Is there a way to convert a dta
file to a csv
?
I do not have a version of Stata installed on my computer, so I cannot do something like:
File --> "Save as csv"
Is there a way to convert a dta
file to a csv
?
I do not have a version of Stata installed on my computer, so I cannot do something like:
File --> "Save as csv"
Python data-analysis library pandas
can read Stata files.
>>> import pandas as pd
>>> data = pd.read_stata('my_stata_file.dta')
>>> data.to_csv('my_stata_file.csv')
Amazing!
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")
Alternatively, visit the link pointed by huntaub in a comment below.
For Stata <= 12 datasets foreign package can also be used
library(foreign)
yourData <- read.dta("yourStataFile.dta")
.dta
files. You should utilize the techniques in this question. –
Frau library(haven)
–
Abrahan 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:
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"
Another way of converting between pretty much any data format using R is with the rio package.
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.
install.packages("rio")
and was good to go. –
Niigata 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 = ",")
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:
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.
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.
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
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')
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.
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')
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:
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