I am trying to learn R and want to bring in an SPSS file, which I can open in SPSS.
I have tried using read.spss from foreign and spss.get from Hmisc. Both error messages are the same.
Here is my code:
## install.packages("Hmisc")
library(foreign)
## change the working directory
getwd()
setwd('C:/Documents and Settings/BTIBERT/Desktop/')
## load in the file
## ?read.spss
asq <- read.spss('ASQ2010.sav', to.data.frame=T)
And the resulting error:
Error in read.spss("ASQ2010.sav", to.data.frame = T) : error
reading system-file header In addition: Warning message: In
read.spss("ASQ2010.sav", to.data.frame = T) : ASQ2010.sav: position
0: character `\000' (
Also, I tried saving out the SPSS file as a SPSS 7 .sav file (was previously using SPSS 18).
Warning messages: 1: In read.spss("ASQ2010_test.sav", to.data.frame =
T) : ASQ2010_test.sav: Unrecognized record type 7, subtype 14
encountered in system file 2: In read.spss("ASQ2010_test.sav",
to.data.frame = T) : ASQ2010_test.sav: Unrecognized record type 7,
subtype 18 encountered in system file
I had a similar issue and solved it following a hint in read.spss help.
Using package memisc instead, you can import a portable SPSS file like this:
data <- as.data.set(spss.portable.file("filename.por"))
Similarly, for .sav files:
data <- as.data.set(spss.system.file('filename.sav'))
although in this case I seem to miss some string values, while the portable import works seamlessly. The help page for spss.portable.file claims:
The importer mechanism is more flexible and extensible than read.spss and read.dta of package "foreign", as most of the parsing of the file headers is done in R. They are also adapted to load efficiently large data sets. Most importantly, importer objects support the labels, missing.values, and descriptions, provided by this package.
The read.spss seems to be outdated a little bit, so I used package called memisc.
To get this to work do this:
install.packages("memisc")
data <- as.data.set(spss.system.file('yourfile.sav'))
You may also try this:
setwd("C:/Users/rest of your path")
library(haven)
data <- read_sav("data.sav")
and if you want to read all files from one folder:
temp <- list.files(pattern = "*.sav")
read.all <- sapply(temp, read_sav)
I know this post is old, but I also had problems loading a Qualtrics SPSS file into R. R's read.spss code came from PSPP a long time ago, and hasn't been updated in a while. (And Hmisc's code uses read.spss(), too, so no luck there.)
The good news is that PSPP 0.6.1 should read the files fine, as long as you specify a "String Width" of "Short - 255 (SPSS 12.0 and earlier)" on the "Download Data" page in Qualtrics. Read it into PSPP, save a new copy, and you should be in business. Awkward, but free.
,
You can read SPSS file from R using above solutions or the one you are currently using. Just make sure that the command is fed with the file, that it can read properly. I had same error and the problem was, SPSS could not access that file. You should make sure the file path is correct, file is accessible and it is in correct format.
library(foreign)
asq <- read.spss('ASQ2010.sav', to.data.frame=TRUE)
As far as warning message is concerned, It does not affect the data. The record type 7 is used to store features in newer SPSS software to make older SPSS software able to read new data. But does not affect data. I have used this numerous times and data is not lost.
You can also read about this at http://r.789695.n4.nabble.com/read-spss-warning-message-Unrecognized-record-type-7-subtype-18-encountered-in-system-file-td3000775.html#a3007945
It looks like the R read.spss implementation is incomplete or broken. R2.10.1 does better than R2.8.1, however. It appears that R gets upset about custom attributes in a sav file even with 2.10.1 (The latest I have). R also may not understand the character encoding field in the file, and in particular it probably does not work with SPSS Unicode files.
You might try opening the file in SPSS, deleting any custom attributes, and resaving the file.
You can see whether there are custom attributes with the SPSS command
display attributes.
If so, delete them (see VARIABLE ATTRIBUTE and DATAFILE ATTRIBUTE commands), and try again.
HTH,
Jon Peck
If you have access to SPSS, save file as .csv, hence import it with read.csv or read.table. I can't recall any problem with .sav file importing. So far it was working like a charm both with read.spss and spss.get. I reckon that spss.get will not give different results, since it depends on foreign::read.spss
Can you provide some info on SPSS/R/Hmisc/foreign version?
