Lapply in a dataframe over different variables using filters

Question

I'm trying to calculate several new variables in my dataframe. Take initial values for example:

Say I 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    AT  1    7
2       1991    AT  2    8
3       1992    AT  3    9
4       1990    DE  4   10
5       1991    DE  5   11
6       1992    DE  6   12

And I want:

        time    geo  var1  var2  var1_1990  var1_1991  var2_1990 var2_1991
1       1990    AT   1     7      1          2          7         8
2       1991    AT   2     8      1          2          7         8
3       1992    AT   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 and the variable are changing for the new variables. Here is my attempt:

intitialyears <- c(1990,1991)
intitialvars <- c("var1", "var2") 
# ideally, I want code where I only have to change these two vectors 
# and where it's possible to change their dimensions

for (i in initialyears){
lapply(initialvars,function(x){
rep(Dataset[time==i,x],each=length(unique(Dataset$time)))
})}

Which runs without error but yields nothing. I would like to assign the variable names in the example (eg. "var1_1990") and immediately make the new variables part of the dataframe. I would also like to avoid the for loop but I don't know how to wrap two lapply's around this function. Should I rather have the function use two arguments? Is the problem that the apply function does not carry the results into my environment? I've been stuck here for a while so I would be grateful for any help!

p.s.: I have the solution to do this combination by combination without apply and the likes but I'm trying to get away from copy and paste:

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

Answer 1

This can be done with subset(), reshape(), and 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  AT 1990    1    7         1         7         2         8
## 2  AT 1991    2    8         1         7         2         8
## 3  AT 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 exactly what you have in your question, but you can fix that up after-the-fact with an index operation, if necessary.

Answer 2

Here's a data.table method:

require(data.table)
dt <- as.data.table(Dataset)
in_cols = c("var1", "var2")
out_cols = do.call("paste", c(CJ(in_cols, unique(dt$time)), sep="_"))

dt[, (out_cols) := unlist(lapply(.SD, as.list), FALSE), by=geo, .SDcols=in_cols]

#    time geo var1 var2 var1_1990 var1_1991 var1_1992 var2_1990 var2_1991 var2_1992
# 1: 1990  AT    1    7         1         2         3         7         8         9
# 2: 1991  AT    2    8         1         2         3         7         8         9
# 3: 1992  AT    3    9         1         2         3         7         8         9
# 4: 1990  DE    4   10         4         5         6        10        11        12
# 5: 1991  DE    5   11         4         5         6        10        11        12
# 6: 1992  DE    6   12         4         5         6        10        11        12

This assumes that the time variable is identical (and in the same order) for each geo value.

Answer 3

With dplyr and tidyr and using a custom function try the following:

Data

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))

Code

library(dplyr); library(tidyr)

intitialyears <- c(1990,1991)
intitialvars <- c("var1", "var2")

#create this function
myTranForm <- function(dataSet, varName, years){

  temp <- dataSet %>% select(time, geo, eval(parse(text=varName))) %>% 
            filter(time %in% years) %>% mutate(time=paste(varName, time, sep="_")) 

   names(temp)[names(temp) %in% varName] <- "someRandomStringForVariableName"
   temp <- temp %>% spread(time, someRandomStringForVariableName)
  return(temp)
}

#Then lapply on intitialvars using the custom function
DatasetList <- lapply(intitialvars, function(x) myTranForm(Dataset, x, intitialyears))

#and loop over the data frames in the list
for(i in 1:length(intitialvars)){
  Dataset <- left_join(Dataset, DatasetList[[i]])
}

Dataset