Reshaping repeated measures data in R wide to long - r

Question

I need to convert a "wide" dataframe of annually repeated measures on individuals into "long" format so that I can model it like lm(y_year2 ~ x_year1) as well as lm(z_year2 ~ y_year2)
I can get it into the format I want "by hand" but cannot get figure out how to melt/dcast it into the shape I want
Below I've illustrated what I'm doing with some simulated data
The dataframe is like this in wide format, one individual per line
ID SITE L_03 M_03 R_03 L_04 M_04 R_04 L_05 M_05 R_05
1 forest X a YES Y b YES Z c NO
2 forest ...
I'd like it in LONG format:
ID SITE L_year1 L_year2 M_year1 M_year2 R_year1 R_year2 year1 year2
1 forest Z Y a b YES YES 03 04
1 forest Y Z b c YES NO 04 05
2 forest ...
2 forest ...
Some Simulated data:
L and M are numeric (length & mass), R is a Yes/No factor (reproductive), 3 years of repeated measurements (2003-2005)
ID <- 1:10; SITE <- c(rep("forest",3), rep("swamp",3), rep("field",4))
L_03 <- round(rnorm(10, 100, 1),3) ; M_03 <- round((10 + L_03*0.25 + rnorm(10, 0, 1)), 3)
R_03 <- sample(c("Yes", "No"), 10, replace = TRUE) ; L_04 <- round((2 + L_03*1.25 + rnorm(10, 1,10)), 3)
M_04 <- round((10 + L_04*0.25 + rnorm(10, 0,10)), 3) ;R_04 <- sample(c("Yes", "No"), 10, replace = TRUE)
L_05 <- round((2 + L_04*1.25 + rnorm(10, 1,10)),3) ; M_05 <- round((10 + L_05*0.25 + abs(rnorm(10, 0,10))),3)
R_05 <- sample(c("Yes", "No"), 10, replace = TRUE); rm_data <- data.frame(ID, SITE, L_03, M_03, R_03, L_04, M_04,R_04, L_05, M_05, R_05)
Approach 1: My ad hoc reshaping "by hand" with rbind
1st, make subset with 2003 & 2004 data, then another w/ 2004 & 2005
rm_data1 <- cbind(rm_data[ ,c(1,2,3:5, 6:8)], rep(2003,10), rep(2004,10))
rm_data2 <- cbind(rm_data[ ,c(1,2,6:8, 9:11)],rep(2004,10), rep(2005,10))
names(rm_data1)[3:10]<- c("L1", "M1", "R1", "L2", "M2", "R2", "yr1", "yr2")
names(rm_data2)[3:10]<- c("L1", "M1", "R1", "L2", "M2", "R2", "yr1", "yr2")
data3 <- rbind(rm_data1, rm_data2)
Approach 2?: I'd like to do this with reshape/melt/dcast. I can't figure out if I can use dcast directly on the wide dataframe or, once I melt it, how to dcast it into the format I want.
library(reshape2)
rm_measure_vars <- c("L_03", "M_03", "R_03", "L_04", "M_04","R_04", "L_05", "M_05", "R_05")
rm_data_melt <- melt(data = rm_data, id.vars = c("ID", "SITE"), measure.vars = rm_measure_vars, value.name = "data")
I add a designator of the year the measurement was taken to the melted data
obs_year <- gsub("(.*)([0-9]{2})", "\\2", rm_data_melt$variable)
rm_data_melt <- cbind(rm_data_melt, obs_year)
The dcast seems like it should be something like this, but this is not yet what I need
dcast(data = rm_data_melt, formula = ID + SITE + obs_year ~ variable)
ID SITE obs_year L_03 M_03 R_03 L_04 M_04 R_04 L_05 M_05 R_05
1 1 forest 03 99.96 35.364 No <NA> <NA> <NA> <NA> <NA> <NA>
2 1 forest 04 <NA> <NA> <NA> 129.595 47.256 Yes <NA> <NA> <NA>
3 1 forest 05 <NA> <NA> <NA> <NA> <NA> <NA> 177.607 58.204 Yes
Any suggestions would be greatly appreciated

