Clean case data
Last updated on 2025-04-09 | Edit this page
Overview
Questions
- How to clean and standardize case data?
Objectives
- Explain how to clean, curate, and standardize case data using cleanepi package
- Perform essential data-cleaning operations to be performed in a raw case dataset.
Prerequisite
This episode requires you to:
- Download the simulated_ebola_2.csv
- Save it in the
data/folder.
Introduction
In the process of analyzing outbreak data, it’s essential to ensure that the dataset is clean, curated, standardized, and validated. This will ensure that analysis is accurate (i.e. you are analysing what you think you are analysing) and reproducible (i.e. if someone wants to go back and repeat your analysis steps with your code, you can be confident they will get the same results). This episode focuses on cleaning epidemics and outbreaks data using the cleanepi package, For demonstration purposes, we’ll work with a simulated dataset of Ebola cases.
Let’s start by loading the package rio to read data
and the package cleanepi to clean it. We’ll use the pipe
%>% to connect some of their functions, including others
from the package dplyr, so let’s also call to the
tidyverse package:
R
# Load packages
library(tidyverse) # for {dplyr} functions and the pipe %>%
library(rio) # for importing data
library(here) # for easy file referencing
library(cleanepi)
The double-colon
The double-colon :: in R lets you call a specific
function from a package without loading the entire package into the
current environment.
For example, dplyr::filter(data, condition) uses
filter() from the dplyr package.
This help us remember package functions and avoid namespace conflicts.
The first step is to import the dataset into working environment,
which can be done by following the guidelines outlined in the Read case data episode. This involves loading
the dataset into R environment and view its structure and
content.
R
# Read data
# e.g.: if path to file is data/simulated_ebola_2.csv then:
raw_ebola_data <- rio::import(
here::here("data", "simulated_ebola_2.csv")
) %>%
dplyr::as_tibble() # for a simple data frame output
R
# Print data frame
raw_ebola_data
OUTPUT
# A tibble: 15,003 × 9
V1 `case id` age gender status `date onset` `date sample` lab region
<int> <int> <chr> <chr> <chr> <chr> <chr> <lgl> <chr>
1 1 14905 90 1 "conf… 03/15/2015 06/04/2015 NA valdr…
2 2 13043 twenty… 2 "" Sep /11/13 03/01/2014 NA valdr…
3 3 14364 54 f <NA> 09/02/2014 03/03/2015 NA valdr…
4 4 14675 ninety <NA> "" 10/19/2014 31/ 12 /14 NA valdr…
5 5 12648 74 F "" 08/06/2014 10/10/2016 NA valdr…
6 5 12648 74 F "" 08/06/2014 10/10/2016 NA valdr…
7 6 14274 sevent… female "" Apr /05/15 01/23/2016 NA valdr…
8 7 14132 sixteen male "conf… Dec /29/Y 05/10/2015 NA valdr…
9 8 14715 44 f "conf… Apr /06/Y 04/24/2016 NA valdr…
10 9 13435 26 1 "" 09/07/2014 20/ 09 /14 NA valdr…
# ℹ 14,993 more rowsDiscussion
Let’s first diagnose the data frame. List all the characteristics in the data frame above that are problematic for data analysis.
Are any of those characteristics familiar from any previous data analysis you have performed?
A quick inspection
Quick exploration and inspection of the dataset are crucial to
identify potential data issues before diving into any analysis tasks.
The cleanepi package simplifies this process with the
scan_data() function. Let’s take a look at how you can use
it:
R
cleanepi::scan_data(raw_ebola_data)
OUTPUT
Field_names missing numeric date character logical
1 age 0.0646 0.8348 0.0000 0.1006 0
2 gender 0.1578 0.0472 0.0000 0.7950 0
3 status 0.0535 0.0000 0.0000 0.9465 0
4 date onset 0.0001 0.0000 0.9159 0.0840 0
5 date sample 0.0001 0.0000 0.9999 0.0000 0
6 region 0.0000 0.0000 0.0000 1.0000 0The results provide an overview of the content of every column, including column names, and the percent of some data types per column. You can see that the column names in the dataset are descriptive but lack consistency, as some they are composed of multiple words separated by white spaces. Additionally, some columns contain more than one data type, and there are missing values in others.
