Convert a dta file to csv without Stata software

Is there a way to convert a dta file to a csv?

I do not have a version of Stata installed on my comp开发者_运维百科uter, so I cannot do something like:

File --> "Save as csv"

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The frankly-incredible data-analysis library for Python called Pandas has a function to read Stata files.

After installing Pandas you can just do:

>>> import pandas as pd
>>> data = pd.io.stata.read_stata('my_stata_file.dta')
>>> data.to_csv('my_stata_file.csv')

Amazing!

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You could try doing it through R:

For Stata <= 15 you can use the haven package to read the dataset and then you simply write it to external CSV file:

library(haven)
yourData = read_dta("path/to/file")
write.csv(yourData, file = "yourStataFile.csv")

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Alternatively, visit the link pointed by huntaub in a comment below.

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For Stata <= 12 datasets foreign package can also be used

library(foreign)
yourData <- read.dta("yourStataFile.dta")

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You can do it in StatTransfer, R or perl (as mentioned by others), but StatTransfer costs $$$ and R/Perl have a learning curve.
There is a free, menu-driven stats program from AM Statistical Software that can open and convert Stata .dta from all versions of Stata, see:

http://am.air.org/

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I have not tried, but if you know Perl you can use the Parse-Stata-DtaReader module to convert the file for you.

The module has a command-line tool dta2csv, which can "convert Stata 8 and Stata 10 .dta files to csv"

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Another way of converting between pretty much any data format using R is with the rio package.

• Install R from CRAN and open R
• Install the rio package using install.packages("rio")

• Load the rio library, then use the convert() function:

  library("rio")
  convert("my_file.dta", "my_file.csv")
  

This method allows you to convert between many formats (e.g., Stata, SPSS, SAS, CSV, etc.). It uses the file extension to infer format and load using the appropriate importing package. More info can be found on the R-project rio page.

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The R method will work reliably, and it requires little knowledge of R. Note that the conversion using the foreign package will preserve data, but may introduce differences. For example, when converting a table without a primary key, the primary key and associated columns will be inserted during the conversion.

From http://www.r-bloggers.com/using-r-for-stata-to-csv-conversion/ I recommend:

library(foreign)
write.table(read.dta(file.choose()), file=file.choose(), quote = FALSE, sep = ",")

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In Python, one can use statsmodels.iolib.foreign.genfromdta to read Stata datasets. In addition, there is also a wrapper of the aforementioned function which can be used to read a Stata file directly from the web: statsmodels.datasets.webuse.

Nevertheless, both of the above rely on the use of the pandas.io.stata.StataReader.data, which is now a legacy function and has been deprecated. As such, the new pandas.read_stata function should now always be used instead.

According to the source file of stata.py, as of version 0.23.0, the following are supported:

Stata data file versions:

• 104
• 105
• 108
• 111
• 113
• 114
• 115
• 117
• 118

Valid encodings:

• ascii
• us-ascii
• latin-1
• latin_1
• iso-8859-1
• iso8859-1
• 8859
• cp819
• latin
• latin1
• L1

As others have noted, the pandas.to_csv function can then be used to save the file into disk. A related function numpy.savetxt can also save the data as a text file.

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EDIT:

The following details come from help dtaversion in Stata 15.1:

        Stata version     .dta file format
        ----------------------------------------
               1               102
            2, 3               103
               4               104
               5               105
               6               108
               7            110 and 111
            8, 9            112 and 113
          10, 11               114
              12               115
              13               117
              14 and 15        118 (# of variables <= 32,767)
              15               119 (# of variables > 32,767, Stata/MP only)
        ----------------------------------------
        file formats 103, 106, 107, 109, and 116
        were never used in any official release.

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StatTransfer is a program that moves data easily between Stata, Excel (or csv), SAS, etc. It is very user friendly (requires no programming skills). See www.stattransfer.com

If you use the program just note that you will have to choose "ASCII/Text - Delimited" to work with .csv files rather than .xls

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Some mentioned SPSS, StatTransfer, they are not free. R and Python (also mentioned above) may be your choice. But personally, I would like to recommend Python, the syntax is much more intuitive than R. You can just use several command lines with Pandas in Python to read and export most of the commonly used data formats:

import pandas as pd

df = pd.read_stata('YourDataName.dta')

df.to_csv('YourDataName.csv')

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SPSS can also read .dta files and export them to .csv, but that costs money. PSPP, an open source version of SPSS, which is rough, might also be able to read/export .dta files.

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PYTHON - CONVERT STATA FILES IN DIRECTORY TO CSV

import glob
import pandas

path=r"{Path to Folder}"

for my_dir in glob.glob("*.dta")[0:1]:
    file = path+my_dir  # collects all the stata files
    # get the file path/name without the ".dta" extension
    file_name, file_extension = os.path.splitext(file)

    # read your data
    df = pandas.read_stata(file, convert_categoricals=False, convert_missing=True)

    # save the data and never think about stata again :)
    df.to_csv(file_name + '.csv')

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For those who have Stata (even though the asker does not) you can use this:

outsheet produces a tab-delimited file so you need to specify the comma option like below

outsheet [varlist] using file.csv , comma

also, if you want to remove labels (which are included by default

outsheet [varlist] using file.csv, comma nolabel

hat tip to:

http://www.ats.ucla.edu/stat/stata/faq/outsheet.htm