RESEARCH SUPPORT
RStudio Data Analysis for Research
From Raw Files to a Clean Dataset
Original files imported with their types and labels intact, data cleaning in R by rules written in a tidyverse script, and an analysis dataset returned with the row counts to show that nothing was lost on the way.
No Commitment Required
Strictly Confidential
In-House PhD-Level Statisticians
Fixed Project Fee
Same-Day Response
What Does Preparing Research Data in RStudio Involve?
Data cleaning in R starts before any model is fitted: the original files are imported with their types, labels and missing-value codes intact, cleaned and reshaped with the tidyverse packages, and saved as an analysis dataset whose every transformation is written in a script. The decisions concern meaning more than syntax: which columns identify participants and which hold measurements, how a code such as 99 becomes a missing value, whether a row is one person or one observation, and how files from different sources are joined without duplicating or losing records. OnlineSPSS, a specialist statistical consultancy for academic, clinical and professional research, provides this support: an in-house PhD-level statistician prepares the data in R and documents each step. Which records belong in the study, and what the findings mean, are yours to decide.
WHEN TO ENGAGE
When Imported Data Stop Adding Up
Preparation problems rarely announce themselves, and they usually surface as an odd count, a warning from a join or a colleague whose totals differ from yours.
Files From Several Sources Spreadsheets, an SPSS or Stata export and a survey platform file must become one dataset, and it is not clear which column identifies the same participant in each.
Totals That Do Not Match A count in R differs from the count in an earlier program or in the protocol, and the cause may lie in the import, in a join or in the handling of missing values.
Messy Coded Values Missing values are typed as 99, a dash or a blank, dates arrive as text, and one category is spelled several ways within the same column.
Code Inherited From Others A script from an earlier analyst still runs, but it reads files through absolute paths on another computer, and its filters and recodes have never been checked against the codebook.
WHAT THE PREPARATION COVERS
From Import to Analysis-Ready Data
Each step of preparation depends on the one before it, and the sequence is written down so that it can be rerun from the original files.
Reading the Files
RStudio data import brings in CSV, Excel and SPSS, Stata or SAS files with readr, readxl and haven, and the column types are checked after reading, not assumed.
Tidy Structure
Tidyverse data cleaning reshapes wide data to long or the reverse with tidyr, so that each row is one observation and each column one variable, a layout many modeling and plotting functions expect.
Cleaning and Recoding
Missing-value codes, impossible values, duplicated records and inconsistent labels are found and handled by rules written in the script, and each decision is on record.
Joining Sources
Files are joined on a checked key, and the row counts before and after each join are compared, so that unmatched or duplicated records are found at once.
Missing Values
The extent and pattern of missing values are summarized before any is handled, and the choice of handling is linked to Missing Data Analysis.
Project Structure
Work sits in an RStudio project with relative file paths and recorded package versions, and the models that follow are carried out through Quantitative Data Analysis.
COMMON PREPARATION ISSUES
Import and Cleaning Errors That Skew Results
R returns a dataset whether or not the import was right, and the only reliable test is a comparison of its totals with the source files.
Types Guessed Wrongly readr infers each column's type from a sample of rows, so an identifier with leading zeros or a column with late text entries can be read as the wrong type.
Codes Left as Numbers A missing-value code such as 99 stays in the column as a number and is averaged with real values, which moves the mean without any warning.
Labels Lost on Import Value labels from an SPSS or Stata file can be dropped when a column is converted, and a coded category is then analyzed as if it were a quantity.
Joins Multiply Rows A join on a key that is not unique repeats records, which inflates counts and gives some participants more weight than others in the analysis.
Dates Read as Text A spreadsheet column that mixes date cells with typed text can be imported as text, with some dates shown as serial numbers, and converting these with the wrong origin moves them by years and distorts every interval that uses them.
Cleaning Without a Record Rows are deleted or values overwritten by hand in a spreadsheet, so nobody can say afterward which records changed or what the reason was.
DELIVERABLES
What the Prepared Dataset Comes With
Six items connect the analysis dataset to the files it came from, so the sample in a methods section can be defended record by record.
Import and Cleaning Script A commented R script that reads the original files and produces the analysis dataset in one run, with no manual edits in between the two.
Variable Dictionary Each analysis variable with its source column, type, units, coding and the rule used to build it, written to match your own codebook.
Row-Count Audit Record counts at each stage (import, exclusions, joins) with the reason for every drop, so the final sample can be reconciled with the source.
Missingness Summary A table of missing values by variable and group, with the patterns noted, as the evidence on which the handling decision is based.
Cleaned Dataset in Your Formats The cleaned dataset saved in the formats your team uses, such as RDS, CSV or a Stata or SPSS file, with labels carried across where the format allows.
Environment Note The R version, the package versions and the project layout, so that another analyst can rebuild the dataset on a different computer.
ILLUSTRATIVE EXAMPLE
Detection Limits Hidden in a Text Column
An illustration of how a preparation problem is found through the row counts, resolved by a stated rule and documented.
