rdhs

API Client and Data Munging for the Demographic and Health Survey Data

https://github.com/ropensci/rdhs

Science Score: 59.0%

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CITATION.cff file
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Found 3 DOI reference(s) in README
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3 of 9 committers (33.3%) from academic institutions
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Low similarity (18.8%) to scientific vocabulary

Keywords

dataset dhs dhs-api extract peer-reviewed r r-package rstats survey-data

Last synced: 4 months ago · JSON representation

Repository

API Client and Data Munging for the Demographic and Health Survey Data

Basic Info

Host: GitHub
Owner: ropensci
License: other
Language: R
Default Branch: main
Homepage: https://docs.ropensci.org/rdhs
Size: 39.6 MB

Statistics

Stars: 37
Watchers: 7
Forks: 10
Open Issues: 36
Releases: 6

Topics

dataset dhs dhs-api extract peer-reviewed r r-package rstats survey-data

Created almost 8 years ago · Last pushed 10 months ago

Metadata Files

Readme Changelog Contributing License Codemeta

README.Rmd

---
output:
  rmarkdown::github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```

# rdhs 

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## Motivation

The Demographic and Health Surveys (DHS) Program has collected population survey data from over 90 countries for over 30 years. In many countries, DHS provide the key data that mark progress towards targets such as the Sustainable Development Goals (SDGs) and inform health policy. Though standard health indicators are routinely published in survey final reports, much of the value of DHS is derived from the ability to download and analyse standardized microdata datasets for subgroup analysis, pooled multi-country analysis, and extended research studies. The suite of tools within `rdhs` improves the accessibility of these datasets for statistical analysis with R, with aim to support reproducible global health research and simplify common analytical pipelines.

 > For questions regarding how to analyse DHS survey data, please read the DHS website's data section first. If you have any questions after this then please create an [issue](https://github.com/ropensci/rdhs/issues) with your question. It is really likely that your question will help other people and so posting them publically as an issue may help others with similar questions.

---

`rdhs` is a package for management and analysis of [Demographic and Health Survey (DHS)](https://www.dhsprogram.com) data. This includes functionality to:

1. Access standard indicator data (i.e. [DHS STATcompiler](https://www.statcompiler.com/)) in R via the [DHS API](https://api.dhsprogram.com/).
1. Identify surveys and datasets relevant to a particular analysis.
1. Download survey datasets from the [DHS website](https://dhsprogram.com/data/available-datasets.cfm).
1. Load datasets and associated metadata into R.
1. Extract variables and combining datasets for pooled multi-survey analyses.

## Installation

You can install the latest version from [`CRAN`](https://cran.r-project.org/package=rdhs) using:

```{r cran-installation, message=FALSE, eval = FALSE}
install.packages("rdhs")
```

You can also install the development version of `rdhs` with the latest patches from github with:

```{r gh_installation, message=FALSE, eval = FALSE}
#install.packages("devtools")
devtools::install_github("ropensci/rdhs")
```

```{r}
# Load the package
library(rdhs)
```

## Getting started

To be able to **download survey datasets from the DHS website**, you will need to **set up an account with the DHS website**, which will enable you to request access to the datasets. Instructions on how to do this can be found [here](https://dhsprogram.com/data/Access-Instructions.cfm). The email, password, and project name that were used to create the account will then need to be provided to `rdhs` when attempting to download datasets. 

---

* Request dataset access from the DHS website [here](https://dhsprogram.com/data/Access-Instructions.cfm).

* Full functionality is described in the tutorial [here](https://docs.ropensci.org/rdhs/articles/introduction.html).

* An example workflow using `rdhs` to calculate trends in anemia prevalence is available [here](https://docs.ropensci.org/rdhs/articles/anemia.html).

