The Liberia Coverage Survey for the assessment of CMAM coverage, micronutrient powder supplementation coverage, vitamin A supplementation coverage, iron folic acid supplementation coverage and IYCF counselling coverage has been commissioned by UNICEF Liberia with funding from Power of Nutrition. In support of this survey, this package for data manipulation, processing and analysis has been developed.
You can install the development version of liberiaData from
GitHub with:
if(!require(remotes)) install.packages("remotes")
remotes::install_github("validmeasures/liberiaData")The liberiaData package has 4 sets of functions that perform the
following tasks:
These functions interface with the server that holds the collected raw
data from the survey conducted. The server is an Open Data
Kit Aggregate server. Two functions fall under this
task. The first is get_liberia_data() which is a wrapper function to
functions in the odkr package (see
repository) for pulling forms
from the server and exporting the forms into datasets. The second is a
utility function called merge_repeats() that merges data from nested
forms within the main forms into the main dataset.
The next set of functions process the raw data to be usable for
analysis. These functions are primarily recode functions (functions
beginning with recode_ followed by a descriptor of what the function
processes) specific to the various indicators in the survey.
In addition to the recode_* functions, there are also a couple of
spatial data processing functions (create_sp() and get_spid()) that
transform datasets into spatial class formats needed for spatial
analysis.
For a detailed description of the indicators in this survey and their definitions, see the section on indicators in the survey’s design document here
The next set of functions are the analysis functions. First are the
estimator functions to report results for the whole survey area. Two
types of estimation are performed for specific types of indicators. A
classic unweighted estimator (estimate_classic()) is used to estimate
the coverage of the Integrated Management of Acute Malnutrition (IMAM)
while for all other indicators a weighted bootstrap estimator
(boot_estimate()) is used. We use the bbw package (see
repository) to apply a blocked
weighted bootstrap estimator as our survey sample is not priorly
weighted.
Second is the spatial interpolation function
(interpolate_indicators()) which is a wrapper to the gstat package
function for inverse distance weighting (IDW) interpolation.
For a detailed description of the analysis approach for this survey, see the section on analysis in the survey’s design document here.
The liberiaData package has a function (run_dashboard()) that runs
an embedded Shiny application to demonstrate the preliminary output of
the analysis.
The liberiaData package contains all the R scripts used to perform the
different steps described above. These scripts can be found in the
data-raw folder - data.R contains the script for pulling the data
from server, cleaning, recoding and then analysis.; maps.R contains
the script for spatial interpolation.
All outputs were then exported as package data (.rda format) found in the data folder.
This package was used to produce the final report using Rmarkdown which can be found in this repository.