Type:	Package
Title:	A Tidy Solution for Epidemiological Data
Version:	0.2.0
Description:	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.
License:	MIT + file LICENSE
URL:	https://github.com/truenomad/epiCleanr, https://truenomad.github.io/epiCleanr/
BugReports:	https://github.com/truenomad/epiCleanr/issues
Encoding:	UTF-8
Depends:	R (≥ 3.6)
Imports:	rio, dplyr, tidyr, tools, withr, ggplot2, stringr, tidyselect, rlang, glue, crayon, countrycode, purrr, tibble, janitor
Suggests:	testthat, knitr, haven, readxl, openxlsx, foreign, yaml, clipr, xml2, readODS, rmatio, jsonlite, fst, feather, arrow, xts, data.table, R.utils, stringdist, stringi, ggtext, ggdist, zoo, wesanderson, scales, spsUtil, rmarkdown, ggh4x
RoxygenNote:	7.2.3
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2023-09-28 11:59:45 UTC; mohamedyusuf
Author:	Mohamed A. Yusuf [aut, cre]
Maintainer:	Mohamed A. Yusuf <mohamedayusuf87@gmail.com>
Repository:	CRAN
Date/Publication:	2023-09-28 12:20:05 UTC

Clean and Match Administrative Names

Description

This function takes administrative names and cleans them using various matching and string distance algorithms. It can also match the cleaned names with a base list provided by the user or fetched from 'GeoNames', which is a official repository of standard spellings of all foreign geographic names.

Usage

clean_admin_names(
  admin_names_to_clean,
  country_code,
  admin_level = "adm2",
  user_base_admin_names = NULL,
  user_base_only = FALSE,
  report_mode = FALSE
)

Arguments

Value

If 'report_mode' is set to TRUE, a data frame containing the original admin names and the matched and cleaned admin names with inormation of the source of data used to clean including the algorithm used, else a cleaned list of names is returned.

See Also

countrycode::codelist() for the full list of codes and naming conventions.

Examples

 
# Example with country code
base_names <- c(
  "Paris", "Marseille", "Lyon",
  "Toulouse", "Nice", "Nantes", "Strasbourg",
  "Montpellier", "Bordeaux", "Lille"
)

unclean_names <- c(
  "Pariis", "Marseill", "Lyone",
  "Toulous", "Niice", "Nantees", "Strasbourgh",
  "Montpeelier", "Bordeuax", "Lilie"
)

france_new <- clean_admin_names(
  country_code = "Fr",
  user_base_admin_names = base_names,
  admin_names_to_clean = unclean_names
)

print(france_new)

Clean variable names or column names in various styles

Description

This function transforms variable names or column names into one of the standard cleaned formats specified by the 'style' argument. It offers more flexibility than janitor::clean_names() function by supporting individual strings and providing multiple naming styles.

Usage

clean_names_strings(input, style = "snake_case")

Arguments

Value

The object with cleaned names.

See Also

janitor::clean_names()

Examples

library(data.table)
library(zoo)
library(xts)

# For data frame with snake_case (default)
data("iris")
cleaned_iris <- clean_names_strings(iris)
colnames(cleaned_iris)

# For data frame with camel_case
cleaned_iris_camel <- clean_names_strings(iris, style = "camel_case")
colnames(cleaned_iris_camel)

# For character vector
original_names <- c("Some Column", "Another-Column!", "Yet Another Column")
cleaned_names <- clean_names_strings(original_names, style = "simple_clean")
print(cleaned_names)

# For matrix
mat <- matrix(1:4, ncol = 2)
colnames(mat) <- c("Some Column", "Another Column")
cleaned_mat <- clean_names_strings(mat)
colnames(cleaned_mat)

# For list
lst <- list("Some Column" = 1, "Another Column" = 2)
cleaned_lst <- clean_names_strings(lst)
names(cleaned_lst)

# For xts object
xts_obj <- xts(x = matrix(1:4, ncol = 2),
               order.by = as.Date('2021-01-01') + 0:1)
colnames(xts_obj) <- c("Some Column", "Another Column")
cleaned_xts <- clean_names_strings(xts_obj)
print(colnames(cleaned_xts))

zoo_obj <- zoo(matrix(1:4, ncol = 2), order.by = 1:2)
colnames(zoo_obj) <- c("Some Column", "Another Column")
cleaned_zoo <- clean_names_strings(zoo_obj)
print(colnames(cleaned_zoo))

# for Data table
dt <- data.table("Some Column" = 1:2, "Another Column" = 3:4)
cleaned_dt <- clean_names_strings(dt)
print(names(cleaned_dt))

Consistency Check Function

Description

This function performs a consistency check to ensure that the number of tests is greater than the number of cases for given columns in a dataset. It returns a ggplot2 object visualizing the results.

Usage

consistency_check(data, tests, cases)

Arguments

Value

A ggplot2::ggplot() object showing the consistency between the number of tests and cases. The x-axis represents the cases, and the y-axis represents the tests. Each facet represents a disease, and the diagonal line shows where the number of tests equals the number of cases.

Examples

# check the consistency between malaria tests and cases

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

fake_epi_df_togo <- import(path)

consistency_check(fake_epi_df_togo,
                  tests = c("malaria_tests","cholera_tests"),
                  cases = c("malaria_cases", "cholera_cases"))

Create Test Function

Description

This function creates a test function to perform various data validation checks. The returned function can be applied to a dataset to perform the specified tests.

