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Try to start stata by ./xstata. If it gives you the following error message (./stata: error while loading shared libraries: libpng12.so.0: cannot open shared object file: No such file or directory), continue with the steps below:
Stata is a general-purpose statistical software package created in 1985 by StataCorp. Most of its users work in research, especially in the fields of economics, sociology, political science, biomedicine and epidemiology citation needed. You can also download COMSOL Multiphysics 5.4.0.225 Windows and Linux.
The full Anaconda installation is quite large, and includes many libraries for Python thatstata_kernel doesn't use. If you don't plan to use Python and want to useless disk space, install Miniconda, a bare-bones version of Anaconda. Then when installing the package any other necessary dependencies will bedownloaded automatically.
To download the software and respective manuals click on the links below. We recommend users to study first the manual, in particular the description of the requirements and installation (e.g.,.NET framework required before installing the software).
Should you encounter problems with the download due to slow internet connection, please send your contact details to the address below and we shall mail you a CD ROM including the software and manuals.
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StataCorp Stata 15 has got a point and click interface along with an intuitive command syntax. You can reproduce and document all the analyses for the publication and review. The version control ensures that the scripts for the analyses continue to produce the same result regardless of the fact when they were written. The data management feature will give you more control over all sorts of data. You can combine as well as reshape the datasets and can also manage the variables. You can point and click for creating a custom graph plus you can also write the scripts in order to produce loads of graphs in reproducible manner. The graphs can be exported to EPS or TIFF for publication to SVG or PNG for the web or PDF files for viewing purpose. You can also download Minitab 18.1 + Portable.
The rdhs package was designed to address these needs and facilitate the management and processing of DHS survey data in the R statistical software environment 6 . This occurs through both functioning as an application programming interface (API) client, allowing access to all data provided within the DHS API, and helping to download the standardised recoded microdatasets from the DHS website and read them into conventional R data structures. Lastly, the package caches data dictionaries associated with the survey datasets, enabling fast querying for survey variables of interest across multiple surveys.
The identified datasets from the API are used by rdhs to specify which files are to be downloaded. Downloaded files are first read in and parsed to create a data dictionary for each survey. Both the dictionary and survey data are saved locally as R objects, which enables efficient querying of downloaded datasets for survey questions of interest. The responses to the identified survey questions are then used to build a curated data set that also includes geographical metadata of the survey locations.
Next we query the surveys endpoint in the API to identify the surveys that have this characteristic ID and were conducted in our countries of interest. Finally, after identifying the relevant surveys, we can query the datasets endpoint to identify the individual recode (IR) survey datasets for each of these surveys. The DHS program provides datasets as SAS, Stata, SPSS, flat or Hierarchical formats. We recommend users to download the flat file format, which are both faster to load and have more complete data dictionaries available.
Between 1987 and March 2019 the DHS Program conducted and published data from 315 surveys, which represents over 12,000 dataset files that can be freely downloaded for further analysis. The published datasets are available in formats that can be analysed using proprietary software including Stata, SAS and SPSS. In this paper we introduced the rdhs package, a freely available software package for the R software environment that extends the available toolkit for analysing DHS datasets.
rdhs enables the DHS API to be accessed directly from within R using a series of functions that wrap each of the twelve API endpoints. API requests made in this way are cached and will be automatically updated in response to any future updates in the API. These functions assist users in both searching for published health and demographic statistic and identifying datasets needed for further analysis. Once identified rdhs enables users to easily download datasets and converts them into standard R data structures and caches the associated data dictionaries, enabling quick searching across multiple datasets for survey questions of interest. We demonstrated in this paper how a user can create a curated data set containing harmonised responses for 154,439 individuals in fewer than ten lines of R code.
The DHS program periodically updates published datasets. However, each update causes a change in the file name that will also be updated in the API. This feature ensures that updated datasets will both be detected by rdhs via the API and consequently the newer dataset files will be downloaded. For example, in our use case the downloaded dataset from the survey conducted in Uganda 2016 has the file name "UGIR7AFL.ZIP". The DHS file naming conventions indicate that this file is the second released version. As such, the code used in this example will ensure that the second released version of the survey will be downloaded, which helps to ensure reproducible research that can be easily shared between individuals. In this way, it is hoped that rdhs will enable more researchers to interact with the wealth of data published by the DHS Program through providing a tool that enables information rich datasets to be easily accessed and reproducibly produced. 2ff7e9595c
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