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Sponsored by the Digital Library Federation, Endangered Data Week, February 25 - March 1, , is an international, collaborative effort, coordinated across campuses, nonprofits, libraries, citizen science initiatives, and cultural heritage institutions to shed light on public datasets that are in danger of being deleted, repressed, mishandled, or lost. Endangered Data Week seeks to promote care for endangered collections by publicizing the availability of datasets; increasing critical engagement with them, including through visualization and analysis; and by encouraging political activism for open data policies and the fostering of data skills through workshops on curation, documentation and discovery, improved access, and preservation.

Subject outline

UNO Libraries is hosting two hands-on workshops on working with data. However, even our structured data will often not be in the ideal format for our desired analysis. As such, we have to spend our time wrangling the data sometimes referred to as munging , though that term is falling out of favor to get it into the shape we want it. Luckily enough, there are a number of versatile tools designed for wrangling our data which will take a lot of the trouble out of the process.

Beyond Basic R – Data Munging | R-bloggers

This is the term used for that spreadsheet-esque data format, where data is neatly kept in columns and rows. Tidy dataframes always take the same shape:.


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Luckily enough, there are tools designed to get you from untidy to tidy data easily, so you can then follow up with the fun parts of analyses. As you might guess from the name, the tidyverse is specifically designed to work with tidy datasets. For instance, imagine a dataframe of seasonal temperatures, built as such:. But the problems with this format become obvious when we, for instance, try to graph the data:. What a mess! This is a good time for us to use those tools I mentioned earlier, to turn our data tidy! Luckily enough, the tidyverse contains a package designed for making our data tidier - called, helpfully enough, tidyr.


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  • We already loaded this package when we called the tidyverse earlier. To change our SeasonalTemps data to a long format, we can use the gather function. This function gathers values stores in multiple columns into a single variable, and makes another variable - the key variable - representing what column the data was originally in. Additionally, we can specify columns that we want to preserve in the new, long dataframe by putting -ColumnName at the end of the function. If, after all our hard work, we want to get back to our original wide format, we can undo our gather using spread.

    If we wanted to rearrage them, I find the easiest way is using the select function from dplyr , another package in the tidyverse. By giving select an argument for data and a vector of column names, we can rearrange the order the columns appear:. Another way many datasets break the tidy format is by storing more than one value in a cell.

    For instance, say we had a dataframe of horse race results from three races, where the results were all written in the same column:.

    This makes some amount of sense as a human reading it, but makes it really hard to do any sort of analysis. So we can use the separate command from tidyr to split that Results column into three columns, one per race.

    R Data Wrangling

    We can also see that there are some times, split into two columns minutes and seconds off to the right side of the table. This function works pretty similarly to separate , with one important difference: while in separate , the second argument was the column to be split , in unite the second argument is the name of the column you want to combine values into:. Looking back to our weather data, we can pick out a common problem facing young analysts. When repeatedly making similar but different dataframes, it can be hard to keep track of which object has which data - and it can be hard to keep coming up with simple, descriptive names, too.

    Data Wrangling in R

    One solution could be to keep overwriting the same object with the new data:. Plus, if you make a mistake while writing over a value that had your original data in it, you have to start all over again - assuming that your data was saved anywhere else!

    This is an example of what we call nested functions , where one function in this case, separate is nested inside of another unite. This lesson covers the basics of using janitor and dplyr to rename and subset messy data. You can then open the. Rproj file to get started. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms. We currently host seminars focused on the programming language R.

    R tutorial: Introduction to cleaning data with R

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