ILLUSTRATIVE EXAMPLE · RIVER WATER QUALITY MONITORING RESEARCH · UNITED STATES
A doctoral researcher in environmental science combined monthly spreadsheets from three analytical laboratories into one dataset to study nutrient concentrations along a river.
Each laboratory formatted its sheet differently, with site names spelled in different ways and concentrations reported in different units. The complication appeared in the row counts. Readings below the detection limit were entered as text, with a less-than sign before the limit, so R read the whole concentration column as text, and a first conversion to numbers turned those entries into missing values. That would have removed the lowest readings silently and raised every average. The statistician kept the censoring in a separate indicator column, harmonized names and units by rules in the script and counted the affected readings by laboratory. How values below the limit would be treated in the models was discussed with the researcher and written into the methods.
Outcome: The researcher received the import and cleaning script, the harmonized dataset with a censoring indicator, the row-count audit, a variable dictionary and a methods note on the handling of values below the detection limit.
This example does not describe a real client or project. We never share details of actual client work, in line with the confidentiality we promise every researcher. This illustrative example is included to show the type, scope, and standard of work involved in this kind of analysis.
SOFTWARE
Source Formats R Reads Directly
Research data often arrive from other programs, and R reads what they wrote without altering the source file.
HOW WE ASSESS YOUR PROJECT
We Review Your Actual Research
Your assessment is based on a review of your actual research materials and analytical requirements, not a generic project category.
You Submit Your Project Details
Share whatever is available at the current stage, such as the research aim, protocol or proposal, dataset, variable list, manuscript or statistical requirements.
We Review Your Research and Define the Scope
Your statistically trained Research Coordinator reviews the materials to understand the study design, research questions, data structure, analytical requirements and expected deliverables. If clarification is needed, additional documents or information can be requested before the assessment is finalized.
Your Analysis Is Completed and Delivered
Once the scope and fixed project fee are agreed, the in-house statistician completes the work and the Research Coordinator remains the communication point through delivery, with the analysis, deliverables and timeline as agreed.
RESEARCH RESPONSIBILITY
Decisions About the Data That Remain Yours
OnlineSPSS prepares and documents the data in R, while which records belong in the study and what each variable means remain decisions of the researcher.
Agreed Statistical Support OnlineSPSS provides the agreed methodological, analytical and statistical reporting support, defined in a scope that is confirmed before any work begins.
No Intellectual Contribution OnlineSPSS does not claim any intellectual contribution to your study, dataset or article. Authorship remains the researcher's.
Your Research Decisions The research question, the definition of the study population, the meaning of each variable and the conclusions remain yours, with overall responsibility.
Confidential Handling Research data and project details are handled confidentially, project information is not published, and an NDA is available where required.
FREQUENTLY ASKED QUESTIONS
Questions About Data Cleaning in R and RStudio
Labels from SPSS and Stata files, joins that change a row count and hand edits in a spreadsheet each get a direct answer below.
What does preparing research data in RStudio include?
Data cleaning in R at OnlineSPSS starts with the original files: reading them, cleaning and reshaping them with the tidyverse, reviewing missing values and saving an analysis dataset with a script that records every step. Model fitting can follow in the same RStudio project when the scope includes it.
Can R open my SPSS and Stata files with their labels?
Yes. The haven package reads SPSS, Stata and SAS files, and for SPSS and Stata files it keeps variable and value labels as attributes of each column. The labels are checked after import, because converting a labeled column to plain numbers drops the label text, and categories are turned into factors where the analysis needs them. Details can vary with package versions, which are recorded.
Why did my row count change after joining two tables?
A changed count usually means the key is not unique, some records have no match, or the matching variable was formatted differently in the two files. The join type, the unmatched records and any duplicated keys are examined, and the result is reconciled with the source files before the analysis starts.
Should data cleaning be done in R or by hand in a spreadsheet?
Cleaning in a script is easier to defend, because every change is written down and can be rerun from the original file, which is never overwritten. Edits made by hand in a spreadsheet leave no record of which cells changed. A correction at the source, such as fixing a data-entry error, is still reasonable if it is logged.
How do you protect my research data and confidentiality?
Your data and project details are handled confidentially, and project information is not published. Researcher names and institutions are not used publicly without permission. An NDA is available where required and can be arranged before sensitive materials are shared. Read more about confidentiality and data security.
What happens when I request an assessment, and how long does it take?
You share your research question, design and relevant materials, such as the original data files, the codebook and any script you already have, and a statistically trained Research Coordinator reviews the request and responds the same day. The analysis, deliverables, timeline and one fixed project fee are then confirmed before any work begins, so the timeline follows from the agreed scope.
Still have a question? Find more answers in our full FAQ.
NEXT STEP
Describe the Files You
Are Starting From
Preparation problems are easiest to fix before the first model runs, and most of them are visible in a few rows of the raw files and the codebook that describes them.
No Commitment Required
Strictly Confidential
Fixed Project Fee
In-House PhD-Level Statisticians
Same-Day Response
Serving Researchers Since 2014