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 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) data. This includes functionality to:
You can install the latest version from
You can also install the development version of
rdhs with the latest patches from github with:
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. 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.
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).
dhs_indicators(indicatorIds = "ML_PMAL_C_RDT", returnFields=c("IndicatorId", "ShortName")) #> ShortName IndicatorId #> 1 Malaria prevalence according to RDT ML_PMAL_C_RDT dhs_data(countryIds = c("CD","TZ"), indicatorIds = "ML_PMAL_C_RDT", surveyYearStart = 2013, returnFields=c("Indicator", "SurveyId", "Value", "SurveyYearLabel", "CountryName")) #> Indicator SurveyId SurveyYearLabel Value #> 1 Malaria prevalence according to RDT CD2013DHS 2013-14 30.8 #> 2 Malaria prevalence according to RDT TZ2015DHS 2015-16 14.4 #> 3 Malaria prevalence according to RDT TZ2017MIS 2017 7.3 #> CountryName #> 1 Congo Democratic Republic #> 2 Tanzania #> 3 Tanzania
Now, obtain survey microdatasets to analyze these same indicators. Query the surveyCharacteristics endpoint to identify the survey characteristic ID for malaria RDT testing.
## call with no arguments to return all characterstics sc <- dhs_survey_characteristics() sc[grepl("Malaria", sc$SurveyCharacteristicName), ] #> SurveyCharacteristicID SurveyCharacteristicName #> 58 96 Malaria - DBS #> 59 90 Malaria - Microscopy #> 60 89 Malaria - RDT #> 61 57 Malaria bednet inventory
dhs_surveys() identify surveys for the countries and years of interest.
## what are the countryIds - we can find that using this API request ids <- dhs_countries(returnFields=c("CountryName", "DHS_CountryCode")) ## find all the surveys that match the search criteria survs <- dhs_surveys(surveyCharacteristicIds = 89, countryIds = c("CD","TZ"), surveyYearStart = 2013)
Lastly, identify the datasets required for download. By default, the recommended option is to download either the spss (.sav),
fileFormat = "SV", or the flat file (.dat),
fileFormat = "FL" datasets. The flat is quicker, but there are still one or two very old datasets that don’t read correctly, whereas the .sav files are slower to read in but so far no datasets have been found that don’t read in correctly. The household member recode (
PR) reports the RDT status for children under five.
datasets <- dhs_datasets(surveyIds = survs$SurveyId, fileFormat = "FL", fileType = "PR") str(datasets) #> 'data.frame': 3 obs. of 13 variables: #> $ FileFormat : chr "Flat ASCII data (.dat)" "Flat ASCII data (.dat)" "Flat ASCII data (.dat)" #> $ FileSize : int 6595349 6491292 2171918 #> $ DatasetType : chr "Survey Datasets" "Survey Datasets" "Survey Datasets" #> $ SurveyNum : int 421 485 529 #> $ SurveyId : chr "CD2013DHS" "TZ2015DHS" "TZ2017MIS" #> $ FileType : chr "Household Member Recode" "Household Member Recode" "Household Member Recode" #> $ FileDateLastModified: chr "September, 19 2016 09:58:23" "September, 28 2019 17:58:28" "June, 11 2019 15:38:22" #> $ SurveyType : chr "DHS" "DHS" "MIS" #> $ SurveyYearLabel : chr "2013-14" "2015-16" "2017" #> $ SurveyYear : chr "2013" "2015" "2017" #> $ DHS_CountryCode : chr "CD" "TZ" "TZ" #> $ FileName : chr "CDPR61FL.ZIP" "TZPR7BFL.ZIP" "TZPR7IFL.ZIP" #> $ CountryName : chr "Congo Democratic Republic" "Tanzania" "Tanzania"
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. 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
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 for specific format for config, or
## login set_rdhs_config(email = "email@example.com", project = "rdhs R package development", config_path = "rdhs.json", global = FALSE) #> Writing your configuration to: #> -> rdhs.json
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.
