ipctools: Utilities to Support Integrated Food Security Phase Classification (IPC) Data Processing and Visualisation
The Integrated Food Security Phase Classification (IPC) is a widely used tool for classifying and analysing the severity and magnitude of food insecurity and malnutrition situations in various countries and regions around the world. It provides a common understanding of the food security situation and enables decision-makers to take appropriate actions to mitigate and respond to food crises. This package provides functions and utilities that support IPC-related data analysis and visualisation.
Please note that ipctools is still highly experimental and is
undergoing a lot of development. Hence, any functionalities described
below have a high likelihood of changing interface or approach as we aim
for a stable working version.
Currently, the package provides utility functions that support the processing, validation, and analysis of acute malnutrition datasets based on IPC data standards and recommendations. Over time, it is aimed that other similar data processing, validation, and analysis steps recommended by IPC on datasets for acute food insecurity and chronic food insecurity can be incorporated.
In addition, it is planned to include a set of functions that would wrap around the IPC-CH API enabling programmatic access to available resources from the IPC for researchers and analysts who use R. For this purpose, a formal request for access to the API has been submitted. Approval of this request will determine whether this plan will come to fruition.
ipctools is not yet on CRAN but can be installed from the nutriverse
R universe as follows:
install.packages(
"ipctools",
repos = c("https://nutriverse.r-universe.dev", "https://cloud.r-project.org")
)For nutrition survey datasets that include MUAC measurements, the IPC recommends that the following tests be performed:
-
Age ratio test - The ratio between those whose age is less than 30 months to those who are 30 months and above.
-
Sex ratio test - The male to female sex ratio test checks whether the ratio of the number of males to the number of females in a survey sample is similar to an expected ratio. It is usually assumed that there should be equal numbers of males and females in the survey sample.
-
Digit preference score - Digit preference is the observation that the final number in a measurement occurs with a greater frequency than is expected by chance. This can occur because of rounding, the practice of increasing or decreasing the value in a measurement to the nearest whole or half unit, or because data are made up.
-
Standard deviation - The standard deviation of the MUAC measurements.
ipctools provides a set of functions that operate on a given
anthropometric dataset containing at least age, sex, and MUAC
measurements of children (if present, oedema information is also
processed) to provide an assessment of data quality.
The following diagram illustrates how this process/workflow using the
ipctools functions:
The functions in ipctools are pipe-friendly hence the workflow
described above can be done through piped operations as follows:
## Check for missing data ----
muac_data |>
process_muac_data() |>
check_missing_data()
#> # A tibble: 1 × 8
#> n_missing_sex p_missing_sex n_missing_age p_missing_age n_missing_muac
#> <int> <dbl> <int> <dbl> <int>
#> 1 0 0 0 0 0
#> # ℹ 3 more variables: p_missing_muac <dbl>, n_missing_oedema <int>,
#> # p_missing_oedema <dbl>
## Check data quality ----
muac_data |>
process_muac_data() |>
ipc_muac_check(
muac_units = "cm",
oedema_recode = c(1, 2)
)
#> $`Age Ratio`
#> $`Age Ratio`$ratio
#> [1] Inf
#>
#> $`Age Ratio`$p
#> [1] 7.785732e-113
#>
#> $`Age Ratio`$class
#> [1] "Problematic"
#>
#>
#> $`Sex Ratio`
#> $`Sex Ratio`$ratio
#> p
#> 0.5057471
#>
#> $`Sex Ratio`$p
#> [1] 0.8479104
#>
#> $`Sex Ratio`$class
#> [1] "Excellent"
#>
#>
#> $`Digit Preference`
#> $`Digit Preference`$score
#> [1] 16.35
#>
#> $`Digit Preference`$class
#> [1] "Acceptable"
#>
#>
#> $`Standard Deviation`
#> $`Standard Deviation`$std_dev
#> [1] 12.45931
#>
#> $`Standard Deviation`$class
#> [1] "Excellent"
#>
#>
#> $`Data Quality`
#> $`Data Quality`$score
#> [1] 1
#>
#> $`Data Quality`$class
#> [1] "Partially OK"The IPC-recommended approach to calculating prevalence of acute
malnutrition based on MUAC is to perform a weighted analysis when either
the age ratio test or the sex ratio test is problematic. For example,
based on the MUAC check shown above, the example dataset muac_data has
some issues with its age ratio and sex ratio. To calculate acute
malnutrition prevalence from this dataset, a weighted analysis will have
to be implemented. This can be done using the
ipc_calculate_prevalence() function as follows:
muac_data |>
process_muac_data() |>
ipc_muac_check(
muac_units = "cm",
oedema_recode = c(1, 2),
.summary = FALSE
) |>
ipc_calculate_prevalence()
#> [1] 0.1768535If you find the ipctools package useful please cite using the
suggested citation provided by a call to the citation() function as
follows:
citation("ipctools")
#> To cite ipctools in publications use:
#>
#> Tomas Zaba and Ernest Guevarra (2024). ipctools: Utilities to Support
#> Integrated Food Security Phase Classification (IPC) Data Analysis and
#> Visualisation. R package version 0.0.9000. URL
#> https://nutriverse.io/ipctools/
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {ipctools: Utilities to Support Integrated Food Security Phase Classification (IPC) Data Analysis and Visualisation},
#> author = {{Tomas Zaba} and {Ernest Guevarra}},
#> year = {2024},
#> note = {R package version 0.0.9000},
#> url = {https://nutriverse.io/ipctools/},
#> }Feedback, bug reports and feature requests are welcome; file issues or seek support here. If you would like to contribute to the package, please see our contributing guidelines.
This project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.