #29 2 3 1 3 0.50 1.00 0.00 3.34
> #30 2 3 2 0 1.00 0.00 1.00 3.34
> #31 2 3 2 1 1.00 0.33 2.00 3.34
> #32 2 3 2 2 1.00 0.67 3.00 4.01
> #33 2 3 2 3 1.00 1.00 4.00 5.01
> A.K.
>
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