HI Usha,
I tried the codes on the full dataset. This is what I get:
BP_2b<-read.csv("BP_2b.csv",sep="\t")
#head(BP_2b,2)
# CODEA Sex MaternalAge Education Birthplace AggScore IntScore Obese14
#1 1 NA 3 4 1 NA NA NA
#2 3 2 3 3 1 0 0 0
# Overweight14 Overweight21 Obese21 hibp14 hibp21
#1 NA NA NA NA NA
#2 0 1 0 0 0
BP.stack1<-
reshape(BP_2b,sep="",timevar=
"time",direction="long",varying=list(names(BP_2b[8:9]),names(BP_2b[10:11]),names(BP_2b[12:13])),v.names=c("Obese","Overweight","HiBP"),idvar="CODEA")
BP_2b<-BP_2b[,c(1:8,11,9:10,12:13)] #Obese21 was not adjacent to Obese14. So,
I rearranged the columns to make it adjacent.
str(BP_2b)
#'data.frame': 6898 obs. of 13 variables:
# $ CODEA : int 1 3 4 7 8 9 10 11 12 13 ...
# $ Sex : int NA 2 2 2 2 1 NA 1 1 2 ...
# $ MaternalAge : int 3 3 3 4 4 3 3 3 3 3 ...
# $ Education : int 4 3 6 6 4 6 3 4 4 4 ...
# $ Birthplace : int 1 1 1 1 1 2 1 1 2 2 ...
# $ AggScore : int NA 0 NA 0 0 0 NA 0 NA NA ...
# $ IntScore : int NA 0 NA 0 0 0 NA 0 NA NA ...
# $ Obese14 : int NA 0 NA 0 0 0 NA NA NA NA ...
# $ Obese21 : int NA 0 NA 1 0 0 NA 0 NA NA ...
# $ Overweight14: int NA 0 NA 0 0 0 NA NA NA NA ...
# $ Overweight21: int NA 1 NA 1 0 0 NA 0 NA NA ...
# $ hibp14 : int NA 0 NA 0 0 0 NA NA NA NA ...
# $ hibp21 : int NA 0 NA 0 0 0 NA 0 NA NA ...
BP.stack2<-
reshape(BP_2b,sep="",timevar=
"time",direction="long",varying=list(names(BP_2b[8:9]),names(BP_2b[10:11]),names(BP_2b[12:13])),v.names=c("Obese","Overweight","HiBP"),idvar="CODEA")
identical(BP.stack1,BP.stack2)
#[1] FALSE
library(car)
BP.stack2$time <- recode(BP.stack2$time,("1=14;2=21"))
head(BP.stack2,2)
# CODEA Sex MaternalAge Education Birthplace AggScore IntScore time Obese
#1.1 1 NA 3 4 1 NA NA 14 NA
#3.1 3 2 3 3 1 0 0 14 0
# Overweight HiBP
#1.1 NA NA
#3.1 0 0
tail(BP.stack2,2)
# CODEA Sex MaternalAge Education Birthplace AggScore IntScore time Obese
#8555.2 8555 1 3 6 2 0 0 21 NA
#8556.2 8556 1 3 4 1 0 0 21 0
# Overweight HiBP
#8555.2 NA NA
#8556.2 0 NA
nrow(BP.stack2)
#[1] 13796
length(BP.stack2$time[BP.stack2$time==14])
#[1] 6898
length(BP.stack2$time[BP.stack2$time==21])
#[1] 6898
#or
names(BP_2b)[grep("\\d+",names(BP_2b))]<-gsub("(\\D+)(\\d+)","\\1_\\2",names(BP_2b)[grep("\\d+",names(BP_2b))])
BP.stack3<-reshape(BP_2b,dir="long",varying=8:13,sep="_")
head(BP.stack3,2)
# CODEA Sex MaternalAge Education Birthplace AggScore IntScore time Obese
#1.14 1 NA 3 4 1 NA NA 14 NA
#2.14 3 2 3 3 1 0 0 14 0
# Overweight hibp id
#1.14 NA NA 1
#2.14 0 0 2
tail(BP.stack3,2)
# CODEA Sex MaternalAge Education Birthplace AggScore IntScore time Obese
#6897.21 8555 1 3 6 2 0 0 21 NA
#6898.21 8556 1 3 4 1 0 0 21 0
# Overweight hibp id
#6897.21 NA NA 6897
#6898.21 0 NA 6898
nrow(BP.stack3)
#[1] 13796
length(BP.stack3$time[BP.stack3$time==21])
#[1] 6898
length(BP.stack3$time[BP.stack3$time==14])
#[1] 6898
#So, I guess this solves the problem. Regarding your second question,
"Do children who are obese or overweight at age 14 experience different
change in blood pressure by 21 years, compared to those with normal
weight at age 14?"