Common operations
This section demonstrate how to perform some common data cleaning operations using the cleanepi package.
Standardizing column names
For this example dataset, standardizing column names typically
involves removing spaces and connecting different words with “_”. This
practice helps maintain consistency and readability in the dataset.
However, the function used for standardizing column names offers more
options. Type ?cleanepi::standardize_column_names for more
details.
R
sim_ebola_data <- cleanepi::standardize_column_names(raw_ebola_data)
names(sim_ebola_data)
OUTPUT
[1] "v1" "case_id" "age" "gender" "status"
[6] "date_onset" "date_sample" "lab" "region" If you want to maintain certain column names without subjecting them
to the standardization process, you can utilize the keep
argument of the function
cleanepi::standardize_column_names(). This argument accepts
a vector of column names that are intended to be kept unchanged.
Challenge
What differences you can observe in the column names?
Standardize the column names of the input dataset, but keep the first column names as it is.
You can try
cleanepi::standardize_column_names(data = raw_ebola_data, keep = "V1")
Removing irregularities
Raw data may contain irregularities such as
duplicated rows, empty rows and
columns, or constant columns (where all entries have
the same value.) Functions from cleanepi like
remove_duplicates() and remove_constants()
remove such irregularities as demonstrated in the below code chunk.
R
# Remove constants
sim_ebola_data <- cleanepi::remove_constants(sim_ebola_data)
Now, print the output to identify what constant column you removed!
R
# Remove duplicates
sim_ebola_data <- cleanepi::remove_duplicates(sim_ebola_data)
OUTPUT
! Found 5 duplicated rows in the dataset.
ℹ Use `attr(dat, "report")[["duplicated_rows"]]` to access them, where "dat" is
the object used to store the output from this operation.You can get the number and location of the duplicated rows that where
found. Run cleanepi::print_report(), wait for the report to
open in your browser, and find the “Duplicates” tab.
R
# Print a report
cleanepi::print_report(sim_ebola_data)
Challenge
In the following data frame:
OUTPUT
# A tibble: 6 × 5
col1 col2 col3 col4 col5
<dbl> <dbl> <chr> <chr> <date>
1 1 1 a b NA
2 2 3 a b NA
3 NA NA a <NA> NA
4 NA NA a <NA> NA
5 NA NA a <NA> NA
6 NA NA <NA> <NA> NA What columns or rows are:
- duplicates?
- empty?
- constant?
Duplicates mostly refers to replicated rows. Empty rows or columns can be a subset within the set of constant rows or columns.
Replacing missing values
In addition to the irregularities, raw data may contain missing
values, and these may be encoded by different strings
(e.g. "NA", "", character(0)). To
ensure robust analysis, it is a good practice to replace all missing
values by NA in the entire dataset. Below is a code snippet
demonstrating how you can achieve this in cleanepi for
missing entries represented by an empty string ":
R
sim_ebola_data <- cleanepi::replace_missing_values(
data = sim_ebola_data,
na_strings = ""
)
sim_ebola_data
OUTPUT
# A tibble: 15,000 × 7
v1 case_id age gender status date_onset date_sample
<int> <int> <chr> <chr> <chr> <chr> <chr>
1 1 14905 90 1 confirmed 03/15/2015 06/04/2015
2 2 13043 twenty-five 2 <NA> Sep /11/13 03/01/2014
3 3 14364 54 f <NA> 09/02/2014 03/03/2015
4 4 14675 ninety <NA> <NA> 10/19/2014 31/ 12 /14
5 5 12648 74 F <NA> 08/06/2014 10/10/2016
6 6 14274 seventy-six female <NA> Apr /05/15 01/23/2016
7 7 14132 sixteen male confirmed Dec /29/Y 05/10/2015
8 8 14715 44 f confirmed Apr /06/Y 04/24/2016
9 9 13435 26 1 <NA> 09/07/2014 20/ 09 /14
10 10 14816 thirty f <NA> 06/29/2015 06/02/2015
# ℹ 14,990 more rowsValidating subject IDs
Each entry in the dataset represents a subject (e.g. a disease case
or study participant) and should be distinguishable by a specific column
formatted in a particular way, such as falling within a specified range,
containing certain prefixes and/or suffixes, containing a specific
number of characters. The cleanepi package offers the
function check_subject_ids() designed precisely for this
task as shown in the below code chunk. This function validates whether
they are unique and meet the required criteria.
