Oct 21, 2025

Public workspaceCleaning a dataset with R functions V.1

DOI
https://dx.doi.org/10.17504/protocols.io.8epv5k3jdv1b/v1
  • Cindy Rivas1,
  • Claudia Treviño1,
  • Andrés Aragón Martínez2
  • 1Institute of Biotechnology, UNAM;
  • 2FES Iztacala, UNAM
Cindy Rivas
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DOI: https://dx.doi.org/10.17504/protocols.io.8epv5k3jdv1b/v1
Protocol Citation: Cindy Rivas, Claudia Treviño, Andrés Aragón Martínez 2025. Cleaning a dataset with R functions . protocols.io https://dx.doi.org/10.17504/protocols.io.8epv5k3jdv1b/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: September 18, 2025
Last Modified: October 21, 2025
Protocol Integer ID: 227653
Keywords: R software, Data Cleaning, Workflow, essential steps of dataset cleaning, dataset cleaning, na value, dataset, factor levels in specific column, reordering factor level, specific column, data, correct typographical error, reproducible workflow
Funders Acknowledgements:
UNAM-DGAPA-PAPIIT
Grant ID: IN224925
UNAM-DGAPA-PAPIIT
Grant ID: IN215425
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Abstract
This protocol provides a reproducible R workflow to:
1. Correct typographical errors (typos) in specific columns.
2. Reordering factor levels in specific columns.
3. Remove NA values.

After completing the essential steps of dataset cleaning, we can proceed to perform statistical analyses or apply machine learning algorithms to our data.
Cleaning a dataset
Set the working directory.

Command
Set the working directory
setwd()

Expected result
> setwd ("/Users/cindyra/Downloads")
> getwd()
[1] "/Users/cindyra/Downloads"



Read the CSV file and create an object that contains your data. In this example, we will not use any R packages or libraries; we will work only with base R. We will clean a small dataset containing kinematic parameter values from a Computer-Assisted Sperm Analysis (CASA) system.
The CSV file for this exercise can be downloaded at:

Dataset
CASA data
NAME
https://osf.io/cq7pg/overview
LINK

Command
Read csv file
 casa_data <- read.csv(casa_data.csv, head=TRUE, sep= , ,stringsAsFactors=TRUE)
Review the structure of casa_data object.
Command
Object structure
str(casa_data)

Expected result
'data.frame': 11 obs. of 8 variables:
$ date : Factor w/ 2 levels "01/03/2025","17/05/2025": 1 1 1 2 1 2 2 2 1 1 ...
$ donor : Factor w/ 3 levels "p32","p45","p67": 3 3 3 1 3 1 3 1 1 1 ...
$ Treatment : Factor w/ 4 levels "5-HT_100nM","5-HT_10nM",..: 4 1 4 4 4 2 1 3 2 3 ...
$ exposure_time: Factor w/ 3 levels "100sec","10min",..: 3 1 2 1 3 2 2 3 2 1 ...
$ VCL : num 110 87.4 117.4 152.1 115.6 ...
$ VSL : num 68 77.4 97.7 77 NA ...
$ LIN : num 0.62 0.89 0.83 0.51 0.75 0.18 0.68 NA 0.5 1.1 ...
$ ALH : num 4.8 3.84 5.24 3.07 NA 4.4 3.11 2.68 4.03 3.57 ...




Now that we know the structure of the data, we can visualize it.
We are going to plot the “Treatment” column against someone of the motility parameter columns, in this example, we use "VCL" column.


Command
Plotting
plot(casa_data_ordered$Treatment, casa_data_ordered$VCL, xlab=Treatment, ylab=VCL)


Data visualization not only allows us to identify patterns or trends within the dataset but also helps detect typographical errors, as illustrated in this example. Cleaning the dataset ensures these errors are corrected, improving the accuracy and reliability of subsequent analyses.

Correct typographical errors (typos) in specific columns.

Command
Correct typos
levels(casa_data$Treatment)[levels(casa_data$Treatment) == Ctl] <- Ctrl

Now, instead of having four factor levels, we have three. After correcting the typographical error, we can clearly visualize the updated and properly labeled factor levels in two ways: first, by printing the factor levels of the column, and second, through a plot.
Command
Print levels
print(levels(casa_data$Treatment))

Expected result
[1] "5-HT_100nM" "5-HT_10nM" "Ctrl"


Command
Plotting
plot(casa_data_ordered$Treatment, casa_data_ordered$VCL, xlab=Treatment, ylab=VCL)



Reordering factor levels in specific columns.

As you can see in the previous plot, the factor levels of the Treatment column are not ordered. It is common practice to plot the control data first, followed by the treatments. Reordering factor levels within a column is an important step in the dataset cleaning process.

Command
Reordering the factor levels
> casa_data_ordered<-casa_data[order(casa_data$Treatment, decreasing=FALSE),]

Let’s visualize the data again to confirm that the factor levels are properly ordered.


Command
Plotting
plot(casa_data_ordered$Treatment, casa_data_ordered$VCL, xlab=Treatment, ylab=VCL)



Remove NA values

A common issue when analyzing data is the presence of NA values, which can bias the results of statistical analyses. To ensure the quality, accuracy, and reliability of the analysis, it is necessary to remove NA values.

One of the functions that allows us to check for NA values in our dataset is 
is.na()
However, this function returns a matrix of the same size as our dataframe.

Expected result
date donor Treatment exposure_time VCL VSL LIN ALH
1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
3 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
5 FALSE FALSE FALSE FALSE FALSE TRUE FALSE TRUE
8 FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE
10 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
11 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
6 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
9 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
2 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
7 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

In our example, we are working with a small dataset. However, when dealing with kinematic parameter data, datasets typically contain a large amount of information, making it more difficult to visualize and identify NA values using this method.

On the other hand, we can simply count the number of NA values per column using 
colSums(is.na())

Command
Count the number of NA values
colSums(is.na(casa_data))

Expected result
date donor Treatment exposure_time VCL VSL LIN ALH
0 0 0 0 1 1 1 1

Now that we know there are NA values, we can remove them.
Command
Remove NA values
> casa_data<- na.omit(casa_data_ordered)

Expected result
date donor Treatment exposure_time VCL VSL LIN ALH
0 0 0 0 0 0 0 0


At this point, we have a clean dataset: we have corrected typographical errors, reordered factor levels, and removed NA values.
We can use this dataset for statistical analyses or machine learning algorithms.
Save cleaned dataset
Finally, it is important to save the CSV file with the cleaned data so that it can be used for future analyses.


Command
Save csv
> write.csv(casa_data, file = casa_data.csv, row.names = FALSE)
The file will be saved in your computer’s working directory.