In
the Obese or Overweight columns, I am not sure what "0" means. For
example, in the overweight column, does "0" means it is normal, or it
has to be both "0" for overweight and obese for a particular ID (CODEA)
in each of the age groups? I guess it should be the latter. If that is
the case, subset data in to normal, Obese, Overweight and then do the
comparisons.
For example:
BP.stacknormal<-subset(BP.stack3,Obese==0 & Overweight==0)
nrow(BP.stacknormal)
[1] 4371
head(BP.stacknormal,3)
# CODEA Sex MaternalAge Education Birthplace AggScore IntScore time Obese
#2.14 3 2 3 3 1 0 0 14 0
#4.14 7 2 4 6 1 0 0 14 0
#5.14 8 2 4 4 1 0 0 14 0
# Overweight hibp id
#2.14 0 0 2
#4.14 0 0 4
#5.14 0 0 5
Similarly, you need to subset for Obese==1 , Overweight==1
BP.stackObese <-subset(BP.stack3,Obese==1)
nrow(BP.stackObese)
#[1] 530
Regarding the analysis, I think logistic regression should fit in this case
because the response variable (hibp) is binary.
#Just a simple logistic regression
BP.stacknormal$Categ<-"Normal"
BP.stackObese$Categ<-"Obese"
BP.newObeseNormal<-rbind(BP.stacknormal,BP.stackObese)
BP.newObeseNormal$time<-factor(BP.newObeseNormal$time)
BP.newObeseNormal$Categ<- factor(BP.newObeseNormal$Categ)
BPlogit<-glm(hibp~time+Categ,data=BP.newObeseNormal,family="binomial")
#or BPlogit<-glm(hibp~time+Categ,data=BP.newObeseNormal,family=binomial(logit))
summary(BPlogit)
#
#Call:
#glm(formula = hibp ~ time + Categ, family = "binomial", data =
BP.newObeseNormal)
#
#Deviance Residuals:
# Min 1Q Median 3Q Max
#-0.8630 -0.5452 -0.5452 -0.4965 2.0759
#Coefficients:
# Estimate Std. Error z value Pr(>|z|)
#(Intercept) -1.83099 0.05768 -31.745 <2e-16 ***
#time21 -0.20038 0.09290 -2.157 0.031 *
#CategObese 1.03506 0.12046 8.593 <2e-16 ***
#---
#Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
For
your question, I guess you need to add interaction term also in the
model. I am not sure whether you need random effects in the model. If
that is the case, you need ?lmer() from library(lme4). You could also
post the question in R mixed models (r-sig-mixed-mod...@r-project.org) mailing
list.
A.K.
----- Original Message -----
From: usha2013 <usha.nat...@gmail.com>
To: r-help@r-project.org
Cc:
Sent: Friday, December 28, 2012 2:02 AM
Subject: Re: [R] help with reshaping wide to long format
Hi, Sorry, but how did you bring it out?
