Using epiCleanr
In epiCleanr: A Tidy Solution for Epidemiological Data

Description

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. These include:

import() imports files of any formats.
export() exports files of any formats.
get_admin_names() downloads admin names using various country codes and naming conventions.
clean_admin_names() cleans admin names using both user-provided and downloaded admin data.
create_test() creates a unit-testing functions to perform various data validation.
consistency_check() plot to see if certain variables exceed others (i.e., tests vs cases).
handle_outliers() detects outliers using various approaches, and offers functionality to manage them.
missing_plot() plots missing data or reporting rate for given variable(s) by different factors.

Installation

The package is yet to be available on Cran, but can be installed using devtools in R.

# The way to install it via CRAN once available
# install.packages("epiCleanr")

You can install the latest development version from GitHub by using:

# If you haven't installed the 'devtools' package, run:
# install.packages("devtools")
devtools::install_github("truenomad/epiCleanr")

Usage

Import and export

Inspired by rio, the import function allows you to read data from a wide range of file formats. Additional reading options specific to each format can be passed through the ellipsis (...) argument. Similarly, the export function provides a simple way to export data into various formats.

# Load the epiCleanr package
library(epiCleanr)

# Reading a CSV file with a specific seperator
data_csv <- import("path/to/your/file.csv", sep = "\n")

# Import the first sheet from an Excel file
data_excel <- import("path/to/your/file.xlsx", sheet = 1)

# Export a Stata DTA file
export(my_data, "path/to/your/file.dta")

# Export an RDS file
export(my_data, "path/to/your/file.rds")

# Export an Excel file with sheets
export(
list(my_data = my_data1, my_data2 = my_data2), "path/to/your/file.xlsx")

Clean strings

The clean_names_strings() function offers additional flexibility by allowing you to clean not just strings within columns but also the column names themselves.

# For data frame with snake_case (default)
data("iris")
colnames(iris)
# > "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"   

cleaned_iris <- clean_names_strings(iris)
colnames(cleaned_iris)
# > "sepal_length" "sepal_width"  "petal_length" "petal_width"  "species"

Download administrative names

You can download administrative names (such as districts, provinces, etc.,) for a given country via the GeoNames website using country name or codes to pull the data.

# Get admin names for Togo
admin_names <- get_admin_names(country_name_or_code = "Togo")

# Different admin levels are saved as a list
str(admin_names$adm2)

#>'data.frame': 30 obs. of  7 variables:
#> $ country_code  : chr  "TG" "TG" "TG" "TG" ...
#> $ asciiname     : chr  "Vo Prefecture" "Zio Prefecture" "Tchaoudjo" "Tchamba" ...
#> $ alternatenames: chr  "Circonscription de Vogan, Prefecture de Vo, Préfecture de Vo, ...
#> $ adm2          : chr  "Vo Prefecture" "Zio Prefecture" "Tchaoudjo" "Tchamba" ...
#> $ latitude      : num  6.42 6.58 9 8.83 6.67 ...
#> $ longitude     : num  1.5 1.17 1.17 1.42 1.5 ...
#> $ last_updated  : IDate, format: "2019-01-08" "2019-01-08" "2022-02-17" "2022-02-17" ...

Clean administrative names

You can clean administrative names using them clean_admin_names function, which uses various matching and string distance algorithms to match your admin data, using your own list of admin names as well admin names from GeoNames website.

# Get simulated epi data for Togo

# get path 
path <- system.file(
  "extdata", 
  "fake_epi_df_togo.rds", 
  package = "epiCleanr")

# get example data
fake_epi_df_togo <- import(path)

# get path 
path <- system.file(
  "extdata", 
  "fake_epi_df_togo.rds", 
  package = "epiCleanr")

# get referecne dataset with clean admin names
fake_epi_df_togo <- import(path)

# Lets check how the matching looks 
clean_admin_names(
     country_code = "Togo",
     admin_names_to_clean = fake_epi_df_togo$district,  
     user_base_admin_names = togo_admin_df$district, 
     report_mode = T) 

#> There are 15 out of 15 (100%) admins that have been perfectly matched!
#>   # A tibble: 15 × 5
#>  names_to_clean  final_names     ource_of_cleaned_name        prop_matched  matching_algorithm  
#>    <chr>         <chr>           <chr>                          <dbl>       <chr>               
#>  1 Bas-Mono      Bas-Mono        User base admin names          100         Levenshtein Distance
#>  2 Bliita        Blitta          Main admin name from geonames  100         Soundex             
#>  3 Centrale      Centrale        User base admin names          100         Levenshtein Distance
#>  4 Cinkaasi      Cinkassé        User base admin names          100         Soundex             
#>  5 Dankben       Dankpen         Main admin name from geonames  100         Soundex             
#>  6 East-Mono     Est-Mono        Main admin name from geonames  100         Soundex             
#>  7 Kaloto        Kloto           Main admin name from geonames  100         Soundex             
#>  8 Keéran        Keran           Alternative name from geonames 100         Soundex             
#>  9 Lomé          Lomé            User base admin names          100         Levenshtein Distance
#> 10 Ogou          Ogou            Main admin name from geonames  100         Levenshtein Distance
#> 11 Sotouboua     Sotouboua       Main admin name from geonames  100         Levenshtein Distance
#> 12 Tchamaba      Tchamba         Main admin name from geonames  100         Soundex             
#> 13 Vo            Vo Prefecture   Main admin name from geonames  100         Levenshtein Distance
#> 14 Yotto         Yoto            Main admin name from geonames  100         Soundex    
#> 15 Zioo          Zio Prefecture  Main admin name from geonames  100         Soundex         