R
sim_ebola_data <-
cleanepi::check_subject_ids(
data = sim_ebola_data,
target_columns = "case_id",
range = c(0, 15000)
)
OUTPUT
! Found 1957 duplicated values in the subject Ids.
ℹ Enter `attr(dat, "report")[["duplicated_rows"]]` to access them, where "dat"
is the object used to store the output from this operation.
ℹ No incorrect subject id was detected.Note that our simulated dataset does contain duplicated subject IDS.
Let’s print a preliminary report with
cleanepi::print_report(sim_ebola_data). Focus on the
“Unexpected subject ids” tab to identify what IDs require an extra
treatment.
After finishing this tutorial, we invite you to explore the package
reference guide of cleanepi::check_subject_ids()
to find the function that can fix this situation.
Standardizing dates
An epidemic dataset typically contains date columns for different events, such as the date of infection, date of symptoms onset, etc. These dates can come in different date formats, and it is good practice to standardize them to ensure that subsequent analysis is comparing like-with-like. The cleanepi package provides functionality for converting date columns of epidemic datasets into ISO format, ensuring consistency across the different date columns. Here’s how you can use it on our simulated dataset:
R
sim_ebola_data <- cleanepi::standardize_dates(
sim_ebola_data,
target_columns = c(
"date_onset",
"date_sample"
)
)
sim_ebola_data
OUTPUT
# A tibble: 15,000 × 7
v1 case_id age gender status date_onset date_sample
<int> <chr> <chr> <chr> <chr> <date> <date>
1 1 14905 90 1 confirmed 2015-03-15 2015-04-06
2 2 13043 twenty-five 2 <NA> 2013-09-11 2014-01-03
3 3 14364 54 f <NA> 2014-02-09 2015-03-03
4 4 14675 ninety <NA> <NA> 2014-10-19 2014-12-31
5 5 12648 74 F <NA> 2014-06-08 2016-10-10
6 6 14274 seventy-six female <NA> 2015-04-05 2016-01-23
7 7 14132 sixteen male confirmed NA 2015-10-05
8 8 14715 44 f confirmed NA 2016-04-24
9 9 13435 26 1 <NA> 2014-07-09 2014-09-20
10 10 14816 thirty f <NA> 2015-06-29 2015-02-06
# ℹ 14,990 more rowsThis function converts the values in the target columns, or will
automatically figure out the date columns within the dataset (if
target_columns = NULL) and convert them into the
Ymd format.
How is this possible?
We invite you to find the key package that makes this standardisation possible inside cleanepi by reading the Details section of the Standardize date variables reference manual!
Converting to numeric values
In the raw dataset, some columns can come with mixture of character
and numerical values, and you will often want to convert character
values for numbers explicitly into numeric values
(e.g. "seven" to 7). For example, in our
simulated data set, in the age column some entries are written in words.
In cleanepi the function
convert_to_numeric() does such conversion as illustrated in
the below code chunk.
R
sim_ebola_data <- cleanepi::convert_to_numeric(sim_ebola_data,
target_columns = "age"
)
sim_ebola_data
OUTPUT
# A tibble: 15,000 × 7
v1 case_id age gender status date_onset date_sample
<int> <chr> <dbl> <chr> <chr> <date> <date>
1 1 14905 90 1 confirmed 2015-03-15 2015-04-06
2 2 13043 25 2 <NA> 2013-09-11 2014-01-03
3 3 14364 54 f <NA> 2014-02-09 2015-03-03
4 4 14675 90 <NA> <NA> 2014-10-19 2014-12-31
5 5 12648 74 F <NA> 2014-06-08 2016-10-10
6 6 14274 76 female <NA> 2015-04-05 2016-01-23
7 7 14132 16 male confirmed NA 2015-10-05
8 8 14715 44 f confirmed NA 2016-04-24
9 9 13435 26 1 <NA> 2014-07-09 2014-09-20
10 10 14816 30 f <NA> 2015-06-29 2015-02-06
# ℹ 14,990 more rowsMultiple language support
Thanks to the numberize package, we can convert numbers written as English, French or Spanish words to positive integer values!