Thanks
On Fri, Dec 28, 2012 at 8:48 AM, arun kirshna [via R] <
ml-node+s789695n4654093...@n4.nabble.com> wrote:
> Hi,
> bp.sub<- structure(list(CODEA = c(1L, 3L, 4L, 7L, 8L, 9L, 10L, 11L, 12L,
> 13L, 14L, 16L, 17L), C45 = c(NA, 2L, 2L, 2L, 2L, 1L, NA, 1L,
> 1L, 2L, 1L, 2L, 1L), ragek = c(3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L), ra80 = c(4L, 3L, 6L, 6L, 4L, 6L, 3L, 4L,
> 4L, 4L, 4L, 4L, 4L), ra98 = c(1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
> 2L, 2L, 2L, 3L, 1L), CBCLAggressionAt1410 = c(NA, 0L, NA, 0L,
> 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), CBCLInternalisingAt1410 = c(NA,
> 0L, NA, 0L, 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), Obese14 = c(NA,
> 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 0L, 0L, NA), Obese21 = c(NA,
> 0L, NA, 1L, 0L, 0L, NA, 0L, NA, NA, 0L, 0L, NA), Overweight14 = c(NA,
> 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 0L, 0L, NA), Overweight21 = c(NA,
> 1L, NA, 1L, 0L, 0L, NA, 0L, NA, NA, 1L, 0L, NA), hibp14 = c(NA,
> 0L, NA, 0L, 0L, 0L, NA, NA, NA, NA, 1L, 1L, NA), hibp21 = c(NA,
> 0L, NA, 0L, 0L, 0L, NA, 0L, NA, NA, 1L, NA, NA)), .Names = c("CODEA",
> "C45", "ragek", "ra80", "ra98", "CBCLAggressionAt1410",
> "CBCLInternalisingAt1410",
> "Obese14", "Obese21", "Overweight14", "Overweight21", "hibp14",
> "hibp21"), row.names = c(NA, 13L), class = "data.frame")
>
> BP.stack1<- reshape(bp.sub,sep="",timevar=
> "time",direction="long",varying=list(names(bp.sub[8:9]),names(bp.sub[10:11]),names(bp.sub[12:13])),v.names=c("Obese","Overweight","HiBP"),idvar="CODEA")
>
> BP.stack1$time <- recode(BP.stack1$time,("1=14;2=21"))
> BP.stack1
> # CODEA C45 ragek ra80 ra98 CBCLAggressionAt1410
> CBCLInternalisingAt1410
> #1.1 1 NA 3 4 1 NA
> NA
> #3.1 3 2 3 3 1 0
> 0
> #4.1 4 2 3 6 1 NA
> NA
> #7.1 7 2 4 6 1 0
> 0
> #8.1 8 2 4 4 1 0
> 0
> #9.1 9 1 3 6 2 0
> 0
> #10.1 10 NA 3 3 1 NA
> NA
> #11.1 11 1 3 4 1 0
> 0
> #12.1 12 1 3 4 2 NA
> NA
> #13.1 13 2 3 4 2 NA
> NA
> #14.1 14 1 3 4 2 0
> 0
> #16.1 16 2 3 4 3 0
> 0
> #17.1 17 1 3 4 1 NA
> NA
> #1.2 1 NA 3 4 1 NA
> NA
> #3.2 3 2 3 3 1 0
> 0
> #4.2 4 2 3 6 1 NA
> NA
> #7.2 7 2 4 6 1 0
> 0
> #8.2 8 2 4 4 1 0
> 0
> #9.2 9 1 3 6 2 0