## Basic Functionality

### Query the [DHS API](https://api.dhsprogram.com/).

Obtain survey estimates for Malaria prevalence among children from the Democratic Republic of Congo and Tanzania in the last 5 years (since 2013) that included rapid diagnostic tests (RDTs).

```{r api, message = FALSE}
dhs_indicators(indicatorIds = "ML_PMAL_C_RDT", returnFields=c("IndicatorId", "ShortName"))

dhs_data(countryIds = c("CD","TZ"), indicatorIds = "ML_PMAL_C_RDT", surveyYearStart = 2013,
       returnFields=c("Indicator", "SurveyId", "Value", "SurveyYearLabel", "CountryName"))
```

### Identify survey datasets

Now, obtain survey microdatasets to analyze these same indicators. Query the *surveyCharacteristics* endpoint to identify the survey characteristic ID for malaria RDT testing.

```{r sc}
## call with no arguments to return all characterstics
sc <- dhs_survey_characteristics()
sc[grepl("Malaria", sc$SurveyCharacteristicName), ]
```

Use `dhs_surveys()` identify surveys for the countries and years of interest.

```{r surv}
## what are the countryIds - we can find that using this API request
ids <- dhs_countries(returnFields=c("CountryName", "DHS_CountryCode"))

## find all the surveys that match the search criteria
survs <- dhs_surveys(surveyCharacteristicIds = 89, countryIds = c("CD","TZ"), surveyYearStart = 2013)
```

Lastly, identify the datasets required for download. By default, the recommended option is to download either the spss (.sav), `fileFormat = "SV"`, or the flat file (.dat), `fileFormat = "FL"` datasets. The flat is quicker, but there are still one or two very old datasets that don't read correctly, whereas the .sav files are slower to read in but so far no datasets have been found that don't read in correctly. The household member recode (`PR`) reports the RDT status for children under five.

```{r }
datasets <- dhs_datasets(surveyIds = survs$SurveyId, fileFormat = "FL", fileType = "PR")
str(datasets)
```

### Download datasets

We can now go ahead and download our datasets. To be able to download survey datasets from the DHS website, you will need to set up an account with them to enable you to request access to the datasets. Instructions on how to do this can be found [here](https://dhsprogram.com/data/Access-Instructions.cfm). The email, password, and project name that were used to create the account will then need to be provided to `rdhs` when attempting to download datasets. 

Once we have created an account, we need to set up our credentials using the function `set_rdhs_config()`. This will require providing as arguments your `email` and `project` for which you want to download datasets from. You will then be prompted for your password.

You can also specify a directory for datasets and API calls to be cached to using `cache_path`. In order to comply with CRAN, this function will also ask you for your permission to write to files outside your temporary directory, and you must type out the filename for the `config_path` - "rdhs.json". (See [introduction vignette](https://docs.ropensci.org/rdhs/articles/introduction.html) for specific format for config, or `?set_rdhs_config`). 

```{r client , R.options = list("rappdir_permission" = TRUE)}
## login
set_rdhs_config(email = "rdhs.tester@gmail.com",
                project = "rdhs R package development",
                config_path = "rdhs.json",
                global = FALSE)
```

The path to your config is saved between sessions so you only have to set this once. With your credentials set, all API requests will be cached within the `cache_path` directory provided so that these can be returned when working remotely or with a poor internet connection.

```{r client_api_cache}
# the first time this will take a few seconds 
microbenchmark::microbenchmark(dhs_datasets(surveyYearStart = 1986),times = 1)

# after caching, results will be available instantly
microbenchmark::microbenchmark(dhs_datasets(surveyYearStart = 1986),times = 1)
```

Now download datasets by providing a list of desired dataset filenames.

```{r download, message=FALSE}
# download datasets
downloads <- get_datasets(datasets$FileName)

str(downloads)
```

### Load datasets into R

The `get_datasets()` function returns a vector with a file path to the saved location of the downloaded datasets. These are read using `readRDS()`:

```{r read a dataset}
# read in first dataset
cdpr <- readRDS(downloads$CDPR61FL)
```

Value labels are stored as attributes to each of the columns of the data frame using the `labelled` class (see `haven::labelled` or our introduction vignette for more details). Variable labels are stored in the `label` attribute.