Epidemiology related operations
In addition to common data cleansing tasks, such as those discussed in the above section, the cleanepi package offers additional functionalities tailored specifically for processing and analyzing outbreak and epidemic data. This section covers some of these specialized tasks.
Checking sequence of dated-events
Ensuring the correct order and sequence of dated events is crucial in
epidemiological data analysis, especially when analyzing infectious
diseases where the timing of events like symptom onset and sample
collection is essential. The cleanepi package provides a
helpful function called check_date_sequence() precisely for
this purpose.
Here’s an example of a code chunk demonstrating the usage of the
function check_date_sequence() in the first 100 records of
our simulated Ebola dataset
R
cleanepi::check_date_sequence(
data = sim_ebola_data[1:100, ],
target_columns = c("date_onset", "date_sample")
)
OUTPUT
! Detected 24 incorrect date sequences at lines: "10, 20, 24, 26, 27, 29, 39,
44, 46, 54, 59, 60, 62, 63, 65, 70, 73, 78, 81, 85, 88, 90, 94, 99".
ℹ Enter `attr(dat, "report")[["incorrect_date_sequence"]]` to access them,
where "dat" is the object used to store the output from this operation.This functionality is crucial for ensuring data integrity and accuracy in epidemiological analyses, as it helps identify any inconsistencies or errors in the chronological order of events, allowing you to address them appropriately.
Dictionary-based substitution
In the realm of data pre-processing, it’s common to encounter scenarios where certain columns in a dataset, such as the “gender” column in our simulated Ebola dataset, are expected to have specific values or factors. However, it’s also common for unexpected or erroneous values to appear in these columns, which need to be replaced with appropriate values. The cleanepi package offers support for dictionary-based substitution, a method that allows you to replace values in specific columns based on mappings defined in a dictionary. This approach ensures consistency and accuracy in data cleaning.
Moreover, cleanepi provides a built-in dictionary specifically tailored for epidemiological data. The example dictionary below includes mappings for the “gender” column.
R
test_dict <- base::readRDS(
system.file("extdata", "test_dict.RDS", package = "cleanepi")
) %>%
dplyr::as_tibble() # for a simple data frame output
test_dict
OUTPUT
# A tibble: 6 × 4
options values grp orders
<chr> <chr> <chr> <int>
1 1 male gender 1
2 2 female gender 2
3 M male gender 3
4 F female gender 4
5 m male gender 5
6 f female gender 6Now, we can use this dictionary to standardize values of the the “gender” column according to predefined categories. Below is an example code chunk demonstrating how to utilize this functionality:
R
sim_ebola_data <- cleanepi::clean_using_dictionary(
sim_ebola_data,
dictionary = test_dict
)
sim_ebola_data
OUTPUT
# A tibble: 15,000 × 7
v1 case_id age gender status date_onset date_sample
<int> <chr> <dbl> <chr> <chr> <date> <date>
1 1 14905 90 male confirmed 2015-03-15 2015-04-06
2 2 13043 25 female <NA> 2013-09-11 2014-01-03
3 3 14364 54 female <NA> 2014-02-09 2015-03-03
4 4 14675 90 <NA> <NA> 2014-10-19 2014-12-31
5 5 12648 74 female <NA> 2014-06-08 2016-10-10
6 6 14274 76 female <NA> 2015-04-05 2016-01-23
7 7 14132 16 male confirmed NA 2015-10-05
8 8 14715 44 female confirmed NA 2016-04-24
9 9 13435 26 male <NA> 2014-07-09 2014-09-20
10 10 14816 30 female <NA> 2015-06-29 2015-02-06
# ℹ 14,990 more rowsThis approach simplifies the data cleaning process, ensuring that categorical data in epidemiological datasets is accurately categorized and ready for further analysis.
Note that, when the column in the dataset contains values that are
not in the dictionary, the function
cleanepi::clean_using_dictionary() will raise an error.