> 0
> #10.2 10 NA 3 3 1 NA
> NA
> #11.2 11 1 3 4 1 0
> 0
> #12.2 12 1 3 4 2 NA
> NA
> #13.2 13 2 3 4 2 NA
> NA
> #14.2 14 1 3 4 2 0
> 0
> #16.2 16 2 3 4 3 0
> 0
> #17.2 17 1 3 4 1 NA
> NA
> # time Obese Overweight HiBP
> #1.1 14 NA NA NA
> #3.1 14 0 0 0
> #4.1 14 NA NA NA
> #7.1 14 0 0 0
> #8.1 14 0 0 0
> #9.1 14 0 0 0
> #10.1 14 NA NA NA
> #11.1 14 NA NA NA
> #2.1 14 NA NA NA
> #13.1 14 NA NA NA
> #14.1 14 0 0 1
> #16.1 14 0 0 1
> #17.1 14 NA NA NA
> #1.2 21 NA NA NA
> #3.2 21 0 1 0
> #4.2 21 NA NA NA
> #7.2 21 1 1 0
> #8.2 21 0 0 0
> #9.2 21 0 0 0
> #10.2 21 NA NA NA
> #11.2 21 0 0 0
> #12.2 21 NA NA NA
> #13.2 21 NA NA NA
> #14.2 21 0 1 1
> #16.2 21 0 0 NA
> #17.2 21 NA NA NA
> names(bp.sub)[grep("\\d+",names(bp.sub))]<-gsub("(\\D+)(\\d+)","\\1_\\2",names(bp.sub)[grep("\\d+",names(bp.sub))])
>
> BP.stack2<-reshape(bp.sub,dir="long",varying=8:13,sep="_")
> Bp.stack2
> CODEA C_45 ragek ra_80 ra_98 CBCLAggressionAt_1410
> 1.14 1 NA 3 4 1 NA
> 2.14 3 2 3 3 1 0
> 3.14 4 2 3 6 1 NA
> 4.14 7 2 4 6 1 0
> 5.14 8 2 4 4 1 0
> 6.14 9 1 3 6 2 0
> 7.14 10 NA 3 3 1 NA
> 8.14 11 1 3 4 1 0
> 9.14 12 1 3 4 2 NA
> 10.14 13 2 3 4 2 NA
> 11.14 14 1 3 4 2 0
> 12.14 16 2 3 4 3 0
> 13.14 17 1 3 4 1 NA
> 1.21 1 NA 3 4 1 NA
> 2.21 3 2 3 3 1 0
> 3.21 4 2 3 6 1 NA
> 4.21 7 2 4 6 1 0
> 5.21 8 2 4 4 1 0
> 6.21 9 1 3 6 2 0
> 7.21 10 NA 3 3 1 NA
> 8.21 11 1 3 4 1 0
> 9.21 12 1 3 4 2 NA
> 10.21 13 2 3 4 2 NA
> 11.21 14 1 3 4 2 0
> 12.21 16 2 3 4 3 0
> 13.21 17 1 3 4 1 NA
> CBCLInternalisingAt_1410 time Obese Overweight hibp id
> 1.14 NA 14 NA NA NA 1
> 2.14 0 14 0 0 0 2
> 3.14 NA 14 NA NA NA 3
> 4.14 0 14 0 0 0 4
> 5.14 0 14 0 0 0 5
> 6.14 0 14 0 0 0 6
> 7.14 NA 14 NA NA NA 7
> 8.14 0 14 NA NA NA 8
> 9.14 NA 14 NA NA NA 9
> 10.14 NA 14 NA NA NA 10
> 11.14 0 14 0 0 1 11
> 12.14 0 14 0 0 1 12
> 13.14 NA 14 NA NA NA 13
> 1.21 NA 21 NA NA NA 1
> 2.21 0 21 0 1 0 2
> 3.21 NA 21 NA NA NA 3
> 4.21 0 21 1 1 0 4
> 5.21 0 21 0 0 0 5
> 6.21 0 21 0 0 0 6
> 7.21 NA 21 NA NA NA 7
> 8.21 0 21 0 0 0 8
> 9.21 NA 21 NA NA NA 9
> 10.21 NA 21 NA NA NA 10
> 11.21 0 21 0 1 1 11
> 12.21 0 21 0 0 NA 12
> 13.21 NA 21 NA NA NA 13
> A.K.
>
>
>
>
>
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