I was wondering if there is a way of using the save() option to save multiple files of data. I wanted to save all these files in the form of .Rdata, but wasn't sure how to approach this without using save() multiple times. I am new to R.
Checking directory
As someone already mentioned above, you can save something as multiple objects and run it that way. Here I used the datasets included in the 'datasets' package within R. First check your directory to see where its getting saved:
getwd()
Then see where that is from the output:
[1] "C:/Users/DELL/Dropbox/My PC (DESKTOP-SUOCLVS)/Desktop/Research Tools/R Directory"
Creating basic rdata file
Then go ahead and run the code:
df1 <- iris
df2 <- mtcars
save(df1, df2,
file = "mydata.rdata")
You'll see now its saved in the directory:
Other file types
If you mean saving multiple objects of different types, that is a bit more of an issue, as something like a csv or spss file isn't easy to coerce. One option is to include the mapply function. I've also used R datasets here as an example:
library(tidyverse)
myList <- list(diamonds = diamonds,
cars = cars)
mapply(write.csv, myList, file=paste0(names(myList), '.csv'))
Which you can now see in the directory:
I am given very big (around 10 Gb each) datasets in both SAS and Stata format. I am going to read them into R for analysis.
Is there a way to show what variables (columns) they contain inside without reading the whole data file? I often only need some of the variables. I can view them of course from File Explorer, but it's not reproducible and takes a lot of time.
Both SAS and Stata are available on the system, but just opening a file might take a minute or so.
If you have SAS run a proc contents or proc datasets to see the details of the dataset without opening it. You may want to do that anyways, so that you can verify variable types, lengths and formats.
libname myFiles 'path to your sas7bdatfiles';
proc contents data=myfiles.datasetName;
run;
See below for the dta solution, which you can update to SAS using read_sas.
library(haven)
# read in first row of dta
dta_head <- read_dta("my_data.dta",
n_max = 1)
# get variable names of dta
dta_names <- names(dta_head)
After examining the names and labels of your dta file, you can then remove the n_max = 1 option and read in full while possibly adding the col_select option specifying the subset of variables you wish to read in.
I am running the abc(Approximate Bayesian Computataion) library in R. I am using the human dataset from abc.data. I run below line of code for model selection example which is working fine.
modsel.it <- postpr(stat.voight["italian",], models, stat.3pops.sim, tol=.05, method="mnlogistic")
summary(modsel.it)
I save the above mentioned human dataset data frames(stat.voight, models, stat.3pops.sim) as .csv(st,mod,stat3) respectively and run the same line of code for .csv files. It works fine but I get an error when I run the postpr functions as mentioned below
t <- postpr(st["italian",], mod, stat3, tol=.05, method="mnlogistic")
It gives me an error of Error: 'Number of summary statistics in 'target' has to be the same as 'sumstat'.
Then I checked the str (structure) of the actual dataframe and then open I saved as .csv. The one I saved as .csv is changed from the actual dataframe. Below shown are the images of the actual(stat.voight) and .csv(st) dataframes. I want to change my dataframe st dataframe be same as dataframestat.voight. Thanks
The write.csv() function has a default argument of row.names = TRUE, which writes the row names as the first column in the CSV. If you set row.names = FALSE, the row names will not be written to the file.
That said, the objects have a number of attributes that aren't written to the output files with write.csv(). As such, you're better off using saveRDS() and readRDS() to serialize these objects and reload them into R.
Simple question, I have stored PMML code of an R object using pmmlcode <- pmml(my.object), and I would like some way to save it directly to a text file. The usual write.table method isn't working because the data is not a table.
You can simply use SaveXML as in the example below:
library(randomForest)
library(pmml)
data(airquality)
ozone.out <- randomForest(Ozone ~ Wind+Temp+Month, data=na.omit(airquality), ntree=200)
saveXML(pmml(ozone.out, data=airquality), "airquality_rf.pmml")
Try toString.XMLNode from XML package and then write to file with writeLines. You'll need to provide example data for a more complete answer.
I am using the iris data just to generate a dummy pmml file and sink command to put your pmml output into a .pmml file,
R > library(pmml)
R > lml <- lm(iris$Sepal.Length~iris$Sepal.Width)
R > sink("myPmml.pmml")
R > cat("<?xml version=\"1.0\"?>\n")
R > pmml(lml)
R > sink()
The output myPmml.pmml should be saved wherever your setwd is set on your .Rprofile , the default is "Mydocuments" in windows. Offcourse this will work even if you put .txt instead of .pmml in the sink() command , something like:
sink("mypmml.txt")
Edit: Added cat command to put xml tags on top, Thanks to J.Dimeo
In the absence of test code to create this but after solving my earlier problem with the availability of the pmml package on the UCLA CRAN mirror. This produces acceptable output for human readability although not in a format that will be interpretable my a PMML-aware application:
cat(paste(unlist(pmmlcode),"\n"), file="yourfile.txt")
Neither of these worked:
If it's just a character vector:
cat(pmmlcode, file="yourfile.txt")
Or if it's a list:
lapply(pmmlcode, cat, file="yourfile.txt", append=TRUE)
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.