### Extract variables and pool datasets

The client also caches all variable labels to quickly query variables in each survey *without* loading the datasets.

```{r questions}
# rapid diagnostic test search
vars <- search_variable_labels(datasets$FileName, search_terms = "malaria rapid test")
```

Then extract these variables from the datasets. Optionally, geographic data may be added.

```{r extract_questions}
# and now extract the data
extract <- extract_dhs(vars, add_geo = FALSE)
```

The returned object is a list of extracted datasets.

Dataset extracts can alternate be specified by providing a vector of surveys and vector of variable names:

```{r extract_variables}
# and grab the questions from this now utilising the survey variables
vars <- search_variables(datasets$FileName, variables = c("hv024","hml35"))

# and now extract the data
extract <- extract_dhs(vars, add_geo = FALSE)
```

Finally, the two datasets are pooled using the function `rbind_labelled()`. This function works specifically with our lists of labelled `data.frame`s. Labels are specified for each variable: for `hv024` all labels are retained (concatenate) but for `hml35` labels across both datasets to be "Neg" and "Pos".

```{r rbind_labelled}
# now let's try our second extraction
extract <- rbind_labelled(extract,
                          labels = list("hv024" = "concatenate",
                                        "hml35" = c("Neg"=0, "Pos"=1)))
```


There is also an option to process downloaded datasets with labelled variables coded as strings, rather than labelled variables. This is specified by the argument `reformat=TRUE`.

```{r reformat}
# identify questions but specifying the reformat argument
questions <- search_variables(datasets$FileName, variables = c("hv024", "hml35"),
                                     reformat=TRUE)

# and now extract the data
extract <- extract_dhs(questions, add_geo = FALSE)

# group our results
extract <- rbind_labelled(extract)

# our hv024 variable is now just character strings, so you can decide when/how to factor/label it later
str(extract)
```

Owner

Name: rOpenSci
Login: ropensci
Kind: organization
Email: info@ropensci.org
Location: Berkeley, CA

Website: https://ropensci.org/
Twitter: rOpenSci
Repositories: 307
Profile: https://github.com/ropensci

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          "volumeNumber": "4",
          "name": "Wellcome Open Research"
        }
      }
    }
  ],
  "relatedLink": [
    "https://docs.ropensci.org/rdhs/",
    "https://CRAN.R-project.org/package=rdhs"
  ],
  "releaseNotes": "https://github.com/ropensci/rdhs/blob/master/NEWS.md",
  "readme": "https://github.com/ropensci/rdhs/blob/main/README.md",
  "contIntegration": [
    "https://github.com/ropensci/rdhs/actions",
    "https://app.codecov.io/github/ropensci/rdhs?branch=main"
  ],
  "developmentStatus": "https://www.repostatus.org/#active",
  "review": {
    "@type": "Review",
    "url": "https://github.com/ropensci/software-review/issues/238",
    "provider": "https://ropensci.org"
  },
  "keywords": [
    "dataset",
    "survey-data",
    "dhs",
    "extract",
    "dhs-api",
    "peer-reviewed",
    "r",
    "r-package",
    "rstats"
  ]
}

GitHub Events

Total

Issues event: 9
Watch event: 4
Issue comment event: 18
Push event: 6
Pull request event: 1
Fork event: 1
Create event: 1

Last Year

Issues event: 9
Watch event: 4
Issue comment event: 18
Push event: 6
Pull request event: 1
Fork event: 1
Create event: 1

Committers

Last synced: almost 2 years ago

All Time

Total Commits: 581
Total Committers: 9
Avg Commits per committer: 64.556
Development Distribution Score (DDS): 0.437

Past Year

Commits: 16
Committers: 2
Avg Commits per committer: 8.0
Development Distribution Score (DDS): 0.063