Another solution not mentioned here is to read SPSS data in R via ODBC. You need:
IBM SPSS Statistics Data File Driver. Standalone driver is enough.
Import SPSS data using RODBC package in R.
See the example here. However I have to admit that, there could be problems with very big data files.
For me it works well using memisc!
install.packages("memisc")
load('memisc')
Daten.Februar <-as.data.set(spss.system.file("NPS_Februar_15_Daten.sav"))
names(Daten.Februar)
I agree with #SDahm that the haven package would be the way to go. I myself have struggled a bit with string values when starting to use it, so I thought I'd share my approach on that here, too.
The "semantics" vignette has some useful information on this topic.
library(tidyverse)
library(haven)
# Some interesting information in here
vignette('semantics')
# Get data from spss file
df <- read_sav(path_to_file)
# get value labels
df <- map_df(.x = df, .f = function(x) {
if (class(x) == 'labelled') as_factor(x)
else x})
# get column names
colnames(df) <- map(.x = spss_file, .f = function(x) {attr(x, 'label')})
There is no such problem with packages you are using. The only requirement for read a spss file is to put the file into a PORTABLE format file. I mean, spss file have *.sav extension. You need to transform your spss file in a portable document that uses *.por extension.
There is more info in http://www.statmethods.net/input/importingdata.html
In my case this warning was combined with a appearance of a new variable before first column of my data with values -100, 2, 2, 2, ..., a shift in the correspondence between labels and values and the deletion of the last variable. A solution that worked was (using SPSS) to create a new dump variable in the last column of the file, fill it with random values and execute the following code:
(filename is the path to the sav file and in my case the original SPSS file had 62 columns, thus 63 with the additional dumb variable)
library(memisc)
data <- as.data.set(spss.system.file(filename))
copyofdata = data
for(i in 2:63){
names(data)[i] <- names(copyofdata)[i-1]
}
data[[1]] <- NULL
newcopyofdata = data
for(i in 2:62){
labels(data[[i]]) <- labels(newcopyofdata[[i-1]])
}
labels(data[[1]]) <- NULL
Hope the above code will help someone else.
Turn your UNICODE in SPSS off
Open SPSS without any data open and run the code below in your syntax editor
SET UNICODE OFF.
Open the data set and resave it to remove the Unicode
read.spss('yourdata.sav', to.data.frame=T) works correctly then
I just came came across an SPSS file that I couldn't get open using haven, foreign, or memisc, but readspss::read.por did the trick for me:
download.file("http://www.tcd.ie/Political_Science/elections/IMSgeneral92.zip",
"IMSgeneral92.zip")
unzip("IMSgeneral92.zip", exdir = "IMSgeneral92")
# rio, haven, foreign, memisc pkgs don't work on this file! But readspss does:
if(!require(readspss)) remotes::install_git("https://github.com/JanMarvin/readspss.git")
ims92 <- readspss::read.por("IMSgeneral92/IMS_Nov7 92.por", convert.factors = FALSE)
Nice! Thanks, #JanMarvin!
1)
I've found the program, stat-transfer, useful for importing spss and stata files into R.
It resolves the issue you mention by converting spss to R dataset. Also very useful for subsetting super large datasets into smaller portions consumable by R. Not free, but a very useful tool for working with datasets from different programs -- especially if you don't have access to them.
2)
Memisc package also has an spss function worth trying.
Related
I have multiple .xls (~100MB) files from which I would like to load multiple sheets (from each) into R as a dataframe. I have tried various functions, such as xlsx::xlsx2 and XLConnect::readWorksheetFromFile, both of which always run for a very long time (>15 mins) and never finish and I have to force-quit RStudio to keep working.
I also tried gdata::read.xls, which does finish, but it takes more than 3 minutes per one sheet and it cannot extract multiple sheets at once (which would be very helpful to speed up my pipeline) like XLConnect::loadWorkbook can.
The time it takes these functions to execute (and I am not even sure the first two would ever finish if I let them go longer) is way too long for my pipeline, where I need to work with many files at once. Is there a way to get these to go/finish faster?
