r - Lapply in a dataframe over different variables using filters -


i'm trying calculate several new variables in dataframe. take initial values example:

say have:

dataset <- data.frame(time=rep(c(1990:1992),2),            geo=c(rep("at",3),rep("de",3)),var1=c(1:6), var2=c(7:12))          time    geo var1 var2 1       1990    @  1    7 2       1991    @  2    8 3       1992    @  3    9 4       1990    de  4   10 5       1991    de  5   11 6       1992    de  6   12

and want:

        time    geo  var1  var2  var1_1990  var1_1991  var2_1990 var2_1991 1       1990    @   1     7      1          2          7         8 2       1991    @   2     8      1          2          7         8 3       1992    @   3     9      1          2          7         8 4       1990    de   4     10     4          5          10        11 5       1991    de   5     11     4          5          10        11 6       1992    de   6     12     4          5          10        11

so both time , variable changing new variables. here attempt:

intitialyears <- c(1990,1991) intitialvars <- c("var1", "var2")  # ideally, want code have change these 2 vectors  # , it's possible change dimensions  (i in initialyears){ lapply(initialvars,function(x){ rep(dataset[time==i,x],each=length(unique(dataset$time))) })}

which runs without error yields nothing. assign variable names in example (eg. "var1_1990") , make new variables part of dataframe. avoid loop don't know how wrap 2 lapply's around function. should rather have function use 2 arguments? problem apply function not carry results environment? i've been stuck here while grateful help!

p.s.: have solution combination combination without apply , likes i'm trying away copy , paste:

dataset$var1_1990 <- c(rep(dataset$var1[which(dataset$time==1990)],                       each=length(unique(dataset$time))))

this can done subset(), reshape(), , merge():

merge(dataset,reshape(subset(dataset,time%in%c(1990,1991)),dir='w',idvar='geo',sep='_')); ##   geo time var1 var2 var1_1990 var2_1990 var1_1991 var2_1991 ## 1  @ 1990    1    7         1         7         2         8 ## 2  @ 1991    2    8         1         7         2         8 ## 3  @ 1992    3    9         1         7         2         8 ## 4  de 1990    4   10         4        10         5        11 ## 5  de 1991    5   11         4        10         5        11 ## 6  de 1992    6   12         4        10         5        11

the column order isn't have in question, can fix after-the-fact index operation, if necessary.


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