Top Committers

Name	Email	Commits
OJWatson	o**5@i**k	327
OJWatson	o**n@h**k	180
jeffeaton	j**n@i**k	62
Rich FitzJohn	r**n@g**m	6
Jeff Eaton	7****n	2
Nicholas G Reich	n**k@s**u	1
Anna Krystalli	a**i@g**m	1
Lucy McGowan	l**o@g**m	1
Jeroen Ooms	j**s@g**m	1

Committer Domains (Top 20 + Academic)

imperial.ac.uk: 2 schoolph.umass.edu: 1 hotmail.co.uk: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 75
Total pull requests: 30
Average time to close issues: about 1 year
Average time to close pull requests: 21 days
Total issue authors: 28
Total pull request authors: 6
Average comments per issue: 2.53
Average comments per pull request: 0.47
Merged pull requests: 27
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 3
Pull requests: 0
Average time to close issues: about 1 hour
Average time to close pull requests: N/A
Issue authors: 3
Pull request authors: 0
Average comments per issue: 0.33
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

OJWatson (36)
jeffeaton (9)
skuchukh (5)
aftollefsen (2)
marek-tph (2)
adejumoridwan (2)
truenomad (1)
m-maheu-giroux (1)
loushangdashabi (1)
camillebelmin (1)
cruzmamo (1)
bpatenaude (1)
andrewcglover (1)
csq-dr (1)
ak45y (1)

Pull Request Authors

OJWatson (18)
jeffeaton (10)
maelle (2)
nickreich (1)
annakrystalli (1)
dependabot[bot] (1)

Top Labels

Issue Labels

enhancement (25) bug (11) parser exceptions (4) wontfix (1) good first issue (1)

Pull Request Labels

dependencies (1)

Dependencies

DESCRIPTION cran

R >= 3.3.0 depends
R6 * imports
brio * imports
digest * imports
foreign * imports
getPass * imports
haven * imports
httr * imports
iotools * imports
jsonlite * imports
magrittr * imports
qdapRegex * imports
rappdirs * imports
rgdal * imports
storr * imports
xml2 * imports
data.table * suggests
ggplot2 * suggests
knitr * suggests
microbenchmark * suggests
rmarkdown * suggests
sf * suggests
survey * suggests
testthat * suggests

docs/articles/geojson_files/leaflet-binding-2.0.2/lib/leaflet-omnivore/package.json npm

browserify 13.0.1 development
jshint 2.9.2 development
mapbox.js 2.4.0 development
mocha ~2.5.3 development
phantomjs-prebuilt 2.1.7 development
st 1.2.2 development
tape 4.5.1 development
uglify-js ^2.6.2 development
zuul ~3.10.1 development
brfs 1.4.3
corslite 0.0.7
csv2geojson ~5.0.0
polyline 0.2.0
togeojson 0.13.0
topojson 1.6.26
wellknown 0.4.2

docs/articles/geojson_files/leaflet-binding-2.0.2/lib/leaflet-providers/package.json npm

chai ^4.1.2 development
eslint >=4.18.2 development
eslint-plugin-html ^2.0.1 development
mocha ^3.2.0 development
mocha-phantomjs-core ^2.1.1 development
mversion >=2.0.0 development
phantomjs-prebuilt ^2.1.16 development
uglify-js ^2.4.15 development

docs/articles/geojson_files/leaflet-binding-2.0.2/plugins/Leaflet.markercluster/package.json npm

jake ~0.5.16 development
jshint ~2.1.3 development
karma >=6.3.16 development
leaflet ~1.0.3 development
magic-string ^0.7.0 development
mocha ~1.10.0 development
uglify-js ~2.6.0 development

.github/workflows/R-CMD-TRAVIS-check.yaml actions

actions/checkout v2 composite
actions/upload-artifact main composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/check-standard.yaml actions

actions/checkout v2 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/pkgdown.yaml actions

JamesIves/github-pages-deploy-action 4.1.4 composite
actions/checkout v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/test-coverage.yaml actions

actions/checkout v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

ecosyste.ms

Data

Tools

Indexes

Applications

Experiments

Open Source Science

rdhs

Science Score: 59.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.Rmd

Owner

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Dependencies