https://github.com/stephenbalogun/tidyndr
Analysis of the Nigeria National Data Repository (NDR)
https://github.com/stephenbalogun/tidyndr
Last synced: 4 months ago
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Analysis of the Nigeria National Data Repository (NDR)
- Host: GitHub
- URL: https://github.com/stephenbalogun/tidyndr
- Owner: stephenbalogun
- License: other
- Created: 2021-01-26T15:15:46.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2023-02-06T13:34:07.000Z (about 2 years ago)
- Last Synced: 2024-08-13T07:13:27.227Z (8 months ago)
- Language: R
- Size: 8.47 MB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
---
title: Tidyndr
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
warning = FALSE,
message = FALSE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
[](https://github.com/stephenbalogun/tidyndr/actions) [](https://app.codecov.io/gh/stephenbalogun/tidyndr?branch=master/) [](https://CRAN.R-project.org/package=tidyndr) [](https://cranlogs.r-pkg.org/#badges) [](https://cranlogs.r-pkg.org/#badges)
The goal of {tidyndr} is to provide a specialized, simple and easy to use functions that wrap around existing functions in `R` for manipulation of the [NDR](https://ndr.phis3project.org.ng/) patient line-list file allowing the user to focus on the tasks to be completed rather than the code/formula details.
The functions presented are similar to the PEPFAR Monitoring Evaluation and Reporting Indicators and are currently grouped into four categories:
- The `read_ndr` function for reading the patient-level line-list downloaded from the front-end of the NDR in 'csv' format.
- The PEPFAR treatment group of indicators that can be performed on the NDR line-list.
- The 'Viral Load' indicators (`tx_vl_eligible()`, `tx_pvls_den()` `tx_pvls_num()` and `tx_vl_unsuppressed()`).
- The summary functions (`summarise_ndr()` and `disaggregrate()`) provides a tabular summary for the tasks that have been completed using any of the functions above.
## Installation
You can install the released version of {tidyndr} from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("tidyndr")
```
Or the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("stephenbalogun/tidyndr",
build_vignette = TRUE)
```
## Usage
```{r load_library}
library(tidyndr)
```
### read_ndr
`read_ndr()` reads the downloaded ".csv" file into [`R`](https://cran.r-project.org) using [`vroom::vroom()`](https://vroom.r-lib.org/) behind the scene and passing appropriate column types to the `col_types` argument. It also formats the variable names using the [`snakecase`](https://en.wikipedia.org/wiki/Snake_case) style.
```{r read_ndr}
## read from a local file path (not run)
# file_path <- system.file("extdata", "ndr_example.csv", package = "tidyndr")
# read_ndr(file_path, time_stamp = "2021-02-15")
### read line-list available on the internet
path <- "https://raw.githubusercontent.com/stephenbalogun/example_files/main/ndr_example.csv"
ndr_example <- read_ndr(path, time_stamp = "2021-02-20")
```
### Treatment Indicators
The functions included in this group are:
- `tx_new()`
- `tx_curr()`
- `tx_ml()` and `tx_ml_outcomes()`
- `tx_rtt()`
- Other supporting functions are: `tx_mmd()`, `tx_regimen()` and `tx_appointment()`
```{r tx_indicators}
## Subset "TX_NEW"
tx_new(ndr_example, from = "2021-01-01", to = "2021-03-31")
## Generate line-list of clients with medication refill in October 2021
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-01-31"
)
## Generate list of clients who were active at the beginning of October 2021 but became inactive at the end of December 2021.
tx_ml(new_data = ndr_example,
from = "2021-01-01",
to = "2021-03-31")
```
### Viral Suppression Indicators
The `tx_vl_eligible()`, `tx_pvls_den()` and the `tx_pvls_num()` functions come in handy when you need to generate the line-list of clients who are eligible for viral load test at a given point for a given facility/state, those who have a valid viral load result (not more than 1 year for people aged 20 years and above and not more than 6 months for paediatrics and adolescents less or equal to 19 years), and those who are virally suppressed (out of those with valid viral load results). When the `sample = TRUE` attribute is supplied to the `tx_vl_eligible()` function, it generates the line-list of only those who are due for a viral load test out of all those who are eligible.
```{r vl_indicators}
## Generate list of clients who are eligible for VL (i.e. expected to have a documented VL result)
ndr_example %>%
tx_vl_eligible(ref = "2021-12-31")
## Generate list of clients that will be expected to have a viral load test done by March 2022
ndr_example %>%
tx_vl_eligible("2022-03-31",
sample = TRUE)
### Calculate the Viral Load Coverage as of December 2021
no_of_vl_results <- tx_pvls_den(ndr_example,
ref = "2021-12-31") %>%
nrow()
no_of_vl_eligible <- tx_vl_eligible(ndr_example,
ref = "2021-12-31") %>%
nrow()
vl_coverage <- scales::percent(no_of_vl_results / no_of_vl_eligible)
print(vl_coverage)
```
For all the 'Treatment' and 'Viral Suppression' indicators (except `tx_ml_outcomes()`, which should be use with `tx_ml()`), you have control over the level of action (state or facility) by supplying to the `states` and/or `facilities` arguments the values of interest . For more than one state or facility, combine the values with the `c()` e.g.
```{r multiple_indicators}
## subset clients that have medication appointment in between January and March of 2021 in
## and are also due for viral load
ndr_example %>%
tx_appointment(from = "2021-01-01",
to = "2021-03-31",
) %>%
tx_vl_eligible(sample = TRUE)
```
### Summarising your Indicators
You might want to generate a summary table of all the indicators you have pulled out. The `summarise_ndr()` (or `summarize_ndr()`) allows you to do this with ease. It accepts all the line-lists you are interested in creating a summary table for, the level at which you want the summary to be created (country/ip, state or facility), and the names you want to give to each of your summary column.
```{r, summarise_ndr}
## generates line-list of TX_NEW between July and December 2021
new <- tx_new(ndr_example, from = "2021-01-01", to = "2021-03-31")
## generates line-list of currently active clients
curr <- tx_curr(ndr_example)
## generates line-list of clients who were active at the beginning of the October but inactive at end of December 2021
ml <- tx_ml(new_data = ndr_example, from = "2021-01-01", to = "2021-03-31")
summarise_ndr(new, curr, ml,
level = "state",
names = c("tx_new", "tx_curr", "tx_ml"))
```
The `disaggregate()` allows you to summarise an indicator of interest into finer details based on "current_age", "sex" "pregnancy_status", "art_duration", "months_dispensed (of ARV)" or "age_sex". These are supplied to the `by` parameter of the function. The default disaggregates the variable of interest at the level of "states" but can also do this at "country/ip", "lga" or "facility" level when any of this is supplied to the `level` parameter.
```{r, disagg}
## generates line-list of TX_NEW between July and September 2021
new_clients <- tx_new(ndr_example, from = "2021-01-01", to = "2021-03-30")
disaggregate(new_clients,
by = "current_age", pivot_wide = FALSE)
## disaggregate 'TX_CURR' by sex
ndr_example %>%
tx_curr() %>%
disaggregate(by = "sex")
```
## Code of Conduct
Please note that the {tidyndr} project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.