You can start a custom dictionary with a data frame inside or outside
R. You can use the function cleanepi::add_to_dictionary()
to include new elements in the dictionary. For example:
R
new_dictionary <- tibble::tibble(
options = "0",
values = "female",
grp = "sex",
orders = 1L
) %>%
cleanepi::add_to_dictionary(
option = "1",
value = "male",
grp = "sex",
order = NULL
)
new_dictionary
OUTPUT
# A tibble: 2 × 4
options values grp orders
<chr> <chr> <chr> <int>
1 0 female sex 1
2 1 male sex 2You can read more details in the section about “Dictionary-based data substituting” in the package “Get started” vignette.
Calculating time span between different date events
In epidemiological data analysis, it is also useful to track and analyze time-dependent events, such as the progression of a disease outbreak (i.e., the time difference between today and the first case reported) or the duration between sample collection and analysis (i.e., the time difference between today and the sample collection). The most common example is to calculate the age of all the subjects given their date of birth (i.e., the time difference between today and the date of birth).
The cleanepi package offers a convenient function for
calculating the time elapsed between two dated events at different time
scales. For example, the below code snippet utilizes the function
cleanepi::timespan() to compute the time elapsed since the
date of sample for the case identified until the 3rd of January 2025
("2025-01-03").
R
sim_ebola_data <- cleanepi::timespan(
sim_ebola_data,
target_column = "date_sample",
end_date = lubridate::ymd("2025-01-03"),
span_unit = "years",
span_column_name = "years_since_collection",
span_remainder_unit = "months"
)
sim_ebola_data %>%
dplyr::select(case_id, date_sample, years_since_collection, remainder_months)
OUTPUT
# A tibble: 15,000 × 4
case_id date_sample years_since_collection remainder_months
<chr> <date> <dbl> <dbl>
1 14905 2015-04-06 9 8
2 13043 2014-01-03 11 0
3 14364 2015-03-03 9 10
4 14675 2014-12-31 10 0
5 12648 2016-10-10 8 2
6 14274 2016-01-23 8 11
7 14132 2015-10-05 9 2
8 14715 2016-04-24 8 8
9 13435 2014-09-20 10 3
10 14816 2015-02-06 9 10
# ℹ 14,990 more rowsAfter executing the function cleanepi::timespan(), two
new columns named years_since_collection and
remainder_months are added to the
sim_ebola_data dataset, containing the calculated time
elapsed since the date of sample collection for each case, measured in
years, and the remaining time measured in months.
Challenge
Age data is useful in any downstream analysis. You can categorize it to generate stratified estimates.
Read the test_df.RDS data frame within the
cleanepi package:
R
dat <- readRDS(
file = system.file("extdata", "test_df.RDS", package = "cleanepi")
) %>%
dplyr::as_tibble()
Calculate the age in years until the 1st of March of the subjects with the date of birth, and the remainder time in months. Clean and standardize the required elements to get this done.
Before calculating the age, you may need to:
- standardize column names
- standardize dates columns
- replace missing as strings to a valid missing entry
In the solution we add date_first_pcr_positive_test
given that it will provide the temporal scale for descriptive and
statistical downstream analysis of the disease outbreak.
R
dat_clean <- dat %>%
# standardize column names and dates
cleanepi::standardize_column_names() %>%
cleanepi::standardize_dates(
target_columns = c("date_of_birth", "date_first_pcr_positive_test")
) %>%
# replace from strings to a valid missing entry
cleanepi::replace_missing_values(
target_columns = "sex",
na_strings = "-99"
) %>%
# calculate the age in 'years' and return the remainder in 'months'
cleanepi::timespan(
target_column = "date_of_birth",
end_date = lubridate::ymd("2025-03-01"),
span_unit = "years",
span_column_name = "age_in_years",
span_remainder_unit = "months"
)
OUTPUT
! Found <numeric> values that could also be of type <Date> in column:
date_of_birth.
ℹ It is possible to convert them into <Date> using: `lubridate::as_date(x,
origin = as.Date("1900-01-01"))`
• where "x" represents here the vector of values from these columns
(`data$target_column`).Now, How would you categorize a numerical variable?