# the first time this will take a few seconds microbenchmark::microbenchmark(dhs_datasets(surveyYearStart = 1986),times = 1) #> Unit: milliseconds #> expr min lq mean median uq #> dhs_datasets(surveyYearStart = 1986) 46.3744 46.3744 46.3744 46.3744 46.3744 #> max neval #> 46.3744 1 # after caching, results will be available instantly microbenchmark::microbenchmark(dhs_datasets(surveyYearStart = 1986),times = 1) #> Unit: milliseconds #> expr min lq mean median #> dhs_datasets(surveyYearStart = 1986) 1.410894 1.410894 1.410894 1.410894 #> uq max neval #> 1.410894 1.410894 1
Now download datasets by providing a list of desired dataset filenames.
# download datasets downloads <- get_datasets(datasets$FileName) str(downloads) #> List of 3 #> $ CDPR61FL: chr "/home/oj/.cache/rdhs/datasets/CDPR61FL.rds" #> $ TZPR7BFL: chr "/home/oj/.cache/rdhs/datasets/TZPR7BFL.rds" #> $ TZPR7IFL: chr "/home/oj/.cache/rdhs/datasets/TZPR7IFL.rds" #> - attr(*, "reformat")= logi FALSE
# read in first dataset cdpr <- readRDS(downloads$CDPR61FL)
Value labels are stored as attributes to each of the columns of the data frame using the
labelled class (see
haven::labelled or our introduction vignette for more details). Variable labels are stored in the
The client also caches all variable labels to quickly query variables in each survey without loading the datasets.
# rapid diagnostic test search vars <- search_variable_labels(datasets$FileName, search_terms = "malaria rapid test")
Then extract these variables from the datasets. Optionally, geographic data may be added.
# and now extract the data extract <- extract_dhs(vars, add_geo = FALSE) #> Starting Survey 1 out of 3 surveys:CDPR61FL #> Starting Survey 2 out of 3 surveys:TZPR7BFL #> Starting Survey 3 out of 3 surveys:TZPR7IFL
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:
# and grab the questions from this now utilising the survey variables vars <- search_variables(datasets$FileName, variables = c("hv024","hml35")) # and now extract the data extract <- extract_dhs(vars, add_geo = FALSE) #> Starting Survey 1 out of 3 surveys:CDPR61FL #> Starting Survey 2 out of 3 surveys:TZPR7BFL #> Starting Survey 3 out of 3 surveys:TZPR7IFL
Finally, the two datasets are pooled using the function
rbind_labelled(). This function works specifically with our lists of labelled
data.frames. Labels are specified for each variable: for
hv024 all labels are retained (concatenate) but for
hml35 labels across both datasets to be “Neg” and “Pos”.
# now let's try our second extraction extract <- rbind_labelled(extract, labels = list("hv024" = "concatenate", "hml35" = c("Neg"=0, "Pos"=1)))
There is also an option to process downloaded datasets with labelled variables coded as strings, rather than labelled variables. This is specified by the argument
# identify questions but specifying the reformat argument questions <- search_variables(datasets$FileName, variables = c("hv024", "hml35"), reformat=TRUE) # and now extract the data extract <- extract_dhs(questions, add_geo = FALSE) #> Starting Survey 1 out of 3 surveys:CDPR61FL #> Starting Survey 2 out of 3 surveys:TZPR7BFL #> Starting Survey 3 out of 3 surveys:TZPR7IFL # 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) #> Classes 'dhs_dataset' and 'data.frame': 208595 obs. of 4 variables: #> $ hv024 : chr "equateur" "equateur" "equateur" "equateur" ... #> ..- attr(*, "label")= chr "Province" #> $ hml35 : chr NA NA NA NA ... #> ..- attr(*, "label")= chr "Result of malaria rapid test" #> $ SurveyId: chr "CD2013DHS" "CD2013DHS" "CD2013DHS" "CD2013DHS" ... #> $ DATASET : chr "CDPR61FL" "CDPR61FL" "CDPR61FL" "CDPR61FL" ...