# If we are happy with the names, we can update the old names with the new ones
# otherwise we can further clean the names manually
fake_epi_df_togo$district <- 
clean_admin_names(
        country_code = "Togo",
        admin_names_to_clean = fake_epi_df_togo$district, 
        user_base_admin_names = togo_admin_df$district, 
        report_mode = F)

Create unit testing functions

The create_test is there so users can create their own functions in which they can use for unit-testing when working with datasets that require lots of manipulation and wrangling. This function (plus the tidylog package) will save users from the headache of troubleshooting issues related to data joins and transformations.

# Set up a unit-testing fucntion
my_tests <- create_test(
        # For checking the dimension of the data
        dimension_test = c(900, 9), 
        # For expected number of combinations in data
        combinations_test = list(
        variables = c("month", "year", "district"),
        expectation = 12 * 5 * 15),
        # Check repeated cols, rows and max and min thresholds
        row_duplicates = TRUE, col_duplicates = TRUE,
        max_threshold_test = list(malaria_tests = 1000, cholera_tests = 1000),
        min_threshold_test = list(cholera_cases = 0, cholera_cases = 0)
        )

# Apply your unit-test on your data
my_tests(fake_epi_df_togo)

#> Test passed! You have the correct number of dimensions!
#> Test passed! No duplicate rows found!
#> Test passed! No repeated columns found!
#> Test passed! You have the correct number of combinations for month, year, district!
#> Test passed! Values in column cholera_cases are above the threshold.
#> Test passed! Values in column cholera_cases are above the threshold.
#> Test passed! Values in column malaria_tests are below the threshold.
#> Test passed! Values in column cholera_tests are below the threshold.

#> Congratulations! All tests passed: 8/8 (100%) 😀

Consistency check

The consistency_check function serves as a function for validating the logical relationships between variables. For instance, if you know that the number of disease cases cannot exceed the number of tests for that disease, this function helps inspect such expected behaviours in your data.

# Run checks using Togo data
consistency_check(fake_epi_df_togo,
                  tests = c("malaria_tests", "cholera_tests"),
                  cases = c("malaria_cases", "cholera_cases"))

#> Consistency test passed for malaria_tests vs malaria_cases: There are more tests than there are cases!
#> Consistency test failed for cholera_tests vs cholera_cases: There are 3 (0.33%) rows where cases are greater than tests.             

# You can even save the plot if you want
ggplot2::ggsave("../man/figures/consistency_plot.png", width = 10, 
                height = 5.75, scale = 0.95, dpi = 400, bg = "white")

Detecting and handling outliers

The handle_outliers function is designed for both identifying and addressing outliers in your dataset. It supports multiple statistical methods for outlier detection, such as Z-score, modified Z-score, and Interquartile Range. Beyond detection, the function offers various options for handling outliers, including removal or replacement with mean, median, grouped means, or quantiles.

# Select the variables
variables <- c("malaria_tests", "malaria_cases",
               "cholera_tests", "cholera_cases")

# Get outliers report
outliers <- handle_outliers(
    fake_epi_df_togo, vars = variables,
    method = "zscore", report_mode = TRUE)

# Get the report
outliers$report

#>   variable      test    outliers prop_outliers
#>   <chr>         <chr>   <glue>   <chr>        
#> 1 malaria_tests Z-Score 5/900    <1%          
#> 2 malaria_cases Z-Score 5/900    <1%          
#> 3 cholera_tests Z-Score 2/900    <1%          
#> 4 cholera_cases Z-Score 1/900    <1%          

# Get the plot
outliers$plot

# You can even save the plot if you want
# ggplot2::ggsave("../man/figures/outliers_plot.png", width = 10, 
#                height = 7, scale = 0.95, dpi = 400)

# If happy, create a dataframe with the outliers handled 
fake_epi_df_togo_no_outliers <- handle_outliers(
                                       fake_epi_df_togo, vars = variables, 
                                       method = "zscore", report_mode = FALSE, 
                                       treat_method = "mean")

Checking missing data and reporting rates over factors

The missing_plot function allows you to visualize missing data across different variables, either by a single factor like time or district, or by two factors simultaneously like date and district. This is particularly useful when you're dealing with epidemiological data that might have varying degrees of completeness across different times or locations.

# Make date columns
fake_epi_df_togo2 <- fake_epi_df_togo |> 
   dplyr::mutate(date = lubridate::as_date(paste0(year, month, "/01")))  

# Missing rate for variables across dates
missing_plot(fake_epi_df_togo2, miss_vars = variables, 
              x_var = "date", use_rep_rate = F)

# Reporting rate for malaria_cases across dates and districts
missing_plot(fake_epi_df_togo2, miss_vars = "malaria_cases", 
              x_var = "date", y_var = "district", use_rep_rate = T)