Cleanepi
R package to clean and standardize epidemiological data
Install / Use
/learn @epiverse-trace/CleanepiREADME
cleanepi: Clean and standardize epidemiological data <img src="inst/extdata/logo.svg" align="right" width="130"/>
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cleanepi is an R package designed for cleaning, curating, and standardizing epidemiological data. It streamlines various data cleaning tasks that are typically expected when working with datasets in epidemiology.
Key functionalities of cleanepi include:
-
Removing irregularities: It removes duplicated and empty rows and columns, as well as columns with constant values.
-
Handling missing values: It replaces missing values with the standard
NAformat, ensuring consistency and ease of analysis. -
Ensuring data integrity: It ensures the uniqueness of uniquely identified columns, thus maintaining data integrity and preventing duplicates.
-
Date conversion: It offers functionality to convert character columns to Date format under specific conditions, enhancing data uniformity and facilitating temporal analysis. It also offers conversion of numeric values written in letters into numbers.
-
Standardizing entries: It can standardize column entries into specified formats, promoting consistency across the dataset.
-
Time span calculation: It calculates the time span between two elements of type
Date, providing valuable demographic insights for epidemiological analysis.
cleanepi operates on data frames or similar structures like tibbles, as well as linelist objects commonly used in epidemiological research. It returns the processed data in the same format, ensuring seamless integration into existing workflows. Additionally, it generates a comprehensive report detailing the outcomes of each cleaning task.
cleanepi is developed by the Epiverse-TRACE team at the Medical Research Council The Gambia unit at the London School of Hygiene and Tropical Medicine.
Installation
cleanepi can be installed from CRAN using
install.packages("cleanepi")
The latest development version of cleanepi can be installed from GitHub.
if (!require("pak")) install.packages("pak")
pak::pak("epiverse-trace/cleanepi")
library(cleanepi)
Quick start
The main function in cleanepi is clean_data(), which internally
makes call of almost all standard data cleaning functions, such as
removal of empty and duplicated rows and columns, replacement of missing
values, etc. However, each function can also be called independently to
perform a specific task. This mechanism is explained in details in the
vignette. Below is typical example of how to use the clean_data()
function.
# READING IN THE TEST DATASET
test_data <- readRDS(
system.file("extdata", "test_df.RDS", package = "cleanepi")
)
# READING IN THE DATA DICTIONARY
test_dictionary <- readRDS(
system.file("extdata", "test_dictionary.RDS", package = "cleanepi")
)
# SCAN THROUGH THE DATA
scan_res <- cleanepi::scan_data(test_data)
# DEFINING THE CLEANING PARAMETERS
replace_missing_values <- list(target_columns = NULL, na_strings = "-99")
remove_duplicates <- list(target_columns = NULL)
standa
