Question

In SAS there’s an method of creating Library (using LIBNAME). This is helpful as when we have to do long data processing, we don’t change always the dataset name. So, if we want to use a dataset again, without changing the name, we can put in a library. So, even if the dataset name are same, but since they are in different libraries, we can work on them together.

My question is there any such option in R that can create Library (or separate folder within R) so that we can save our data there?

Here’s the example:

Suppose I’ve a dataset "dat1". I summarize variables in dat1 var1 & var2 for var3.

proc summary data=dat1 nway missing;
  var var1 var2;
  class var3;
  output out=tmp.dat1 (drop = _freq_ _type_) sum = ;
  run;

Then I merged dat1 with dat2, which is another dataset.Both dat1 & dat2 has common variable var3, with which I merged. I created new dataset dat1 again.

proc sql;
   create table dat1 as
   select a.*,b.*
   from dat1 a left join tmp.dat2 b
   on a.var3=b.var3;
  quit;

Now, I’m again summarizing dataset dat1 after merging to check if the values of var1 & var 2 remain the same before & after merging.

proc summary data=dat1 nway missing;
  var var1 var2;
  class var3;
  output out=tmp1.dat1 (drop = _freq_ _type_) sum = ;
  run;

The equivalent code in R will be

dat3 <- ddply(dat1,
              .(var3),
              summarise,
              var1 = sum(var1,na.rm=TRUE),
              var2 = sum(var2,na.rm=TRUE))

dat1 <- sqldf("select a.*,b.* 
                 from dat1 a 
                      left join dat2 b 
                             on a.var3=b.var3")

dat4 <- ddply(dat1,
              .(var3),
              summarise,
              var1 = sum(var1,na.rm=TRUE),
              var2 = sum(var2,na.rm=TRUE))

In case of SAS I used just 2 dataset name. But in case of R, I’m using 4 dataset name. So, if I’m writing 4000 line code for data processing, having too many dataset name sometimes become overwhelming. In sas it became easy to have same dataset name as I’m using 2 libraries tmp, tmp1 other than the default work library.

In SAS, library is defined as:

LIBNAME tmp "directory_path\folder_name";

In this folder, dat1 will be stored.

Answer 1

Here is an example using the SOAR package and named environments

To quote from the vignette

Objects need not be always held in memory. The function save may be used to save objects on the disc in a file, typically with an .RData extension. The objects may then be removed from memory and later recalled explicitly with the load function.

The SOAR package provides simple way to store objects on the disc, but in such a way that they remain visible on the search path as promises, that is, if and when an object is needed again it is automatically loaded into memory. It uses the same lazy loading mechanism as packages, but the functionality provided here is more dynamic and exible

It will be useful to read the whole vignette

library(SOAR)
library(plyr)
library(sqldf)
set.seed(1)

# create some dummy data create a named envirment
tmp <- new.env(parent = .GlobalEnv)
dat1 <- data.frame(var1 = rnorm(50),
                   var2 = sample(50, replace = TRUE),
                   var3 = sample(letters[1:5], 50, replace = TRUE))

tmp$dat1 <- ddply(dat1, .(var3), summarise,
                  var1 = sum(var1, na.rm = TRUE), 
                  var2 = sum(var2, na.rm = TRUE))

tmp$dat2 <- data.frame(Var3 = sample(letters[1:5], 20, replace = TRUE), 
                       Var4 = 1:20)

# store as a SOAR cached object (on disc)
Store(tmp, lib = "tmp")

# replace dat1 within the global enviroment using sqldf create a new
# environment to work in with the correct version of dat1 and dat2
sqlenv <- tmp
sqlenv$dat1 <- dat1

dat1 <- sqldf("select a.*,b.* from dat1 a left join dat2 b on a.var3=b.var3", 
              envir = sqlenv)

# create a new named enviroment tmp1
tmp1 <- new.env(parent = .GlobalEnv)

tmp1$dat1 <- ddply(dat1, .(var3), summarise, 
                   var1 = sum(var1, na.rm = TRUE), 
                   var2 = sum(var2, na.rm = TRUE))

# store using a SOAR cache
Store(tmp1, lib = "tmp")


tmp1$dat1

##   var3   var1 var2
## 1    a  1.336  378
## 2    b  8.514 1974
## 3    c  5.795  624
## 4    d -8.828  936
## 5    e 20.846 1490

tmp$dat1

##   var3    var1 var2
## 1    a  0.4454  126
## 2    b  1.4190  329
## 3    c  1.9316  208
## 4    d -2.9427  312
## 5    e  4.1691  298

I’m not sure you should expect tmp1$dat1 and tmp$dat1 to be identical (given my example anyway)