Usage

create_test(
  dimension_test = NULL,
  combinations_test = NULL,
  row_duplicates = FALSE,
  col_duplicates = FALSE,
  min_threshold_test = NULL,
  max_threshold_test = NULL
)

Arguments

Value

A function to be applied to the dataset.

Examples

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

fake_epi_df_togo <- import(path)

# Set up unit-test function
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)
)

result <- my_tests(fake_epi_df_togo)

Export Data to Various File Formats

Description

This function provides a unified interface for exporting data to various file formats supported by the rio::export() function. The format is automatically detected from the file extension to simplify the exporting process.

Usage

export(data, file_path, ...)

Arguments

Value

No return value, called for side effects.

See Also

rio::export(), which this function is based on.

Examples

# Create temporary account
tmpdir <- tempfile()
dir.create(tmpdir)

# Export a CSV file
export(mtcars, file_path = file.path(tmpdir, "file.csv"))

# Export an Excel file
export(mtcars, file_path = file.path(tmpdir, "file.xlsx"))

# Export a Stata DTA file
export(mtcars, file_path = file.path(tmpdir, "file.dta"))

# Export an RDS file
export(mtcars, file_path = file.path(tmpdir, "file.rds"))

# Export an RData file
export(list(mtcars = mtcars, iris = iris),
       file_path = file.path(tmpdir, "file.RData"))

# Remove the temporary directory and its contents
unlink(tmpdir, recursive = TRUE)

Retrieve Administrative Names from GeoNames

Description

This function grabs administrative region names (such as districts, provinces, etc.) for a given country from the 'GeoNames' website. It accepts both country names and various country coding schemes.

Usage

get_admin_names(country_name_or_code, silent_mode = TRUE)

Arguments

Value

A list containing administrative region names and details for different administrative levels (e.g., ADM1, ADM2, etc.). Each element of the list corresponds to a different administrative level and contains a data frame with columns such as country_code, ascii name, alternate names, latitude, longitude, and date last updated.

See Also

'Geonames' website for the source of admin names data

Examples

# example using different naming/code conventions
three_digit <- get_admin_names("TGO")   # using 3 digit iso codes
two_digit <- get_admin_names("TG")      # using 2 digit iso codes
un_code <- get_admin_names(768)         # using UN codes
full_name <-  get_admin_names("Togo")   # using full names

str(full_name$adm2)

Detect and Handle Outliers in Dataset

Description

This function identifies and handles outliers in a given dataset using various methods including Z-Score, Modified Z-Score, and Inter-Quartile Range (IQR). It also provides options to treat the identified outliers, using mean, median, rolling mean by group and inter-quartile range. It also has the option to generate a summary report and a plot.

Usage

handle_outliers(
  data,
  vars = NULL,
  method = NULL,
  zscore_threshold = 3,
  mod_zscore_threshold = 3.5,
  iqr_k_value = 1.5,
  treat_method = "none",
  grouping_vars = NULL,
  report_mode = FALSE
)

Arguments

Value

If report_mode is TRUE, a list containing a summary dataframe and a ggplot object. Otherwise, a dataframe with outliers treated according to treat_method.

Examples

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

fake_epi_df_togo <- import(path)

variables <- c("malaria_tests", "malaria_cases",
                 "cholera_tests", "cholera_cases")
result <- handle_outliers(fake_epi_df_togo, vars = variables,
               method = "zscore", report_mode = TRUE)

print(result$report)

print(result$plot)

Import Data from Various File Formats

Description

This function provides a unified interface for importing data from various file formats supported by the rio package. The format is automatically detected from the file extension to simplify the importing process.

Usage

import(file_path, ...)

Arguments

Value

A data frame or appropriate R object containing the imported data.

See Also

rio::import(), which this function is based on.

Examples

# Locate test data directory
path <-  system.file("extdata",
                     package = "epiCleanr")

# Import a CSV file
data_csv <- import(file_path = file.path(path, "test_data.csv"))

# Import an Excel file
data_excel <- import(file_path = file.path(path, "test_data.xlsx"))

# Import a Stata DTA file
data_dta <- import(file_path = file.path(path, "test_data.dta"))

# Import an RDS file
data_rds <- import(file_path = file.path(path, "test_data.rds"))

# Import an RData file
data_rdata <- import(file_path = file.path(path, "test_data.RData"))

# Import an SPSS file
data_spss <- import(file_path = file.path(path, "test_data.sav"))

Plot Missing data over time

Description

This function visualizes the proportion of missing data or reporting rate for specified variables in a dataset. It creates a tile plot using ggplot2; where the x-axis can represent any categorical time such as time (e.g., year, month), and the y-axis can represents either variables or groupings (e.g., state). The output can further be manipulated to one's needs.

Usage

missing_plot(data, x_var, y_var = NULL, miss_vars = NULL, use_rep_rate = FALSE)

Arguments

Value

A ggplot2 object representing the tile plot.

Examples

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

fake_epi_df_togo <- import(path)

# Check misisng data by year
result <- missing_plot(fake_epi_df_togo,
             x_var = "year", use_rep_rate = FALSE)