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Export R outputs to SAS dataset

6 messages · Jomy Jose, Rasmus Liland, Daniel Nordlund +1 more

Jomy Jose
# 
Hi
I was able to run R code via PROC IML in SAS,so is there any way to export
the generated outputs to SAS datasets since the R outputs don't follow data
frame structure.

Thanks in advance
Jose
Rasmus Liland
#
On 2020-08-22 08:17 +0530, Jomy Jose wrote:
| Hi
| I was able to run R code via PROC IML 
| in SAS,so is there any way to export 
| the generated outputs to SAS datasets 
| since the R outputs don't follow data 
| frame structure.

Dear Jomy,

But perhaps you can take the outputs in 
SAS and work on them inside from there?

To export a data frame from R to SAS via 
a file[1], you can use 

	foreign::write.foreign(..., package="SAS")

But I have not tried it.

I have used foreign::read.spss before, 
hehe :-)  

I know R is also possible to call from 
Julia, and the df appearing in Julia, 
this sounds like it should be possible 
SAS too[2], yes?

Best,
Rasmus

[1] https://www.statmethods.net/input/exportingdata.html
[2] https://documentation.sas.com/?docsetId=imlug&docsetTarget=imlug_r_sect012.htm&docsetVersion=15.1&locale=en

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Daniel Nordlund
#
On 8/22/2020 9:05 AM, Rasmus Liland wrote:
Can you give a reproducible example of the R-code you are running and 
the R "output" you want to get back in SAS?? It is difficult from way 
over here to know if you are wanting numerical results like means or 
regression coefficients ... or if you just want printed output in your 
SAS log or listing.

Dan
Jomy Jose
# 
Hi Daniel

Thanks,please find the code and output

 #--------R libraries---------
      library(tidyverse)
      library(MF)


MFSubj(lesion ~ group, calflung)
HLBoot(lesion ~ group,  calflung, compare = c("con", "vac"), b = 100,
          B = 100, alpha = 0.05, hpd = TRUE, bca = FALSE,
          return.boot = FALSE, trace.it = FALSE, seed = NULL)


10000 bootstrap samples
95% confidence intervals
Comparing vac to con


Mitigated Fraction

                observed median  lower  upper
Equal Tailed        0.44 0.4464 0.1360 0.7024
Highest Density     0.44 0.4464 0.1456 0.7088


Hodges-Lehmann

                observed   median     lower    upper
Equal Tailed    -0.07335 -0.07125 -0.170425 -0.01480
Highest Density -0.07335 -0.07125 -0.156350 -0.00975


Quartile Differences (quartiles of vac - quartiles of con)

     observed    median    lower     upper
Q25 -0.041500 -0.041300 -0.10340 -0.000905
Q50 -0.112525 -0.111175 -0.28115  0.019350
Q75 -0.168000 -0.170425 -0.38650  0.030000


Quartiles of con
    observed   median   lower   upper
Q25 0.054000 0.054000 0.01525 0.11275
Q50 0.139275 0.139275 0.06140 0.31000
Q75 0.315000 0.315000 0.17300 0.45250


Quartiles of vac
    observed  median   lower    upper
Q25  0.01250 0.01250 0.00125 0.026000
Q50  0.02675 0.02675 0.01665 0.144575
Q75  0.14700 0.14700 0.02810 0.292000


Best
Jose

On Sun, Aug 23, 2020 at 2:44 AM Daniel Nordlund <djnordlund at gmail.com>
wrote:

  
  


  


      

    

    
1 day later

    

      
      

        


  

        

      Daniel Nordlund
    

    

      
      #
    

  


  

      
It is still not clear to me (1) if you just want the printed output in 
your SAS list file, or (2) if you want the actual numerical results 
returned to SAS so that you can do more manipulation with the numbers.

If (1) you can precede your R code with sink() to output to your SAS 
list file

 ?#--------R libraries---------
sink('path/to/your/listfile.lst', append=TRUE)
 ?library(tidyverse)
 ?library(MF)

MFSubj(lesion ~ group, calflung)
HLBoot(lesion ~ group, ?calflung, compare = c("con", "vac"), b = 100,
 ? ? ? ? ? B = 100, alpha = 0.05, hpd = TRUE, bca = FALSE,
 ? ? ? ? ? return.boot = FALSE, trace.it <http://trace.it> = FALSE, seed 
= NULL)

You will probably need to redirect your SAS list file to the same location
 ?? PROC PRINTTO file='path/to/your/listfile.lst' new;


If (2), then you need to store the output from you function into 
variables that you can examine to see what you may want to import into 
SAS.? So, something like this in R

mfsubj <- MFSubj(lesion ~ group, calflung)
hlboot <- HLBoot(lesion ~ group,? calflung, compare = c("con", "vac"), b 
= 100,
 ????????? B = 100, alpha = 0.05, hpd = TRUE, bca = FALSE,
 ????????? return.boot = FALSE, trace.it = FALSE, seed = NULL)
str(mfsubj)
str(hlboot)

After examining the output, you will know what variables/dataframes you 
want to import and you can use the functions provided by PROC IML for 
that purpose.? You will need to read the SAS documentation to understand 
how to do that.

This is becoming off topic for R-Help, so let me end with suggesting you 
pursue this question either on SAScommunity or the SAS-L listserve.? You 
might also want to look into SAS Viya for running your R code. If you 
want to continue this off-list, I can try to help you more, but I will 
need to better understand what it is that you want to get back into SAS.

Dan

        
On 8/23/2020 6:46 AM, Jomy Jose wrote:

            

      
      
      
    

        

      
      
    

        

  
    

      
      
    

  


  


      

    

    
1 day later

    

      
      

        


  

        

      d@vidwk@tz m@iii@g oii gm@ii@com
    

    

      
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Daniel,

I have used this package with success:

https://cran.r-project.org/web/packages/SASxport/SASxport.pdf

On Mon, Aug 24, 2020 at 3:57 PM Daniel Nordlund <djnordlund at gmail.com>
wrote: