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pachadotdev/open-redatam: Recover information of REDATAM databases for statistical analysis using standard tools such as SPSS, STATA, R, etc.

Open Redatam is an open source software for extracting raw information from REDATAM databases. It was created to recover information of REDATAM databases for statistical analysis using standard tools such as SPSS, STATA, R, etc.

Please read our article for the full context of this project (Open Access):

Vargas Sepúlveda, Mauricio and Barkai, Lital. 2025. "The REDATAM format and its challenges for data access and information creation in public policy." Data & Policy 7 (January): e18. https://dx.doi.org/10.1017/dap.2025.4.

This software is a full C++ ground-up rewrite of the original Redatam Converter created by Pablo de Grande and written in C#. Rewriting the original C# code in C++ allows for better portability and the ability to use the program within R, Python, and other languages.

If you use R: We have an R package 📦 that allows to directly read REDATAM databases in R.

If you use Python: We have a Python package 📦 that allows to directly read REDATAM databases in Python.

If you only need the processed data: We provide tidy microdata 📊 in R and CSV format.

For a given census, such as the Chilean Census 2017, the following options are equivalent.

redatam input-dir/dictionary.dicx output-dir

The REDATAM database will be exported to CSV files and an XML summary of the tables and variables.

Download the DEB file and install it. This will install redatam and redatamgui in /usr/local/bin/ with the necessary dependencies and a desktop entry. The installer creates an entry in the Applications folder for Open Redatam GUI and you can also use the command line tool from the Terminal by calling redatam.

Download the DMG file. The image contains redatam and redatamgui, which you can copy to "Applications". The app is not verified because it does not make sense for us to pay 200 USD/year just to sign one image. You can install it anyways this by going to the System Settings in the Apple menu and then:

1. Select Privacy & Security.
2. Scroll down to the Security section.
3. Click "Open Anyway" beneath the message "RedatamGUI was blocked for use because it is not from an identified developer."

Download the EXE file and install it. The installer creates an entry in the Start Menu for Open Redatam GUI and you can also use the command line tool from Power Shell by calling C:\Program Files (x86)\Open Redatam\redatam.exe.

The software requires C++11 or higher to compile.

On Linux, run the following commands:

git clone https://github.com/pachadotdev/open-redatam.git
sudo apt-get update
sudo apt-get install -y qtbase5-dev qtbase5-dev-tools qt5-qmake
make

Then run ./redatam or ./redatamgui.

On Mac, run the following commands:

git clone https://github.com/pachadotdev/open-redatam.git
brew install qt@5
export PATH="/opt/homebrew/opt/qt@5/bin:$PATH"
export LDFLAGS="-L/opt/homebrew/opt/qt@5/lib"
export CPPFLAGS="-I/opt/homebrew/opt/qt@5/include"
export PKG_CONFIG_PATH="/opt/homebrew/opt/qt@5/lib/pkgconfig"
make

Then run ./redatam or ./redatamgui.

On Windows, you need Visual Studio Code 2019 with C++ development tools and Qt 5 for MSVC 2019 64-bit.

Then run the following commands:

git clone https://github.com/pachadotdev/open-redatam.git

cd redatamwindows
cmake -G "Visual Studio 16 2019" .
cmake --build . --config Release
cmake --install . --config Release

cd redatamguiwindows
cmake . -G "Visual Studio 16 2019"
cmake --build . --config Release
"C:\Qt\5.15.2\msvc2019_64\bin\windeployqt.exe" --release .\Release\redatamgui.exe
cd ..

A simple validation exercise is to compare counts and percentages by sex and age groups against the IPUMS data to determine if the data was converted correctly.

Some census files feature a sample. For this exercise, we used the dictionaries for the countries containing the universe in DIC and DICX formats.

DIC and DICX parsing leads to the same results.

  sex         n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
  <int+lbl>     <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
1 1 [Male]   5020766.      49.9 5019447     49.9  1319.  0.00839
2 2 [Female] 5040041.      50.1 5040409     50.1  -368. -0.00839

   age2           n_ipums pct_ipums  n_rdtm pct_rdtm
   <int+lbl>        <dbl>     <dbl>   <int>    <dbl>
 1  1 [0 to 4]   1091540.    10.8   1089948   10.8  
 2  2 [5 to 9]    988984.     9.83   992654    9.87 
 3  3 [10 to 14] 1076853.    10.7   1078164   10.7  
 4  4 [15 to 19] 1107708.    11.0   1106284   11.0  
 5 12 [20 to 24]  981961.     9.76   978606    9.73 
 6 13 [25 to 29]  812924.     8.08   817395    8.13 
 7 14 [30 to 34]  754287.     7.50   753831    7.49 
 8 15 [35 to 39]  631461.     6.28   631032    6.27 
 9 16 [40 to 44]  543112.     5.40   544701    5.41 
10 17 [45 to 49]  461436.     4.59   461984    4.59 
11 18 [50 to 54]  404489.     4.02   403220    4.01 
12 19 [55 to 59]  322182.     3.20   324025    3.22 
13 20 [60 to 64]  280089.     2.78   279867    2.78 
14 21 [65 to 69]  207331.     2.06   204529    2.03 
15 22 [70 to 74]  152851.     1.52   152423    1.52 
16 23 [75 to 79]   99799.     0.992   99276    0.987
17 24 [80 to 84]   81862.     0.814   81095    0.806
18 25 [85+]        61937.     0.616   60822    0.605

DIC and DICX parsing leads to the same results.

  sex        n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
  <int+lbl>    <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
1 1 [Male]   8607570      49.0 8601989     48.9   5581   0.0457
2 2 [Female] 8961420      51.0 8972014     51.1 -10594  -0.0457

   age2          n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
   <int+lbl>       <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
 1  1 [0 to 4]   1157530      6.59 1166146     6.64  -8616 -0.0471 
 2  2 [5 to 9]   1215960      6.92 1210189     6.89   5771  0.0348 
 3  3 [10 to 14] 1147610      6.53 1147415     6.53    195  0.00297
 4  4 [15 to 19] 1236800      7.04 1244697     7.08  -7897 -0.0429 
 5 12 [20 to 24] 1384320      7.88 1387822     7.90  -3502 -0.0177 
 6 13 [25 to 29] 1475500      8.40 1474150     8.39   1350  0.0101 
 7 14 [30 to 34] 1294480      7.37 1293637     7.36    843  0.00690
 8 15 [35 to 39] 1202670      6.85 1207777     6.87  -5107 -0.0271 
 9 16 [40 to 44] 1195460      6.80 1198503     6.82  -3043 -0.0154 
10 17 [45 to 49] 1162380      6.62 1160763     6.61   1617  0.0111 
11 18 [50 to 54] 1186210      6.75 1184954     6.74   1256  0.00907
12 19 [55 to 59] 1047910      5.96 1047779     5.96    131  0.00245
13 20 [60 to 64]  849470      4.84  846915     4.82   2555  0.0159 
14 21 [65 to 69]  656700      3.74  653002     3.72   3698  0.0221 
15 22 [70 to 74]  518120      2.95  515909     2.94   2211  0.0134 
16 23 [75 to 79]  365350      2.08  363589     2.07   1761  0.0106 
17 24 [80 to 84]  240640      1.37  239446     1.36   1194  0.00718
18 25 [85+]       231880      1.32  231310     1.32    570  0.00362

DIC and DICX parsing leads to the same results.

  sex        n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
  <int+lbl>    <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
1 1 [Male]   4270670      49.8 4265215     49.8   5455  -0.0149
2 2 [Female] 4305390      50.2 4297326     50.2   8064   0.0149

   age2          n_ipums pct_ipums n_rdtm pct_rdtm n_diff   pct_diff
   <dbl+lbl>       <dbl>     <dbl>  <int>    <dbl>  <dbl>      <dbl>
 1  1 [0 to 4]    977620  11.4     973644   11.4     3976  0.0284   
 2  2 [5 to 9]    974680  11.4     971881   11.4     2799  0.0147   
 3  3 [10 to 14]  960410  11.2     959338   11.2     1072 -0.00516  
 4  4 [15 to 19]  839980   9.79    838239    9.79    1741  0.00487  
 5 12 [20 to 24]  784940   9.15    785802    9.18    -862 -0.0245   
 6 13 [25 to 29]  687440   8.02    687785    8.03    -345 -0.0167   
 7 14 [30 to 34]  649820   7.58    646112    7.55    3708  0.0313   
 8 15 [35 to 39]  591470   6.90    590750    6.90     720 -0.00248  
 9 16 [40 to 44]  475180   5.54    476647    5.57   -1467 -0.0259   
10 17 [45 to 49]  379460   4.42    380028    4.44    -568 -0.0136   
11 18 [50 to 54]  331210   3.86    330713    3.86     497 -0.000293 
12 19 [55 to 59]  233910   2.73    233976    2.73     -66 -0.00508  
13 20 [60 to 64]  209800   2.45    207933    2.43    1867  0.0179   
14 21 [65 to 69]  157940   1.84    158365    1.85    -425 -0.00787  
15 22 [70 to 74]  135330   1.58    136068    1.59    -738 -0.0111   
16 23 [75 to 79]   77990   0.909    77871    0.909    119 -0.0000460
17 24 [80 to 84]   54960   0.641    54402    0.635    558  0.00550  
18 25 [85+]        53660   0.626    52987    0.619    673  0.00687  
19 98 [Unknown]      260   0.00303     NA   NA         NA NA  

DIC and DICX parsing leads to the same results.

  sex        n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
  <int+lbl>    <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
1 1 [Male]   7178200      49.6 7177683     49.6    517  0.00757
2 2 [Female] 7304130      50.4 7305816     50.4  -1686 -0.00757

   age2          n_ipums pct_ipums  n_rdtm pct_rdtm
   <int+lbl>       <dbl>     <dbl>   <int>    <dbl>
 1  1 [0 to 4]   1459980    10.1   1462277   10.1  
 2  2 [5 to 9]   1530380    10.6   1526806   10.5  
 3  3 [10 to 14] 1542170    10.6   1539342   10.6  
 4  4 [15 to 19] 1421490     9.82  1419537    9.80 
 5 12 [20 to 24] 1288560     8.90  1292126    8.92 
 6 13 [25 to 29] 1203030     8.31  1200564    8.29 
 7 14 [30 to 34] 1066870     7.37  1067289    7.37 
 8 15 [35 to 39]  941870     6.50   938726    6.48 
 9 16 [40 to 44]  819470     5.66   819002    5.65 
10 17 [45 to 49]  753060     5.20   750141    5.18 
11 18 [50 to 54]  608370     4.20   610132    4.21 
12 19 [55 to 59]  513910     3.55   515893    3.56 
13 20 [60 to 64]  397050     2.74   400759    2.77 
14 21 [65 to 69]  322970     2.23   323817    2.24 
15 22 [70 to 74]  238280     1.65   240091    1.66 
16 23 [75 to 79]  164550     1.14   165218    1.14 
17 24 [80 to 84]  115750     0.799  115552    0.798
18 25 [85+]        94570     0.653   96227    0.664

DIC and DICX parsing leads to the same results

  sex        n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
  <int+lbl>    <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
1 1 [Male]   2718590      47.3 2719371     47.3   -781 -0.00970
2 2 [Female] 3025050      52.7 3024742     52.7    308  0.00970

   age2          n_ipums pct_ipums n_rdtm pct_rdtm n_diff pct_diff
   <dbl+lbl>       <dbl>     <dbl>  <int>    <dbl>  <dbl>    <dbl>
 1  1 [0 to 4]    556940     9.70  555893    9.68    1047  0.0190 
 2  2 [5 to 9]    683410    11.9   684727   11.9    -1317 -0.0219 
 3  3 [10 to 14]  706600    12.3   706347   12.3      253  0.00542
 4  4 [15 to 19]  599880    10.4   600565   10.5     -685 -0.0111 
 5 12 [20 to 24]  485100     8.45  486542    8.47   -1442 -0.0244 
 6 13 [25 to 29]  459660     8.00  457890    7.97    1770  0.0315 
 7 14 [30 to 34]  400100     6.97  402249    7.00   -2149 -0.0368 
 8 15 [35 to 39]  355850     6.20  353147    6.15    2703  0.0476 
 9 16 [40 to 44]  303920     5.29  303631    5.29     289  0.00547
10 17 [45 to 49]  251620     4.38  252122    4.39    -502 -0.00838
11 18 [50 to 54]  217660     3.79  215734    3.76    1926  0.0338 
12 19 [55 to 59]  182920     3.18  183075    3.19    -155 -0.00244
13 20 [60 to 64]  150930     2.63  151864    2.64    -934 -0.0160 
14 21 [65 to 69]  125590     2.19  125157    2.18     433  0.00772
15 22 [70 to 74]   97110     1.69   97457    1.70    -347 -0.00590
16 23 [75 to 79]   75400     1.31   75984    1.32    -584 -0.0101 
17 24 [80 to 84]   46770     0.814  46870    0.816   -100 -0.00167
18 25 [85+]        44180     0.769  44859    0.781   -679 -0.0118

DIC and DICX parsing leads to the same results.

  sex         n_ipums pct_ipums   n_rdtm pct_rdtm n_diff pct_diff
  <int+lbl>     <dbl>     <dbl>    <int>    <dbl>  <dbl>    <dbl>
1 1 [Male]   13659450      49.7 13622640     49.7  36810   0.0494
2 2 [Female] 13799500      50.3 13789517     50.3   9983  -0.0494

   age2          n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
   <dbl+lbl>       <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
 1  1 [0 to 4]   2730740     9.94  2724620    9.94    6120  0.00535
 2  2 [5 to 9]   2689060     9.79  2683928    9.79    5132  0.00200
 3  3 [10 to 14] 2947320    10.7   2948985   10.8    -1665 -0.0244 
 4  4 [15 to 19] 2732690     9.95  2730785    9.96    1905 -0.0100 
 5 12 [20 to 24] 2542550     9.26  2531554    9.24   10996  0.0243 
 6 13 [25 to 29] 2304810     8.39  2291865    8.36   12945  0.0329 
 7 14 [30 to 34] 2073730     7.55  2074691    7.57    -961 -0.0164 
 8 15 [35 to 39] 1880710     6.85  1871852    6.83    8858  0.0206 
 9 16 [40 to 44] 1653130     6.02  1642059    5.99   11071  0.0301 
10 17 [45 to 49] 1374440     5.01  1371385    5.00    3055  0.00260
11 18 [50 to 54] 1152430     4.20  1152647    4.20    -217 -0.00796
12 19 [55 to 59]  885160     3.22   892143    3.25   -6983 -0.0310 
13 20 [60 to 64]  731310     2.66   730956    2.67     354 -0.00325
14 21 [65 to 69]  579310     2.11   579302    2.11       8 -0.00357
15 22 [70 to 74]  452130     1.65   452998    1.65    -868 -0.00598
16 23 [75 to 79]  342750     1.25   343999    1.25   -1249 -0.00669
17 24 [80 to 84]  200710     0.731  203636    0.743  -2926 -0.0119 
18 25 [85+]       185970     0.677  184752    0.674   1218  0.00329

DIC and DICX parsing leads to the same results.

  sex        n_ipums pct_ipums  n_rdtm pct_rdtm n_diff pct_diff
  <int+lbl>    <dbl>     <dbl>   <int>    <dbl>  <dbl>    <dbl>
1 1 [Male]   1577700      48.0 1577416     48.0    284   0.0324
2 2 [Female] 1706550      52.0 1708461     52.0  -1911  -0.0324

   age2          n_ipums pct_ipums n_rdtm pct_rdtm n_diff pct_diff
   <int+lbl>       <dbl>     <dbl>  <int>    <dbl>  <dbl>    <dbl>
 1  1 [0 to 4]    219860      6.69 220345     6.71   -485 -0.0114 
 2  2 [5 to 9]    236080      7.19 238068     7.25  -1988 -0.0569 
 3  3 [10 to 14]  255570      7.78 256552     7.81   -982 -0.0260 
 4  4 [15 to 19]  263090      8.01 261691     7.96   1399  0.0465 
 5 12 [20 to 24]  240700      7.33 241006     7.33   -306 -0.00568
 6 13 [25 to 29]  229820      7.00 228385     6.95   1435  0.0471 
 7 14 [30 to 34]  232930      7.09 233365     7.10   -435 -0.00973
 8 15 [35 to 39]  221690      6.75 222521     6.77   -831 -0.0219 
 9 16 [40 to 44]  203740      6.20 203098     6.18    642  0.0226 
10 17 [45 to 49]  198160      6.03 198773     6.05   -613 -0.0157 
11 18 [50 to 54]  195040      5.94 194565     5.92    475  0.0174 
12 19 [55 to 59]  172560      5.25 173007     5.27   -447 -0.0110 
13 20 [60 to 64]  151050      4.60 150775     4.59    275  0.0106 
14 21 [65 to 69]  132090      4.02 131563     4.00    527  0.0180 
15 22 [70 to 74]  111500      3.39 112395     3.42   -895 -0.0256 
16 23 [75 to 79]   94810      2.89  93659     2.85   1151  0.0365 
17 24 [80 to 84]   69880      2.13  70505     2.15   -625 -0.0180 
18 25 [85+]        55680      1.70  55604     1.69     76  0.00315

ByteArrayReader:

• Provides basic file reading and byte manipulation.
• Used by: BitArrayReader, FuzzyEntityParser, Variable.

BitArrayReader:

• Splits binary data into variable-sized chunks.
• Used by: Variable.

FuzzyEntityParser:

• Reads and parses .dic files.
• Depends on: ByteArrayReader.
• Used by: RedatamDatabase.

XMLParser: - Reads and parses .dicx` files.

• Depends on: pugixml (src/vendor).
• Used by: RedatamDatabase.

Variable:

• Represents a data structure within the system.
• Depends on: ByteArrayReader, BitArrayReader.
• Used by: Entity.

Entity:

• A higher-level representation grouping Variable and other metadata.
• Depends on: Variable.
• Used by: RedatamDatabase.

CSVExporter:

• Converts entities to CSV-compatible structures.
• Depends on: Entity, ParentIDCalculator and utils.
• Used by: RedatamDatabase.

XMLExporter:

• Converts entities and their variables into an XML-compatible structure.
• Depends on: Entity, utils, and pugixml (src/vendor).
• Used by: RedatamDatabase.

ParentIDCalculator:

• Calculates parent IDs for a child Entity based on row data.
• Depends on: Entity.
• Used by: CSVExporter.

RListExporter (R package):

• Converts entities to R-compatible structures.
• Depends on: Entity.
• Used by: RedatamDatabase.

PyDictExporter (Python package):

• Converts entities to Python-compatible structures.
• Depends on: Entity.
• Used by: RedatamDatabase.

RedatamDatabase:

• The central orchestrator that manages entities and interacts with parsers and exporters.
• Depends on: Entity, FuzzyEntityParser, XMLParser, RListExporter, PyDictExporter.

If you find this software useful, please consider donating. You can donate via BuyMeACoffee.

De Grande, Pablo. 2016. “El formato Redatam.” Estudios demográficos y urbanos 31 (3): 811–32. Ruggles, Steven, Lara Cleveland, Rodrigo Lovaton, Sula Sarkar, Matthew Sobek, Derek Burk, Dan Ehrlich, Quinn Heimann, and Jane Lee. 2024. “Integrated Public Use Microdata Series (IPUMS).” 2024. https://international.ipums.org/international/.

Open Redatam was created and is supported by Lital Barkai (barkailital@gmail.com).

The tests, installation instructions and R and Python package were created by Mauricio "Pacha" Vargas Sepulveda (m.sepulveda@mail.utoronto.ca)

The original converter was created by Pablo De Grande. See here for more information.

This project uses pugixml created by Arseny Kapoulkine to structure a part of the output data.

The author wishes to acknowledge the statistical offices that provided the underlying data used for the validation: National Institute of Statistics, Bolivia; National Institute of Statistics, Chile; National Institute of Statistics and Censuses, Ecuador; National Institute of Statistics, Uruguay.

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