In several places, I have seen a recommendation to use the function readxl::read_xls, which seems to be widely recommended for this task and should be faster per sheet. This one, however, gives me an error:
> # Minimal reproducible example:
> setwd("/Users/USER/Desktop")
> library(readxl)
> data <- read_xls(path="test_file.xls")
Error:
filepath: /Users/USER/Desktop/test_file.xls
libxls error: Unable to open file
I also did some elementary testing to make sure the file exists and is in the correct format:
> # Testing existence & format of the file
> file.exists("test_file.xls")
[1] TRUE
> format_from_ext("test_file.xls")
[1] "xls"
> format_from_signature("test_file.xls")
[1] "xls"
The test_file.xls used above is available here.
Any advice would be appreciated in terms of making the first functions run faster or the read_xls run at all - thank you!
UPDATE:
It seems that some users are able to open the file above using the readxl::read_xls function, while others are not, both on Mac and Windows, using the most up to date versions of R, Rstudio, and readxl. The issue has been posted on the readxl GitHub and has not been resolved yet.
I downloaded your dataset and read each excel sheet in this way (for example, for sheets "Overall" and "Area"):
install.packages("readxl")
library(readxl)
library(data.table)
dt_overall <- as.data.table(read_excel("test_file.xls", sheet = "Overall"))
area_sheet <- as.data.table(read_excel("test_file.xls", sheet = "Area"))
Finally, I get dt like this (for example, only part of the dataset for the "Area" sheet):
Just as well, you can use the read_xls function instead read_excel.
I checked, it also works correctly and even a little faster, since read_excel is a wrapper over read_xls and read_xlsx functions from readxl package.
Also, you can use excel_sheets function from readxl package to read all sheets of your Excel file.
UPDATE
Benchmarking is done with microbenchmark package for the following packages/functions: gdata::read.xls, XLConnect::readWorksheetFromFile and readxl::read_excel.
But XLConnect it's a Java-based solution, so it requires a lot of RAM.
I found that I was unable to open the file with read_xl immediately after downloading it, but if I opened the file in Excel, saved it, and closed it again, then read_xl was able to open it without issue.
My suggested workaround for handling hundreds of files is to build a little C# command line utility that opens, saves, and closes an Excel file. Source code is below, the utility can be compiled with visual studio community edition.
using System.IO;
using Excel = Microsoft.Office.Interop.Excel;
namespace resaver
{
class Program
{
static void Main(string[] args)
{
string srcFile = Path.GetFullPath(args[0]);
Excel.Application excelApplication = new Excel.Application();
excelApplication.Application.DisplayAlerts = false;
Excel.Workbook srcworkBook = excelApplication.Workbooks.Open(srcFile);
srcworkBook.Save();
srcworkBook.Close();
excelApplication.Quit();
}
}
}
Once compiled, the utility can be called from R using e.g. system2().
I will propose a different workflow. If you happen to have LibreOffice installed, then you can convert your excel files to csv programatically. I have Linux, so I do it in bash, but I'm sure it can be possible in macOS.
So open a terminal and navigate to the folder with your excel files and run in terminal:
for i in *.xls
do soffice --headless --convert-to csv "$i"
done
Now in R you can use data.table::fread to read your files with a loop:
Scenario 1: the structure of files is different
If the structure of files is different, then you wouldn't want to rbind them together. You could run in R:
files <- dir("path/to/files", pattern = ".csv")
all_files <- list()
for (i in 1:length(files)){
fileName <- gsub("(^.*/)(.*)(.csv$)", "\\2", files[i])
all_files[[fileName]] <- fread(files[i])
}
If you want to extract your named elements within the list into the global environment, so that they can be converted into objects, you can use list2env:
list2env(all_files, envir = .GlobalEnv)
Please be aware of two things: First, in the gsub call, the direction of the slash. And second, list2env may overwrite objects in your Global Environment if they have the same name as the named elements within the list.
Scenario 2: the structure of files is the same
In that case it's likely you want to rbind them all together. You could run in R:
files <- dir("path/to/files", pattern = ".csv")
joined <- list()
for (i in 1:length(files)){
joined <- rbindlist(joined, fread(files[i]), fill = TRUE)
}
On my system, i had to use path.expand.
R> file = "~/blah.xls"
R> read_xls(file)
Error:
filepath: ~/Dropbox/signal/aud/rba/balsheet/data/a03.xls
libxls error: Unable to open file
R> read_xls(path.expand(file)) # fixed
Resaving your file and you can solve your problem easily.
I also find this problem before but I get the answer from your discussion.
