The khisr package is designed to seamlessly integrate with the Kenya Health Information System (KHIS), providing R users with a powerful interface for efficient data retrieval. KHIS is a cornerstone in health information management in Kenya, and khisr simplifies the process of accessing and working with KHIS data directly within the R environment.
Installation
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')Usage
Auth
khisr will, by default, help you interact with KHIS as an authenticated user. Before calling any function that makes an API call you need credentials to KHIS. You will be expected to set this credential to download data. See the article set you credentials for more
# Set the credentials using username and password
khis_cred(username = 'KHIS username', password = 'KHIS password')
# Set credentials using configuration path
khis_cred(config_path = 'path/to/secret.json')After setting the credential you can invoke any function to download data from the API.
For this overview, we’ve logged into KHIS 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
#> # 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 pregnancyWhere 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.