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I have an R dataset (an .Rdata file) that I need to convert to either SAS (.sas7bdat or .xpt) or SPSS (.sav or .por). How can I import this dataset into SAS or SPSS?
If you want to use this in SPSS, consider using the STATS_GETR extension command. It can read R workspace or data files and map appropriate elements directly to an SPSS dataset. This extension command is available from the SPSS Community (www.ibm.com/developerworks/spssdevcentral) website or, for Statistics 22, it can be installed via the Utilities menu.
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This is a broader/more-general question, but I find reading .sav files in R to be a nightmare. I use the haven package, but often run into errors due to the format in which .sav files are read (as an ex., of many, it refuses to let me coerce the dbl+lbl format into a numeric data frame).
What I typically do to get around this annoying process is to just save the .sav file as a .csv, then re-read it into R, but I'm sure there's got to be a better way, right?!
n/a this is a more-general question
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How to work with excel file larger than 100 MB, I already imported but it doesn't running the shiny app?
if the excel is format ,you can save as csv .then you can usedata.table::fread() to read. it's more effective and easily , csv is ligthter than xlsx
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Unfortunately I have to deal with a data file with .int file format. This has the effect of littering any search results with unrelated information about integers.
I can't figure out how to open this file in R. I have an example with the Julia language, shown below:
filename = "mnist_train.int"
open(filename) do f
...
end
But when I try to search for a similar function in R, I either find results about opening excel files, results for other languages, or results that deal with integers. Could someone please point me to some resources for dealing with this filetype?
Because I am not sure about what the content type, guess you trying to open a binary file format.
You can have a look at ?readBin
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I know that there is a Python package that imports RData file.
But I was wondering if that is the best option for me.
I have Dataframes in R that I want to use in Python.
I was wondering if I should save this as json or csv and then read with pandas in Python, or I should just save it as RData and use the rpy2 package.
All I need is just turn these R dataframes into Python data frame, so I can manipulate and combine with other results I calculated in Python...
You can use feather.
It's a data format for data frames (created by #Wes McKinney and #hadley) to make data sharing between R and python easy (and some other languages too).
In R:
library(feather)
file_path <- "foo.feather"
data_frame <- read_feather(file_path)
write_feather(data_frame, file_path)
In python:
import feather
file_path = 'foo.feather'
data_frame = feather.read_dataframe(file_path)
feather.write_dataframe(data_frame, file_path)
PS.: Podcast on feather where authors discuss it's application, pros/cons and future.
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I have the next link
[1] https://drive.google.com/open?id=0ByCmoyvCype7ODBMQjFTSlNtTzQ
This is a pdf file. The author of a paper gave the list of mutation in this format.
I need to annotate the mutation of this file.
I need a txt or TVS or VCF file to be reading by annovar.
Can you help me to convert this using R or other software in ubuntu?
In principle this is a job for tabulizer but I couldn't get it to work in this instance; I suspect the single table over so many pages confused it.
You can read it in to R as text with the pdftools package easily enough
library(pdftools)
txt <- pdf_text("selection.pdf")
Now txt is an R list, with each element of the list a character string for a single page in the original document. You might be able to do something fancy with regular expressions to convert this to more meaningful data.
However, it makes more sense to ask the original author for their data in an appropriate format. Publishing a 561 page PDF of tabular data is just nuts.