I used the read_excel() to open those files.
I was seeing a similar error and wanted to share a short-term solution.
library(readxl)
download.file("https://mjwebster.github.io/DataJ/spreadsheets/MLBpayrolls.xls", "MLBPayrolls.xls")
MLBpayrolls <- read_excel("MLBpayrolls.xls", sheet = "MLB Payrolls", na = "n/a")
Yields (on some systems in my classroom but not others):
Error: filepath: MLBPayrolls.xls libxls error: Unable to open file
The temporary solution was to paste the URL of the xls file into Firefox and download it via the browser. Once this was done we could run the read_excel line without error.
This was happening today on Windows 10, with R 3.6.2 and R Studio 1.2.5033.
If you have downloaded the .xls data from the internet, even if you are opening it in Ms.Excel, it will open a prompt first asking to confirm if you trust the source, see below screenshot, I am guessing this is the reason R (read_xls) also can't open it, as it's considered unsafe. Save it as .xlsx file and then use read_xlsx() or read_excel().
Even thought this is not a code-based solution, I just changed the type file. For instance, instead of xls I saved as csv or xlsx. Then I opened it as regular one.
I worked it for me, because when I opened my xlsfile, I popped up the message: "The file format and extension of 'file.xls'' don't match. The file could be corrupted or unsafe..."
Using expss package I am creating cross tabs by reading SPSS files in R. This actually works perfectly but the process takes lots of time to load. I have a folder which contains various SPSS files(usually 3 files only) and through R script I am fetching the last modified file among the three.
setwd('/file/path/for/this/file/SPSS')
library(expss)
expss_output_viewer()
#get all .sav files
all_sav <- list.files(pattern ='\\.sav$')
#use file.info to get the index of the file most recently modified
pass<-all_sav[with(file.info(all_sav), which.max(mtime))]
mydata = read_spss(pass,reencode = TRUE) # read SPSS file mydata
w <- data.frame(mydata)
args <- commandArgs(TRUE)
Everything is perfect and works absolutely fine but it generally takes too much time to load large files(112MB,48MB for e.g) which isn't good.
Is there a way I can make it more time-efficient and takes less time to create the table. The dropdowns are created using PHP.
I have searched for this and found another library called 'haven' but I am not sure whether that can give me significance as well. Can anyone help me with this? I would really appreciate that. Thanks in advance.
As written in the expss vignette (https://cran.r-project.org/web/packages/expss/vignettes/labels-support.html) you can use in the following way:
# we need to load packages strictly in this order to avoid conflicts
library(haven)
library(expss)
spss_data = haven::read_spss("spss_file.sav")
# add missing 'labelled' class
spss_data = add_labelled_class(spss_data)
I am attempting to read data from the National Health Interview Survey in R: http://www.cdc.gov/nchs/nhis/nhis_2011_data_release.htm . The data is Sample Adult. The SAScii library actually has a function read.SAScii whose documentation has an example for the same data set I would like to use. The issue is it "doesn't work":
NHIS.11.samadult.SAS.read.in.instructions <-
"ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Program_Code/NHIS/2011/SAMADULT.sas"
NHIS.11.samadult.file.location <-
"ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2011/samadult.zip"
#store the NHIS file as an R data frame!
NHIS.11.samadult.df <-
read.SAScii (
NHIS.11.samadult.file.location ,
NHIS.11.samadult.SAS.read.in.instructions ,
zipped = T, )
#or store the NHIS SAS import instructions for use in a
#read.fwf function call outside of the read.SAScii function
NHIS.11.samadult.sas <- parse.SAScii( NHIS.11.samadult.SAS.read.in.instructions )
#save the data frame now for instantaneous loading later
save( NHIS.11.samadult.df , file = "NHIS.11.samadult.data.rda" )
However, when running it I get the error Error in toupper(SASinput) : invalid multibyte string 533.
Others on Stack Overflow with a similar error, but for functions such as read.delim and read.csv, have recommended to try changing the argument to fileEncoding="latin1" for example. The problem with read.SAScii is it has no such parameter fileEncoding.
See:
R: invalid multibyte string and Invalid multibyte string in read.csv
Just in case anyone has a similar problem, the issue and solution for me was to run options( encoding = "windows-1252" ) right before running the above code for read.SAScii since the ASCII file is meant for use in SAS and therefore on Windows. And I am using Linux.