The simplest alternative is using Hmisc::cut2(). You can
also use dplyr::case_when() however, this requires more
lines of code and is more appropriate for custom categorizations. Here
we provide one solution using base::cut():
R
dat_clean %>%
# select to conveniently view timespan output
dplyr::select(
study_id,
sex,
date_first_pcr_positive_test,
date_of_birth,
age_in_years
) %>%
# categorize the age numerical variable [add as a challenge hint]
dplyr::mutate(
age_category = base::cut(
x = age_in_years,
breaks = c(0, 20, 35, 60, Inf), # replace with max value if known
include.lowest = TRUE,
right = FALSE
)
)
OUTPUT
# A tibble: 10 × 6
study_id sex date_first_pcr_posit…¹ date_of_birth age_in_years age_category
<chr> <int> <date> <date> <dbl> <fct>
1 PS001P2 1 2020-12-01 1972-01-06 53 [35,60)
2 PS002P2 1 2021-01-01 1952-02-20 73 [60,Inf]
3 PS004P2… NA 2021-02-11 1961-06-15 63 [60,Inf]
4 PS003P2 1 2021-02-01 1947-11-11 77 [60,Inf]
5 P0005P2 2 2021-02-16 2000-09-26 24 [20,35)
6 PS006P2 2 2021-05-02 NA NA <NA>
7 PB500P2 1 2021-02-19 1989-03-11 35 [35,60)
8 PS008P2 2 2021-09-20 1976-05-10 48 [35,60)
9 PS010P2 1 2021-02-26 1991-09-23 33 [20,35)
10 PS011P2 2 2021-03-03 1991-08-02 33 [20,35)
# ℹ abbreviated name: ¹date_first_pcr_positive_testYou can investigate the maximum values of variables using
skimr::skim(). Instead of base::cut() you can
also use Hmisc::cut2(x = age_in_years,cuts = c(20,35,60)),
which gives calculate the maximum value and do not require more
arguments.
Multiple operations at once
Performing data cleaning operations individually can be
time-consuming and error-prone. The cleanepi package
simplifies this process by offering a convenient wrapper function called
clean_data(), which allows you to perform multiple
operations at once.
The clean_data() function applies a series of predefined
data cleaning operations to the input dataset. Here’s an example code
chunk illustrating how to use clean_data() on a raw
simulated Ebola dataset:
Further more, you can combine multiple data cleaning tasks via the pipe operator in “%>%”, as shown in the below code snippet.
R
# Perfom the cleaning operations using the pipe (%>%) operator
cleaned_data <- raw_ebola_data %>%
cleanepi::standardize_column_names() %>%
cleanepi::remove_constants() %>%
cleanepi::remove_duplicates() %>%
cleanepi::replace_missing_values(na_strings = "") %>%
cleanepi::check_subject_ids(
target_columns = "case_id",
range = c(1, 15000)
) %>%
cleanepi::standardize_dates(
target_columns = c("date_onset", "date_sample")
) %>%
cleanepi::convert_to_numeric(target_columns = "age") %>%
cleanepi::check_date_sequence(
target_columns = c("date_onset", "date_sample")
) %>%
cleanepi::clean_using_dictionary(dictionary = test_dict) %>%
cleanepi::timespan(
target_column = "date_sample",
end_date = lubridate::ymd("2025-01-03"),
span_unit = "years",
span_column_name = "years_since_collection",
span_remainder_unit = "months"
)
Challenge
Have you noticed that cleanepi contains a set of functions to diagnose the cleaning status and another set to perform cleaning actions?
To identify both groups:
- On a piece of paper, write the names of each function under the corresponding column:
| Diagnose cleaning status | Perform cleaning action |
|---|---|
| … | … |
Cleaning report
The cleanepi package generates a comprehensive report detailing the findings and actions of all data cleansing operations conducted during the analysis. This report is presented as a webpage with multiple sections. Each section corresponds to a specific data cleansing operation, and clicking on each section allows you to access the results of that particular operation. This interactive approach enables users to efficiently review and analyze the outcomes of individual cleansing steps within the broader data cleansing process.
You can view the report using the functioncleanepi::print_report(cleaned_data).
Example of data cleaning report generated by cleanepi
