This package offers a tidy solution for epidemiological data. It houses a range of functions for epidemiologists and public health data wizards for data management and cleaning. For more details on how to use this package, visit the epiCleanr website.
The package is available on Cran and can be installed in the following way:
install.packages("epiCleanr")
library("epiCleanr")Or install the development version from GitHub:
# If you haven't installed the 'devtools' package, run:
# install.packages("devtools")
devtools::install_github("truenomad/epiCleanr")Load the package:
library(epiCleanr)epiCleanr could be used as a helper package for
end-to-end epidemiological data management, offering functionalities
ranging from data importation and quality assessment to cleaning and
exporting files. Below are some of the workflow steps this package
streamlines:
Utilise import() to seamlessly read data from a wide
array of file formats, from CSV to Excel to JSON, all within one
function.
consistency_check(): Generate plots to identify
inconsistencies, such as when the number of tests exceeds the number of
cases.
missing_plot(): Visualize patterns of missing data
or reporting rates across different variables and factors.
create_test(): Establish unit-testing functions to
automate data validation, ensuring the robustness of your
dataset.
clean_admin_names(): Normalize administrative names
in your dataset using either user-supplied data or downloaded reference
data via get_admin_names().
cleaning_names_strings(): Use this function to clean
and standardize string columns in your data.
handle_outliers(): Detect and manage outliers using
a variety of statistical methods, providing you with options to either
remove or impute them.
Finally, use export() to save your cleaned data back
into multiple file formats, be it CSV, Excel, or other specialized
formats.