![]() Although much more complex examples are probably available to you, we will start with this for now. This is a very simple example of the differences between wide and long format. Generally, this is the format we want for most modeling techniques and most visualizations. Therefore, each row corresponds to a single time point. Here, time is nested within individuals making it the lowest unit. Notice that a single ID spans multiple columns and that each row has only one time point. This means that a single ID can span multiple rows, usually with a unique time point for each row as so: # ID Time Var In contrast, long format has the lowest nested unit as a single row. Taking your time to learn these methods will be well worth it. The copy-and-paste approach is seriously error prone and is not reproducible. Much of these may be things you have done in other tools such as spreadsheets. Because of this, we suggest taking your time to fully understand what each function is doing with the data. All of these are necessary to work with data flexibly. For example, reshaping can refer to moving data into a more wide-format or long-format, can refer to summarizing or aggregating, and can refer to joining or binding. Heads up! Understanding these tools requires an understanding of what ways data can be moved around. There are several methods that help create tidy data: It is the form that data needs to be in to analyze it, whether that analysis is by graphing, modeling, or other means. This depends largely on your data and research design but the definition is still the same-columns are variables and rows are observations. Tidy data is based on columns being variables and rows being observations. The goal of these packages is to help tidy up your data. ![]() The majority of what you’ll need to do with data as a researcher will be covered by these functions. The most influential individuals in the R world, including the makers and maintainers of RStudio, use these methods and syntax. It is the cutting edge of all things R.It is often worthwhile to make sure the code is readable for, as the saying goes, there are always at least two collaborators on any project: you and future you. It simplifies the code and makes the code more readable.I’m introducing this to you for a couple reasons. That’s a bit of an aside, but know that you can always get at a function even if it is “masked” from your current session. To review, the :: grabs the function from inside of the package and let’s you use just that function. We can do this by: awesome :: make_really_cool(args) We can still access the awesome version of the function (because, again, even though the name is the same, they won’t necessarily do the same things for you). R will automatically use the function from amazing. For example, if we loaded two packages- awesome and amazing-and both had the function make_really_cool and we loaded awesome and then amazing as so: library(awesome) In this situation, the last loaded package is the one that R will use by default. These functions with the same name will almost invariably differ in what they do. These conflicts are where two or more functions across different packages have the same name. Note that when we loaded tidyverse it loaded several packages and told you of “conflicts”. # ✖ dplyr::filter() masks stats::filter() ![]() They form a sort of “grammar” of data manipulation that simplifies both the coding approach and the way researchers think about working with data. The tidyverse 10 is a group of packages 11 that provide a simple syntax that can do many basic (and complex) data manipulating. In order to work with and clean your data in the most modern and straightforward way, we are going to be using the “tidyverse” group of methods. “Organizing is what you do before you do something, so that when you do it, it is not all mixed up.” - A. Chapter 10: Where to Go from Here and Common PitfallsĬhapter 2: Working with and Cleaning Your Data.Chapter 9: Reproducible Workflow with RMarkdown.Chapter 3: Exploring Your Data with Tables and Visuals.Select Variables and Filter Observations.Chapter 2: Working with and Cleaning Your Data.
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