The author of the SAScii library actually has another Github repository asdfree where he has working code for downloading CDC-NHIS datasets for all available years as well as as many other datasets from various surveys such as the American Housing Survey, FDA Drug Surveys, and many more.
The following links to the author's solution to the issue in this question. From there, you can easily find a link to the asdfree repository: https://github.com/ajdamico/SAScii/issues/3 .
As far as this dataset goes, the code in https://github.com/ajdamico/asdfree/blob/master/National%20Health%20Interview%20Survey/download%20all%20microdata.R#L8-L13 does the trick, however it doesn't encode the columns as factors or numeric properly. The good thing is that for any given dataset in an NHIS year, there are only about less than ten to twenty numeric columns where encoding these as numeric one by one is not so painful, and encoding the rest of the columns as numeric requires only a loop through the non-numeric columns.
The easiest solution for me, since I only require the Sample Adult dataset for 2011, and I was able to get my hands on a machine with SAS installed, was to run the SAS program included at http://www.cdc.gov/nchs/nhis/nhis_2011_data_release.htm to encode the columns as necessary. Finally, I used proc export to export the sas dataset onto a CSV file which I then opened in R easily with no necessary edits to the data except in dealing with missing values.
In case you want to work with NHIS datasets besides Sample Adult, it is worth noting that when I ran the available SAS program for 2010 "Sample Adult Cancer" (http://www.cdc.gov/nchs/nhis/nhis_2010_data_release.htm) and exported the data to a CSV, there was an issue with having less column names than actual columns when I attempted to read in the CSV file in R. Skipping the first line resolves this issue but you lose the descriptive column names. You can however import this same data easily without encoding with the R code in the asdfree repository. Please read the documentation there for more info.
I have a textfile of 4.5 million rows and 90 columns to import into R. Using read.table I get the cannot allocate vector of size... error message so am trying to import using the ff package before subsetting the data to extract the observations which interest me (see my previous question for more details: Add selection crteria to read.table).
So, I use the following code to import:
test<-read.csv2.ffdf("FD_INDCVIZC_2010.txt", header=T)
but this returns the following error message :
Error in read.table.ffdf(FUN = "read.csv2", ...) :
only ffdf objects can be used for appending (and skipping the first.row chunk)
What am I doing wrong?
Here are the first 5 rows of the text file:
CANTVILLE.NUMMI.AEMMR.AGED.AGER20.AGEREV.AGEREVQ.ANAI.ANEMR.APAF.ARM.ASCEN.BAIN.BATI.CATIRIS.CATL.CATPC.CHAU.CHFL.CHOS.CLIM.CMBL.COUPLE.CS1.CUIS.DEPT.DEROU.DIPL.DNAI.EAU.EGOUL.ELEC.EMPL.ETUD.GARL.HLML.ILETUD.ILT.IMMI.INAI.INATC.INFAM.INPER.INPERF.IPO ...
1 1601;1;8;052;54;051;050;1956;03;1;ZZZZZ;2;Z;Z;Z;1;0;Z;4;Z;Z;6;1;1;Z;16;Z;03;16;Z;Z;Z;21;2;2;2;Z;1;2;1;1;1;4;4;4,02306147485403;ZZZZZZZZZ;1;1;1;4;M;22;32;AZ;AZ;00;04;2;2;0;1;2;4;1;00;Z;54;2;ZZ;1;32;2;10;2;11;111;11;11;1;2;ZZZZZZ;1;2;1;4;41;2;Z
2 1601;1;8;012;14;011;010;1996;03;3;ZZZZZ;2;Z;Z;Z;1;0;Z;4;Z;Z;6;2;8;Z;16;Z;ZZ;16;Z;Z;Z;ZZ;1;2;2;2;Z;2;1;1;1;4;4;4,02306147485403;ZZZZZZZZZ;3;3;3;1;M;11;11;ZZ;ZZ;00;04;2;2;0;1;2;4;1;14;Z;54;2;ZZ;1;32;Z;10;2;23;230;11;11;Z;Z;ZZZZZZ;1;2;1;4;41;2;Z
