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The khisr package is designed to seamlessly integrate with DHIS2, providing R users with a powerful interface for efficient data retrieval. DHIS2 is a cornerstone in health information management for many organisations, and khisr simplifies the process of accessing and working with DHIS2 data directly within the R environment.
You can install the release version of khisr from CRAN with:
install.packages("khisr")And the development version of khisr like so:
#install.packages('pak')
pak::pak('damurka/khisr')library("khisr")The khisr package operates in authenticated mode by default. This means you’ll need to provide credentials before using any functions that interact with your DHIS2 instance to download data. To ensure secure access, khisr offers a convenient way to store your credentials within your R environment. Refer to the following resource for detailed instructions on setting your credentials: set you credentials
# Option 1: Set credentials directly in R (less secure)
khis_cred(username = 'DHIS2 username',
password = 'DHIS2 password',
base_url = 'https://<dhis2 server instance>/api')
# Option 2: Set credentials from a secure configuration file (recommended)
khis_cred(config_path = 'path/to/secret.json')Once you’ve established your credentials, you’re ready to leverage khisr’s functions to download data from your DHIS2 instance.
For this overview, we’ve logged into DHIS2 as a specific user in a hidden chunk.
This is a basic example which shows you how to solve a common problem:
# Retrieve the organisation units by county (level 2)
counties <- get_organisation_units(level %.eq% '2')
counties
#> # A tibble: 47 × 2
#> name id
#> <chr> <chr>
#> 1 Baringo County vvOK1BxTbet
#> 2 Bomet County HMNARUV2CW4
#> 3 Bungoma County KGHhQ5GLd4k
#> 4 Busia County Tvf1zgVZ0K4
#> 5 Elgeyo Marakwet County MqnLxQBigG0
#> 6 Embu County PFu8alU2KWG
#> 7 Garissa County uyOrcHZBpW0
#> 8 Homa Bay County nK0A12Q7MvS
#> 9 Isiolo County bzOfj0iwfDH
#> 10 Kajiado County Hsk1YV8kHkT
#> # ℹ 37 more rows
# Retrieve organisation units by name (level included to ensure it refers to county)
kiambu_county <- get_organisation_units(level %.eq% '2',
name %.like% 'Kiambu')
kiambu_county
#> # A tibble: 1 × 2
#> name id
#> <chr> <chr>
#> 1 Kiambu County qKzosKQPl6G
# Retrieve all data elements by data element group for outpatient (data element group name MOH 705)
moh_705 <- get_data_elements(dataElementGroups.name %.like% 'moh 705')
moh_705
#> # A tibble: 96 × 2
#> name id
#> <chr> <chr>
#> 1 Abortion IrWSgk9GsUm
#> 2 All other diseases KxT47tbKHsd
#> 3 Anaemia cases kkUHOwGMawD
#> 4 Arthritis, Joint pains etc. waNhWrS3HL6
#> 5 Asthma L82lvvxVaqt
#> 6 Autism L529r3Wvtcf
#> 7 Bilharzia (Schistosomiasis) ojFSHMwbkHK
#> 8 Brucellosis nb9cfWgxYFc
#> 9 Burns dkEYL9Sous9
#> 10 Cardiovascular conditions sZETzNe1To8
#> # ℹ 86 more rows
# Filter the data element to element that contain malaria
malaria <- get_data_elements(dataElementGroups.name %.like% 'moh 705',
name %.like% 'malaria')
malaria
#> # A tibble: 4 × 2
#> name id
#> <chr> <chr>
#> 1 Confirmed Malaria (only Positive cases) OoakJhWiyZp
#> 2 Malaria in pregnancy gvZmXInRLuD
#> 3 MOH 705A Rev 2020_ Tested for Malaria siOyOiOJpI8
#> 4 Suspected Malaria Lt0FqtnHraW
# Retrieve data for malaria in Kiambu county in the outpatient data element groups
data <- get_analytics(
dx %.d% malaria$id,
pe %.d% 'LAST_YEAR',
ou %.f% kiambu_county$id
) %>%
left_join(malaria, by = c('dx'='id'))
data
#> # A tibble: 4 × 4
#> dx pe value name
#> <chr> <chr> <dbl> <chr>
#> 1 Lt0FqtnHraW 2023 31101 Suspected Malaria
#> 2 OoakJhWiyZp 2023 5092 Confirmed Malaria (only Positive cases)
#> 3 siOyOiOJpI8 2023 20554 MOH 705A Rev 2020_ Tested for Malaria
#> 4 gvZmXInRLuD 2023 397 Malaria in pregnancyGet Started is a more extensive general introduction to khisr.
Browse the articles index to find articles that cover various topics in more depth.
See the function index for an organized, exhaustive listing.
Please note that the khisr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.