Code chunks will not be evaluated, because:
Set credential unsuccessful.

khisr

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.

Key Features

• Data Retrieval: Easily download and manage data from DHIS2.
• Flexible Queries: Customize data queries to retrieve specific data elements, periods, and organizational units.
• Secure Access: Manage credentials securely within your R environment.

Use Cases

• Health data analysis for research.
• Monitoring and evaluation of health programs
• Generating reports and dashboards for health information systems.

Installation

Stable Release

You can install the release version of khisr from CRAN with:

install.packages("khisr")

Development Version

And the development version of khisr like so:

#install.packages('pak')
pak::pak('damurka/khisr')

Usage

Load khisr package

library("khisr")

Auth

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.

Basic Overview

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

# 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

# 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

# Filter the data element to element that contain malaria
malaria <- get_data_elements(dataElementGroups.name %.like% 'moh 705', 
                             name %.like% 'malaria')
malaria

# 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

Where to learn more

Get 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.

Code of Conduct

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.