Data Wrangling Process


The process of data wrangling often seems very situation dependent and there isn’t a unifying process. However this isn’t completely true. The process can be thought of as having four distinct steps.

Step Result Name Description
Import data_raw Get the data into R somehow. The structure of the data is just however it came in.
Tidy data_tidy Restructure the data so that each row is an observation, and each column is a variable.
Clean data Correct variable types, consistent and useful labels, validation and correction.
Use data_small Sub-setting the full data into a smaller set that addresses a particular question.

I tend to break my data cleaning scripts into three chunks and the cleaning script looks something like this:

In the above script the ... represent a bunch commands that I pipe together. The clean data set doesn’t include any _modifier tag because that clean data set is the one that I want to save and then subsequently use in any analysis.

In many real world examples, the data wrangling work is concentrated in only one or two of the first three steps. Typically you might be able to skip one or more steps, but I would encourage using the naming convention above so that it is clear where the skipped steps are and what step you are on.

When working in large research groups, it is inevitably only a few people understand the data import and cleaning and all of the other researchers are interested in building models. If the data owner handles the cleaning process and saves it in a format that make is easy to work with, plenty of frustration will be saved.


Obviously getting the data into R is an obvious first step. Often this is a simple step of reading a .csv file, but more complicated scenarios such as messy excel files, importing data tables from a database, or pulling data out of web pages or pdf documents are common. Another case is when the data is spread across a bunch of files (e.g. one excel file per month of data) and we need to read multiple files and squish them together before proceeding to the next step.


The terminology of “tidy data” popularized by Hadley Wickham and his introduction to the concept lives in a vignette in the tidyr package. I highly recommend reading Hadley’s introduction as well as what I present here.

Data is usually described as “tidy” if it satisfies the following:

  1. Each row represents an observation.
  2. Each column represents a variable.
  3. Each table of data represents a different type of observational unit.

The difficult part is recognizing what constitutes an observation and what constitutes a variable. Often I like to think that the observations represent a noun and each now has multiple variables that adjectives that describe the noun. In particular I think that the attributes should be applicable to every single observation. If your data has a large number of NA values, that is a symptom of storing the data in a messy (non-tidy) format.

Suppose I have an address book where I keep email addresses, phone numbers, and other contact information. However, because different people have several different types of contact information, it would be a bad idea to have one row per person because then we’d need a column for work email, personal email, home phone, work phone, cell phone, twitter handle, reddit user name, etc. Instead, store the information with a single row representing a particular contact.

## # A tibble: 5 x 3
##   Person Type       Value                    
##   <chr>  <chr>      <chr>                    
## 1 Derek  Work Email
## 2 Derek  Cell Phone 970-867-5309             
## 3 Derek  Twitter    @D_Sonderegger           
## 4 Derek  Github     dereksonderegger         
## 5 Mom    Home Phone 555-867-5309

For a more challenging example, suppose we have grade book where we’ve stored students scores for four different homework assignments.

##      name HW.1 HW.2 HW.3 HW.4
## 1  Alison    8    5    8    4
## 2 Brandon    5    3    6    9
## 3 Charles    9    7    9   10

In this case we are considering each row to represent a student and each variable represents homework score. An alternative representation would be for each row to represent a single score.

##       name Assignment Score
## 1   Alison       HW.1     8
## 2  Brandon       HW.1     5
## 3  Charles       HW.1     9
## 4   Alison       HW.2     5
## 5  Brandon       HW.2     3
## 6  Charles       HW.2     7
## 7   Alison       HW.3     8
## 8  Brandon       HW.3     6
## 9  Charles       HW.3     9
## 10  Alison       HW.4     4
## 11 Brandon       HW.4     9
## 12 Charles       HW.4    10

Either representation is fine in this case, because each student should have the same number of assignments. However, if I was combining grade books from multiple times I’ve taught the course, the first option won’t work because sometimes I assign projects and sometimes not. So the tidy version of the data would be to have a table scores where each row represents a single assignment from a particular student.


The cleaning step is usually highly dependent on the data set content. This step involves

  1. Making sure every variable has the right type. For example, make sure that dates are dates, not character strings.
  2. Fix factor labels and sort order.
  3. Verify numeric values are reasonable. I often do this by examining summary statistics and/or histograms for each column.
  4. Create any calculated variables we need.

Most of our data frame manipulation tools are designed to work with tidy data. As a result, cleaning is most easily done after the data set structure has been tidied. Therefore, I recommend first performing the reshaping tidying step and then perform the cleaning.


In the previous three steps, we tried to keep all of the data present and not filter anything out. In this step we transition to using data to build a much deeper understanding.

In the simplest case, we just take the full dataset and pass it into a graph or statistical model. But in a more complicated situation, we might want to filter out a bunch of data and focus on a particular subset. For example, we might make a graph for a particular subgroup comparing two covariates.

In this step, the data manipulation is often to just filter the final cleaned up data. Because I often want to consider many different small filtered sets, it can be convenient to not actually save these sets, but rather just pipe them into the graphing or modeling function.


Why is it good to separate the steps in our thinking?

  1. It forces me to keep my data wrangling code organized and encourages documenting any decisions I have to make.

  2. By separating the data wrangling step code from the analysis, I think more deeply about verification issues and initial exploration. I find that by thinking about how best to store the data I better understand exactly what the data represents.

  3. Because all my subsequent analysis will depend on the same tidy data set, I make fewer mistakes when I clean the data correctly in once. If every analysis had some cleaning steps, inevitably I will mess up and forget one or more steps that I did in other analyses. So do the cleaning all in one place and when you discover an additional problem that you need to address, there is only one script you need to modify to include the fix.

  4. Packages make it very easy to share my data analysis projects with anyone around the world because I can just post it on GitHub and anyone can view, download, and understand everything because of the standardized R package structure. It is useful in the package to separate the import/tidying/cleaning into one location and then the analysis (which is what my collaborators are interested in) into another location.