Answer 1

I gave it some try. The reshape is the easy part. The rest needs some semi-manual handling, I believe. The following should give you what you want.
output <- reshape(rm_data, idvar=c("ID","SITE"), varying=3:11,
v.names=c("L_","M_","R_"), direction="long")
output$time <- output$time + 2 # to get the year
names(output)[3:6] <- c("year1", "L_year1", "M_year1", "R_year1")
output$year2 <- output$year1+1
rownames(output) <- c()
sapply(output[,4:6], function(x) {
i <- ncol(output)+1
output[,i] <<- x[c(2:length(x), NA)]
names(output)[i] <<- sub("1","2",names(output)[i-4])
})
output <- output[,c(1,2,4,8,5,9,6,10,3,7)] # rearrange columns as necessary
Hope this helps!

Answer 2

Install onetree packages.
devtools::install_github("yikeshu0611/onetree")
library(onetree)
3 steps, using onetree package
1 step
reshape the data to a long data
long1=reshape_toLong(data = rm_data,
id = "ID",
j = "year",
value.var.prefix = c("L_","M_","R_"))
2nd step
drop 5 year, choose 3 and 4 year; duplicated year as y
long2=long1[long1$year!=5,]
long2$y=long2$year
reshape long2 to a wide data by year
wide1=reshape_toWide(data = long2,
id = "ID",
j = "year",
value.var.prefix = c("L_","M_","R_","y")
)
Now, we get data with year 3 and year 4, whic is year1 and year2 in your purpose data.
So we repalce 3 with 1, 4 with 2 in the colnames.
colnames(wide1)=gsub(3,1,colnames(wide1))
colnames(wide1)=gsub(4,2,colnames(wide1))
3rd step
do 2nd step again, this time, we drop year3, we choose year4 and year5.
long3=long1[long1$year!=3,]
long3$y=long3$year
wide2=reshape_toWide(data = long3,
id = "ID",
j = "year",
value.var.prefix = c("L_","M_","R_","y")
)
colnames(wide2)=gsub(4,1,colnames(wide2))
colnames(wide2)=gsub(5,2,colnames(wide2))
last
rbind wide1 and wide2
data=rbind(wide1,wide2)
data[order(data$ID),]
ID SITE L_1 M_1 R_1 y1 L_2 M_2 R_2 y2
1 1 forest 100.181 34.279 Yes 3 131.88 50.953 No 4
11 1 forest 131.88 50.953 No 4 158.642 50.255 No 5
2 2 forest 101.645 36.667 Yes 3 123.923 43.915 No 4
12 2 forest 123.923 43.915 No 4 163.81 55.979 No 5
3 3 forest 98.961 33.901 Yes 3 125.928 41.611 No 4
13 3 forest 125.928 41.611 No 4 165.865 57.417 No 5
4 4 swamp 100.807 36.254 No 3 117.856 48.634 Yes 4
14 4 swamp 117.856 48.634 Yes 4 137.487 50.945 No 5
5 5 swamp 99.75 33.881 No 3 132.419 50.563 Yes 4
15 5 swamp 132.419 50.563 Yes 4 168.461 58.373 Yes 5
6 6 swamp 100.463 34.859 Yes 3 122.884 40.301 No 4
16 6 swamp 122.884 40.301 No 4 152.85 57.491 No 5
7 7 field 102.527 34.521 No 3 123.363 35.935 No 4
17 7 field 123.363 35.935 No 4 168 55.692 No 5
8 8 field 99.957 35.236 Yes 3 139.083 34.793 No 4
18 8 field 139.083 34.793 No 4 177.648 62.638 Yes 5
9 9 field 100.16 36.454 No 3 135.468 45.115 Yes 4
19 9 field 135.468 45.115 Yes 4 180.666 57.233 No 5
10 10 field 100.037 35.612 No 3 139.165 46.95 No 4
20 10 field 139.165 46.95 No 4 169.333 55.782 Yes 5