3 1601;1;8;006;05;005;005;2002;03;3;ZZZZZ;2;Z;Z;Z;1;0;Z;4;Z;Z;6;2;8;Z;16;Z;ZZ;16;Z;Z;Z;ZZ;1;2;2;2;Z;2;1;1;1;4;4;4,02306147485403;ZZZZZZZZZ;3;3;3;1;M;11;11;ZZ;ZZ;00;04;2;2;0;1;2;4;1;14;Z;54;2;ZZ;1;32;Z;10;2;23;230;11;11;Z;Z;ZZZZZZ;1;2;1;4;41;2;Z
4 1601;1;8;047;54;046;045;1961;03;2;ZZZZZ;2;Z;Z;Z;1;0;Z;4;Z;Z;6;1;6;Z;16;Z;14;974;Z;Z;Z;16;2;2;2;Z;2;2;4;1;1;4;4;4,02306147485403;ZZZZZZZZZ;2;2;2;1;M;22;32;MN;GU;14;04;2;2;0;1;2;4;1;14;Z;54;2;ZZ;2;32;1;10;2;11;111;11;11;1;4;ZZZZZZ;1;2;1;4;41;2;Z
5 1601;2;9;053;54;052;050;1958;02;1;ZZZZZ;2;Z;Z;Z;1;0;Z;2;Z;Z;2;1;2;Z;16;Z;12;87;Z;Z;Z;22;2;1;2;Z;1;2;3;1;1;2;2;4,21707670353782;ZZZZZZZZZ;1;1;1;2;M;21;40;GZ;GU;00;07;0;0;0;0;0;2;1;00;Z;54;2;ZZ;1;30;2;10;3;11;111;ZZ;ZZ;1;1;ZZZZZZ;2;2;1;4;42;1;Z
I encountered a similar problem related to reading csv into ff objects. On using
read.csv2.ffdf(file = "FD_INDCVIZC_2010.txt")
instead of implicit call
read.csv2.ffdf("FD_INDCVIZC_2010.txt")
I got rid of the error. The explicitly passing values to the argument seems specific to ff functions.
You could try the following code:
read.csv2.ffdf("FD_INDCVIZC_2010.txt",
sep = "\t",
VERBOSE = TRUE,
first.rows = 100000,
next.rows = 200000,
header=T)
I am assuming that since its a txt file, its a tab-delimited file.
Sorry I came across the question just now. Using the VERBOSE option, you can actually see how much time your each block of data is taking to be read. Hope this helps.
If possible try to filter the data at the OS level, that is before they are loaded into R. The simplest way to do this in R is to use a combination of pipe and grep command:
textpipe <- pipe('grep XXXX file.name |')
mutable <- read.table(textpipe)
You can use grep, awk, sed and basically all the machinery of unix command tools to add the necessary selection criteria and edit the csv files before they are imported into R. This works very fast and by this procedure you can strip unnecessary data before R begins to read them from pipe.
This works well under Linux and Mac, perhaps you need to install cygwin to make this work under Windows or use some other windows-specific utils.
perhaps you could try the following code:
read.table.ffdf(x = NULL, file = 'your/file/path', seq=';' )
I wish to read data into R from SAS data sets in Windows. The read.ssd function allows me to do so, however, it seems to have an issue when I try to import a SAS data set that has any non-alphabetic symbols in its name. For example, I can import table.sas7bdat using the following:
directory <- "C:/sas data sets"
sashome <- "/Program Files/SAS/SAS 9.1"
table.df <- read.ssd(directory, "table", sascmd = file.path(sashome, "sas.exe"))
but I can't do the same for a table SAS data set named table1.sas7bdat. It returns an error:
Error in file.symlink(oldPath, linkPath) :
symbolic links are not supported on this version of Windows
Given that I do not have the option to rename these data sets, is there a way to read a SAS data set that has non-alphabetic symbols in its name in to R?
Looking about, it looks like others have your problem as well. Perhaps it's just a bug.
Anyway, try the suggestion from this (old) R help post, posted by the venerable Dan Nordlund who's pretty good at this stuff - and also active on SASL (sasl#listserv.uga.edu) if you want to try cross-posting your question there.
https://stat.ethz.ch/pipermail/r-help/2008-December/181616.html
Also, you might consider the transport method if you don't mind 8 character long variable names.
Use:
directory <- "C:/sas data sets"
sashome <- "/Program Files/SAS/SAS 9.1"
table.df <- read.ssd(library=directory, mem="table1", formats=F,
sasprog=file.path(sashome, "sas.exe"))